Malegori, Cristina; Nascimento Marques, Emanuel José; de Freitas, Sergio Tonetto; Pimentel, Maria Fernanda; Pasquini, Celio; Casiraghi, Ernestina
2017-04-01
The main goal of this study was to investigate the analytical performances of a state-of-the-art device, one of the smallest dispersion NIR spectrometers on the market (MicroNIR 1700), making a critical comparison with a benchtop FT-NIR spectrometer in the evaluation of the prediction accuracy. In particular, the aim of this study was to estimate in a non-destructive manner, titratable acidity and ascorbic acid content in acerola fruit during ripening, in a view of direct applicability in field of this new miniaturised handheld device. Acerola (Malpighia emarginata DC.) is a super-fruit characterised by a considerable amount of ascorbic acid, ranging from 1.0% to 4.5%. However, during ripening, acerola colour changes and the fruit may lose as much as half of its ascorbic acid content. Because the variability of chemical parameters followed a non-strictly linear profile, two different regression algorithms were compared: PLS and SVM. Regression models obtained with Micro-NIR spectra give better results using SVM algorithm, for both ascorbic acid and titratable acidity estimation. FT-NIR data give comparable results using both SVM and PLS algorithms, with lower errors for SVM regression. The prediction ability of the two instruments was statistically compared using the Passing-Bablok regression algorithm; the outcomes are critically discussed together with the regression models, showing the suitability of the portable Micro-NIR for in field monitoring of chemical parameters of interest in acerola fruits. Copyright © 2016 Elsevier B.V. All rights reserved.
Krepper, Gabriela; Romeo, Florencia; Fernandes, David Douglas de Sousa; Diniz, Paulo Henrique Gonçalves Dias; de Araújo, Mário César Ugulino; Di Nezio, María Susana; Pistonesi, Marcelo Fabián; Centurión, María Eugenia
2018-01-01
Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12 mg kg- 1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (w w- 1). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59 mg kg- 1, REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis.
Linear regression models and k-means clustering for statistical analysis of fNIRS data.
Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro
2015-02-01
We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
Quantum algorithm for linear regression
Wang, Guoming
2017-07-01
We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.
Regression algorithm for emotion detection
Berthelon , Franck; Sander , Peter
2013-01-01
International audience; We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor m...
Laurens, L M L; Wolfrum, E J
2013-12-18
One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.
Isingizwe Nturambirwe, J. Frédéric; Perold, Willem J.; Opara, Umezuruike L.
2016-02-01
Near infrared (NIR) spectroscopy has gained extensive use in quality evaluation. It is arguably one of the most advanced spectroscopic tools in non-destructive quality testing of food stuff, from measurement to data analysis and interpretation. NIR spectral data are interpreted through means often involving multivariate statistical analysis, sometimes associated with optimisation techniques for model improvement. The objective of this research was to explore the extent to which genetic algorithms (GA) can be used to enhance model development, for predicting fruit quality. Apple fruits were used, and NIR spectra in the range from 12000 to 4000 cm-1 were acquired on both bruised and healthy tissues, with different degrees of mechanical damage. GAs were used in combination with partial least squares regression methods to develop bruise severity prediction models, and compared to PLS models developed using the full NIR spectrum. A classification model was developed, which clearly separated bruised from unbruised apple tissue. GAs helped improve prediction models by over 10%, in comparison with full spectrum-based models, as evaluated in terms of error of prediction (Root Mean Square Error of Cross-validation). PLS models to predict internal quality, such as sugar content and acidity were developed and compared to the versions optimized by genetic algorithm. Overall, the results highlighted the potential use of GA method to improve speed and accuracy of fruit quality prediction.
A Scalable Local Algorithm for Distributed Multivariate Regression
National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm can be used for distributed...
An Efficient Local Algorithm for Distributed Multivariate Regression
National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed...
Finite Algorithms for Robust Linear Regression
DEFF Research Database (Denmark)
Madsen, Kaj; Nielsen, Hans Bruun
1990-01-01
The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...
Superquantile Regression: Theory, Algorithms, and Applications
2014-12-01
Highway, Suite 1204, Arlington, Va 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1...Navy submariners, reliability engineering, uncertainty quantification, and financial risk management . Superquantile, superquantile regression...Royset Carlos F. Borges Associate Professor of Operations Research Dissertation Supervisor Professor of Applied Mathematics Lyn R. Whitaker Javier
A semi-learning algorithm for noise rejection: an fNIRS study on ADHD children
Sutoko, Stephanie; Funane, Tsukasa; Katura, Takusige; Sato, Hiroki; Kiguchi, Masashi; Maki, Atsushi; Monden, Yukifumi; Nagashima, Masako; Yamagata, Takanori; Dan, Ippeita
2017-02-01
In pediatrics studies, the quality of functional near infrared spectroscopy (fNIRS) signals is often reduced by motion artifacts. These artifacts likely mislead brain functionality analysis, causing false discoveries. While noise correction methods and their performance have been investigated, these methods require several parameter assumptions that apparently result in noise overfitting. In contrast, the rejection of noisy signals serves as a preferable method because it maintains the originality of the signal waveform. Here, we describe a semi-learning algorithm to detect and eliminate noisy signals. The algorithm dynamically adjusts noise detection according to the predetermined noise criteria, which are spikes, unusual activation values (averaged amplitude signals within the brain activation period), and high activation variances (among trials). Criteria were sequentially organized in the algorithm and orderly assessed signals based on each criterion. By initially setting an acceptable rejection rate, particular criteria causing excessive data rejections are neglected, whereas others with tolerable rejections practically eliminate noises. fNIRS data measured during the attention response paradigm (oddball task) in children with attention deficit/hyperactivity disorder (ADHD) were utilized to evaluate and optimize the algorithm's performance. This algorithm successfully substituted the visual noise identification done in the previous studies and consistently found significantly lower activation of the right prefrontal and parietal cortices in ADHD patients than in typical developing children. Thus, we conclude that the semi-learning algorithm confers more objective and standardized judgment for noise rejection and presents a promising alternative to visual noise rejection
Directory of Open Access Journals (Sweden)
Gongliang Yu
2014-04-01
Full Text Available Satellite remote sensing is a highly useful tool for monitoring chlorophyll-a concentration (Chl-a in water bodies. Remote sensing algorithms based on near-infrared-red (NIR-red wavelengths have demonstrated great potential for retrieving Chl-a in inland waters. This study tested the performance of a recently developed NIR-red based algorithm, SAMO-LUT (Semi-Analytical Model Optimizing and Look-Up Tables, using an extensive dataset collected from five Asian lakes. Results demonstrated that Chl-a retrieved by the SAMO-LUT algorithm was strongly correlated with measured Chl-a (R2 = 0.94, and the root-mean-square error (RMSE and normalized root-mean-square error (NRMS were 8.9 mg∙m−3 and 72.6%, respectively. However, the SAMO-LUT algorithm yielded large errors for sites where Chl-a was less than 10 mg∙m−3 (RMSE = 1.8 mg∙m−3 and NRMS = 217.9%. This was because differences in water-leaving radiances at the NIR-red wavelengths (i.e., 665 nm, 705 nm and 754 nm used in the SAMO-LUT were too small due to low concentrations of water constituents. Using a blue-green algorithm (OC4E instead of the SAMO-LUT for the waters with low constituent concentrations would have reduced the RMSE and NRMS to 1.0 mg∙m−3 and 16.0%, respectively. This indicates (1 the NIR-red algorithm does not work well when water constituent concentrations are relatively low; (2 different algorithms should be used in light of water constituent concentration; and thus (3 it is necessary to develop a classification method for selecting the appropriate algorithm.
A flexible fuzzy regression algorithm for forecasting oil consumption estimation
International Nuclear Information System (INIS)
Azadeh, A.; Khakestani, M.; Saberi, M.
2009-01-01
Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.
Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms
Directory of Open Access Journals (Sweden)
Zhongyi Hu
2013-01-01
Full Text Available Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA based memetic algorithm (FA-MA to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.
Liu, Ke; Chen, Xiaojing; Li, Limin; Chen, Huiling; Ruan, Xiukai; Liu, Wenbin
2015-02-09
The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named "consensus SPA-MLR" (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques. Copyright © 2014 Elsevier B.V. All rights reserved.
Jiang, Hui; Liu, Guohai; Mei, Congli; Yu, Shuang; Xiao, Xiahong; Ding, Yuhan
2012-11-01
The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV = 0.0776, Rc = 0.9777, RMSEP = 0.0963, and Rp = 0.9686 for pH model; RMSECV = 1.3544% w/w, Rc = 0.8871, RMSEP = 1.4946% w/w, and Rp = 0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry.
Virtual machine consolidation enhancement using hybrid regression algorithms
Directory of Open Access Journals (Sweden)
Amany Abdelsamea
2017-11-01
Full Text Available Cloud computing data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. The reason for this extremely high energy consumption is not just the quantity of computing resources and the power inefficiency of hardware, but rather lies in the inefficient usage of these resources. VM consolidation involves live migration of VMs hence the capability of transferring a VM between physical servers with a close to zero down time. It is an effective way to improve the utilization of resources and increase energy efficiency in cloud data centers. VM consolidation consists of host overload/underload detection, VM selection and VM placement. Most of the current VM consolidation approaches apply either heuristic-based techniques, such as static utilization thresholds, decision-making based on statistical analysis of historical data; or simply periodic adaptation of the VM allocation. Most of those algorithms rely on CPU utilization only for host overload detection. In this paper we propose using hybrid factors to enhance VM consolidation. Specifically we developed a multiple regression algorithm that uses CPU utilization, memory utilization and bandwidth utilization for host overload detection. The proposed algorithm, Multiple Regression Host Overload Detection (MRHOD, significantly reduces energy consumption while ensuring a high level of adherence to Service Level Agreements (SLA since it gives a real indication of host utilization based on three parameters (CPU, Memory, Bandwidth utilizations instead of one parameter only (CPU utilization. Through simulations we show that our approach reduces power consumption by 6 times compared to single factor algorithms using random workload. Also using PlanetLab workload traces we show that MRHOD improves
A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis
Directory of Open Access Journals (Sweden)
Zhiming Song
2015-01-01
Full Text Available As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.
Nocita, M.; Stevens, A.; Toth, G.; van Wesemael, B.; Montanarella, L.
2012-12-01
In the context of global environmental change, the estimation of carbon fluxes between soils and the atmosphere has been the object of a growing number of studies. This has been motivated notably by the possibility to sequester CO2 into soils by increasing the soil organic carbon (SOC) stocks and by the role of SOC in maintaining soil quality. Spatial variability of SOC masks its slow accumulation or depletion, and the sampling density required to detect a change in SOC content is often very high and thus very expensive and labour intensive. Visible near infrared diffuse reflectance spectroscopy (Vis-NIR DRS) has been shown to be a fast, cheap and efficient tool for the prediction of SOC at fine scales. However, when applied to regional or country scales, Vis-NIR DRS did not provide sufficient accuracy as an alternative to standard laboratory soil analysis for SOC monitoring. Under the framework of Land Use/Cover Area Frame Statistical Survey (LUCAS) project of the European Commission's Joint Research Centre (JRC), about 20,000 samples were collected all over European Union. Soil samples were analyzed for several physical and chemical parameters, and scanned with a Vis-NIR spectrometer in the same laboratory. The scope of our research was to predict SOC content at European scale using LUCAS spectral library. We implemented a modified local partial least square regression (l-PLS) including, in addition to spectral distance, other potentially useful covariates (geography, texture, etc.) to select for each unknown sample a group of predicting neighbours. The dataset was split in mineral soils under cropland, mineral soils under grassland, mineral soils under woodland, and organic soils due to the extremely diverse spectral response of the four classes. Four every class training (70%) and test (30%) sets were created to calibrate and validate the SOC prediction models. The results showed very good prediction ability for mineral soils under cropland and mineral soils
Variable selection in Logistic regression model with genetic algorithm.
Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi
2018-02-01
Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.
Al-Harrasi, Ahmed; Rehman, Najeeb Ur; Mabood, Fazal; Albroumi, Muhammaed; Ali, Liaqat; Hussain, Javid; Hussain, Hidayat; Csuk, René; Khan, Abdul Latif; Alam, Tanveer; Alameri, Saif
2017-09-01
In the present study, for the first time, NIR spectroscopy coupled with PLS regression as a rapid and alternative method was developed to quantify the amount of Keto-β-Boswellic Acid (KBA) in different plant parts of Boswellia sacra and the resin exudates of the trunk. NIR spectroscopy was used for the measurement of KBA standards and B. sacra samples in absorption mode in the wavelength range from 700-2500 nm. PLS regression model was built from the obtained spectral data using 70% of KBA standards (training set) in the range from 0.1 ppm to 100 ppm. The PLS regression model obtained was having R-square value of 98% with 0.99 corelationship value and having good prediction with RMSEP value 3.2 and correlation of 0.99. It was then used to quantify the amount of KBA in the samples of B. sacra. The results indicated that the MeOH extract of resin has the highest concentration of KBA (0.6%) followed by essential oil (0.1%). However, no KBA was found in the aqueous extract. The MeOH extract of the resin was subjected to column chromatography to get various sub-fractions at different polarity of organic solvents. The sub-fraction at 4% MeOH/CHCl3 (4.1% of KBA) was found to contain the highest percentage of KBA followed by another sub-fraction at 2% MeOH/CHCl3 (2.2% of KBA). The present results also indicated that KBA is only present in the gum-resin of the trunk and not in all parts of the plant. These results were further confirmed through HPLC analysis and therefore it is concluded that NIRS coupled with PLS regression is a rapid and alternate method for quantification of KBA in Boswellia sacra. It is non-destructive, rapid, sensitive and uses simple methods of sample preparation.
Outlier detection algorithms for least squares time series regression
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Bent
We review recent asymptotic results on some robust methods for multiple regression. The regressors include stationary and non-stationary time series as well as polynomial terms. The methods include the Huber-skip M-estimator, 1-step Huber-skip M-estimators, in particular the Impulse Indicator Sat...
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
Ulbrich, Norbert Manfred
2013-01-01
A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.
A comparison of regression algorithms for wind speed forecasting at Alexander Bay
CSIR Research Space (South Africa)
Botha, Nicolene
2016-12-01
Full Text Available to forecast 1 to 24 hours ahead, in hourly intervals. Predictions are performed on a wind speed time series with three machine learning regression algorithms, namely support vector regression, ordinary least squares and Bayesian ridge regression. The resulting...
Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation
Directory of Open Access Journals (Sweden)
Ching-Wei Chen
2016-01-01
Full Text Available This study covers records of various parameters affecting the power consumption of air-conditioning systems. Using the Support Vector Machine (SVM, the chiller power consumption model, secondary chilled water pump power consumption model, air handling unit fan power consumption model, and air handling unit load model were established. In addition, it was found that R2 of the models all reached 0.998, and the training time was far shorter than that of the neural network. Through genetic programming, a combination of operating parameters with the least power consumption of air conditioning operation was searched. Moreover, the air handling unit load in line with the air conditioning cooling load was predicted. The experimental results show that for the combination of operating parameters with the least power consumption in line with the cooling load obtained through genetic algorithm search, the power consumption of the air conditioning systems under said combination of operating parameters was reduced by 22% compared to the fixed operating parameters, thus indicating significant energy efficiency.
Baratieri, Sabrina C; Barbosa, Juliana M; Freitas, Matheus P; Martins, José A
2006-01-23
A multivariate method of analysis of nystatin and metronidazole in a semi-solid matrix, based on diffuse reflectance NIR measurements and partial least squares regression, is reported. The product, a vaginal cream used in the antifungal and antibacterial treatment, is usually, quantitatively analyzed through microbiological tests (nystatin) and HPLC technique (metronidazole), according to pharmacopeial procedures. However, near infrared spectroscopy has demonstrated to be a valuable tool for content determination, given the rapidity and scope of the method. In the present study, it was successfully applied in the prediction of nystatin (even in low concentrations, ca. 0.3-0.4%, w/w, which is around 100,000 IU/5g) and metronidazole contents, as demonstrated by some figures of merit, namely linearity, precision (mean and repeatability) and accuracy.
Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.
Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo
2015-08-01
Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.
Directory of Open Access Journals (Sweden)
ELİF BULUT
2013-06-01
Full Text Available Partial Least Squares Regression (PLSR is a multivariate statistical method that consists of partial least squares and multiple linear regression analysis. Explanatory variables, X, having multicollinearity are reduced to components which explain the great amount of covariance between explanatory and response variable. These components are few in number and they don’t have multicollinearity problem. Then multiple linear regression analysis is applied to those components to model the response variable Y. There are various PLSR algorithms. In this study NIPALS and PLS-Kernel algorithms will be studied and illustrated on a real data set.
Balabin, Roman M; Lomakina, Ekaterina I
2011-04-21
In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.
Cheng, Jun-Hu; Jin, Huali; Liu, Zhiwei
2018-01-01
The feasibility of developing a multispectral imaging method using important wavelengths from hyperspectral images selected by genetic algorithm (GA), successive projection algorithm (SPA) and regression coefficient (RC) methods for modeling and predicting protein content in peanut kernel was investigated for the first time. Partial least squares regression (PLSR) calibration model was established between the spectral data from the selected optimal wavelengths and the reference measured protein content ranged from 23.46% to 28.43%. The RC-PLSR model established using eight key wavelengths (1153, 1567, 1972, 2143, 2288, 2339, 2389 and 2446 nm) showed the best predictive results with the coefficient of determination of prediction (R2P) of 0.901, and root mean square error of prediction (RMSEP) of 0.108 and residual predictive deviation (RPD) of 2.32. Based on the obtained best model and image processing algorithms, the distribution maps of protein content were generated. The overall results of this study indicated that developing a rapid and online multispectral imaging system using the feature wavelengths and PLSR analysis is potential and feasible for determination of the protein content in peanut kernels.
International Nuclear Information System (INIS)
Hong, W.-C.
2009-01-01
Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. Recently, support vector regression (SVR), with nonlinear mapping capabilities of forecasting, has been successfully employed to solve nonlinear regression and time series problems. However, it is still lack of systematic approaches to determine appropriate parameter combination for a SVR model. This investigation elucidates the feasibility of applying chaotic particle swarm optimization (CPSO) algorithm to choose the suitable parameter combination for a SVR model. The empirical results reveal that the proposed model outperforms the other two models applying other algorithms, genetic algorithm (GA) and simulated annealing algorithm (SA). Finally, it also provides the theoretical exploration of the electric load forecasting support system (ELFSS)
Strecht, Pedro; Cruz, Luís; Soares, Carlos; Mendes-Moreira, João; Abreu, Rui
2015-01-01
Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is…
Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K
2015-01-01
Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (pmachine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273
Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong
2018-01-01
Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.
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Xu Yu
2018-01-01
Full Text Available Cross-domain collaborative filtering (CDCF solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR. We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.
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Cheng-Wen Lee
2017-11-01
Full Text Available Accurate electricity forecasting is still the critical issue in many energy management fields. The applications of hybrid novel algorithms with support vector regression (SVR models to overcome the premature convergence problem and improve forecasting accuracy levels also deserve to be widely explored. This paper applies chaotic function and quantum computing concepts to address the embedded drawbacks including crossover and mutation operations of genetic algorithms. Then, this paper proposes a novel electricity load forecasting model by hybridizing chaotic function and quantum computing with GA in an SVR model (named SVRCQGA to achieve more satisfactory forecasting accuracy levels. Experimental examples demonstrate that the proposed SVRCQGA model is superior to other competitive models.
Zhang, Shen; Zheng, Yanchun; Wang, Daifa; Wang, Ling; Ma, Jianai; Zhang, Jing; Xu, Weihao; Li, Deyu; Zhang, Dan
2017-08-10
Motor imagery is one of the most investigated paradigms in the field of brain-computer interfaces (BCIs). The present study explored the feasibility of applying a common spatial pattern (CSP)-based algorithm for a functional near-infrared spectroscopy (fNIRS)-based motor imagery BCI. Ten participants performed kinesthetic imagery of their left- and right-hand movements while 20-channel fNIRS signals were recorded over the motor cortex. The CSP method was implemented to obtain the spatial filters specific for both imagery tasks. The mean, slope, and variance of the CSP filtered signals were taken as features for BCI classification. Results showed that the CSP-based algorithm outperformed two representative channel-wise methods for classifying the two imagery statuses using either data from all channels or averaged data from imagery responsive channels only (oxygenated hemoglobin: CSP-based: 75.3±13.1%; all-channel: 52.3±5.3%; averaged: 64.8±13.2%; deoxygenated hemoglobin: CSP-based: 72.3±13.0%; all-channel: 48.8±8.2%; averaged: 63.3±13.3%). Furthermore, the effectiveness of the CSP method was also observed for the motor execution data to a lesser extent. A partial correlation analysis revealed significant independent contributions from all three types of features, including the often-ignored variance feature. To our knowledge, this is the first study demonstrating the effectiveness of the CSP method for fNIRS-based motor imagery BCIs. Copyright © 2017 Elsevier B.V. All rights reserved.
A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover
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Akpona Okujeni
2014-07-01
Full Text Available Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR, kernel ridge regression (KRR, artificial neural networks (NN, random forest regression (RFR and partial least squares regression (PLSR. Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN or limited (RFR and PLSR performances when comprehensively mapping urban land cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.
Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.
Hu, Yi-Chung
2014-01-01
On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.
de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino
2018-05-01
This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.
Pair- ${v}$ -SVR: A Novel and Efficient Pairing nu-Support Vector Regression Algorithm.
Hao, Pei-Yi
This paper proposes a novel and efficient pairing nu-support vector regression (pair--SVR) algorithm that combines successfully the superior advantages of twin support vector regression (TSVR) and classical -SVR algorithms. In spirit of TSVR, the proposed pair--SVR solves two quadratic programming problems (QPPs) of smaller size rather than a single larger QPP, and thus has faster learning speed than classical -SVR. The significant advantage of our pair--SVR over TSVR is the improvement in the prediction speed and generalization ability by introducing the concepts of the insensitive zone and the regularization term that embodies the essence of statistical learning theory. Moreover, pair--SVR has additional advantage of using parameter for controlling the bounds on fractions of SVs and errors. Furthermore, the upper bound and lower bound functions of the regression model estimated by pair--SVR capture well the characteristics of data distributions, thus facilitating automatic estimation of the conditional mean and predictive variance simultaneously. This may be useful in many cases, especially when the noise is heteroscedastic and depends strongly on the input values. The experimental results validate the superiority of our pair--SVR in both training/prediction speed and generalization ability.This paper proposes a novel and efficient pairing nu-support vector regression (pair--SVR) algorithm that combines successfully the superior advantages of twin support vector regression (TSVR) and classical -SVR algorithms. In spirit of TSVR, the proposed pair--SVR solves two quadratic programming problems (QPPs) of smaller size rather than a single larger QPP, and thus has faster learning speed than classical -SVR. The significant advantage of our pair--SVR over TSVR is the improvement in the prediction speed and generalization ability by introducing the concepts of the insensitive zone and the regularization term that embodies the essence of statistical learning theory
Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)
Lestander, Torbjörn
2003-01-01
This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potenti...
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Jianzhou Wang
2015-01-01
Full Text Available This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP and optimized support vector regression (SVR. Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA, particle swarm optimization algorithm (PSO, and cuckoo optimization algorithm (COA. Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1 analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2 the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3 the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.
International Nuclear Information System (INIS)
Hong, Wei-Chiang
2011-01-01
Support vector regression (SVR), with hybrid chaotic sequence and evolutionary algorithms to determine suitable values of its three parameters, not only can effectively avoid converging prematurely (i.e., trapping into a local optimum), but also reveals its superior forecasting performance. Electric load sometimes demonstrates a seasonal (cyclic) tendency due to economic activities or climate cyclic nature. The applications of SVR models to deal with seasonal (cyclic) electric load forecasting have not been widely explored. In addition, the concept of recurrent neural networks (RNNs), focused on using past information to capture detailed information, is helpful to be combined into an SVR model. This investigation presents an electric load forecasting model which combines the seasonal recurrent support vector regression model with chaotic artificial bee colony algorithm (namely SRSVRCABC) to improve the forecasting performance. The proposed SRSVRCABC employs the chaotic behavior of honey bees which is with better performance in function optimization to overcome premature local optimum. A numerical example from an existed reference is used to elucidate the forecasting performance of the proposed SRSVRCABC model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. Therefore, the SRSVRCABC model is a promising alternative for electric load forecasting. -- Highlights: → Hybridizing the seasonal adjustment and the recurrent mechanism into an SVR model. → Employing chaotic sequence to improve the premature convergence of artificial bee colony algorithm. → Successfully providing significant accurate monthly load demand forecasting.
Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H
2016-01-01
Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.
Yulia, M.; Suhandy, D.
2018-03-01
NIR spectra obtained from spectral data acquisition system contains both chemical information of samples as well as physical information of the samples, such as particle size and bulk density. Several methods have been established for developing calibration models that can compensate for sample physical information variations. One common approach is to include physical information variation in the calibration model both explicitly and implicitly. The objective of this study was to evaluate the feasibility of using explicit method to compensate the influence of different particle size of coffee powder in NIR calibration model performance. A number of 220 coffee powder samples with two different types of coffee (civet and non-civet) and two different particle sizes (212 and 500 µm) were prepared. Spectral data was acquired using NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement. A discrimination method based on PLS-DA was conducted and the influence of different particle size on the performance of PLS-DA was investigated. In explicit method, we add directly the particle size as predicted variable results in an X block containing only the NIR spectra and a Y block containing the particle size and type of coffee. The explicit inclusion of the particle size into the calibration model is expected to improve the accuracy of type of coffee determination. The result shows that using explicit method the quality of the developed calibration model for type of coffee determination is a little bit superior with coefficient of determination (R2) = 0.99 and root mean square error of cross-validation (RMSECV) = 0.041. The performance of the PLS2 calibration model for type of coffee determination with particle size compensation was quite good and able to predict the type of coffee in two different particle sizes with relatively high R2 pred values. The prediction also resulted in low bias and RMSEP values.
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Meiping Wang
2016-01-01
Full Text Available We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR model-optimized particle swarm optimization (PSO algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR, and artificial neural network (ANN through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.
International Nuclear Information System (INIS)
Kumar, Akansha; Tsvetkov, Pavel V.
2015-01-01
Highlights: • This paper presents a new method useful for the optimization of complex dynamic systems. • The method uses the strengths of; genetic algorithms (GA), and regression splines. • The method is applied to the design of a gas cooled fast breeder reactor design. • Tools like Java, R, and codes like MCNP, Matlab are used in this research. - Abstract: A module based optimization method using genetic algorithms (GA), and multivariate regression analysis has been developed to optimize a set of parameters in the design of a nuclear reactor. GA simulates natural evolution to perform optimization, and is widely used in recent times by the scientific community. The GA fits a population of random solutions to the optimized solution of a specific problem. In this work, we have developed a genetic algorithm to determine the values for a set of nuclear reactor parameters to design a gas cooled fast breeder reactor core including a basis thermal–hydraulics analysis, and energy transfer. Multivariate regression is implemented using regression splines (RS). Reactor designs are usually complex and a simulation needs a significantly large amount of time to execute, hence the implementation of GA or any other global optimization techniques is not feasible, therefore we present a new method of using RS in conjunction with GA. Due to using RS, we do not necessarily need to run the neutronics simulation for all the inputs generated from the GA module rather, run the simulations for a predefined set of inputs, build a multivariate regression fit to the input and the output parameters, and then use this fit to predict the output parameters for the inputs generated by GA. The reactor parameters are given by the, radius of a fuel pin cell, isotopic enrichment of the fissile material in the fuel, mass flow rate of the coolant, and temperature of the coolant at the core inlet. And, the optimization objectives for the reactor core are, high breeding of U-233 and Pu-239 in
Forecasting systems reliability based on support vector regression with genetic algorithms
International Nuclear Information System (INIS)
Chen, K.-Y.
2007-01-01
This study applies a novel neural-network technique, support vector regression (SVR), to forecast reliability in engine systems. The aim of this study is to examine the feasibility of SVR in systems reliability prediction by comparing it with the existing neural-network approaches and the autoregressive integrated moving average (ARIMA) model. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using real-value genetic algorithms, and then adopts the optimal parameters to construct the SVR models. A real reliability data for 40 suits of turbochargers were employed as the data set. The experimental results demonstrate that SVR outperforms the existing neural-network approaches and the traditional ARIMA models based on the normalized root mean square error and mean absolute percentage error
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Avval Zhila Mohajeri
2015-01-01
Full Text Available This paper deals with developing a linear quantitative structure-activity relationship (QSAR model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-α] indole, diazepino [1,2-α] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR technique combined with the stepwise (SW and the genetic algorithm (GA methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.
Asencio-Cortés, G.; Morales-Esteban, A.; Shang, X.; Martínez-Álvarez, F.
2018-06-01
Earthquake magnitude prediction is a challenging problem that has been widely studied during the last decades. Statistical, geophysical and machine learning approaches can be found in literature, with no particularly satisfactory results. In recent years, powerful computational techniques to analyze big data have emerged, making possible the analysis of massive datasets. These new methods make use of physical resources like cloud based architectures. California is known for being one of the regions with highest seismic activity in the world and many data are available. In this work, the use of several regression algorithms combined with ensemble learning is explored in the context of big data (1 GB catalog is used), in order to predict earthquakes magnitude within the next seven days. Apache Spark framework, H2 O library in R language and Amazon cloud infrastructure were been used, reporting very promising results.
Application of an Intelligent Fuzzy Regression Algorithm in Road Freight Transportation Modeling
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Pooya Najaf
2013-07-01
Full Text Available Road freight transportation between provinces of a country has an important effect on the traffic flow of intercity transportation networks. Therefore, an accurate estimation of the road freight transportation for provinces of a country is so crucial to improve the rural traffic operation in a large scale management. Accordingly, the focused case study database in this research is the information related to Iran’s provinces in the year 2008. Correlation between road freight transportation with variables such as transport cost and distance, population, average household income and Gross Domestic Product (GDP of each province is calculated. Results clarify that the population is the most effective factor in the prediction of provinces’ transported freight. Linear Regression Model (LRM is calibrated based on the population variable, and afterwards Fuzzy Regression Algorithm (FRA is generated on the basis of the LRM. The proposed FRA is an intelligent modified algorithm with an accurate prediction and fitting ability. This methodology can be significantly useful in macro-level planning problems where decreasing prediction error values is one of the most important concerns for decision makers. In addition, Back-Propagation Neural Network (BPNN is developed to evaluate the prediction capability of the models and to be compared with FRA. According to the final results, the modified FRA estimates road freight transportation values more accurately than the BPNN and LRM. Finally, in order to predict the road freight transportation values, the reliability of the calibrated models is analyzed using the information of the year 2009. Results show higher reliability for the proposed modified FRA.
A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data
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Ruzzo Walter L
2006-03-01
Full Text Available Abstract Background As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. Methods In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. Results We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly. Conclusion Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets.
A robust background regression based score estimation algorithm for hyperspectral anomaly detection
Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei
2016-12-01
Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement
Yamamoto, M; Hayata, I; Furuta, S
1992-03-01
Since 1989 we have promoted a project to develop an automated scoring system of radiation induced chromosome aberrations. As a first step, a high resolution image processing system for study purposes, NIRS-1000:CHROMO STUDY, has been developed. It is composed of: (1) CHROMO MARKER whose main purpose is to mark on images to make image data base, (2) CHROMO ALGO whose purpose is algorithm development, and (3) METAPHASE RANKER whose purposes are metaphase finding and ranking with a high power objective lens. However, METAPHASE RANKER is presently under development. The system utilizes a high definition video system so as to realize the best spatial resolution that is achievable with an optical microscope using an objective lens (x 100, numerical aperture 1.4). The video camera has 1024 effective scan lines to realize 0.1 microns sampling on a specimen. The system resolution achieved on the hard copy is less than 0.3 microns on a specimen. A preliminary algorithm has been developed to classify the aberrations on the system using projection information of gray level. The preliminary test results on excellent 10 metaphases show that the correct classification ratio is 92.7%, that the detection rate of the aberrations is 83.3% and that the false positive rate is 6.1%.
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Marco F. Ferrão
2007-08-01
Full Text Available Least-squares support vector machines (LS-SVM were used as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants found in powdered milk samples, using near-infrared spectroscopy. Excellent models were built using LS-SVM for determining R², RMSECV and RMSEP values. LS-SVMs show superior performance for quantifying starch, whey and sucrose in powdered milk samples in relation to PLSR. This study shows that it is possible to determine precisely the amount of one and two common adulterants simultaneously in powdered milk samples using LS-SVM and NIR spectra.
An integrated study of surface roughness in EDM process using regression analysis and GSO algorithm
Zainal, Nurezayana; Zain, Azlan Mohd; Sharif, Safian; Nuzly Abdull Hamed, Haza; Mohamad Yusuf, Suhaila
2017-09-01
The aim of this study is to develop an integrated study of surface roughness (Ra) in the die-sinking electrical discharge machining (EDM) process of Ti-6AL-4V titanium alloy with positive polarity of copper-tungsten (Cu-W) electrode. Regression analysis and glowworm swarm optimization (GSO) algorithm were considered for modelling and optimization process. Pulse on time (A), pulse off time (B), peak current (C) and servo voltage (D) were selected as the machining parameters with various levels. The experiments have been conducted based on the two levels of full factorial design with an added center point design of experiments (DOE). Moreover, mathematical models with linear and 2 factor interaction (2FI) effects of the parameters chosen were developed. The validity test of the fit and the adequacy of the developed mathematical models have been carried out by using analysis of variance (ANOVA) and F-test. The statistical analysis showed that the 2FI model outperformed with the most minimal value of Ra compared to the linear model and experimental result.
Demand analysis of flood insurance by using logistic regression model and genetic algorithm
Sidi, P.; Mamat, M. B.; Sukono; Supian, S.; Putra, A. S.
2018-03-01
Citarum River floods in the area of South Bandung Indonesia, often resulting damage to some buildings belonging to the people living in the vicinity. One effort to alleviate the risk of building damage is to have flood insurance. The main obstacle is not all people in the Citarum basin decide to buy flood insurance. In this paper, we intend to analyse the decision to buy flood insurance. It is assumed that there are eight variables that influence the decision of purchasing flood assurance, include: income level, education level, house distance with river, building election with road, flood frequency experience, flood prediction, perception on insurance company, and perception towards government effort in handling flood. The analysis was done by using logistic regression model, and to estimate model parameters, it is done with genetic algorithm. The results of the analysis shows that eight variables analysed significantly influence the demand of flood insurance. These results are expected to be considered for insurance companies, to influence the decision of the community to be willing to buy flood insurance.
International Nuclear Information System (INIS)
Bunyamin, Muhammad Afif; Yap, Keem Siah; Aziz, Nur Liyana Afiqah Abdul; Tiong, Sheih Kiong; Wong, Shen Yuong; Kamal, Md Fauzan
2013-01-01
This paper presents a new approach of gas emission estimation in power generation plant using a hybrid Genetic Algorithm (GA) and Linear Regression (LR) (denoted as GA-LR). The LR is one of the approaches that model the relationship between an output dependant variable, y, with one or more explanatory variables or inputs which denoted as x. It is able to estimate unknown model parameters from inputs data. On the other hand, GA is used to search for the optimal solution until specific criteria is met causing termination. These results include providing good solutions as compared to one optimal solution for complex problems. Thus, GA is widely used as feature selection. By combining the LR and GA (GA-LR), this new technique is able to select the most important input features as well as giving more accurate prediction by minimizing the prediction errors. This new technique is able to produce more consistent of gas emission estimation, which may help in reducing population to the environment. In this paper, the study's interest is focused on nitrous oxides (NOx) prediction. The results of the experiment are encouraging.
Energy Technology Data Exchange (ETDEWEB)
Azadeh, A; Seraj, O [Department of Industrial Engineering and Research Institute of Energy Management and Planning, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, University of Tehran, P.O. Box 11365-4563 (Iran); Saberi, M [Department of Industrial Engineering, University of Tafresh (Iran); Institute for Digital Ecosystems and Business Intelligence, Curtin University of Technology, Perth (Australia)
2010-06-15
This study presents an integrated fuzzy regression and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data. Furthermore, it is difficult to model uncertain behavior of energy consumption with only conventional fuzzy regression (FR) or time series and the integrated algorithm could be an ideal substitute for such cases. At First, preferred Time series model is selected from linear or nonlinear models. For this, after selecting preferred Auto Regression Moving Average (ARMA) model, Mcleod-Li test is applied to determine nonlinearity condition. When, nonlinearity condition is satisfied, the preferred nonlinear model is selected and defined as preferred time series model. At last, the preferred model from fuzzy regression and time series model is selected by the Granger-Newbold. Also, the impact of data preprocessing on the fuzzy regression performance is considered. Monthly electricity consumption of Iran from March 1994 to January 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with other intelligent tools such as Genetic Algorithm (GA) and Artificial Neural Network (ANN). (author)
[Determination of acidity and vitamin C in apples using portable NIR analyzer].
Yang, Fan; Li, Ya-Ting; Gu, Xuan; Ma, Jiang; Fan, Xing; Wang, Xiao-Xuan; Zhang, Zhuo-Yong
2011-09-01
Near infrared (NIR) spectroscopy technology based on a portable NIR analyzer, combined with kernel Isomap algorithm and generalized regression neural network (GRNN) has been applied to establishing quantitative models for prediction of acidity and vitamin C in six kinds of apple samples. The obtained results demonstrated that the fitting and the predictive accuracy of the models with kernel Isomap algorithm were satisfactory. The correlation between actual and predicted values of calibration samples (R(c)) obtained by the acidity model was 0.999 4, and for prediction samples (R(p)) was 0.979 9. The root mean square error of prediction set (RMSEP) was 0.055 8. For the vitamin C model, R(c) was 0.989 1, R(p) was 0.927 2, and RMSEP was 4.043 1. Results proved that the portable NIR analyzer can be a feasible tool for the determination of acidity and vitamin C in apples.
Energy Technology Data Exchange (ETDEWEB)
Hemmateenejad, Bahram, E-mail: hemmatb@sums.ac.ir [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of); Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Shamsipur, Mojtaba [Department of Chemistry, Razi University, Kermanshah (Iran, Islamic Republic of); Zare-Shahabadi, Vali [Young Researchers Club, Mahshahr Branch, Islamic Azad University, Mahshahr (Iran, Islamic Republic of); Akhond, Morteza [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of)
2011-10-17
Highlights: {yields} Ant colony systems help to build optimum classification and regression trees. {yields} Using of genetic algorithm operators in ant colony systems resulted in more appropriate models. {yields} Variable selection in each terminal node of the tree gives promising results. {yields} CART-ACS-GA could model the melting point of organic materials with prediction errors lower than previous models. - Abstract: The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.
Diagnostic Algorithm to Reflect Regressive Changes of Human Papilloma Virus in Tissue Biopsies
Lhee, Min Jin; Cha, Youn Jin; Bae, Jong Man; Kim, Young Tae
2014-01-01
Purpose Landmark indicators have not yet to be developed to detect the regression of cervical intraepithelial neoplasia (CIN). We propose that quantitative viral load and indicative histological criteria can be used to differentiate between atypical squamous cells of undetermined significance (ASCUS) and a CIN of grade 1. Materials and Methods We collected 115 tissue biopsies from women who tested positive for the human papilloma virus (HPV). Nine morphological parameters including nuclear size, perinuclear halo, hyperchromasia, typical koilocyte (TK), abortive koilocyte (AK), bi-/multi-nucleation, keratohyaline granules, inflammation, and dyskeratosis were examined for each case. Correlation analyses, cumulative logistic regression, and binary logistic regression were used to determine optimal cut-off values of HPV copy numbers. The parameters TK, perinuclear halo, multi-nucleation, and nuclear size were significantly correlated quantitatively to HPV copy number. Results An HPV loading number of 58.9 and AK number of 20 were optimal to discriminate between negative and subtle findings in biopsies. An HPV loading number of 271.49 and AK of 20 were optimal for discriminating between equivocal changes and obvious koilocytosis. Conclusion We propose that a squamous epithelial lesion with AK of >20 and quantitative HPV copy number between 58.9-271.49 represents a new spectrum of subtle pathological findings, characterized by AK in ASCUS. This can be described as a distinct entity and called "regressing koilocytosis". PMID:24532500
Directory of Open Access Journals (Sweden)
S.K. Lahiri
2009-09-01
Full Text Available Soft sensors have been widely used in the industrial process control to improve the quality of the product and assure safety in the production. The core of a soft sensor is to construct a soft sensing model. This paper introduces support vector regression (SVR, a new powerful machine learning methodbased on a statistical learning theory (SLT into soft sensor modeling and proposes a new soft sensing modeling method based on SVR. This paper presents an artificial intelligence based hybrid soft sensormodeling and optimization strategies, namely support vector regression – genetic algorithm (SVR-GA for modeling and optimization of mono ethylene glycol (MEG quality variable in a commercial glycol plant. In the SVR-GA approach, a support vector regression model is constructed for correlating the process data comprising values of operating and performance variables. Next, model inputs describing the process operating variables are optimized using genetic algorithm with a view to maximize the process performance. The SVR-GA is a new strategy for soft sensor modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics etc. is not required. Using SVR-GA strategy, a number of sets of optimized operating conditions were found. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the quality.
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Xiangbing Zhou
2018-04-01
Full Text Available Rapidly growing GPS (Global Positioning System trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM, our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.
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Yongquan Dong
2018-04-01
Full Text Available Providing accurate electric load forecasting results plays a crucial role in daily energy management of the power supply system. Due to superior forecasting performance, the hybridizing support vector regression (SVR model with evolutionary algorithms has received attention and deserves to continue being explored widely. The cuckoo search (CS algorithm has the potential to contribute more satisfactory electric load forecasting results. However, the original CS algorithm suffers from its inherent drawbacks, such as parameters that require accurate setting, loss of population diversity, and easy trapping in local optima (i.e., premature convergence. Therefore, proposing some critical improvement mechanisms and employing an improved CS algorithm to determine suitable parameter combinations for an SVR model is essential. This paper proposes the SVR with chaotic cuckoo search (SVRCCS model based on using a tent chaotic mapping function to enrich the cuckoo search space and diversify the population to avoid trapping in local optima. In addition, to deal with the cyclic nature of electric loads, a seasonal mechanism is combined with the SVRCCS model, namely giving a seasonal SVR with chaotic cuckoo search (SSVRCCS model, to produce more accurate forecasting performances. The numerical results, tested by using the datasets from the National Electricity Market (NEM, Queensland, Australia and the New York Independent System Operator (NYISO, NY, USA, show that the proposed SSVRCCS model outperforms other alternative models.
Dai, Wensheng
2014-01-01
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740
Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie
2014-01-01
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.
Directory of Open Access Journals (Sweden)
Wensheng Dai
2014-01-01
Full Text Available Sales forecasting is one of the most important issues in managing information technology (IT chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR, is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA, temporal ICA (tICA, and spatiotemporal ICA (stICA to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.
Li, Zhongwei; Xin, Yuezhen; Wang, Xun; Sun, Beibei; Xia, Shengyu; Li, Hui
2016-01-01
Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization. PMID:27610365
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Adimi Maryam
2012-01-01
Full Text Available A quantitative structure activity relationship (QSAR model has been produced for predicting antagonist potency of biphenyl derivatives as human histamine (H3 receptors. The molecular structures of the compounds are numerically represented by various kinds of molecular descriptors. The whole data set was divided into training and test sets. Genetic algorithm based multiple linear regression is used to select most statistically effective descriptors. The final QSAR model (N =24, R2=0.916, F = 51.771, Q2 LOO = 0.872, Q2 LGO = 0.847, Q2 BOOT = 0.857 was fully validated employing leaveone- out (LOO cross-validation approach, Fischer statistics (F, Yrandomisation test, and predictions based on the test data set. The test set presented an external prediction power of R2 test=0.855. In conclusion, the QSAR model generated can be used as a valuable tool for designing similar groups of new antagonists of histamine (H3 receptors.
Fisz, Jacek J
2006-12-07
The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi
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Daniel Vasiliu
Full Text Available Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED. Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.
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Ji Zhou
2014-06-01
Full Text Available The land surface temperature (LST is one of the most important parameters of surface-atmosphere interactions. Methods for retrieving LSTs from satellite remote sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface. Many split-window (SW algorithms, which can be applied to satellite sensors with two adjacent thermal channels located in the atmospheric window between 10 μm and 12 μm, require auxiliary atmospheric parameters (e.g., water vapor content. In this research, the Heihe River basin, which is one of the most arid regions in China, is selected as the study area. The Moderate-resolution Imaging Spectroradiometer (MODIS is selected as a test case. The Global Data Assimilation System (GDAS atmospheric profiles of the study area are used to generate the training dataset through radiative transfer simulation. Significant correlations between the atmospheric upwelling radiance in MODIS channel 31 and the other three atmospheric parameters, including the transmittance in channel 31 and the transmittance and upwelling radiance in channel 32, are trained based on the simulation dataset and formulated with three regression models. Next, the genetic algorithm is used to estimate the LST. Validations of the RM-GA method are based on the simulation dataset generated from in situ measured radiosonde profiles and GDAS atmospheric profiles, the in situ measured LSTs, and a pair of daytime and nighttime MOD11A1 products in the study area. The results demonstrate that RM-GA has a good ability to estimate the LSTs directly from the MODIS data without any auxiliary atmospheric parameters. Although this research is for local application in the Heihe River basin, the findings and proposed method can easily be extended to other satellite sensors and regions with arid climates and high elevations.
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Dwi Marisa Efendi
2018-04-01
Full Text Available Cassava is one type of plant that can be planted in tropical climates. Cassava commodity is one of the leading sub-sectors in the plantation area. Cassava plant is the main ingredient of sago flour which is now experiencing price decline. The condition of the abundant supply of sago or tapioca flour production is due to the increase of cassava planting in each farmer. With the increasing number of cassava planting in farmer's plantation cause the price of cassava received by farmer is not suitable. So for the need of making sago or tapioca flour often excess in buying raw material of cassava This resulted in a lot of rotten cassava and the factory bought cassava for a low price. Based on the problem, this research is done using data mining modeled with multiple linear regression algorithm which aim to estimate the amount of Sago or Tapioca flour that can be produced, so that the future can improve the balance between the amount of cassava supply and tapioca production. The variables used in linear regression analysis are dependent variable and independent variable . From the data obtained, the dependent variable is the number of Tapioca (kg symbolized by Y while the independent variable is milled cassava symbolized by X. From the results obtained with an accuracy of 95% confidence level, then obtained coefficient of determination (R2 is 1.00. While the estimation results almost closer to the actual data value, with an average error of 0.00.
Improving NIR snow pit stratigraphy observations by introducing a controlled NIR light source
Dean, J.; Marshall, H.; Rutter, N.; Karlson, A.
2013-12-01
Near-infrared (NIR) photography in a prepared snow pit measures mm-/grain-scale variations in snow structure, as reflectivity is strongly dependent on microstructure and grain size at the NIR wavelengths. We explore using a controlled NIR light source to maximize signal to noise ratio and provide uniform incident, diffuse light on the snow pit wall. NIR light fired from the flash is diffused across and reflected by an umbrella onto the snow pit; the lens filter transmits NIR light onto the spectrum-modified sensor of the DSLR camera. Lenses are designed to refract visible light properly, not NIR light, so there must be a correction applied for the subsequent NIR bright spot. To avoid interpolation and debayering algorithms automatically performed by programs like Adobe's Photoshop on the images, the raw data are analyzed directly in MATLAB. NIR image data show a doubling of the amount of light collected in the same time for flash over ambient lighting. Transitions across layer boundaries in the flash-lit image are detailed by higher camera intensity values than ambient-lit images. Curves plotted using median intensity at each depth, normalized to the average profile intensity, show a separation between flash- and ambient-lit images in the upper 10-15 cm; the ambient-lit image curve asymptotically approaches the level of the flash-lit image curve below 15cm. We hypothesize that the difference is caused by additional ambient light penetrating the upper 10-15 cm of the snowpack from above and transmitting through the wall of the snow pit. This indicates that combining NIR ambient and flash photography could be a powerful technique for studying penetration depth of radiation as a function of microstructure and grain size. The NIR flash images do not increase the relative contrast at layer boundaries; however, the flash more than doubles the amount of recorded light and controls layer noise as well as layer boundary transition noise.
Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.
2017-01-01
Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...
Rosero-Vlasova, O.; Borini Alves, D.; Vlassova, L.; Perez-Cabello, F.; Montorio Lloveria, R.
2017-10-01
Deforestation in Amazon basin due, among other factors, to frequent wildfires demands continuous post-fire monitoring of soil and vegetation. Thus, the study posed two objectives: (1) evaluate the capacity of Visible - Near InfraRed - ShortWave InfraRed (VIS-NIR-SWIR) spectroscopy to estimate soil organic matter (SOM) in fire-affected soils, and (2) assess the feasibility of SOM mapping from satellite images. For this purpose, 30 soil samples (surface layer) were collected in 2016 in areas of grass and riparian vegetation of Campos Amazonicos National Park, Brazil, repeatedly affected by wildfires. Standard laboratory procedures were applied to determine SOM. Reflectance spectra of soils were obtained in controlled laboratory conditions using Fieldspec4 spectroradiometer (spectral range 350nm- 2500nm). Measured spectra were resampled to simulate reflectances for Landsat-8, Sentinel-2 and EnMap spectral bands, used as predictors in SOM models developed using Partial Least Squares regression and step-down variable selection algorithm (PLSR-SD). The best fit was achieved with models based on reflectances simulated for EnMap bands (R2=0.93; R2cv=0.82 and NMSE=0.07; NMSEcv=0.19). The model uses only 8 out of 244 predictors (bands) chosen by the step-down variable selection algorithm. The least reliable estimates (R2=0.55 and R2cv=0.40 and NMSE=0.43; NMSEcv=0.60) resulted from Landsat model, while Sentinel-2 model showed R2=0.68 and R2cv=0.63; NMSE=0.31 and NMSEcv=0.38. The results confirm high potential of VIS-NIR-SWIR spectroscopy for SOM estimation. Application of step-down produces sparser and better-fit models. Finally, SOM can be estimated with an acceptable accuracy (NMSE 0.35) from EnMap and Sentinel-2 data enabling mapping and analysis of impacts of repeated wildfires on soils in the study area.
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Lüdtke Rainer
2008-08-01
Full Text Available Abstract Background Regression to the mean (RTM occurs in situations of repeated measurements when extreme values are followed by measurements in the same subjects that are closer to the mean of the basic population. In uncontrolled studies such changes are likely to be interpreted as a real treatment effect. Methods Several statistical approaches have been developed to analyse such situations, including the algorithm of Mee and Chua which assumes a known population mean μ. We extend this approach to a situation where μ is unknown and suggest to vary it systematically over a range of reasonable values. Using differential calculus we provide formulas to estimate the range of μ where treatment effects are likely to occur when RTM is present. Results We successfully applied our method to three real world examples denoting situations when (a no treatment effect can be confirmed regardless which μ is true, (b when a treatment effect must be assumed independent from the true μ and (c in the appraisal of results of uncontrolled studies. Conclusion Our method can be used to separate the wheat from the chaff in situations, when one has to interpret the results of uncontrolled studies. In meta-analysis, health-technology reports or systematic reviews this approach may be helpful to clarify the evidence given from uncontrolled observational studies.
Rafiei, Hamid; Khanzadeh, Marziyeh; Mozaffari, Shahla; Bostanifar, Mohammad Hassan; Avval, Zhila Mohajeri; Aalizadeh, Reza; Pourbasheer, Eslam
2016-01-01
Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors . A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r(2), concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained.
Li, Yongxin; Li, Yuanqian; Zheng, Bo; Qu, Lingli; Li, Can
2009-06-08
A rapid and sensitive method based on microchip capillary electrophoresis with condition optimization of genetic algorithm-support vector regression (GA-SVR) was developed and applied to simultaneous analysis of multiplex PCR products of four foodborne pathogenic bacteria. Four pairs of oligonucleotide primers were designed to exclusively amplify the targeted gene of Vibrio parahemolyticus, Salmonella, Escherichia coli (E. coli) O157:H7, Shigella and the quadruplex PCR parameters were optimized. At the same time, GA-SVR was employed to optimize the separation conditions of DNA fragments in microchip capillary electrophoresis. The proposed method was applied to simultaneously detect the multiplex PCR products of four foodborne pathogenic bacteria under the optimal conditions within 8 min. The levels of detection were as low as 1.2 x 10(2) CFU mL(-1) of Vibrio parahemolyticus, 2.9 x 10(2) CFU mL(-1) of Salmonella, 8.7 x 10(1) CFU mL(-1) of E. coli O157:H7 and 5.2 x 10(1) CFU mL(-1) of Shigella, respectively. The relative standard deviation of migration time was in the range of 0.74-2.09%. The results demonstrated that the good resolution and less analytical time were achieved due to the application of the multivariate strategy. This study offers an efficient alternative to routine foodborne pathogenic bacteria detection in a fast, reliable, and sensitive way.
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Ping Jiang
2015-01-01
Full Text Available Wind speed/power has received increasing attention around the earth due to its renewable nature as well as environmental friendliness. With the global installed wind power capacity rapidly increasing, wind industry is growing into a large-scale business. Reliable short-term wind speed forecasts play a practical and crucial role in wind energy conversion systems, such as the dynamic control of wind turbines and power system scheduling. In this paper, an intelligent hybrid model for short-term wind speed prediction is examined; the model is based on cross correlation (CC analysis and a support vector regression (SVR model that is coupled with brainstorm optimization (BSO and cuckoo search (CS algorithms, which are successfully utilized for parameter determination. The proposed hybrid models were used to forecast short-term wind speeds collected from four wind turbines located on a wind farm in China. The forecasting results demonstrate that the intelligent hybrid models outperform single models for short-term wind speed forecasting, which mainly results from the superiority of BSO and CS for parameter optimization.
Pharmaceutical applications using NIR technology in the cloud
Grossmann, Luiz; Borges, Marco A.
2017-05-01
NIR technology has been available for a long time, certainly more than 50 years. Without any doubt, it has found many niche applications, especially in the pharmaceutical, food, agriculture and other industries due to its flexibility. There are a number of advantages over other existing analytical technologies we can list, for example virtually no need for sample preparation; usually NIR does not demand sample destruction and subsequent discard; NIR provides fast results; NIR does not require extensive operator training and carries small operating costs. However, the key point about NIR technology is the fact that it's more related to statistics than chemistry or, in other words, we are more concerned about analyzing and distinguishing features within the data than looking deep into the chemical entities themselves. A simple scan reading in the NIR range usually involves huge inflows of data points. Usually we decompose the signals into hundreds of predictor variables and use complex algorithms to predict classes or quantify specific content. NIR is all about math, especially by converting chemical information into numbers. Easier said than done. A NIR signal is a very complex one. Usually the signal responses are not specific to a particular material, rather, each grouṕs responses add up, thus providing low specificity of a spectral reading. This paper proposes a simple and efficient method to analyze and compare NIR spectra for the purpose of identifying the presence of active pharmaceutical ingredients in finished products using low cost NIR scanning devices connected to the internet cloud.
DEFF Research Database (Denmark)
Mozaffari, Ahmad; Gorji-Bandpy, Mofid; Samadian, Pendar
2013-01-01
Optimizing and controlling of complex engineering systems is a phenomenon that has attracted an incremental interest of numerous scientists. Until now, a variety of intelligent optimizing and controlling techniques such as neural networks, fuzzy logic, game theory, support vector machines...... and stochastic algorithms were proposed to facilitate controlling of the engineering systems. In this study, an extended version of mutable smart bee algorithm (MSBA) called Pareto based mutable smart bee (PBMSB) is inspired to cope with multi-objective problems. Besides, a set of benchmark problems and four...... well-known Pareto based optimizing algorithms i.e. multi-objective bee algorithm (MOBA), multi-objective particle swarm optimization (MOPSO) algorithm, non-dominated sorting genetic algorithm (NSGA-II), and strength Pareto evolutionary algorithm (SPEA 2) are utilized to confirm the acceptable...
Indian Academy of Sciences (India)
polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming.
Peterson, David L.; Condon, Estelle (Technical Monitor)
2000-01-01
Proponents of near infrared reflectance spectroscopy (NIRS) have been exceptionally successful in applying NIRS techniques to many instances of organic material analyses. While this research and development began in the 1950s, in recent years, stimulation of advancements in instrumentation is allowing NIRS to begin to find its way into the food processing systems, into food quality and safety, textiles and much more. And, imaging high spectral resolution spectrometers are now being evaluated for the rapid scanning of foodstuffs, such as the inspection of whole chicken carcasses for fecal contamination. The imaging methods are also finding their way into medical applications, such as the non-intrusive monitoring of blood oxygenation in newborns. Can these scientific insights also be taken into space and successfully used to measure the Earth's condition? Is there an analog between the organic analyses in the laboratory and clinical settings and the study of Earth's living biosphere? How are the methods comparable and how do they differ?
Bogler, Carsten; Mehnert, Jan; Steinbrink, Jens; Haynes, John-Dylan
2014-01-01
Sustained, long-term cognitive workload is associated with variations and decrements in performance. Such fluctuations in vigilance can be a risk factor especially during dangerous attention demanding activities. Functional MRI studies have shown that attentional performance is correlated with BOLD-signals, especially in parietal and prefrontal cortical regions. An interesting question is whether these BOLD-signals could be measured in real-world scenarios, say to warn in a dangerous workplace whenever a subjects' vigilance is low. Because fMRI lacks the mobility needed for such applications, we tested whether the monitoring of vigilance might be possible using Near-Infrared Spectroscopy (NIRS). NIRS is a highly mobile technique that measures hemodynamics in the surface of the brain. We demonstrate that non-invasive NIRS signals correlate with vigilance. These signals carry enough information to decode subjects' reaction times at a single trial level.
Directory of Open Access Journals (Sweden)
Carsten Bogler
Full Text Available Sustained, long-term cognitive workload is associated with variations and decrements in performance. Such fluctuations in vigilance can be a risk factor especially during dangerous attention demanding activities. Functional MRI studies have shown that attentional performance is correlated with BOLD-signals, especially in parietal and prefrontal cortical regions. An interesting question is whether these BOLD-signals could be measured in real-world scenarios, say to warn in a dangerous workplace whenever a subjects' vigilance is low. Because fMRI lacks the mobility needed for such applications, we tested whether the monitoring of vigilance might be possible using Near-Infrared Spectroscopy (NIRS. NIRS is a highly mobile technique that measures hemodynamics in the surface of the brain. We demonstrate that non-invasive NIRS signals correlate with vigilance. These signals carry enough information to decode subjects' reaction times at a single trial level.
Shastri, Niket; Pathak, Kamlesh
2018-05-01
The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.
Indian Academy of Sciences (India)
to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...
Directory of Open Access Journals (Sweden)
María-Teresa Sánchez
2011-06-01
Full Text Available NIR spectroscopy was used as a non-destructive technique for the assessment of chemical changes in the main internal quality properties of wine grapes (Vitis vinifera L. during on-vine ripening and at harvest. A total of 363 samples from 25 white and red grape varieties were used to construct quality-prediction models based on reference data and on NIR spectral data obtained using a commercially-available diode-array spectrophotometer (380–1,700 nm. The feasibility of testing bunches of intact grapes was investigated and compared with the more traditional must-based method. Two regression approaches (MPLS and LOCAL algorithms were tested for the quantification of changes in soluble solid content (SSC, reducing sugar content, pH-value, titratable acidity, tartaric acid, malic acid and potassium content. Cross-validation results indicated that NIRS technology provided excellent precision for sugar-related parameters (r2 = 0.94 for SSC and reducing sugar content and good precision for acidity-related parameters (r2 ranging between 0.73 and 0.87 for the bunch-analysis mode assayed using MPLS regression. At validation level, comparison of LOCAL and MPLS algorithms showed that the non-linear strategy improved the predictive capacity of the models for all study parameters, with particularly good results for acidity-related parameters and potassium content.
de Oliveira Neves, Ana Carolina; Soares, Gustavo Mesquita; de Morais, Stéphanie Cavalcante; da Costa, Fernanda Saadna Lopes; Porto, Dayanne Lopes; de Lima, Kássio Michell Gomes
2012-01-05
This work utilized the near-infrared spectroscopy (NIRS) and multivariate calibration to measure the percentage drug dissolution of four active pharmaceutical ingredients (APIs) (isoniazid, rifampicin, pyrazinamide and ethambutol) in finished pharmaceutical products produced in the Federal University of Rio Grande do Norte (Brazil). The conventional analytical method employed in quality control tests of the dissolution by the pharmaceutical industry is high-performance liquid chromatography (HPLC). The NIRS is a reliable method that offers important advantages for the large-scale production of tablets and for non-destructive analysis. NIR spectra of 38 samples (in triplicate) were measured using a Bomen FT-NIR 160 MB in the range 1100-2500nm. Each spectrum was the average of 50 scans obtained in the diffuse reflectance mode. The dissolution test, which was initially carried out in 900mL of 0.1N hydrochloric acid at 37±0.5°C, was used to determine the percentage a drug that dissolved from each tablet measured at the same time interval (45min) at pH 6.8. The measurement of the four API was performed by HPLC (Shimadzu, Japan) in the gradiente mode. The influence of various spectral pretreatments (Savitzky-Golay smoothing, Multiplicative Scatter Correction (MSC), and Savitzky-Golay derivatives) and multivariate analysis using the partial least squares (PLS) regression algorithm was calculated by the Unscrambler 9.8 (Camo) software. The correlation coefficient (R(2)) for the HPLC determination versus predicted values (NIRS) ranged from 0.88 to 0.98. The root-mean-square error of prediction (RMSEP) obtained from PLS models were 9.99%, 8.63%, 8.57% and 9.97% for isoniazid, rifampicin, ethambutol and pyrazinamide, respectively, indicating that the NIR method is an effective and non-destructive tool for measurement of drug dissolution from tablets. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.
Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok
2013-02-01
The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm
Lo, Ching F.
1999-01-01
The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.
Determination of drug, excipients and coating distribution in pharmaceutical tablets using NIR-CI
Directory of Open Access Journals (Sweden)
Anna Palou
2012-04-01
Full Text Available The growing interest of the pharmaceutical industry in Near Infrared-Chemical Imaging (NIR-CI is a result of its high usefulness for quality control analyses of drugs throughout their production process (particularly of its non-destructive nature and expeditious data acquisition. In this work, the concentration and distribution of the major and minor components of pharmaceutical tablets are determined and the spatial distribution from the internal and external sides has been obtained. In addition, the same NIR-CI allowed the coating thickness and its surface distribution to be quantified. Images were processed to extract the target data and calibration models constructed using the Partial Least Squares (PLS algorithms. The concentrations of Active Pharmaceutical Ingredient (API and excipients obtained for uncoated cores were essentially identical to the nominal values of the pharmaceutical formulation. But the predictive ability of the calibration models applied to the coated tablets decreased as the coating thickness increased. Keywords: Near infrared Chemical Imaging (NIR-CI, Hyperspectral imaging, Component distribution, Tablet coating distribution, Partial Least Squares (PLS regression
On children's dyslexia with NIRS
Gan, Zhuo; Li, Chengjun; Gong, Hui; Luo, Qingming; Yao, Bin; Song, Ranran; Wu, Hanrong
2003-12-01
Developmental dyslexia is a kind of prevalent psychologic disease. Some functional imaging technologies, such as FMRI and PET, have been used to study the brain activities of dyslexics. NIRS is a kind of novel technology which is more and more widely being used for study of the cognitive psychology. However, there aren"t reports about the dyslexic research using NIRS to be found until now. This paper introduces a NIRS system of four measuring channels. Brain activities of dyslexic subjects and normal subjects during reading task were studied with the NIRS system. Two groups of subjects, the group of dyslexia and the group of normal, were appointed to perform two reading tasks. At the same time, their cortical activities were measured with the NIRS system. This experimental result indicates that the brain activities of the dyslexic group were significantly higher than the control group in BA 48 and that NIRS can be used for the study of human brain activity.
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...
Indian Academy of Sciences (India)
ticians but also forms the foundation of computer science. Two ... with methods of developing algorithms for solving a variety of problems but ... applications of computers in science and engineer- ... numerical calculus are as important. We will ...
Shrivastava, Prashant Kumar; Pandey, Arun Kumar
2018-06-01
Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.
Indian Academy of Sciences (India)
algorithm design technique called 'divide-and-conquer'. One of ... Turtle graphics, September. 1996. 5. ... whole list named 'PO' is a pointer to the first element of the list; ..... Program for computing matrices X and Y and placing the result in C *).
Indian Academy of Sciences (India)
algorithm that it is implicitly understood that we know how to generate the next natural ..... Explicit comparisons are made in line (1) where maximum and minimum is ... It can be shown that the function T(n) = 3/2n -2 is the solution to the above ...
Ghavami, Raoof; Najafi, Amir; Sajadi, Mohammad; Djannaty, Farhad
2008-09-01
In order to accurately simulate (13)C NMR spectra of hydroxy, polyhydroxy and methoxy substituted flavonoid a quantitative structure-property relationship (QSPR) model, relating atom-based calculated descriptors to (13)C NMR chemical shifts (ppm, TMS=0), is developed. A dataset consisting of 50 flavonoid derivatives was employed for the present analysis. A set of 417 topological, geometrical, and electronic descriptors representing various structural characteristics was calculated and separate multilinear QSPR models were developed between each carbon atom of flavonoid and the calculated descriptors. Genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used to select the descriptors and to generate the correlation models. Analysis of the results revealed a correlation coefficient and root mean square error (RMSE) of 0.994 and 2.53ppm, respectively, for the prediction set.
Fouad, Marwa A; Tolba, Enas H; El-Shal, Manal A; El Kerdawy, Ahmed M
2018-05-11
The justified continuous emerging of new β-lactam antibiotics provokes the need for developing suitable analytical methods that accelerate and facilitate their analysis. A face central composite experimental design was adopted using different levels of phosphate buffer pH, acetonitrile percentage at zero time and after 15 min in a gradient program to obtain the optimum chromatographic conditions for the elution of 31 β-lactam antibiotics. Retention factors were used as the target property to build two QSRR models utilizing the conventional forward selection and the advanced nature-inspired firefly algorithm for descriptor selection, coupled with multiple linear regression. The obtained models showed high performance in both internal and external validation indicating their robustness and predictive ability. Williams-Hotelling test and student's t-test showed that there is no statistical significant difference between the models' results. Y-randomization validation showed that the obtained models are due to significant correlation between the selected molecular descriptors and the analytes' chromatographic retention. These results indicate that the generated FS-MLR and FFA-MLR models are showing comparable quality on both the training and validation levels. They also gave comparable information about the molecular features that influence the retention behavior of β-lactams under the current chromatographic conditions. We can conclude that in some cases simple conventional feature selection algorithm can be used to generate robust and predictive models comparable to that are generated using advanced ones. Copyright © 2018 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Boris Campillo-Gimenez
Full Text Available Case-based reasoning (CBR is an emerging decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs and a label (output. Extracting useful information from this database can help the CBR system providing more reliable results on the yet to be solved cases.We suggest a general framework where a CBR system, viz. K-Nearest Neighbour (K-NN algorithm, is combined with various information obtained from a Logistic Regression (LR model, in order to improve prediction of access to the transplant waiting list.LR is applied, on the case database, to assign weights to the attributes as well as the solved cases. Thus, five possible decision making systems based on K-NN and/or LR were identified: a standalone K-NN, a standalone LR and three soft K-NN algorithms that rely on the weights based on the results of the LR. The evaluation was performed under two conditions, either using predictive factors known to be related to registration, or using a combination of factors related and not related to registration.The results show that our suggested approach, where the K-NN algorithm relies on both weighted attributes and cases, can efficiently deal with non relevant attributes, whereas the four other approaches suffer from this kind of noisy setups. The robustness of this approach suggests interesting perspectives for medical problem solving tools using CBR methodology.
Indian Academy of Sciences (India)
will become clear in the next article when we discuss a simple logo like programming language. ... Rod B may be used as an auxiliary store. The problem is to find an algorithm which performs this task. ... No disks are moved from A to Busing C as auxiliary rod. • move _disk (A, C);. (No + l)th disk is moved from A to C directly ...
International Nuclear Information System (INIS)
1988-01-01
The fourth annual report of the Niedersaechsisches Institut fuer Radiooekologie (NIR) is intended to describe the scientific work of the institute and its members in 1987. The central part of this publication are the fourteen reports on scientific activities, to be divided into four large categories: - Behaviour of tritium in the atmosphere and the soil - on this, important new knowledge was gained in 1987 in an experiment in Canada on the release of this substance; - Investigations in the radioecology of iodine 129, the dependence of its mobility in the soil on humus substances and microorganisms, and its enrichment in the human thyroid gland; - Establishment of transfer factors in the food chain for fission products like cesium 137, cesium 134 and strontium 90 - this being a field where exact knowledge has regained great importance after the accident at Chernobyl; - Aerosol-physical investigations: on the one hand, to obtain data on the propagation of nutrient aerosols and aerosols carrying harmful substances in areas with vegetation, and on the other hand to measure 'snow-out' and 'fog-out' coefficients. To this are added a number of papers on the stability of the decontamination substance for cesium 137 - ammonium-iron-hexacyanoferrate (AIHCF) - in the soil, on the translocation of cesium in apple-trees, and on the improvement of the analytics for uranium and plutonium in environmental specimens. (orig./MG) [de
Dynamic Filtering Improves Attentional State Prediction with fNIRS
Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.
2016-01-01
Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).
International Nuclear Information System (INIS)
Sato, Yukio; Yamaguchi, Hiroshi
2000-01-01
Radiation biophysics or microdosimetry has suggested radiation effect mechanism. Full understanding of it has not yet been obtained. There are vast variety of events in physical, chemical and biological processes from at the time of irradiation to biological endpoints. Analysis of RBE-LET relation for biological endpoints like survival, mutation and transformation in cultured mammalian cells is still the leading subject to study the physical processes. The biological and repair processes have been studied phenomenologically through dose rate effect or fractionation experiment. Human genome project has accelerated biological sciences as a whole taking methodology of the molecular biology, where the mechanism is explained by molecules involved. We have thus to know entity and its (biological) function in every single process. Molecular biological approach in radiation biology has started and revealed several proteins being involved in the repair processes. Quantitative relation between phenomenological data like cell survivals and molecular processes, however, has been little known yet. A promising approach to fill this gap should be the study by microbeam, which enables us to see, for example, a deletion in chromosomal level by a single particle traverse of cell nucleus and may suggest possible molecular processes. Under this motivation we started feasibility study on installation of a microbeam port in our Tandem accelerator (5.1 MeV 4 He 2+ ). We have planned to adopt a lens focusing and a scanning system developed (by the Oxford microbeam Ltd) for the existing micro PIXE system in NIRS, which has basically achieved irradiation to a cell within a position resolution of 2 micrometer. There are two practical requirements, i.e. precise positioning and faster irradiation. These are described including research subjects planned. (author)
Funane, Tsukasa; Sato, Hiroki; Yahata, Noriaki; Takizawa, Ryu; Nishimura, Yukika; Kinoshita, Akihide; Katura, Takusige; Atsumori, Hirokazu; Fukuda, Masato; Kasai, Kiyoto; Koizumi, Hideaki; Kiguchi, Masashi
2015-01-01
Abstract. It has been reported that a functional near-infrared spectroscopy (fNIRS) signal can be contaminated by extracerebral contributions. Many algorithms using multidistance separations to address this issue have been proposed, but their spatial separation performance has rarely been validated with simultaneous measurements of fNIRS and functional magnetic resonance imaging (fMRI). We previously proposed a method for discriminating between deep and shallow contributions in fNIRS signals, referred to as the multidistance independent component analysis (MD-ICA) method. In this study, to validate the MD-ICA method from the spatial aspect, multidistance fNIRS, fMRI, and laser-Doppler-flowmetry signals were simultaneously obtained for 12 healthy adult males during three tasks. The fNIRS signal was separated into deep and shallow signals by using the MD-ICA method, and the correlation between the waveforms of the separated fNIRS signals and the gray matter blood oxygenation level–dependent signals was analyzed. A three-way analysis of variance (signal depth×Hb kind×task) indicated that the main effect of fNIRS signal depth on the correlation is significant [F(1,1286)=5.34, pdeep and shallow signals, and the accuracy and reliability of the fNIRS signal will be improved with the method. PMID:26157983
Duan, Libin; Xiao, Ning-cong; Li, Guangyao; Cheng, Aiguo; Chen, Tao
2017-07-01
Tailor-rolled blank thin-walled (TRB-TH) structures have become important vehicle components owing to their advantages of light weight and crashworthiness. The purpose of this article is to provide an efficient lightweight design for improving the energy-absorbing capability of TRB-TH structures under dynamic loading. A finite element (FE) model for TRB-TH structures is established and validated by performing a dynamic axial crash test. Different material properties for individual parts with different thicknesses are considered in the FE model. Then, a multi-objective crashworthiness design of the TRB-TH structure is constructed based on the ɛ-support vector regression (ɛ-SVR) technique and non-dominated sorting genetic algorithm-II. The key parameters (C, ɛ and σ) are optimized to further improve the predictive accuracy of ɛ-SVR under limited sample points. Finally, the technique for order preference by similarity to the ideal solution method is used to rank the solutions in Pareto-optimal frontiers and find the best compromise optima. The results demonstrate that the light weight and crashworthiness performance of the optimized TRB-TH structures are superior to their uniform thickness counterparts. The proposed approach provides useful guidance for designing TRB-TH energy absorbers for vehicle bodies.
Spady, Richard; Stouli, Sami
2012-01-01
We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...
Energy Technology Data Exchange (ETDEWEB)
Syunyaev, R.Z.; Balabin, R.M. [Russian State Univ. of Oil and Gas, Moscow (Russian Federation). Dept. of Physics; Akhatov, I.S. [North Dakota State Univ., Fargo, ND (United States). Dept. of Mechanical Engineering and Center for Nanoscale Science and Engineering
2008-07-01
The presence of asphaltene and resin in crude oil is known to cause well bore plugging and pipeline deposition; stabilization of water/oil emulsions; sedimentation and plugging during crude oil storage; adsorption on refining equipment and coke formation. Kinetic and thermodynamic parameters of adsorption are also known to influence wettability and the capillary number. In this study, adsorption parameters of petroleum resins and asphaltenes were evaluated by Near Infrared (NIR) spectroscopy. Fractioned quartz, dolomite, mica and kaolinite sands were used as adsorbent. The particle size distribution was evaluated using an optical microscope. Porosity and permeability of each fraction were designed and benzene was used as the solvent. Various approaches for calibrating NIR spectra-macromolecules concentration were discussed. In this study, the partial least squares (PLS) regression method was used and the Langmuir model was chosen for experimental data fitting. Kinetic and isothermic data was used to evaluate the maximal adsorbed mass density, the equilibrium constant of adsorption, and the rate constants of adsorption and desorption. The rate constants of resins adsorption and desorption depended on the concentration. A numerical algorithm was developed to estimate the diffusion coefficient and relaxation time from the experimental data.
NIR detects, destroys insect pests
International Nuclear Information System (INIS)
McGraw, L.C.
1998-01-01
What’s good for Georgia peanuts may also be good for Kansas wheat. An electric eye that scans all food-grade peanuts for visual defects could one day do the same for wheat kernels. For peanuts, it’s a proven method for monitoring quality. In wheat, scanning with near-infrared (NIR) energy can reveal hidden insect infestations that lower wheat quality. ARS entomologists James E. Throne and James E. Baker and ARS agricultural engineer Floyd E. Dowell are the first to combine NIR with an automated grain-handling system to rapidly detect insects hidden in single wheat kernels
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak; Ghosh, Malay; Mallick, Bani K.
2012-01-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik's ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak
2012-07-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik\\'s ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
[On-site evaluation of raw milk qualities by portable Vis/NIR transmittance technique].
Wang, Jia-Hua; Zhang, Xiao-Wei; Wang, Jun; Han, Dong-Hai
2014-10-01
To ensure the material safety of dairy products, visible (Vis)/near infrared (NIR) spectroscopy combined with che- mometrics methods was used to develop models for fat, protein, dry matter (DM) and lactose on-site evaluation. A total of 88 raw milk samples were collected from individual livestocks in different years. The spectral of raw milk were measured by a porta- ble Vis/NIR spectrometer with diffused transmittance accessory. To remove the scatter effect and baseline drift, the diffused transmittance spectra were preprocessed by 2nd order derivative with Savitsky-Golay (polynomial order 2, data point 25). Changeable size moving window partial least squares (CSMWPLS) and genetic algorithms partial least squares (GAPLS) meth- ods were suggested to select informative regions for PLS calibration. The PLS and multiple linear regression (MLR) methods were used to develop models for predicting quality index of raw milk. The prediction performance of CSMWPLS models were similar to GAPLS models for fat, protein, DM and lactose evaluation, the root mean standard errors of prediction (RMSEP) were 0.115 6/0.103 3, 0.096 2/0.113 7, 0.201 3/0.123 7 and 0.077 4/0.066 8, and the relative standard deviations of prediction (RPD) were 8.99/10.06, 3.53/2.99, 5.76/9.38 and 1.81/2.10, respectively. Meanwhile, the MLR models were also cal- ibrated with 8, 10, 9 and 7 variables for fat, protein, DM and lactose, respectively. The prediction performance of MLR models was better than or close to PLS models. The MLR models to predict fat, protein, DM and lactose yielded the RMSEP of 0.107 0, 0.093 0, 0.136 0 and 0.065 8, and the RPD of 9.72, 3.66, 8.53 and 2.13, respectively. The results demonstrated the usefulness of Vis/NIR spectra combined with multivariate calibration methods as an objective and rapid method for the quality evaluation of complicated raw milks. And the results obtained also highlight the potential of portable Vis/NIR instruments for on-site assessing quality indexes of
Monitoring tissue oxygen availability with near infrared spectroscopy (NIRS) in health and disease
DEFF Research Database (Denmark)
Boushel, Robert Christopher; Langberg, H; Olesen, J
2001-01-01
, brain and connective tissue, and more recently it has been used in the clinical setting to assess circulatory and metabolic abnormalities. Quantitative measures of blood flow are also possible using NIRS and a light-absorbing tracer, which can be applied to evaluate circulatory responses to exercise......Near infrared spectroscopy (NIRS) is becoming a widely used research instrument to measure tissue oxygen (O2) status non-invasively. Continuous-wave spectrometers are the most commonly used devices, which provide semi-quantitative changes in oxygenated and deoxygenated hemoglobin in small blood...... vessels (arterioles, capillaries and venules). Refinement of NIRS hardware and the algorithms used to deconvolute the light absorption signal have improved the resolution and validity of cytochrome oxidase measurements. NIRS has been applied to measure oxygenation in a variety of tissues including muscle...
Using Massive Multivariate NIRS Data in Ryegrass
DEFF Research Database (Denmark)
Edriss, Vahid; Greve-Pedersen, Morten; Jensen, Christian S
2015-01-01
Near infrared spectroscopy (NIRS) analytical techniques is a simple, fast and low cost method of high dimensional phenotyping compared to usual chemical techniques. To use this method there is no need for special sample preparation. The aim of this study is to use NIRS data to predict plant traits...... (e.g. dry matter, protein content, etc.) for the next generation. In total 1984 NIRS data from 995 ryegrass families (first cut) were used. The Absorption of radiation in the region of 960 – 1690 nm in every 2 nm distance produced 366 bins to represent the NIRS spectrum. The amount of genetic...
Sun, Zhongyu; Li, Can; Li, Lian; Nie, Lei; Dong, Qin; Li, Danyang; Gao, Lingling; Zang, Hengchang
2018-08-05
N-acetyl-d-glucosamine (GlcNAc) is a microbial fermentation product, and NIR spectroscopy is an effective process analytical technology (PAT) tool in detecting the key quality attribute: the GlcNAc content. Meanwhile, the design of NIR spectrometers is under the trend of miniaturization, portability and low-cost nowadays. The aim of this study was to explore a portable micro NIR spectrometer with the fermentation process. First, FT-NIR spectrometer and Micro-NIR 1700 spectrometer were compared with simulated fermentation process solutions. The R c 2 , R p 2 , RMSECV and RMSEP of the optimal FT-NIR and Micro-NIR 1700 models were 0.999, 0.999, 3.226 g/L, 1.388 g/L and 0.999, 0.999, 1.821 g/L, 0.967 g/L. Passing-Bablok regression method and paired t-test results showed there were no significant differences between the two instruments. Then the Micro-NIR 1700 was selected for the practical fermentation process, 135 samples from 10 batches were collected. Spectral pretreatment methods and variables selection methods (BiPLS, FiPLS, MWPLS and CARS-PLS) for PLS modeling were discussed. The R c 2 , R p 2 , RMSECV and RMSEP of the optimal GlcNAc content PLS model of the practical fermentation process were 0.994, 0.995, 2.792 g/L and 1.946 g/L. The results have a positive reference for application of the Micro-NIR spectrometer. To some extent, it could provide theoretical supports in guiding the microbial fermentation or the further assessment of bioprocess. Copyright © 2018. Published by Elsevier B.V.
de Oliveira, Isadora R. N.; Roque, Jussara V.; Maia, Mariza P.; Stringheta, Paulo C.; Teófilo, Reinaldo F.
2018-04-01
A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.
Greenhouse cooling by NIR-reflection
Hemming, S.; Kempkes, F.; Braak, van der N.; Dueck, T.A.; Marissen, A.
2007-01-01
Wageningen UR investigated the potential of several NIR-filtering methods to be applied in horticulture. In this paper the analysis of the optical properties of available NIR-filtering materials is given including a calculation method to quantify the energy reduction under these materials and to
Non-contact finger vein acquisition system using NIR laser
Kim, Jiman; Kong, Hyoun-Joong; Park, Sangyun; Noh, SeungWoo; Lee, Seung-Rae; Kim, Taejeong; Kim, Hee Chan
2009-02-01
Authentication using finger vein pattern has substantial advantage than other biometrics. Because human vein patterns are hidden inside the skin and tissue, it is hard to forge vein structure. But conventional system using NIR LED array has two drawbacks. First, direct contact with LED array raise sanitary problem. Second, because of discreteness of LEDs, non-uniform illumination exists. We propose non-contact finger vein acquisition system using NIR laser and Laser line generator lens. Laser line generator lens makes evenly distributed line laser from focused laser light. Line laser is aimed on the finger longitudinally. NIR camera was used for image acquisition. 200 index finger vein images from 20 candidates are collected. Same finger vein pattern extraction algorithm was used to evaluate two sets of images. Acquired images from proposed non-contact system do not show any non-uniform illumination in contrary with conventional system. Also results of matching are comparable to conventional system. We developed Non-contact finger vein acquisition system. It can prevent potential cross contamination of skin diseases. Also the system can produce uniformly illuminated images unlike conventional system. With the benefit of non-contact, proposed system shows almost equivalent performance compared with conventional system.
An image analysis system for near-infrared (NIR) fluorescence lymph imaging
Zhang, Jingdan; Zhou, Shaohua Kevin; Xiang, Xiaoyan; Rasmussen, John C.; Sevick-Muraca, Eva M.
2011-03-01
Quantitative analysis of lymphatic function is crucial for understanding the lymphatic system and diagnosing the associated diseases. Recently, a near-infrared (NIR) fluorescence imaging system is developed for real-time imaging lymphatic propulsion by intradermal injection of microdose of a NIR fluorophore distal to the lymphatics of interest. However, the previous analysis software3, 4 is underdeveloped, requiring extensive time and effort to analyze a NIR image sequence. In this paper, we develop a number of image processing techniques to automate the data analysis workflow, including an object tracking algorithm to stabilize the subject and remove the motion artifacts, an image representation named flow map to characterize lymphatic flow more reliably, and an automatic algorithm to compute lymph velocity and frequency of propulsion. By integrating all these techniques to a system, the analysis workflow significantly reduces the amount of required user interaction and improves the reliability of the measurement.
Rohwedder, J J R; Pasquini, C; Fortes, P R; Raimundo, I M; Wilk, A; Mizaikoff, B
2014-07-21
A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Pellegrino, Giovanni; Machado, Alexis; von Ellenrieder, Nicolas; Watanabe, Satsuki; Hall, Jeffery A.; Lina, Jean-Marc; Kobayashi, Eliane; Grova, Christophe
2016-01-01
Objective: We aimed at studying the hemodynamic response (HR) to Interictal Epileptic Discharges (IEDs) using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG) and functional Near InfraRed Spectroscopy (fNIRS) recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF) and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (seven patients), followed by oxy-hemoglobin decreases (six patients). HR was lateralized in six patients and lasted from 8.5 to 30 s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result). The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30 s. Conclusions: (i) EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; (ii) cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function (iii) the HR is often bilateral and lasts up to 30 s. PMID:27047325
Directory of Open Access Journals (Sweden)
Giovanni ePellegrino
2016-03-01
Full Text Available Objective: We aimed at studying the hemodynamic response (HR to Interictal Epileptic Discharges (IEDs using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG and functional Near InfraRed Spectroscopy (fNIRS recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (7 patients, followed by oxy-hemoglobin decreases (6 patients. HR was lateralized in 6 patients and lasted from 8.5 to 30s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result. The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30s. Conclusions: i EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; ii cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function iii the HR is often bilateral and lasts up to 30s.
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
DEFF Research Database (Denmark)
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Directory of Open Access Journals (Sweden)
Weibo Zhao
2017-12-01
Full Text Available Power generation industry is the key industry of carbon dioxide (CO2 emission in China. Assessing its future CO2 emissions is of great significance to the formulation and implementation of energy saving and emission reduction policies. Based on the Stochastic Impacts by Regression on Population, Affluence and Technology model (STIRPAT, the influencing factors analysis model of CO2 emission of power generation industry is established. The ridge regression (RR method is used to estimate the historical data. In addition, a wavelet neural network (WNN prediction model based on Cuckoo Search algorithm optimized by Gauss (GCS is put forward to predict the factors in the STIRPAT model. Then, the predicted values are substituted into the regression model, and the CO2 emission estimation values of the power generation industry in China are obtained. It’s concluded that population, per capita Gross Domestic Product (GDP, standard coal consumption and thermal power specific gravity are the key factors affecting the CO2 emission from the power generation industry. Besides, the GCS-WNN prediction model has higher prediction accuracy, comparing with other models. Moreover, with the development of science and technology in the future, the CO2 emission growth in the power generation industry will gradually slow down according to the prediction results.
Combining Alphas via Bounded Regression
Directory of Open Access Journals (Sweden)
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Kang, Qian; Ru, Qingguo; Liu, Yan; Xu, Lingyan; Liu, Jia; Wang, Yifei; Zhang, Yewen; Li, Hui; Zhang, Qing; Wu, Qing
2016-01-01
An on-line near infrared (NIR) spectroscopy monitoring method with an appropriate multivariate calibration method was developed for the extraction process of Fu-fang Shuanghua oral solution (FSOS). On-line NIR spectra were collected through two fiber optic probes, which were designed to transmit NIR radiation by a 2 mm flange. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were used comparatively for building the calibration regression models. During the extraction process, the feasibility of NIR spectroscopy was employed to determine the concentrations of chlorogenic acid (CA) content, total phenolic acids contents (TPC), total flavonoids contents (TFC) and soluble solid contents (SSC). High performance liquid chromatography (HPLC), ultraviolet spectrophotometric method (UV) and loss on drying methods were employed as reference methods. Experiment results showed that the performance of siPLS model is the best compared with PLS and iPLS. The calibration models for AC, TPC, TFC and SSC had high values of determination coefficients of (R2) (0.9948, 0.9992, 0.9950 and 0.9832) and low root mean square error of cross validation (RMSECV) (0.0113, 0.0341, 0.1787 and 1.2158), which indicate a good correlation between reference values and NIR predicted values. The overall results show that the on line detection method could be feasible in real application and would be of great value for monitoring the mixed decoction process of FSOS and other Chinese patent medicines.
Gross, Samuel M; Tibshirani, Robert
2015-04-01
We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with these type of data is "sparse multiple canonical correlation analysis" (sparse mCCA). All of the current sparse mCCA techniques are biconvex and thus have no guarantees about reaching a global optimum. We propose a method for performing sparse supervised canonical correlation analysis (sparse sCCA), a specific case of sparse mCCA when one of the datasets is a vector. Our proposal for sparse sCCA is convex and thus does not face the same difficulties as the other methods. We derive efficient algorithms for this problem that can be implemented with off the shelf solvers, and illustrate their use on simulated and real data. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
`VIS/NIR mapping of TOC and extent of organic soils in the Nørre Å valley
Knadel, M.; Greve, M. H.; Thomsen, A.
2009-04-01
Organic soils represent a substantial pool of carbon in Denmark. The need for carbon stock assessment calls for more rapid and effective mapping methods to be developed. The aim of this study was to compare traditional soil mapping with maps produced from the results of a mobile VIS/NIR system and to evaluate the ability to estimate TOC and map the area of organic soils. The Veris mobile VIS/NIR spectroscopy system was compared to traditional manual sampling. The system is developed for in-situ near surface measurements of soil carbon content. It measures diffuse reflectance in the 350 nm-2200 nm region. The system consists of two spectrophotometers mounted on a toolbar and pulled by a tractor. Optical measurements are made through a sapphire window at the bottom of the shank. The shank was pulled at a depth of 5-7 cm at a speed of 4-5 km/hr. 20-25 spectra per second with 8 nm resolution were acquired by the spectrometers. Measurements were made on 10-12 m spaced transects. The system also acquired soil electrical conductivity (EC) for two soil depths: shallow EC-SH (0- 31 cm) and deep conductivity EC-DP (0- 91 cm). The conductivity was recorded together with GPS coordinates and spectral data for further construction of the calibration models. Two maps of organic soils in the Nørre Å valley (Central Jutland) were generated: (i) based on a conventional 25 m grid with 162 sampling points and laboratory analysis of TOC, (ii) based on in-situ VIS/NIR measurements supported by chemometrics. Before regression analysis, spectral information was compressed by calculating principal components. The outliers were determined by a mahalanobis distance equation and removed. Clustering using a fuzzy c- means algorithm was conducted. Within each cluster a location with the minimal spatial variability was selected. A map of 15 representative sample locations was proposed. The interpolation of the spectra into a single spectrum was performed using a Gaussian kernel weighting
Gu, Huidong; Liu, Guowen; Wang, Jian; Aubry, Anne-Françoise; Arnold, Mark E
2014-09-16
A simple procedure for selecting the correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm in bioanalytical LC-MS/MS assays is reported. The correct weighting factor is determined by the relationship between the standard deviation of instrument responses (σ) and the concentrations (x). The weighting factor of 1, 1/x, or 1/x(2) should be selected if, over the entire concentration range, σ is a constant, σ(2) is proportional to x, or σ is proportional to x, respectively. For the first time, we demonstrated with detailed scientific reasoning, solid historical data, and convincing justification that 1/x(2) should always be used as the weighting factor for all bioanalytical LC-MS/MS assays. The impacts of using incorrect weighting factors on curve stability, data quality, and assay performance were thoroughly investigated. It was found that the most stable curve could be obtained when the correct weighting factor was used, whereas other curves using incorrect weighting factors were unstable. It was also found that there was a very insignificant impact on the concentrations reported with calibration curves using incorrect weighting factors as the concentrations were always reported with the passing curves which actually overlapped with or were very close to the curves using the correct weighting factor. However, the use of incorrect weighting factors did impact the assay performance significantly. Finally, the difference between the weighting factors of 1/x(2) and 1/y(2) was discussed. All of the findings can be generalized and applied into other quantitative analysis techniques using calibration curves with weighted least-squares regression algorithm.
Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks.
Directory of Open Access Journals (Sweden)
Alessio Paolo Buccino
Full Text Available Non-invasive Brain-Computer Interfaces (BCI have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG and functional Near-Infrared Spectroscopy (fNIRS in an asynchronous Sensory Motor rhythm (SMR-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm-Left-Arm-Right-Hand-Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes.
Quantification of fructan concentration in grasses using NIR spectroscopy and PLSR
DEFF Research Database (Denmark)
Shetty, Nisha; Gislum, Rene
2011-01-01
Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to quantify fructan concentration in samples from seven grass species. Savitzky-Golay first derivative with filter width 7 and polynomial order 2 with mean centering was applied as a spectral pre-treatment method...... to remove unimportant baseline signals. In order to model the NIR spectroscopy data the partial least squares regression (PLSR) approach was used on the full spectra. Variable selection based on PLSR by jack-knifing within a cross-model validation (CMV) framework was applied in order to remove non...... quantification of fructans by NIR spectroscopy is possible and that jack-knifing PLSR within a CMV framework is an effective way to eliminate the wavelengths of no interest. Jack-knifing PLSR did not improve the predictive ability because the root mean square error of prediction (RMSEP) increased (1.37) compared...
PENENTUAN BAHAN KERING BUAH SAWO SECARA TIDAK MERUSAK MENGGUNAKAN NIR SPECTROSCOPY
Directory of Open Access Journals (Sweden)
Diding Suhandy
2012-12-01
Full Text Available This work was conducted to develop a new measuring system for nondestructive dry matter prediction in sawo fruit using short wavelength near infrared (SW-NIR spectroscopy. In this research, a number of 100 sawo fruits were used as samples. Spectra were acquired using a portable spectrometer (VIS-NIR USB4000, The Ocean Optics, USA with 100 ms integration time and 50 scans for number of scanning. Dry matter was measured using oven drying. The calibration and validation model was developed using the partial least squares (PLS regression method. The result showed that the best calibration model could be developed for original spectra in the wavelength range of 700-990 nm with F= 8, r = 0.92, SEC = 0.68 and SEP = 0.86. Keywords: Absorbance mode, dry matter, nondestructive method, sawo fruit, SW-NIR spectroscopy.
Directory of Open Access Journals (Sweden)
Djéssica Tatiana Raspe
2013-01-01
Full Text Available This work reports the use of FT-NIR spectroscopy coupled with multivariate calibration to determine the percentage of free fatty acids (FFA in samples obtained by the esterification of FFA in vegetable oils. The analytical method used as calibration matrix samples of the reaction medium of esterification of oleic acid in soybean oil in proportions of 0.3 to 40 wt% (by weight of oleic acid obtained under different experimental conditions and utilized the partial least squares (PLS regression. The efficiency of the method was tested to predict the content of FFA in reactions of esterification of oleic acid in soybean oil catalysed by KSF clay and Amberlyst 15 commercial resin, both in a batch mode. Good Correlations were observed between the FT-NIR/PLS method and the reference method (AOCS. The results confirm that FT-NIR spectroscopy, in combination with multivariate calibration, is a promising technique for monitoring esterification reaction for biodiesel production.
Potable NIR spectroscopy predicting soluble solids content of pears based on LEDs
Energy Technology Data Exchange (ETDEWEB)
Liu Yande; Liu Wei; Sun Xudong; Gao Rongjie; Pan Yuanyuan; Ouyang Aiguo, E-mail: jxliuyd@163.com [School of Mechatronics Engineering, East China Jiaotong University, Changbei Open and Developing District, Nanchang, 330013 (China)
2011-01-01
A portable near-infrared (NIR) instrument was developed for predicting soluble solids content (SSC) of pears equipped with light emitting diodes (LEDs). NIR spectra were collected on the calibration and prediction sets (145:45). Relationships between spectra and SSC were developed by multivariate linear regression (MLR), partial least squares (PLS) and artificial neural networks (ANNs) in the calibration set. The 45 unknown pears were applied to evaluate the performance of them in terms of root mean square errors of prediction (RMSEP) and correlation coefficients (r). The best result was obtained by PLS with RMSEP of 0.62{sup 0}Brix and r of 0.82. The results showed that the SSC of pears could be predicted by the portable NIR instrument.
Potable NIR spectroscopy predicting soluble solids content of pears based on LEDs
International Nuclear Information System (INIS)
Liu Yande; Liu Wei; Sun Xudong; Gao Rongjie; Pan Yuanyuan; Ouyang Aiguo
2011-01-01
A portable near-infrared (NIR) instrument was developed for predicting soluble solids content (SSC) of pears equipped with light emitting diodes (LEDs). NIR spectra were collected on the calibration and prediction sets (145:45). Relationships between spectra and SSC were developed by multivariate linear regression (MLR), partial least squares (PLS) and artificial neural networks (ANNs) in the calibration set. The 45 unknown pears were applied to evaluate the performance of them in terms of root mean square errors of prediction (RMSEP) and correlation coefficients (r). The best result was obtained by PLS with RMSEP of 0.62 0 Brix and r of 0.82. The results showed that the SSC of pears could be predicted by the portable NIR instrument.
Energy Technology Data Exchange (ETDEWEB)
Brunet, Didier, E-mail: didier.brunet@ird.f [IRD - Institut de Recherche pour le Developpement, UMR Eco and Sols (Ecologie Fonctionnelle et Biogeochimie des Sols), Montpellier SupAgro, Batiment 12, 2 place Viala, 34060 Montpellier Cedex 1 (France); Woignier, Thierry [IRD, UMR Eco and Sols, PRAM (Pole de Recherche Agronomique de la Martinique), BP 213, Petit Morne, 97232 Le Lamentin, Martinique (French West Indies) (France); CNRS - Centre National de la Recherche Scientifique, Universite Montpellier 2, place Eugene Bataillon, 34095 Montpellier Cedex 5 (France); Lesueur-Jannoyer, Magalie; Achard, Raphael [CIRAD (Centre de Cooperation Internationale en Recherche Agronomique pour le Developpement), PRAM, BP 213, Petit Morne, 97232 Le Lamentin, Martinique (French West Indies) (France); Rangon, Luc [IRD, UMR Eco and Sols, PRAM (Pole de Recherche Agronomique de la Martinique), BP 213, Petit Morne, 97232 Le Lamentin, Martinique (French West Indies) (France); Barthes, Bernard G. [IRD - Institut de Recherche pour le Developpement, UMR Eco and Sols (Ecologie Fonctionnelle et Biogeochimie des Sols), Montpellier SupAgro, Batiment 12, 2 place Viala, 34060 Montpellier Cedex 1 (France)
2009-11-15
Chlordecone is a toxic organochlorine insecticide that was used in banana plantations until 1993 in the French West Indies. This study aimed at assessing the potential of near infrared reflectance spectroscopy (NIRS) for determining chlordecone content in Andosols, Nitisols and Ferralsols from Martinique. Using partial least square regression, chlordecone content conventionally determined through gas chromatography-mass spectrometry could be correctly predicted by NIRS (Q{sup 2} = 0.75, R{sup 2} = 0.82 for the total set), especially for samples with chlordecone content <12 mg kg{sup -1} or when the sample set was rather homogeneous (Q{sup 2} = 0.91, R{sup 2} = 0.82 for the Andosols). Conventional measures and NIRS predictions were poorly correlated for chlordecone content >12 mg kg{sup -1}, nevertheless ca. 80% samples were correctly predicted when the set was divided into three or four classes of chlordecone content. Thus NIRS could be considered a time- and cost-effective method for characterising soil contamination by chlordecone. - Soil content in chlordecone, an organochlorine insecticide, can be determined time- and cost-effectively using near infrared reflectance spectroscopy (NIRS).
International Nuclear Information System (INIS)
Brunet, Didier; Woignier, Thierry; Lesueur-Jannoyer, Magalie; Achard, Raphael; Rangon, Luc; Barthes, Bernard G.
2009-01-01
Chlordecone is a toxic organochlorine insecticide that was used in banana plantations until 1993 in the French West Indies. This study aimed at assessing the potential of near infrared reflectance spectroscopy (NIRS) for determining chlordecone content in Andosols, Nitisols and Ferralsols from Martinique. Using partial least square regression, chlordecone content conventionally determined through gas chromatography-mass spectrometry could be correctly predicted by NIRS (Q 2 = 0.75, R 2 = 0.82 for the total set), especially for samples with chlordecone content -1 or when the sample set was rather homogeneous (Q 2 = 0.91, R 2 = 0.82 for the Andosols). Conventional measures and NIRS predictions were poorly correlated for chlordecone content >12 mg kg -1 , nevertheless ca. 80% samples were correctly predicted when the set was divided into three or four classes of chlordecone content. Thus NIRS could be considered a time- and cost-effective method for characterising soil contamination by chlordecone. - Soil content in chlordecone, an organochlorine insecticide, can be determined time- and cost-effectively using near infrared reflectance spectroscopy (NIRS).
Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.
Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina
2014-07-15
This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. Copyright © 2014 Elsevier Ltd. All rights reserved.
Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui
2016-01-01
In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Du, Chen-Zhao; Wu, Zhi-Sheng; Zhao, Na; Zhou, Zheng; Shi, Xin-Yuan; Qiao, Yan-Jiang
2016-10-01
To establish a rapid quantitative analysis method for online monitoring of chlorogenic acid in aqueous solution of Lonicera Japonica Flos extraction by using micro-electromechanical near infrared spectroscopy (MEMS-NIR). High performance liquid chromatography(HPLC) was used as reference method．Kennard-Stone (K-S) algorithm was used to divide sample sets, and partial least square(PLS) regression was adopted to establish the multivariate analysis model between the HPLC analysis contents and NIR spectra. The synergy interval partial least squares (SiPLS) was used to selected modeling waveband to establish PLS models. RPD was used to evaluate the prediction performance of the models. MDLs was calculated based on two types of error detection theory, on-line analytical modeling approach of Lonicera Japonica Flos extraction process was expressed scientifically by MDL. The result shows that the model established by multiplicative scatter correction(MSC) was the best, with the root mean square with cross validation(RMSECV), root mean square error of correction(RMSEC) and root mean square error of prediction(RMSEP) of chlorogenic acid as 1.707, 1.489, 2.362, respectively, the determination coefficient of the calibration model was 0.998 5, and the determination coefficient of the prediction was 0.988 1．The value of RPD is 9.468.The MDL (0.042 15 g•L⁻¹) selected by SiPLS is less than the original,which demonstrated that SiPLS was beneficial to improve the prediction performance of the model. In this study, a more accurate expression of the prediction performance of the model from the two types of error detection theory, to further illustrate MEMS-NIR spectroscopy can be used for on-line monitoring of Lonicera Japonica Flos extraction process. Copyright© by the Chinese Pharmaceutical Association.
Palou, Anna; Miró, Aira; Blanco, Marcelo; Larraz, Rafael; Gómez, José Francisco; Martínez, Teresa; González, Josep Maria; Alcalà, Manel
2017-06-01
Even when the feasibility of using near infrared (NIR) spectroscopy combined with partial least squares (PLS) regression for prediction of physico-chemical properties of biodiesel/diesel blends has been widely demonstrated, inclusion in the calibration sets of the whole variability of diesel samples from diverse production origins still remains as an important challenge when constructing the models. This work presents a useful strategy for the systematic selection of calibration sets of samples of biodiesel/diesel blends from diverse origins, based on a binary code, principal components analysis (PCA) and the Kennard-Stones algorithm. Results show that using this methodology the models can keep their robustness over time. PLS calculations have been done using a specialized chemometric software as well as the software of the NIR instrument installed in plant, and both produced RMSEP under reproducibility values of the reference methods. The models have been proved for on-line simultaneous determination of seven properties: density, cetane index, fatty acid methyl esters (FAME) content, cloud point, boiling point at 95% of recovery, flash point and sulphur.
NIR: optimerer produktionen af gammeldags modnede sild
DEFF Research Database (Denmark)
Svensson, T.; Bro, Rasmus; Nielsen, Henrik Hauch
2005-01-01
Måling med nærinfrarødt (NIR) lys er et godt supplement til de nuværende metoder til at følge modningen af sild saltede i tønder. Det viser resultaterne af et forskningsprojekt udført i samarbejde mellem Lykkeberg A/S, Danmarks Fiskeriundersøgelser og Den Kgl Veterinær- og Landbohøjskole. Ved hjælp...... af avanceret matematik er det nemt og hurtigt at bestemme modningsgraden af sild direkte fra en NIR måling....
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Zhang, Y; Sun, J W; Rolfe, P
2012-01-01
The non-invasive measurement of cerebral functional haemodynamics using near-infrared spectroscopy (NIRS) instruments is often affected by physiological interference. The suppression of this interference is crucial for reliable recovery of brain activity measurements because it can significantly affect the signal quality. In this study, we present a recursive least-squares (RLS) algorithm for adaptive filtering to reduce the magnitude of the physiological interference component. To evaluate it, we implemented Monte Carlo simulations based on a five-layer slab model of a human adult head with a multidistance source–detector arrangement, of a short pair and a long pair, for NIRS measurement. We derived measurements by adopting different interoptode distances, which is relevant to the process of optimizing the NIRS probe configuration. Both RLS and least mean squares (LMS) algorithms were used to attempt the removal of physiological interference. The results suggest that the RLS algorithm is more capable of minimizing the effect of physiological interference due to its advantages of faster convergence and smaller mean squared error (MSE). The influence of superficial layer thickness on the performance of the RLS algorithm was also investigated. We found that the near-detector position is an important variable in minimizing the MSE and a short source–detector separation less than 9 mm is robust to superficial layer thickness variation. (paper)
Prediction of pH and color in pork meat using VIS-NIR Near-infrared Spectroscopy (NIRS
Directory of Open Access Journals (Sweden)
Elton Jhones Granemann FURTADO
2018-06-01
Full Text Available Abstract The potential of near-infrared spectroscopy (NIRS to predict the physicochemical characteristics of the porcine longissimus dorsi (LD muscle was evaluated in comparison to the standard methods of pH and color for meat quality analysis compared to the pH results with Colorimeter and pH meter. Spectral information from each sample (n = 77 was obtained as the average of 32 successive scans acquired over a spectral range from 400 - 2498 nm with a 2 - nm gap for calibration and validation models. Partial least squares (PLS regression was used for each individual model. An R2 and a residual predictive deviation (RPD of 0.67/1.7, 0.86/2, and 0.76/1.9 were estimated for color parameters L*, a *, and b*, respectively. Final pH had an R2 of 0.67 and a RPD of 1.6. NIRS showed great potential to predict color parameter a * of porcine LD muscle. Further studies with larger samples should help improve model quality.
Monitoring of whey quality with NIR spectroscopy
DEFF Research Database (Denmark)
Kucheryavskiy, Sergey; Lomborg, Carina
2015-01-01
The possibility of using near-infrared (NIR) spectroscopy for monitoring of liquid whey quality parameters during protein production process has been tested. The parameters included total solids, lactose, protein and fat content. The samples for the experiment were taken from real industrial...
Experimental radiation carcinogenesis is studies at NIRS
International Nuclear Information System (INIS)
Sado, Toshihiko
1992-01-01
Experimental radiation carcinogenesis studies conducted during the past decade at NIRS are briefly reviewed. They include the following: 1) Age dependency of susceptibility to radiation carcinogenesis. 2) Radiation-induced myeloid leukemia. 3) Mechanism of fractionated X-irradiation (FX) induced thymic lymphomas. 4) Significance of radiation-induced immunosuppression in radiation carcinogenesis in vivo. 5) Other ongoing studies. (author)
Directory of Open Access Journals (Sweden)
A. PITTAS
2001-03-01
Full Text Available Near Infrared Reflectance Spectroscopy (NIRS, coupled with fiber optic probes, has been shown to be a quick and reliable analytical tool for quality assurance and quality control in the pharmaceutical industry, both for verifications of raw materials and quantification of the active ingredients in final products. In this paper, a typical pharmaceutical product, hydrocortisone sodium succinate, is used as an example for the application of NIR spectroscopy for quality control. In order to develop an NIRS method with higher precision and accuracy than the official UV/VIS spectroscopic method (BP '99, 19 samples, taken from one years production and several prepared in the laboratory, having a hydrocortisone sodium succinate concentration in the range from 89.05%to 95.83 %, were analysed by NIR and UV/VIS spectroscopy. Three frequency ranges: 5939.735627.32 cm-1; 4863.64 4574.36 cm-1; 4308.234200.24 cm-1, with the best positive correlation between the changes in the spectral and concentration data, were chosen. The validity of the developed NIRS chemometric method for the determination of the hydrocortisone sodium succinate concentration, constructed by the partial least squares (PLS regression technique, is discussed. A correlation coefficient of 0.9758 and a standard error of cross validation (RMSECVof 1.06%were found between the UV/VI Sand òhe NIR spectroscopic results of the hydrocortisone sodium succinate concentration in the samples.
Multispectral imaging algorithms were developed using visible-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging (HSI) techniques to detect worms on fresh-cut lettuce. The optimal wavebands that detect worm on fresh-cut lettuce for each type of HSI were investigated using the one-way...
Li, Meng; Ford, Tim; Li, Xiaoyan; Gu, Ji-Dong
2011-04-15
A newly designed primer set (AnnirS), together with a previously published primer set (ScnirS), was used to detect anammox bacterial nirS genes from sediments collected from three marine environments. Phylogenetic analysis demonstrated that all retrieved sequences were clearly different from typical denitrifiers' nirS, but do group together with the known anammox bacterial nirS. Sequences targeted by ScnirS are closely related to Scalindua nirS genes recovered from the Peruvian oxygen minimum zone (OMZ), whereas sequences targeted by AnnirS are more closely affiliated with the nirS of Candidatus 'Kuenenia stuttgartiensis' and even form a new phylogenetic nirS clade, which might be related to other genera of the anammox bacteria. Analysis demonstrated that retrieved sequences had higher sequence identities (>60%) with known anammox bacterial nirS genes than with denitrifiers' nirS, on both nucleotide and amino acid levels. Compared to the 16S rRNA and hydrazine oxidoreductase (hzo) genes, the anammox bacterial nirS not only showed consistent phylogenetic relationships but also demonstrated more reliable quantification of anammox bacteria because of the single copy of the nirS gene in the anammox bacterial genome and the specificity of PCR primers for different genera of anammox bacteria, thus providing a suitable functional biomarker for investigation of anammox bacteria.
Upconverting and NIR emitting rare earth based nanostructures for NIR-bioimaging
Hemmer, Eva; Venkatachalam, Nallusamy; Hyodo, Hiroshi; Hattori, Akito; Ebina, Yoshie; Kishimoto, Hidehiro; Soga, Kohei
2013-11-01
In recent years, significant progress was achieved in the field of nanomedicine and bioimaging, but the development of new biomarkers for reliable detection of diseases at an early stage, molecular imaging, targeting and therapy remains crucial. The disadvantages of commonly used organic dyes include photobleaching, autofluorescence, phototoxicity and scattering when UV (ultraviolet) or visible light is used for excitation. The limited penetration depth of the excitation light and the visible emission into and from the biological tissue is a further drawback with regard to in vivo bioimaging. Lanthanide containing inorganic nanostructures emitting in the near-infrared (NIR) range under NIR excitation may overcome those problems. Due to the outstanding optical and magnetic properties of lanthanide ions (Ln3+), nanoscopic host materials doped with Ln3+, e.g. Y2O3:Er3+,Yb3+, are promising candidates for NIR-NIR bioimaging. Ln3+-doped gadolinium-based inorganic nanostructures, such as Gd2O3:Er3+,Yb3+, have a high potential as opto-magnetic markers allowing the combination of time-resolved optical imaging and magnetic resonance imaging (MRI) of high spatial resolution. Recent progress in our research on over-1000 nm NIR fluorescent nanoprobes for in vivo NIR-NIR bioimaging will be discussed in this review.In recent years, significant progress was achieved in the field of nanomedicine and bioimaging, but the development of new biomarkers for reliable detection of diseases at an early stage, molecular imaging, targeting and therapy remains crucial. The disadvantages of commonly used organic dyes include photobleaching, autofluorescence, phototoxicity and scattering when UV (ultraviolet) or visible light is used for excitation. The limited penetration depth of the excitation light and the visible emission into and from the biological tissue is a further drawback with regard to in vivo bioimaging. Lanthanide containing inorganic nanostructures emitting in the near
Radioecological studies in early period of NIRS
International Nuclear Information System (INIS)
Ichikawa, Ryushi
2004-01-01
Japanese tuna-fishing boat Fukuryumaru No.5 was exposed to heavy radioactive fallout due to the nuclear test explosion carried out by U.S.A. at Bikini Atoll of Marshal Islands in the central part of Pacific Ocean on March 1, 1954. Following this accident, radioactivity was detected in various environmental samples including rain, marine fishes and agricultural crops. Science Council of Japan organized the new research group of many scientists in the field of fisheries, agricultural, medical and biological studies and radiation protection studies. Government of Japan established National Institute of Radiological Sciences (NIRS) in 1957. In this Institute various radioecological studies have been carried out. In this paper, some of these radioecological studies carried out in early period of NIRS are described. (author)
NIR spectroscopic properties of aqueous acids solutions.
Omar, Ahmad Fairuz; Atan, Hanafi; Matjafri, Mohd Zubir
2012-06-15
Acid content is one of the important quality attributes in determining the maturity index of agricultural product, particularly fruits. Despite the fact that much research on the measurement of acidity in fruits through non-destructive spectroscopy analysis at NIR wavelengths between 700 to 1,000 nm has been conducted, the same response towards individual acids is not well known. This paper presents NIR spectroscopy analysis on aqueous citric, tartaric, malic and oxalic solutions through quantitative analysis by selecting a set of wavelengths that can best be used to measure the pH of the solutions. The aquaphotomics study of the acid solutions has generated R² above 0.9 for the measurement of all acids. The most important wavelengths for pH are located at 918-925 nm and 990-996 nm, while at 975 nm for water.
Tomuta, Ioan; Iovanov, Rares; Bodoki, Ede; Vonica, Loredana
2014-04-01
Near-Infrared (NIR) spectroscopy is an important component of a Process Analytical Technology (PAT) toolbox and is a key technology for enabling the rapid analysis of pharmaceutical tablets. The aim of this research work was to develop and validate NIR-chemometric methods not only for the determination of active pharmaceutical ingredients content but also pharmaceutical properties (crushing strength, disintegration time) of meloxicam tablets. The development of the method for active content assay was performed on samples corresponding to 80%, 90%, 100%, 110% and 120% of meloxicam content and the development of the methods for pharmaceutical characterization was performed on samples prepared at seven different compression forces (ranging from 7 to 45 kN) using NIR transmission spectra of intact tablets and PLS as a regression method. The results show that the developed methods have good trueness, precision and accuracy and are appropriate for direct active content assay in tablets (ranging from 12 to 18 mg/tablet) and also for predicting crushing strength and disintegration time of intact meloxicam tablets. The comparative data show that the proposed methods are in good agreement with the reference methods currently used for the characterization of meloxicam tablets (HPLC-UV methods for the assay and European Pharmacopeia methods for determining the crushing strength and disintegration time). The results show the possibility to predict both chemical properties (active content) and physical/pharmaceutical properties (crushing strength and disintegration time) directly, without any sample preparation, from the same NIR transmission spectrum of meloxicam tablets.
Agricultural applications of NIR reflectance and transmittance
DEFF Research Database (Denmark)
Gislum, René
2009-01-01
There has been a considerable increase in the use of near infrared (NIR) reflectance and transmittance spectroscopy technologies for rapid determination of quality parameters in agriculture, including applications within crop product quality, feed and food quality, manure quality, soil analyses etc....... As a result it was decided to arrange a seminar within the Nordic Association of Agricultural Scientists. This is a report of the meeting....
Design and construction of a NIR spectrometer
Barcala-Riveira, J M; Fernandez-Marron, J L; Molero-Menendez, F; Navarrete-Marin, J J; Oller-Gonzalez, J C
2003-01-01
This document describes the design and construction of a NIR spectrometer based on an acoustic-optic tunable filter. The spectrometer will be used for automatic identification of plastics in domestic waste. The system works between 1200 and 1800 nm. Instrument is controlled by a personal computer. Computer receives and analyses data. A software package has been developed to do these tasks. (Author) 27 refs.
NIRS database of the original research database
International Nuclear Information System (INIS)
Morita, Kyoko
1991-01-01
Recently, library staffs arranged and compiled the original research papers that have been written by researchers for 33 years since National Institute of Radiological Sciences (NIRS) established. This papers describes how the internal database of original research papers has been created. This is a small sample of hand-made database. This has been cumulating by staffs who have any knowledge about computer machine or computer programming. (author)
Design and construction of a NIR spectrometer
International Nuclear Information System (INIS)
Barcala Riveira, J. M.; Fernandez Marron, J. L.; Alberdi Primicia, J.; Molero Menendez, F.; Navarrete Marin, J. J.; Oller Gonzalez, J. C.
2003-01-01
This document describes the design and construction of a NIR spectrometer based on an acoustic-optic tunable filter. The spectrometer will be used for automatic identification of plastics in domestic waste. The system works between 1200 and 1800 nm. Instrument is controlled by a personal computer. Computer receives and analyses data. A software package has been developed to do these tasks. (Author) 27 refs
Towards NIRS-based hand movement recognition.
Paleari, Marco; Luciani, Riccardo; Ariano, Paolo
2017-07-01
This work reports on preliminary results about on hand movement recognition with Near InfraRed Spectroscopy (NIRS) and surface ElectroMyoGraphy (sEMG). Either basing on physical contact (touchscreens, data-gloves, etc.), vision techniques (Microsoft Kinect, Sony PlayStation Move, etc.), or other modalities, hand movement recognition is a pervasive function in today environment and it is at the base of many gaming, social, and medical applications. Albeit, in recent years, the use of muscle information extracted by sEMG has spread out from the medical applications to contaminate the consumer world, this technique still falls short when dealing with movements of the hand. We tested NIRS as a technique to get another point of view on the muscle phenomena and proved that, within a specific movements selection, NIRS can be used to recognize movements and return information regarding muscles at different depths. Furthermore, we propose here three different multimodal movement recognition approaches and compare their performances.
Distraction decreases prefrontal oxygenation: A NIRS study.
Ozawa, Sachiyo; Hiraki, Kazuo
2017-04-01
When near-infrared spectroscopy (NIRS) is used to measure emotion-related cerebral blood flow (CBF) changes in the prefrontal cortex regions, the functional distinction of CBF changes is often difficult because NIRS is unable to measure neural activity in deeper brain regions that play major roles in emotional processing. The CBF changes could represent cognitive control of emotion and emotional responses to emotional materials. Supposing that emotion-related CBF changes in the prefrontal cortex regions during distraction are emotional responses, we examined whether oxygenated hemoglobin (oxyHb) decreases. Attention-demanding tasks cause blood flow decreases, and we thus compared the effects of visually paced tapping with different tempos, on distraction. The results showed that the oxyHb level induced by emotional stimulation decreased with fast-tempo tapping significantly more than slow-tempo tapping in ventral medial prefrontal cortex regions. Moreover, a Global-Local task following tapping showed significantly greater local-minus-global response time (RT) difference scores in the fast- and mid-tempo condition compared with those in the slow-tempo, suggesting an increased attentional focus, and decreased negative emotion. The overall findings indicate that oxyHb changes in a relatively long distraction task, as measured by NIRS, are associated with emotional responses, and oxyHb can be decreased by successfully performing attention-demanding distraction tasks. Copyright © 2017 Elsevier Inc. All rights reserved.
Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.
Differentiating regressed melanoma from regressed lichenoid keratosis.
Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A
2017-04-01
Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Maeda, Koki; Toyoda, Sakae; Philippot, Laurent; Hattori, Shohei; Nakajima, Keiichi; Ito, Yumi; Yoshida, Naohiro
2017-12-19
The relative contribution of fungi, bacteria, and nirS and nirK denirifiers to nitrous oxide (N 2 O) emission with unknown isotopic signature from dairy manure compost was examined by selective inhibition techniques. Chloramphenicol (CHP), cycloheximide (CYH), and diethyl dithiocarbamate (DDTC) were used to suppress the activity of bacteria, fungi, and nirK-possessing denitrifiers, respectively. Produced N 2 O were surveyed to isotopocule analysis, and its 15 N site preference (SP) and δ 18 O values were compared. Bacteria, fungi, nirS, and nirK gene abundances were compared by qPCR. The results showed that N 2 O production was strongly inhibited by CHP addition in surface pile samples (82.2%) as well as in nitrite-amended core samples (98.4%), while CYH addition did not inhibit the N 2 O production. N 2 O with unknown isotopic signature (SP = 15.3-16.2‰), accompanied by δ 18 O (19.0-26.8‰) values which were close to bacterial denitrification, was also suppressed by CHP and DDTC addition (95.3%) indicating that nirK denitrifiers were responsible for this N 2 O production despite being less abundant than nirS denitrifiers. Altogether, our results suggest that bacteria are important for N 2 O production with different SP values both from compost surface and pile core. However, further work is required to decipher whether N 2 O with unknown isotopic signature is mostly due to nirK denitrifiers that are taxonomically different from the SP-characterized strains and therefore have different SP values rather than also being interwoven with the contribution of the NO-detoxifying pathway and/or of co-denitrification.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Abstract Expression Grammar Symbolic Regression
Korns, Michael F.
This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.
Wan, Jian; Chen, Yi-Chieh; Morris, A Julian; Thennadil, Suresh N
2017-07-01
Near-infrared (NIR) spectroscopy is being widely used in various fields ranging from pharmaceutics to the food industry for analyzing chemical and physical properties of the substances concerned. Its advantages over other analytical techniques include available physical interpretation of spectral data, nondestructive nature and high speed of measurements, and little or no need for sample preparation. The successful application of NIR spectroscopy relies on three main aspects: pre-processing of spectral data to eliminate nonlinear variations due to temperature, light scattering effects and many others, selection of those wavelengths that contribute useful information, and identification of suitable calibration models using linear/nonlinear regression . Several methods have been developed for each of these three aspects and many comparative studies of different methods exist for an individual aspect or some combinations. However, there is still a lack of comparative studies for the interactions among these three aspects, which can shed light on what role each aspect plays in the calibration and how to combine various methods of each aspect together to obtain the best calibration model. This paper aims to provide such a comparative study based on four benchmark data sets using three typical pre-processing methods, namely, orthogonal signal correction (OSC), extended multiplicative signal correction (EMSC) and optical path-length estimation and correction (OPLEC); two existing wavelength selection methods, namely, stepwise forward selection (SFS) and genetic algorithm optimization combined with partial least squares regression for spectral data (GAPLSSP); four popular regression methods, namely, partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). The comparative study indicates that, in general, pre-processing of spectral data can play a significant
NIRS as an alternative to conventional soil analysis for Greenland soils (focus on SOC)
DEFF Research Database (Denmark)
Knadel, Maria; Ogric, Mateja; Adhikari, Kabindra
Soil organic carbon (SOC) is an important soil property. It is the main constituents of soil organic matter and a good indicator of soil quality. The estimation and mapping of SOC content could be used to select potential agricultural areas in the Arctic areas. However, conventional analysis of SOC...... are time consuming and expensive. They involve a lot of sample preparation, and chemicals and are destructive. Near infrared spectroscopy (NIRS) in the range between 400 and 2500 nm is an alternative method for SOC analysis. It is fast and non-destructive. The aims of this study where to test...... the feasibility of using NIRS to estimate SOC content on a landscape and field scale in Greenland. Partial Least squares regression models were built to correlated soil spectra and their reference SOC data to develop calibration models. Very good predictive ability for both landscape and field scale were obtained...
New laser design for NIR lidar applications
Vogelmann, H.; Trickl, T.; Perfahl, M.; Biggel, S.
2018-04-01
Recently, we quantified the very high spatio-temporal short term variability of tropospheric water vapor in a three dimensional study [1]. From a technical point of view this also depicted the general requirement of short integration times for recording water-vapor profiles with lidar. For this purpose, the only suitable technique is the differential absorption lidar (DIAL) working in the near-infrared (NIR) spectral region. The laser emission of most water vapor DIAL systems is generated by Ti:sapphire or alexandrite lasers. The water vapor absorption band at 817 nm is predominated for the use of Ti:sapphire. We present a new concept of transversely pumping in a Ti:Sapphire amplification stage as well as a compact laser design for the generation of single mode NIR pulses with two different DIAL wavelengths inside a single resonator. This laser concept allows for high output power due to repetitions rates up to 100Hz or even more. It is, because of its compactness, also suitable for mobile applications.
Tsakiridis, Nikolaos L.; Tziolas, Nikolaos; Dimitrakos, Agathoklis; Galanis, Georgios; Ntonou, Eleftheria; Tsirika, Anastasia; Terzopoulou, Evangelia; Kalopesa, Eleni; Zalidis, George C.
2017-09-01
Soil Spectral Libraries facilitate agricultural production taking into account the principles of a low-input sustainable agriculture and provide more valuable knowledge to environmental policy makers, enabling improved decision making and effective management of natural resources in the region. In this paper, a comparison in the predictive performance of two state of the art algorithms, one linear (Partial Least Squares Regression) and one non-linear (Cubist), employed in soil spectroscopy is conducted. The comparison was carried out in a regional Soil Spectral Library developed in the Eastern Macedonia and Thrace region of Northern Greece, comprised of roughly 450 Entisol soil samples from soil horizons A (0-30 cm) and B (30-60 cm). The soil spectra were acquired in the visible - Near Infrared Red region (vis- NIR, 350nm-2500nm) using a standard protocol in the laboratory. Three soil properties, which are essential for agriculture, were analyzed and taken into account for the comparison. These were the Organic Matter, the Clay content and the concentration of nitrate-N. Additionally, three different spectral pre-processing techniques were utilized, namely the continuum removal, the absorbance transformation, and the first derivative. Following the removal of outliers using the Mahalanobis distance in the first 5 principal components of the spectra (accounting for 99.8% of the variance), a five-fold cross-validation experiment was considered for all 12 datasets. Statistical comparisons were conducted on the results, which indicate that the Cubist algorithm outperforms PLSR, while the most informative transformation is the first derivative.
Lin, Z. D.; Wang, Y. B.; Wang, R. J.; Wang, L. S.; Lu, C. P.; Zhang, Z. Y.; Song, L. T.; Liu, Y.
2017-07-01
A total of 130 topsoil samples collected from Guoyang County, Anhui Province, China, were used to establish a Vis-NIR model for the prediction of organic matter content (OMC) in lime concretion black soils. Different spectral pretreatments were applied for minimizing the irrelevant and useless information of the spectra and increasing the spectra correlation with the measured values. Subsequently, the Kennard-Stone (KS) method and sample set partitioning based on joint x-y distances (SPXY) were used to select the training set. Successive projection algorithm (SPA) and genetic algorithm (GA) were then applied for wavelength optimization. Finally, the principal component regression (PCR) model was constructed, in which the optimal number of principal components was determined using the leave-one-out cross validation technique. The results show that the combination of the Savitzky-Golay (SG) filter for smoothing and multiplicative scatter correction (MSC) can eliminate the effect of noise and baseline drift; the SPXY method is preferable to KS in the sample selection; both the SPA and the GA can significantly reduce the number of wavelength variables and favorably increase the accuracy, especially GA, which greatly improved the prediction accuracy of soil OMC with Rcc, RMSEP, and RPD up to 0.9316, 0.2142, and 2.3195, respectively.
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun
2016-07-01
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Giovenzana, Valentina; Civelli, Raffaele; Beghi, Roberto; Oberti, Roberto; Guidetti, Riccardo
2015-11-01
The aim of this work was to test a simplified optical prototype for a rapid estimation of the ripening parameters of white grape for Franciacorta wine directly in field. Spectral acquisition based on reflectance at four wavelengths (630, 690, 750 and 850 nm) was proposed. The integration of a simple processing algorithm in the microcontroller software would allow to visualize real time values of spectral reflectance. Non-destructive analyses were carried out on 95 grape bunches for a total of 475 berries. Samplings were performed weekly during the last ripening stages. Optical measurements were carried out both using the simplified system and a portable commercial vis/NIR spectrophotometer, as reference instrument for performance comparison. Chemometric analyses were performed in order to extract the maximum useful information from optical data. Principal component analysis (PCA) was performed for a preliminary evaluation of the data. Correlations between the optical data matrix and ripening parameters (total soluble solids content, SSC; titratable acidity, TA) were carried out using partial least square (PLS) regression for spectra and using multiple linear regression (MLR) for data from the simplified device. Classification analysis were also performed with the aim of discriminate ripe and unripe samples. PCA, MLR and classification analyses show the effectiveness of the simplified system in separating samples among different sampling dates and in discriminating ripe from unripe samples. Finally, simple equations for SSC and TA prediction were calculated. Copyright © 2015 Elsevier B.V. All rights reserved.
NIRS - Near infrared spectroscopy - investigations in neurovascular diseases
DEFF Research Database (Denmark)
Schytz, Henrik Winther
2015-01-01
The purpose of this thesis was to explore and develop methods, where continuous wave near infrared spectroscopy (CW-NIRS) can be applied in different neurovascular diseases, in order to find biological markers that are useful in clinical neurology. To develop a new method to detect changes...... tomography (133Xe-SPECT) and the corrected BFI value. It was concluded, that it was not possible to obtain reliable BFI data with the ICG CW-NIRS method. NIRS measurements of low frequency oscillations (LFOs) may be a reliable method to investigate vascular alterations in neurovascular diseases......, but this requires an acceptable LFOs variation between hemispheres and over time in the healthy brain. The second study therefore investigated day-to-day and hemispheric variations in LFOs with NIRS. It was shown that NIRS might be useful in assessing LFOs between hemispheres, as well as interhemispheric phase...
Sut, Magdalena; Fischer, Thomas; Repmann, Frank; Raab, Thomas
2013-04-01
In Germany, at more than 1000 sites, soil is polluted with an anthropogenic contaminant in form of iron-cyanide complexes. These contaminations are caused by former Manufactured Gas Plants (MGPs), where electricity for lighting was produced in the process of coal gasification. The production of manufactured gas was restrained in 1950, which caused cessation of MGPs. Our study describes the application of Polychromix Handheld Field Portable Near-Infrared (NIR) Analyzer to predict the cyanide concentrations in soil. In recent times, when the soil remediation is of major importance, there is a need to develop rapid and non-destructive methods for contaminant determination in the field. In situ analysis enables determination of 'hot spots', is cheap and time saving in comparison to laboratory methods. This paper presents a novel usage of NIR spectroscopy, where a calibration model was developed, using multivariate calibration algorithms, in order to determine NIR spectral response to the cyanide concentration in soil samples. As a control, the contaminant concentration was determined using conventional Flow Injection Analysis (FIA). The experiments revealed that portable near-infrared spectrometers could be a reliable device for identification of contamination 'hot spots', where cyanide concentration are higher than 2400 mg kg-1 in the field and >1750 mg kg-1 after sample preparation in the laboratory, but cannot replace traditional laboratory analyses due to high limits of detection.
DEFF Research Database (Denmark)
da Silva, Neirivaldo Cavalcante; Cavalcanti, Claudia Jessica; Honorato, Fernanda Araujo
2017-01-01
spectral responses of fuel samples (gasoline and biodiesel blends) from a high-resolution benchtop Frontier FT-NIR (PerkinElmer) spectrometer and a handheld MicroNIR™1700 (JDSU). These virtual standards can be created by mathematically mixing spectra from the pure solvents present in gasoline or diesel...... to the handheld MicroNIR using virtual standards as transfer samples...
Diseno y construccion de un espectrometro NIR; Design and construction of a NIR spectrometer
Energy Technology Data Exchange (ETDEWEB)
Barcala Riveira, J M; Fernandez Marron, J L; Alberdi Primicia, J; Molero Menendez, F; Navarrete Marin, J J; Oller Gonzalez, J C
2003-07-01
This document describes the design and construction of a NIR spectrometer based on an acoustic-optic tunable filter. The spectrometer will be used for automatic identification of plastics in domestic waste. The system works between 1200 and 1800 nm. Instrument is controlled by a personal computer. Computer receives and analyses data. A software package has been developed to do these tasks. (Author) 27 refs.
International Nuclear Information System (INIS)
Fernández-Ahumada, E; Gómez, A; Vallesquino, P; Guerrero, J E; Pérez-Marín, D; Garrido-Varo, A; Fearn, T
2008-01-01
According to the current demands of the authorities, the manufacturers and the consumers, controls and assessments of the feed compound manufacturing process have become a key concern. Among others, it must be assured that a given compound feed is well manufactured and labelled in terms of the ingredient composition. When near-infrared spectroscopy (NIRS) together with linear models were used for the prediction of the ingredient composition, the results were not always acceptable. Therefore, the performance of nonlinear methods has been investigated. Artificial neural networks and least squares support vector machines (LS-SVM) have been applied to a large (N = 20 320) and heterogeneous population of non-milled feed compounds for the NIR prediction of the inclusion percentage of wheat and sunflower meal, as representative of two different classes of ingredients. Compared to partial least squares regression, results showed considerable reductions of standard error of prediction values for both methods and ingredients: reductions of 45% with ANN and 49% with LS-SVM for wheat and reductions of 44% with ANN and 46% with LS-SVM for sunflower meal. These improvements together with the facility of NIRS technology to be implemented in the process make it ideal for meeting the requirements of the animal feed industry
Alamar, Priscila D; Caramês, Elem T S; Poppi, Ronei J; Pallone, Juliana A L
2016-07-01
The present study investigated the application of near infrared spectroscopy as a green, quick, and efficient alternative to analytical methods currently used to evaluate the quality (moisture, total sugars, acidity, soluble solids, pH and ascorbic acid) of frozen guava and passion fruit pulps. Fifty samples were analyzed by near infrared spectroscopy (NIR) and reference methods. Partial least square regression (PLSR) was used to develop calibration models to relate the NIR spectra and the reference values. Reference methods indicated adulteration by water addition in 58% of guava pulp samples and 44% of yellow passion fruit pulp samples. The PLS models produced lower values of root mean squares error of calibration (RMSEC), root mean squares error of prediction (RMSEP), and coefficient of determination above 0.7. Moisture and total sugars presented the best calibration models (RMSEP of 0.240 and 0.269, respectively, for guava pulp; RMSEP of 0.401 and 0.413, respectively, for passion fruit pulp) which enables the application of these models to determine adulteration in guava and yellow passion fruit pulp by water or sugar addition. The models constructed for calibration of quality parameters of frozen fruit pulps in this study indicate that NIR spectroscopy coupled with the multivariate calibration technique could be applied to determine the quality of guava and yellow passion fruit pulp. Copyright © 2016 Elsevier Ltd. All rights reserved.
Genisheva, Z; Quintelas, C; Mesquita, D P; Ferreira, E C; Oliveira, J M; Amaral, A L
2018-04-25
This work aims to explore the potential of near infrared (NIR) spectroscopy to quantify volatile compounds in Vinho Verde wines, commonly determined by gas chromatography. For this purpose, 105 Vinho Verde wine samples were analyzed using Fourier transform near infrared (FT-NIR) transmission spectroscopy in the range of 5435 cm -1 to 6357 cm -1 . Boxplot and principal components analysis (PCA) were performed for clusters identification and outliers removal. A partial least square (PLS) regression was then applied to develop the calibration models, by a new iterative approach. The predictive ability of the models was confirmed by an external validation procedure with an independent sample set. The obtained results could be considered as quite good with coefficients of determination (R 2 ) varying from 0.94 to 0.97. The current methodology, using NIR spectroscopy and chemometrics, can be seen as a promising rapid tool to determine volatile compounds in Vinho Verde wines. Copyright © 2017 Elsevier Ltd. All rights reserved.
[Study on predicting firmness of watermelon by Vis/NIR diffuse transmittance technique].
Tian, Hai-Qing; Ying, Yi-Bin; Lu, Hui-Shan; Xu, Hui-Rong; Xie, Li-Juan; Fu, Xia-Ping; Yu, Hai-Yan
2007-06-01
Watermelon is a popular fruit in the world and firmness (FM) is one of the major characteristics used for assessing watermelon quality. The objective of the present research was to study the potential of visible/near Infrared (Vis/NIR) diffuse transmittance spectroscopy as a way for the nondestructive measurement of FM of watermelon. Statistical models between the spectra and FM were developed using partial least square (PLS) and principle component regression (PCR) methods. Performance of different models was assessed in terms of correlation coefficients (r) of validation set of samples and root mean square errors of prediction (RMSEP). Models for three kinds of mathematical treatments of spectra (original, first derivative and second derivative) were established. Savitsky-Goaly filter smoothing method was used for spectra data smoothing. The PLS model of the second derivative spectra gave the best prediction of FM, with a correlation coefficient (r) of 0. 974 and root mean square errors of prediction (RMSEP) of 0. 589 N using Savitsky-Goaly filter smoothing method. The results of this study indicate that NIR diffuse transmittance spectroscopy can be used to predict the FM of watermelon. The Vis/NIR diffuse transmittance technique will be valuable for the nandestructive detection large shape and thick peel fruits'.
Prediction of ethanol in bottled Chinese rice wine by NIR spectroscopy
Ying, Yibin; Yu, Haiyan; Pan, Xingxiang; Lin, Tao
2006-10-01
To evaluate the applicability of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining ethanol concentration of Chinese rice wine in square brown glass bottle, transmission spectra of 100 bottled Chinese rice wine samples were collected in the spectral range of 350-1200 nm. Statistical equations were established between the reference data and VIS-NIR spectra by partial least squares (PLS) regression method. Performance of three kinds of mathematical treatment of spectra (original spectra, first derivative spectra and second derivative spectra) were also discussed. The PLS models of original spectra turned out better results, with higher correlation coefficient in calibration (R cal) of 0.89, lower root mean standard error of calibration (RMSEC) of 0.165, and lower root mean standard error of cross validation (RMSECV) of 0.179. Using original spectra, PLS models for ethanol concentration prediction were developed. The R cal and the correlation coefficient in validation (R val) were 0.928 and 0.875, respectively; and the RMSEC and the root mean standard error of validation (RMSEP) were 0.135 (%, v v -1) and 0.177 (%, v v -1), respectively. The results demonstrated that VIS-NIR spectroscopy could be used to predict ethanol concentration in bottled Chinese rice wine.
Fluckiger, Miriam; Brown, Malcolm R; Ward, Louise R; Moltschaniwskyj, Natalie A
2011-06-15
Near infrared reflectance spectroscopy (NIRS) was used to predict glycogen concentrations in the foot muscle of cultured abalone. NIR spectra of live, shucked and freeze-dried abalones were modelled against chemically measured glycogen data (range: 0.77-40.9% of dry weight (DW)) using partial least squares (PLS) regression. The calibration models were then used to predict glycogen concentrations of test abalone samples and model robustness was assessed from coefficient of determination of the validation (R2(val)) and standard error of prediction (SEP) values. The model for freeze-dried abalone gave the best prediction (R2(val) 0.97, SEP=1.71), making it suitable for quantifying glycogen. Models for live and shucked abalones had R2(val) of 0.86 and 0.90, and SEP of 3.46 and 3.07 respectively, making them suitable for producing estimations of glycogen concentration. As glycogen is a taste-active component associated with palatability in abalone, this study demonstrated the potential of NIRS as a rapid method to monitor the factors associated with abalone quality. Copyright © 2011 Elsevier Ltd. All rights reserved.
Near infrared spectroscopic (NIRS) analysis of grapes and red-wines
International Nuclear Information System (INIS)
Guggenbichler, W.
2003-04-01
In this work vine varieties of the genus Vitis as well as grape-must and fully developed wines were examined by Near Infrared Spectroscopy (NIRS). The spectra were obtained by methods of transflection and transmission measurements. It was shown, that spectra of different varieties of grapes and red-wines can be combined in clusters by means of NIR spectroscopy and subsequent principle components analysis (PCA). In addition to this, it was possible to identify blends of two different varieties of wines as such and to determine the ratio of mixture. In several varieties of grape-must these NIR spectroscopic measurements further allowed a quantitative determination of important parameters concerning the quality of grapes, such as: sugar, total acidity, tartaric acid, malic acid, and pH-value. The content of polyphenols in grapes was also analyzed by this method. The total parameter for polyphenols in grapes is a helpful indicator for the optimal harvest time and the quality of grapes. All quantitative calculations were made by the method of partial least square regression (PLS). As these spectroscopic measurements require minimal sample preparations and due to the fact that measurements can be accomplished and results obtained within a few seconds, this method turned out to be a promising option in order to classify wines and to quantify relevant ingredients in grapes. (author)
Measurement of soluble solids content in watermelon by Vis/NIR diffuse transmittance technique.
Tian, Hai-qing; Ying, Yi-bin; Lu, Hui-shan; Fu, Xia-ping; Yu, Hai-yan
2007-02-01
Watermelon is a popular fruit in the world with soluble solids content (SSC) being one of the major characteristics used for assessing its quality. This study was aimed at obtaining a method for nondestructive SSC detection of watermelons by means of visible/near infrared (Vis/NIR) diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer operating over the range 350~1000 nm. Spectra data were analyzed by two multivariate calibration techniques: partial least squares (PLS) and principal component regression (PCR) methods. Two experiments were designed for two varieties of watermelons [Qilin (QL), Zaochunhongyu (ZC)], which have different skin thickness range and shape dimensions. The influences of different data preprocessing and spectra treatments were also investigated. Performance of different models was assessed in terms of root mean square errors of calibration (RMSEC), root mean square errors of prediction (RMSEP) and correlation coefficient (r) between the predicted and measured parameter values. Results showed that spectra data preprocessing influenced the performance of the calibration models. The first derivative spectra showed the best results with high correlation coefficient of determination [r=0.918 (QL); r=0.954 (ZC)], low RMSEP [0.65 degrees Brix (QL); 0.58 degrees Brix (ZC)], low RMSEC [0.48 degrees Brix (QL); 0.34 degrees Brix (ZC)] and small difference between the RMSEP and the RMSEC by PLS method. The nondestructive Vis/NIR measurements provided good estimates of SSC index of watermelon, and the predicted values were highly correlated with destructively measured values for SSC. The models based on smoothing spectra (Savitzky-Golay filter smoothing method) did not enhance the performance of calibration models obviously. The results indicated the feasibility of Vis/NIR diffuse transmittance spectral analysis for predicting watermelon SSC in a
Beganović, Anel; Beć, Krzysztof B.; Henn, Raphael; Huck, Christian W.
2018-05-01
The applicability of two elimination techniques for interferences occurring in measurements with cells of short pathlength using Fourier transform near-infrared (FT-NIR) spectroscopy was evaluated. Due to the growing interest in the field of vibrational spectroscopy in aqueous biological fluids (e.g. glucose in blood), aqueous solutions of D-(+)-glucose were prepared and split into a calibration set and an independent validation set. All samples were measured with two FT-NIR spectrometers at various spectral resolutions. Moving average smoothing (MAS) and fast Fourier transform filter (FFT filter) were applied to the interference affected FT-NIR spectra in order to eliminate the interference pattern. After data pre-treatment, partial least squares regression (PLSR) models using different NIR regions were constructed using untreated (interference affected) spectra and spectra treated with MAS and FFT filter. The prediction of the independent validation set revealed information about the performance of the utilized interference elimination techniques, as well as the different NIR regions. The results showed that the combination band of water at approx. 5200 cm-1 is of great importance since its performance was superior to the one of the so-called first overtone of water at approx. 6800 cm-1. Furthermore, this work demonstrated that MAS and FFT filter are fast and easy-to-use techniques for the elimination of interference fringes in FT-NIR transmittance spectroscopy.
Time domain functional NIRS imaging for human brain mapping.
Torricelli, Alessandro; Contini, Davide; Pifferi, Antonio; Caffini, Matteo; Re, Rebecca; Zucchelli, Lucia; Spinelli, Lorenzo
2014-01-15
This review is aimed at presenting the state-of-the-art of time domain (TD) functional near-infrared spectroscopy (fNIRS). We first introduce the physical principles, the basics of modeling and data analysis. Basic instrumentation components (light sources, detection techniques, and delivery and collection systems) of a TD fNIRS system are described. A survey of past, existing and next generation TD fNIRS systems used for research and clinical studies is presented. Performance assessment of TD fNIRS systems and standardization issues are also discussed. Main strengths and weakness of TD fNIRS are highlighted, also in comparison with continuous wave (CW) fNIRS. Issues like quantification of the hemodynamic response, penetration depth, depth selectivity, spatial resolution and contrast-to-noise ratio are critically examined, with the help of experimental results performed on phantoms or in vivo. Finally we give an account on the technological developments that would pave the way for a broader use of TD fNIRS in the neuroimaging community. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
Designing and testing a wearable, wireless fNIRS patch.
Abtahi, Mohammadreza; Cay, Gozde; Saikia, Manob Jyoti; Mankodiya, Kunal
2016-08-01
Optical brain monitoring using near infrared (NIR) light has got a lot of attention in order to study the complexity of the brain due to several advantages as oppose to other methods such as EEG, fMRI and PET. There are a few commercially available functional NIR spectroscopy (fNIRS) brain monitoring systems, but they are still non-wearable and pose difficulties in scanning the brain while the participants are in motion. In this work, we present our endeavors to design and test a low-cost, wireless fNIRS patch using NIR light sources at wavelengths of 770 and 830nm, photodetectors and a microcontroller to trigger the light sources, read photodetector's output and transfer data wirelessly (via Bluetooth) to a smart-phone. The patch is essentially a 3-D printed wearable system, recording and displaying the brain hemodynamic responses on smartphone, also eliminates the need for complicated wiring of the electrodes. We have performed rigorous lab experiments on the presented system for its functionality. In a proof of concept experiment, the patch detected the NIR absorption on the arm. Another experiment revealed that the patch's battery could last up to several hours with continuous fNIRS recording with and without wireless data transfer.
Kosmowski, Frédéric; Worku, Tigist
2018-01-01
Crop cultivar identification is fundamental for agricultural research, industry and policies. This paper investigates the feasibility of using visible/near infrared hyperspectral data collected with a miniaturized NIR spectrometer to identify cultivars of barley, chickpea and sorghum in the context of Ethiopia. A total of 2650 grains of barley, chickpea and sorghum cultivars were scanned using the SCIO, a recently released miniaturized NIR spectrometer. The effects of data preprocessing techniques and choosing a machine learning algorithm on distinguishing cultivars are further evaluated. Predictive multiclass models of 24 barley cultivars, 19 chickpea cultivars and 10 sorghum cultivars delivered an accuracy of 89%, 96% and 87% on hold-out sample. The Support Vector Machine (SVM) and Partial least squares discriminant analysis (PLS-DA) algorithms consistently outperformed other algorithms. Several cultivars, believed to be widely adopted in Ethiopia, were identified with perfect accuracy. These results advance the discussion on cultivar identification survey methods by demonstrating that miniaturized NIR spectrometers represent a low-cost, rapid and viable tool. We further discuss the potential utility of the method for adoption surveys, field-scale agronomic studies, socio-economic impact assessments and value chain quality control. Finally, we provide a free tool for R to easily carry out crop cultivar identification and measure uncertainty based on spectral data.
NIRS inaugurated as IAEA Collaborating Centre. Its presence and function
International Nuclear Information System (INIS)
Yonekura, Yoshiharu; Watanabe, Naoyuki; Sakai, Kazuo; Kamada, Tadashi; Imai, Reiko; Fujibayashi, Yasuhisa; Nakane, Takeshi; Burkart, W.; Chhem, R.; Matsuura, Shojiro
2010-01-01
The feature article is the collection of documents commemorating the 2010 designation of National Institute of Radiological Sciences (NIRS) as one of International Atomic Energy Agency (IAEA) Collaborating Centres (CC) again, involving 4 introductory chapters containing 9 sections in total. The IAEA-CC concept, essentially for the 4-year project, started to globally give shape by designating 3 organizations in some countries in 2004, NIRS as a CC worked from 2006 and the present designation is the renewed one. There are 17 IAEA-CCs at present. The title of Chapter 1 of the article is the same as above title by NIRS President and of Chapter 2, ''IAEA-CC scheme'' by NIRS Senior Specialist/ professor of Gunma Pref. College of Health Sciences/ former IAEA staff. Chapter 3 entitled ''Research Development of Next Four Years in Three Collaboration Areas'', contains 3 topics of the very areas mainly responsible to the project, of biological effect and mechanism of low dose radiation by NIRS Director of Res. Center for Radiation Protection, IAEA-CC plan (radiotherapy) by the Director for Charged Particle Therapy, and IAEA-CC activity and research at Molecular Imaging Center by its Director. Chapter 4 entitled ''Expectation to NIRS'' contains four topics; Expectations for the reinforcement of collaboration with IAEA whose new priority is cancer control by the Japanese Ambassador Extraordinary and Plenipotentiary in Vienna; Welcoming NIRS to join IAEA-CC network (an interview with IAEA Deputy Director General and Head of Nuclear Sciences and Applications); Honoured to invite NIRS to establish a new partnership with IAEA (an interview with IAEA Director of Division of Human Health, Dept. of Nuclear Sciences and Applications); Expectation to NIRS in peaceful use of nuclear and radiation by President of the Nuclear Safety Research Association. (T.T.)
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
Brain Functional Connectivity in MS: An EEG-NIRS Study
2015-10-01
1 AWARD NUMBER: W81XWH-14-1-0582 TITLE: Brain Functional Connectivity in MS: An EEG -NIRS Study PRINCIPAL INVESTIGATOR: Heather Wishart...Functional Connectivity in MS: An EEG -NIRS Study 5b. GRANT NUMBER W81XWH-14-1-0582 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER Heather...electrical ( EEG ) and blood volume and blood oxygen-based (NIRS and fMRI) signals, and to use the results to help optimize blood oxygen level
Regularized Label Relaxation Linear Regression.
Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung; Fang, Bingwu
2018-04-01
Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on -norm and -norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.
Directory of Open Access Journals (Sweden)
Masayuki Satoh
2017-11-01
Full Text Available Aims: The aims of this study were (1 to investigate the influence of physical movement on near-infrared spectroscopy (NIRS data, (2 to establish a video-NIRS system which simultaneously records NIRS data and the subject’s movement, and (3 to measure the oxygenated hemoglobin (oxy-Hb concentration change (Δoxy-Hb during a word fluency (WF task. Experiment 1: In 5 healthy volunteers, we measured the oxy-Hb and deoxygenated hemoglobin (deoxy-Hb concentrations during 11 kinds of facial, head, and extremity movements. The probes were set in the bilateral frontal regions. The deoxy-Hb concentration was increased in 85% of the measurements. Experiment 2: Using a pillow on the backrest of the chair, we established the video-NIRS system with data acquisition and video capture software. One hundred and seventy-six elderly people performed the WF task. The deoxy-Hb concentration was decreased in 167 subjects (95%. Experiment 3: Using the video-NIRS system, we measured the Δoxy-Hb, and compared it with the results of the WF task. Δoxy-Hb was significantly correlated with the number of words. Conclusion: Like the blood oxygen level-dependent imaging effect in functional MRI, the deoxy-Hb concentration will decrease if the data correctly reflect the change in neural activity. The video-NIRS system might be useful to collect NIRS data by recording the waveforms and the subject’s appearance simultaneously.
Gutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, Maria P
2016-02-16
Plant phenotyping is a very important topic in agriculture. In this context, data mining strategies may be applied to agricultural data retrieved with new non-invasive devices, with the aim of yielding useful, reliable and objective information. This work presents some applications of machine learning algorithms along with in-field acquired NIR spectral data for plant phenotyping in viticulture, specifically for grapevine variety discrimination and assessment of plant water status. Support vector machine (SVM), rotation forests and M5 trees models were built using NIR spectra acquired in the field directly on the adaxial side of grapevine leaves, with a non-invasive portable spectrophotometer working in the spectral range between 1600 and 2400 nm. The ν-SVM algorithm was used for the training of a model for varietal classification. The classifiers' performance for the 10 varieties reached, for cross- and external validations, the 88.7% and 92.5% marks, respectively. For water stress assessment, the models developed using the absorbance spectra of six varieties yielded the same determination coefficient for both cross- and external validations (R² = 0.84; RMSEs of 0.164 and 0.165 MPa, respectively). Furthermore, a variety-specific model trained only with samples of Tempranillo from two different vintages yielded R² = 0.76 and RMSE of 0.16 MPa for cross-validation and R² = 0.79, RMSE of 0.17 MPa for external validation. These results show the power of the combined use of data mining and non-invasive NIR sensing for in-field grapevine phenotyping and their usefulness for the wine industry and precision viticulture implementations.
Directory of Open Access Journals (Sweden)
Salvador Gutiérrez
2016-02-01
Full Text Available Plant phenotyping is a very important topic in agriculture. In this context, data mining strategies may be applied to agricultural data retrieved with new non-invasive devices, with the aim of yielding useful, reliable and objective information. This work presents some applications of machine learning algorithms along with in-field acquired NIR spectral data for plant phenotyping in viticulture, specifically for grapevine variety discrimination and assessment of plant water status. Support vector machine (SVM, rotation forests and M5 trees models were built using NIR spectra acquired in the field directly on the adaxial side of grapevine leaves, with a non-invasive portable spectrophotometer working in the spectral range between 1600 and 2400 nm. The ν-SVM algorithm was used for the training of a model for varietal classification. The classifiers’ performance for the 10 varieties reached, for cross- and external validations, the 88.7% and 92.5% marks, respectively. For water stress assessment, the models developed using the absorbance spectra of six varieties yielded the same determination coefficient for both cross- and external validations (R2 = 0.84; RMSEs of 0.164 and 0.165 MPa, respectively. Furthermore, a variety-specific model trained only with samples of Tempranillo from two different vintages yielded R2 = 0.76 and RMSE of 0.16 MPa for cross-validation and R2 = 0.79, RMSE of 0.17 MPa for external validation. These results show the power of the combined use of data mining and non-invasive NIR sensing for in-field grapevine phenotyping and their usefulness for the wine industry and precision viticulture implementations.
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...
Compensation techniques in NIRS proton beam radiotherapy
International Nuclear Information System (INIS)
Akanuma, A.; Majima, H.; Furukawa, S.
1982-01-01
Proton beam has the dose distribution advantage in radiation therapy, although it has little advantage in biological effects. One of the best advantages is its sharp fall off of dose after the peak. With proton beam, therefore, the dose can be given just to cover a target volume and potentially no dose is delivered thereafter in the beam direction. To utilize this advantage, bolus techniques in conjunction with CT scanning are employed in NIRS proton beam radiation therapy planning. A patient receives CT scanning first so that the target volume can be clearly marked and the radiation direction and fixation method can be determined. At the same time bolus dimensions are calculated. The bolus frames are made with dental paraffin sheets according to the dimensions. The paraffin frame is replaced with dental resin. Alginate (a dental impression material with favorable physical density and skin surface contact) is now employed for the bolus material. With fixation device and bolus on, which are constructed individually, the patient receives CT scanning again prior to a proton beam treatment in order to prove the devices are suitable. Alginate has to be poured into the frame right before each treatments. Further investigations are required to find better bolus materials and easier construction methods
Compensation techniques in NIRS proton beam radiotherapy
Energy Technology Data Exchange (ETDEWEB)
Akanuma, A. (Univ. of Tokyo, Japan); Majima, H.; Furukawa, S.
1982-09-01
Proton beam has the dose distribution advantage in radiation therapy, although it has little advantage in biological effects. One of the best advantages is its sharp fall off of dose after the peak. With proton beam, therefore, the dose can be given just to cover a target volume and potentially no dose is delivered thereafter in the beam direction. To utilize this advantage, bolus techniques in conjunction with CT scanning are employed in NIRS proton beam radiation therapy planning. A patient receives CT scanning first so that the target volume can be clearly marked and the radiation direction and fixation method can be determined. At the same time bolus dimensions are calculated. The bolus frames are made with dental paraffin sheets according to the dimensions. The paraffin frame is replaced with dental resin. Alginate (a dental impression material with favorable physical density and skin surface contact) is now employed for the bolus material. With fixation device and bolus on, which are constructed individually, the patient receives CT scanning again prior to a proton beam treatment in order to prove the devices are suitable. Alginate has to be poured into the frame right before each treatments. Further investigations are required to find better bolus materials and easier construction methods.
SHARK-NIR system design analysis overview
Viotto, Valentina; Farinato, Jacopo; Greggio, Davide; Vassallo, Daniele; Carolo, Elena; Baruffolo, Andrea; Bergomi, Maria; Carlotti, Alexis; De Pascale, Marco; D'Orazi, Valentina; Fantinel, Daniela; Magrin, Demetrio; Marafatto, Luca; Mohr, Lars; Ragazzoni, Roberto; Salasnich, Bernardo; Verinaud, Christophe
2016-08-01
In this paper, we present an overview of the System Design Analysis carried on for SHARK-NIR, the coronagraphic camera designed to take advantage of the outstanding performance that can be obtained with the FLAO facility at the LBT, in the near infrared regime. Born as a fast-track project, the system now foresees both coronagraphic direct imaging and spectroscopic observing mode, together with a first order wavefront correction tool. The analysis we here report includes several trade-offs for the selection of the baseline design, in terms of optical and mechanical engineering, and the choice of the coronagraphic techniques to be implemented, to satisfy both the main scientific drivers and the technical requirements set at the level of the telescope. Further care has been taken on the possible exploitation of the synergy with other LBT instrumentation, like LBTI. A set of system specifications is then flown down from the upper level requirements to finally ensure the fulfillment of the science drivers. The preliminary performance budgets are presented, both in terms of the main optical planes stability and of the image quality, including the contributions of the main error sources in different observing modes.
HARDERSEN IRTF ASTEROID NIR REFLECTANCE SPECTRA V1.0
National Aeronautics and Space Administration — This dataset includes average near-infrared (NIR) reflectance spectra for 68 main-belt asteroids that were observed at the NASA Infrared Telescope Facility (IRTF),...
NIRS Characterization of Paper Pulps to Predict Kappa Number
Directory of Open Access Journals (Sweden)
Ana Moral
2015-01-01
Full Text Available Rice is one of the most abundant food crops in the world and its straw stands as an important source of fibres both from an economic and an environmental point of view. Pulp characterization is of special relevance in works involving alternative raw materials, since pulp properties are closely linked to the quality of the final product. One of the analytical techniques that can be used in pulp characterization is near-infrared spectroscopy (NIRS. The use of NIRS has economic and technical advantages over conventional techniques. This paper aims to discuss the convenience of using NIRS to predict Kappa number in rice straw pulps produced under different conditions. We found that the resulting Kappa number can be acceptably estimated by NIRS, as the errors obtained with that method are similar to those found for other techniques.
Toward Adaptation of fNIRS Instrumentation to Airborne Environments
Adamovsky, Grigory; Mackey, Jeffrey; Harrivel, Angela; Hearn, Tristan; Floyd, Bertram
2014-01-01
The paper reviews potential applications of functional Near-Infrared Spectroscopy (fNIRS), a well-known medical diagnostic technique, to monitoring the cognitive state of pilots with a focus on identifying ways to adopt this technique to airborne environments. We also discuss various fNIRS techniques and the direction of technology maturation of associated hardware in view of their potential for miniaturization, maximization of data collection capabilities, and user friendliness.
Motor learning and modulation of prefrontal cortex: an fNIRS assessment
Ono, Yumie; Noah, Jack Adam; Zhang, Xian; Nomoto, Yasunori; Suzuki, Tatsuya; Shimada, Sotaro; Tachibana, Atsumichi; Bronner, Shaw; Hirsch, Joy
2015-12-01
Objective. Prefrontal hemodynamic responses are observed during performance of motor tasks. Using a dance video game (DVG), a complex motor task that requires temporally accurate footsteps with given visual and auditory cues, we investigated whether 20 h of DVG training modified hemodynamic responses of the prefrontal cortex in six healthy young adults. Approach. Fronto-temporal activity during actual DVG play was measured using functional near-infrared spectroscopy (fNIRS) pre- and post-training. To evaluate the training-induced changes in the time-courses of fNIRS signals, we employed a regression analysis using the task-specific template fNIRS signals that were generated from alternate well-trained and/or novice DVG players. The HRF was also separately incorporated as a template to construct an alternate regression model. Change in coefficients for template functions at pre- and post- training were determined and compared among different models. Main results. Training significantly increased the motor performance using the number of temporally accurate steps in the DVG as criteria. The mean oxygenated hemoglobin (ΔoxyHb) waveform changed from an activation above baseline pattern to that of a below baseline pattern. Participants showed significantly decreased coefficients for regressors of the ΔoxyHb response of novice players and HRF. The model using ΔoxyHb responses from both well-trained and novice players of DVG as templates showed the best fit for the ΔoxyHb responses of the participants at both pre- and post-training when analyzed with Akaike information criteria. Significance. These results suggest that the coefficients for the template ΔoxyHb responses of the novice players are sensitive indicators of motor learning during the initial stage of training and thus clinically useful to determine the improvement in motor performance when patients are engaged in a specific rehabilitation program.
In vivo near infrared (NIRS) sensor attachment using fibrin bioadhesive
Macnab, Andrew; Pagano, Roberto; Kwon, Brian; Dumont, Guy; Shadgan, Babak
2018-02-01
Background: `Tisseel' (Baxter Healthcare, Deerfield, IL) is a fibrin-based sealant that is commonly used during spine surgery to augment dural repairs. We wish to intra-operatively secure a near infrared spectroscopy (NIRS) sensor to the dura in order to monitor the tissue hemodynamics of the underlying spinal cord. To determine if `Tisseel' sealant adversely attenuates NIR photon transmission. Methods: We investigated `Tisseel' in both an in vitro and in vivo paradigm. For in vitro testing, we used a 1 mm pathlength cuvette containing either air or `Tisseel' interposed between a NIR light source (760 and 850 nm) and a photodiode detector and compared transmittance. For in vivo testing, a continuous wave (760 and 850 nm) spatiallyresolved NIRS device was placed over the triceps muscle using either conventional skin apposition (overlying adhesive bandage) or bioadhesion with `Tisseel'. Raw optical data and tissue saturation index (TSI%) collected at rest were compared. Results: In-vitro NIR light absorption by `Tisseel' was very high, with transmittance reduced by 95% compared to air. In-vivo muscle TSI% values were 80% with conventional attachment and 20% using fibrin glue. Conclusion: The optical properties of `Tisseel' significantly attenuate NIR light during in-vitro transmittance and critically compromise photon transmission in-vivo.
Epoch making NIRS studies seen through citation trends
International Nuclear Information System (INIS)
Dan, Ippeita
2009-01-01
Near-infrared spectroscopy (NIRS) studies through citation trends are investigated of literature concerning only the brain function measurement and its methodology together with NIRS principle, technological development, present state and future view. Investigation is conducted firstly for the survey of important author name of those concerned papers in Web of Science and Google Scholar with search words of NIRS, brain and optical topography as an option. Second, >100 papers of those authors citing any of them are picked up and their papers are ranked in accordance with Web of Science citation number, of which top-nineteen are presented here. Impact and epoch making papers are reviewed with explanations of: the establishment of measuring technology of cerebral blood flow change and subsequent brain function by NIRS; development with multi-channel detection; simultaneous measurement with other imaging modalities; examination of NIRS validity; spatial analysis of NIRS; and measurement of brain function. The highest times of citation are 1,238 of the paper by F. F. Jobsis in 'Science' (1977). It should be noted that 10 of top 19 papers are those by Japanese authors. However, review articles omitted in the present literature survey are mostly described by foreign authors: an effort to systemize the concerned fields might be required in this country. (K.T.)
Understanding logistic regression analysis
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Alternative Methods of Regression
Birkes, David
2011-01-01
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s
Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany
2016-07-01
Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.
DEFF Research Database (Denmark)
Shetty, Nisha; Gislum, René; Jensen, Anne Mette Dahl
2012-01-01
Near-infrared (NIR) spectroscopy was used in combination with chemometrics to quantify total nonstructural carbohydrates (TNC) in grass samples in order to overcome year-to-year variation. A total of 1103 above-ground plant and root samples were collected from different field and pot experiments...... and with various experimental designs in the period from 2001 to 2005. A calibration model was developed using partial least squares regression (PLSR). The calibration model on a large data set spanning five years demonstrated that quantification of TNC using NIR spectroscopy was possible with an acceptable low...
Polylinear regression analysis in radiochemistry
International Nuclear Information System (INIS)
Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.
1995-01-01
A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis
Sensitivity of fNIRS to cognitive state and load
Directory of Open Access Journals (Sweden)
Frank Anthony Fishburn
2014-02-01
Full Text Available Functional near-infrared spectroscopy (fNIRS is an emerging low-cost noninvasive neuroimaging technique that measures cortical bloodflow. While fNIRS has gained interest as a potential alternative to fMRI for use with clinical and pediatric populations, it remains unclear whether fNIRS has the necessary sensitivity to serve as a replacement for fMRI. The present study set out to examine whether fNIRS has the sensitivity to detect linear changes in activation and functional connectivity in response to cognitive load, and functional connectivity changes when transitioning from a task-free resting state to a task. Sixteen young adult subjects were scanned with a continuous-wave fNIRS system during a 10-minute resting-state scan followed by a letter n-back task with three load conditions. Five optical probes were placed over frontal and parietal cortices, covering bilateral dorsolateral PFC (dlPFC, bilateral ventrolateral PFC (vlPFC, frontopolar cortex (FP, and bilateral parietal cortex. Activation was found to scale linearly with working memory load in bilateral prefrontal cortex. Functional connectivity increased with increasing n-back loads for fronto-parietal, interhemispheric dlPFC, and local connections. Functional connectivity differed between the resting state scan and the n-back scan, with fronto-parietal connectivity greater during the n-back, and interhemispheric vlPFC connectivity greater during rest. These results demonstrate that fNIRS is sensitive to both cognitive load and state, suggesting that fNIRS is well-suited to explore the full complement of neuroimaging research questions and will serve as a viable alternative to fMRI.
Directory of Open Access Journals (Sweden)
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Understanding logistic regression analysis.
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Data preprocessing methods of FT-NIR spectral data for the classification cooking oil
Ruah, Mas Ezatul Nadia Mohd; Rasaruddin, Nor Fazila; Fong, Sim Siong; Jaafar, Mohd Zuli
2014-12-01
This recent work describes the data pre-processing method of FT-NIR spectroscopy datasets of cooking oil and its quality parameters with chemometrics method. Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modelling. Hence, this work is dedicated to investigate the utility and effectiveness of pre-processing algorithms namely row scaling, column scaling and single scaling process with Standard Normal Variate (SNV). The combinations of these scaling methods have impact on exploratory analysis and classification via Principle Component Analysis plot (PCA). The samples were divided into palm oil and non-palm cooking oil. The classification model was build using FT-NIR cooking oil spectra datasets in absorbance mode at the range of 4000cm-1-14000cm-1. Savitzky Golay derivative was applied before developing the classification model. Then, the data was separated into two sets which were training set and test set by using Duplex method. The number of each class was kept equal to 2/3 of the class that has the minimum number of sample. Then, the sample was employed t-statistic as variable selection method in order to select which variable is significant towards the classification models. The evaluation of data pre-processing were looking at value of modified silhouette width (mSW), PCA and also Percentage Correctly Classified (%CC). The results show that different data processing strategies resulting to substantial amount of model performances quality. The effects of several data pre-processing i.e. row scaling, column standardisation and single scaling process with Standard Normal Variate indicated by mSW and %CC. At two PCs model, all five classifier gave high %CC except Quadratic Distance Analysis.
Function approximation with polynomial regression slines
International Nuclear Information System (INIS)
Urbanski, P.
1996-01-01
Principles of the polynomial regression splines as well as algorithms and programs for their computation are presented. The programs prepared using software package MATLAB are generally intended for approximation of the X-ray spectra and can be applied in the multivariate calibration of radiometric gauges. (author)
Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena
2007-11-05
A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.
Estimation of Anthocyanin Content of Berries by NIR Method
International Nuclear Information System (INIS)
Zsivanovits, G.; Ludneva, D.; Iliev, A.
2010-01-01
Anthocyanin contents of fruits were estimated by VIS spectrophotometer and compared with spectra measured by NIR spectrophotometer (600-1100 nm step 10 nm). The aim was to find a relationship between NIR method and traditional spectrophotometric method. The testing protocol, using NIR, is easier, faster and non-destructive. NIR spectra were prepared in pairs, reflectance and transmittance. A modular spectrocomputer, realized on the basis of a monochromator and peripherals Bentham Instruments Ltd (GB) and a photometric camera created at Canning Research Institute, were used. An important feature of this camera is the possibility offered for a simultaneous measurement of both transmittance and reflectance with geometry patterns T0/180 and R0/45. The collected spectra were analyzed by CAMO Unscrambler 9.1 software, with PCA, PLS, PCR methods. Based on the analyzed spectra quality and quantity sensitive calibrations were prepared. The results showed that the NIR method allows measuring of the total anthocyanin content in fresh berry fruits or processed products without destroying them.
Understanding poisson regression.
Hayat, Matthew J; Higgins, Melinda
2014-04-01
Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.
Directory of Open Access Journals (Sweden)
Li eXiao
2014-08-01
Full Text Available Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR and pyrolysis-molecular beam mass spectrometry (Py-mbms are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis and for building regression models (partial least square regression between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated
Error propagation of partial least squares for parameters optimization in NIR modeling.
Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng
2018-03-05
A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. Copyright © 2017. Published by Elsevier B.V.
Error propagation of partial least squares for parameters optimization in NIR modeling
Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng
2018-03-01
A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.
Thermostatic system of sensor in NIR spectrometer based on PID control
Wang, Zhihong; Qiao, Liwei; Ji, Xufei
2016-11-01
Aiming at the shortcomings of the primary sensor thermostatic control system in the near infrared (NIR) spectrometer, a novel thermostatic control system based on proportional-integral-derivative (PID) control technology was developed to improve the detection precision of the NIR spectrometer. There were five parts including bridge amplifier circuit, analog-digital conversion (ADC) circuit, microcontroller, digital-analog conversion (DAC) circuit and drive circuit in the system. The five parts formed a closed-loop control system based on PID algorithm that was used to control the error between the temperature calculated by the sampling data of ADC and the designed temperature to ensure the stability of the spectrometer's sensor. The experimental results show that, when the operating temperature of sensor is -11°, compared with the original system, the temperature control precision of the new control system is improved from ±0.64° to ±0.04° and the spectrum signal to noise ratio (SNR) is improved from 4891 to 5967.
Combined data mining/NIR spectroscopy for purity assessment of lime juice
Shafiee, Sahameh; Minaei, Saeid
2018-06-01
This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.
DEFF Research Database (Denmark)
Mahnke, Martina; Uprichard, Emma
2014-01-01
Imagine sailing across the ocean. The sun is shining, vastness all around you. And suddenly [BOOM] you’ve hit an invisible wall. Welcome to the Truman Show! Ever since Eli Pariser published his thoughts on a potential filter bubble, this movie scenario seems to have become reality, just with slight...... changes: it’s not the ocean, it’s the internet we’re talking about, and it’s not a TV show producer, but algorithms that constitute a sort of invisible wall. Building on this assumption, most research is trying to ‘tame the algorithmic tiger’. While this is a valuable and often inspiring approach, we...
fNIRS-based brain-computer interfaces: a review
Directory of Open Access Journals (Sweden)
Noman eNaseer
2015-01-01
Full Text Available A brain-computer interface (BCI is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis, multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.
[Advances of NIR spectroscopy technology applied in seed quality detection].
Zhu, Li-wei; Ma, Wen-guang; Hu, Jin; Zheng, Yun-ye; Tian, Yi-xin; Guan, Ya-jing; Hu, Wei-min
2015-02-01
Near infrared spectroscopy (NIRS) technology developed fast in recent years, due to its rapid speed, less pollution, high-efficiency and other advantages. It has been widely used in many fields such as food, chemical industry, pharmacy, agriculture and so on. The seed is the most basic and important agricultural capital goods, and seed quality is important for agricultural production. Most methods presently used for seed quality detecting were destructive, slow and needed pretreatment, therefore, developing one kind of method that is simple and rapid has great significance for seed quality testing. This article reviewed the application and trends of NIRS technology in testing of seed constituents, vigor, disease and insect pests etc. For moisture, starch, protein, fatty acid and carotene content, the model identification rates were high as their relative contents were high; for trace organic, the identification rates were low as their relative content were low. The heat-damaged seeds with low vigor were discriminated by NIRS, the seeds stored for different time could also been identified. The discrimination of frost-damaged seeds was impossible. The NIRS could be used to identify health and infected disease seeds, and did the classification for the health degree; it could identify parts of the fungal pathogens. The NIRS could identify worm-eaten and health seeds, and further distinguished the insect species, however the identification effects for small larval and low injury level of insect pests was not good enough. Finally, in present paper existing problems and development trends for NIRS in seed quality detection was discussed, especially the single seed detecting technology which was characteristic of the seed industry, the standardization of its spectral acquisition accessories will greatly improve its applicability.
Directory of Open Access Journals (Sweden)
Mok Tik
2014-06-01
Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.
Analysis of task-evoked systemic interference in fNIRS measurements: insights from fMRI.
Erdoğan, Sinem B; Yücel, Meryem A; Akın, Ata
2014-02-15
Functional near infrared spectroscopy (fNIRS) is a promising method for monitoring cerebral hemodynamics with a wide range of clinical applications. fNIRS signals are contaminated with systemic physiological interferences from both the brain and superficial tissues, resulting in a poor estimation of the task related neuronal activation. In this study, we use the anatomical resolution of functional magnetic resonance imaging (fMRI) to extract scalp and brain vascular signals separately and construct an optically weighted spatial average of the fMRI blood oxygen level-dependent (BOLD) signal for characterizing the scalp signal contribution to fNIRS measurements. We introduce an extended superficial signal regression (ESSR) method for canceling physiology-based systemic interference where the effects of cerebral and superficial systemic interference are treated separately. We apply and validate our method on the optically weighted BOLD signals, which are obtained by projecting the fMRI image onto optical measurement space by use of the optical forward problem. The performance of ESSR method in removing physiological artifacts is compared to i) a global signal regression (GSR) method and ii) a superficial signal regression (SSR) method. The retrieved signals from each method are compared with the neural signals that represent the 'ground truth' brain activation cleaned from cerebral systemic fluctuations. We report significant improvements in the recovery of task induced neural activation with the ESSR method when compared to the other two methods as reflected in the Pearson R(2) coefficient and mean square error (MSE) metrics (two tailed paired t-tests, pnoise (CNR) improvement (60%). Our findings suggest that, during a cognitive task i) superficial scalp signal contribution to fNIRS signals varies significantly among different regions on the forehead and ii) using an average scalp measurement together with a local measure of superficial hemodynamics better accounts
Multicollinearity and Regression Analysis
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
Development of VIS/NIR spectroscopic system for real-time prediction of fresh pork quality
Zhang, Haiyun; Peng, Yankun; Zhao, Songwei; Sasao, Akira
2013-05-01
Quality attributes of fresh meat will influence nutritional value and consumers' purchasing power. The aim of the research was to develop a prototype for real-time detection of quality in meat. It consisted of hardware system and software system. A VIS/NIR spectrograph in the range of 350 to 1100 nm was used to collect the spectral data. In order to acquire more potential information of the sample, optical fiber multiplexer was used. A conveyable and cylindrical device was designed and fabricated to hold optical fibers from multiplexer. High power halogen tungsten lamp was collected as the light source. The spectral data were obtained with the exposure time of 2.17ms from the surface of the sample by press down the trigger switch on the self-developed system. The system could automatically acquire, process, display and save the data. Moreover the quality could be predicted on-line. A total of 55 fresh pork samples were used to develop prediction model for real time detection. The spectral data were pretreated with standard normalized variant (SNV) and partial least squares regression (PLSR) was used to develop prediction model. The correlation coefficient and root mean square error of the validation set for water content and pH were 0.810, 0.653, and 0.803, 0.098 respectively. The research shows that the real-time non-destructive detection system based on VIS/NIR spectroscopy can be efficient to predict the quality of fresh meat.
Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration
International Nuclear Information System (INIS)
Xu Lu; Zhou Yanping; Tang Lijuan; Wu Hailong; Jiang Jianhui; Shen Guoli; Yu Ruqin
2008-01-01
Preprocessing of raw near-infrared (NIR) spectral data is indispensable in multivariate calibration when the measured spectra are subject to significant noises, baselines and other undesirable factors. However, due to the lack of sufficient prior information and an incomplete knowledge of the raw data, NIR spectra preprocessing in multivariate calibration is still trial and error. How to select a proper method depends largely on both the nature of the data and the expertise and experience of the practitioners. This might limit the applications of multivariate calibration in many fields, where researchers are not very familiar with the characteristics of many preprocessing methods unique in chemometrics and have difficulties to select the most suitable methods. Another problem is many preprocessing methods, when used alone, might degrade the data in certain aspects or lose some useful information while improving certain qualities of the data. In order to tackle these problems, this paper proposes a new concept of data preprocessing, ensemble preprocessing method, where partial least squares (PLSs) models built on differently preprocessed data are combined by Monte Carlo cross validation (MCCV) stacked regression. Little or no prior information of the data and expertise are required. Moreover, fusion of complementary information obtained by different preprocessing methods often leads to a more stable and accurate calibration model. The investigation of two real data sets has demonstrated the advantages of the proposed method
NIR detection of honey adulteration reveals differences in water spectral pattern.
Bázár, György; Romvári, Róbert; Szabó, András; Somogyi, Tamás; Éles, Viktória; Tsenkova, Roumiana
2016-03-01
High fructose corn syrup (HFCS) was mixed with four artisanal Robinia honeys at various ratios (0-40%) and near infrared (NIR) spectra were recorded with a fiber optic immersion probe. Levels of HFCS adulteration could be detected accurately using leave-one-honey-out cross-validation (RMSECV=1.48; R(2)CV=0.987), partial least squares regression and the 1300-1800nm spectral interval containing absorption bands related to both water and carbohydrates. Aquaphotomics-based evaluations showed that unifloral honeys contained more highly organized water than the industrial sugar syrup, supposedly because of the greater variety of molecules dissolved in the multi-component honeys. Adulteration with HFCS caused a gradual reduction of water molecular structures, especially water trimers, which facilitate interaction with other molecules. Quick, non-destructive NIR spectroscopy combined with aquaphotomics could be used to describe water molecular structures in honey and to detect a rather common form of adulteration. Copyright © 2015 Elsevier Ltd. All rights reserved.
Casale, M; Oliveri, P; Casolino, C; Sinelli, N; Zunin, P; Armanino, C; Forina, M; Lanteri, S
2012-01-27
An authentication study of the Italian PDO (protected designation of origin) extra virgin olive oil Chianti Classico was performed; UV-visible (UV-vis), Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopies were applied to a set of samples representative of the whole Chianti Classico production area. The non-selective signals (fingerprints) provided by the three spectroscopic techniques were utilised both individually and jointly, after fusion of the respective profile vectors, in order to build a model for the Chianti Classico PDO olive oil. Moreover, these results were compared with those obtained by the gas chromatographic determination of the fatty acids composition. In order to characterise the olive oils produced in the Chianti Classico PDO area, UNEQ (unequal class models) and SIMCA (soft independent modelling of class analogy) were employed both on the MIR, NIR and UV-vis spectra, individually and jointly, and on the fatty acid composition. Finally, PLS (partial least square) regression was applied on the UV-vis, NIR and MIR spectra, in order to predict the content of oleic and linoleic acids in the extra virgin olive oils. UNEQ, SIMCA and PLS were performed after selection of the relevant predictors, in order to increase the efficiency of both classification and regression models. The non-selective information obtained from UV-vis, NIR and MIR spectroscopy allowed to build reliable models for checking the authenticity of the Italian PDO extra virgin olive oil Chianti Classico. Copyright © 2011 Elsevier B.V. All rights reserved.
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....
Directory of Open Access Journals (Sweden)
Gumieniczek Anna
2018-03-01
Full Text Available It is well known that drugs can directly react with excipients. In addition, excipients can be a source of impurities that either directly react with drugs or catalyze their degradation. Thus, binary mixtures of three diuretics, torasemide, furosemide and amiloride with different excipients, i.e. citric acid anhydrous, povidone K25 (PVP, magnesium stearate (Mg stearate, lactose, D-mannitol, glycine, calcium hydrogen phosphate anhydrous (CaHPO4 and starch, were examined to detect interactions. High temperature and humidity or UV/VIS irradiation were applied as stressing conditions. Differential scanning calorimetry (DSC, FT-IR and NIR were used to adequately collect information. In addition, chemometric assessments of NIR signals with principal component analysis (PCA and ANOVA were applied.
Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A
2016-08-01
The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data
Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo
2018-06-01
Objective. Brain–computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. Approach. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. Main results. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. Significance. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Bounded Gaussian process regression
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....
and Multinomial Logistic Regression
African Journals Online (AJOL)
This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
NIR monitoring of in-service wood structures
Michela Zanetti; Timothy G. Rials; Douglas Rammer
2005-01-01
Near infrared spectroscopy (NIRS) was used to study a set of Southern Yellow Pine boards exposed to natural weathering for different periods of exposure time. This non-destructive spectroscopic technique is a very powerful tool to predict the weathering of wood when used in combination with multivariate analysis (Principal Component Analysis, PCA, and Projection to...
Developing and evaluating a multisite and multispecies NIR ...
African Journals Online (AJOL)
To elevate NIR from proof-of-concept to a pilot scale, a large multisite, multispecies calibration was developed over iterative cycles to: (1) determine whether KPY in eucalypts can be predicted from a single calibration independent of site and species, and (2) identify the potential limits of accuracy and precision. This paper ...
Solar Energy Delivering Greenhouse with an Integrated NIR filter
Sonneveld, P.J.; Swinkels, G.L.A.M.; Holterman, H.J.; Tuijl, van B.A.J.; Bot, G.P.A.
2008-01-01
The scope of this investigation is the design and development of a new type of greenhouse with an integrated filter for rejecting near infrared radiation (NIR) and a solar energy delivery system. Cooled greenhouses are an important issue to cope with the combination of high global radiation and high
VIS/NIR imaging application for honey floral origin determination
Minaei, Saeid; Shafiee, Sahameh; Polder, Gerrit; Moghadam-Charkari, Nasrolah; Ruth, van Saskia; Barzegar, Mohsen; Zahiri, Javad; Alewijn, Martin; Kuś, Piotr M.
2017-01-01
Nondestructive methods are of utmost importance for honey characterization. This study investigates the potential application of VIS-NIR hyperspectral imaging for detection of honey flower origin using machine learning techniques. Hyperspectral images of 52 honey samples were taken in
TROPOMI and TROPI: UV/VIS/NIR/SWIR instruments
Levelt, P.F.; Oord, G.H.J. van den; Dobber, M.; Eskes, H.; Weele, M. van; Veefkind, P.; Oss, R. van; Aben, I.; Jongma, R.T.; Landgraf, J.; Vries, J. de; Visser, H.
2006-01-01
TROPOMI (Tropospheric Ozone-Monitoring Instrument) is a five-channel UV-VIS-NIR-SWIR non-scanning nadir viewing imaging spectrometer that combines a wide swath (114°) with high spatial resolution (10 × 10 km 2). The instrument heritage consists of GOME on ERS-2, SCIAMACHY on Envisat and, especially,
Tissue oxygenation and haemodynamics measurement with spatially resolved NIRS
Zhang, Y.; Scopesi, F.; Serra, G.; Sun, J. W.; Rolfe, P.
2010-08-01
We describe the use of Near Infrared Spectroscopy (NIRS) for the non-invasive investigation of changes in haemodynamics and oxygenation of human peripheral tissues. The goal was to measure spatial variations of tissue NIRS oxygenation variables, namely deoxy-haemoglobin (HHb), oxy-haemoglobin (HbO2), total haemoglobin (HbT), and thereby to evaluate the responses of the peripheral circulation to imposed physiological challenges. We present a skinfat- muscle heterogeneous tissue model with varying fat thickness up to 15mm and a Monte Carlo simulation of photon transport within this model. The mean partial path length and the mean photon visit depth in the muscle layer were derived for different source-detector spacing. We constructed NIRS instrumentation comprising of light-emitting diodes (LED) as light sources at four wavelengths, 735nm, 760nm, 810nm and 850nm and sensitive photodiodes (PD) as the detectors. Source-detector spacing was varied to perform measurements at different depths within forearm tissue. Changes in chromophore concentration in response to venous and arterial occlusion were calculated using the modified Lambert-Beer Law. Studies in fat and thin volunteers indicated greater sensitivity in the thinner subjects for the tissue oxygenation measurement in the muscle layer. These results were consistent with those found using Monte Carlo simulation. Overall, the results of this investigation demonstrate the usefulness of the NIRS instrument for deriving spatial information from biological tissues.
Less transpiration and good quality thanks to NIR-screen
Stanghellini, C.; Kempkes, F.L.K.; Hemming, S.; Jianfeng, D.
2009-01-01
Materials or additives for greenhouse cover that reflect or absorb a part of the NIR radiation can decrease the cooling requirement for the greenhouse and increase water use efficiency of the crop. By reducing the ventilation requirement, it might even decrease emissions of carbon dioxide from
Lu, Jia-hui; Zhang, Yi-bo; Zhang, Zhuo-yong; Meng, Qing-fan; Guo, Wei-liang; Teng, Li-rong
2008-06-01
A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.
Jiang, Hao; Lu, Jiangang
2018-05-01
Corn starch is an important material which has been traditionally used in the fields of food and chemical industry. In order to enhance the rapidness and reliability of the determination for starch content in corn, a methodology is proposed in this work, using an optimal CC-PLSR-RBFNN calibration model and near-infrared (NIR) spectroscopy. The proposed model was developed based on the optimal selection of crucial parameters and the combination of correlation coefficient method (CC), partial least squares regression (PLSR) and radial basis function neural network (RBFNN). To test the performance of the model, a standard NIR spectroscopy data set was introduced, containing spectral information and chemical reference measurements of 80 corn samples. For comparison, several other models based on the identical data set were also briefly discussed. In this process, the root mean square error of prediction (RMSEP) and coefficient of determination (Rp2) in the prediction set were used to make evaluations. As a result, the proposed model presented the best predictive performance with the smallest RMSEP (0.0497%) and the highest Rp2 (0.9968). Therefore, the proposed method combining NIR spectroscopy with the optimal CC-PLSR-RBFNN model can be helpful to determine starch content in corn.
Using decision trees and their ensembles for analysis of NIR spectroscopic data
DEFF Research Database (Denmark)
Kucheryavskiy, Sergey V.
and interpretation of the models. In this presentation, we are going to discuss an applicability of decision trees based methods (including gradient boosting) for solving classification and regression tasks with NIR spectra as predictors. We will cover such aspects as evaluation, optimization and validation......Advanced machine learning methods, like convolutional neural networks and decision trees, became extremely popular in the last decade. This, first of all, is directly related to the current boom in Big data analysis, where traditional statistical methods are not efficient. According to the kaggle.......com — the most popular online resource for Big data problems and solutions — methods based on decision trees and their ensembles are most widely used for solving the problems. It can be noted that the decision trees and convolutional neural networks are not very popular in Chemometrics. One of the reasons...
Determination of moisture content of lyophilized allergen vaccines by NIR spectroscopy
DEFF Research Database (Denmark)
Zheng, Yiwu; Lai, Xuxin; Bruun, Susanne Wrang
2008-01-01
Moisture content is an important parameter for lyophilized vaccines. Currently, Karl Fischer titration is widely used for moisture determination in routine analysis. However, this method is time-consuming, sample destructive and requires environment polluting reagents, as well as the results rely...... on the random samplings. In this study, near infrared spectroscopy was used as a fast, non-invasive and non-destructive method to determine the moisture content in lyophilized allergy vaccines. Five different vaccine products were investigated, which contained water in the range of 0.17-1.51% (w/w, KF......). Different data pre-treatments, wavelength selection and partial least squares regression were applied to construct calibration models. Multi-products model and product-specific models were obtained, which show the possibility of NIR as a rapid method to discriminate whether moisture content fit...
Ridge Regression Signal Processing
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
Multi-band algorithms for the estimation of chlorophyll concentration in the Chesapeake Bay
Gilerson, Alexander; Ondrusek, Michael; Tzortziou, Maria; Foster, Robert; El-Habashi, Ahmed; Tiwari, Surya Prakash; Ahmed, Sam
2015-01-01
on the two- or three band ratio algorithms in the red/NIR part of the spectrum, which require 665, 708, 753 nm bands (or similar) and which work well in various waters all over the world. The critical 708 nm band for these algorithms is not available on MODIS
Better Autologistic Regression
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2017-11-01
Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.
Regression in organizational leadership.
Kernberg, O F
1979-02-01
The choice of good leaders is a major task for all organizations. Inforamtion regarding the prospective administrator's personality should complement questions regarding his previous experience, his general conceptual skills, his technical knowledge, and the specific skills in the area for which he is being selected. The growing psychoanalytic knowledge about the crucial importance of internal, in contrast to external, object relations, and about the mutual relationships of regression in individuals and in groups, constitutes an important practical tool for the selection of leaders.
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
Physiological Aging Influence on Brain Hemodynamic Activity during Task-Switching: A fNIRS Study.
Vasta, Roberta; Cutini, Simone; Cerasa, Antonio; Gramigna, Vera; Olivadese, Giuseppe; Arabia, Gennarina; Quattrone, Aldo
2017-01-01
Task-switching (TS) paradigm is a well-known validated tool useful for exploring the neural substrates of cognitive control, in particular the activity of the lateral and medial prefrontal cortex. This work is aimed at investigating how physiological aging influences hemodynamic response during the execution of a color-shape TS paradigm. A multi-channel near infrared spectroscopy (fNIRS) was used to measure hemodynamic activity in 27 young (30.00 ± 7.90 years) and 11 elderly participants (57.18 ± 9.29 years) healthy volunteers (55% male, age range: (19-69) years) during the execution of a TS paradigm. Two holders were placed symmetrically over the left/right hemispheres to record cortical activity [oxy-(HbO) and deoxy-hemoglobin (HbR) concentration] of the dorso-lateral prefrontal cortex (DLPFC), the dorsal premotor cortex (PMC), and the dorso-medial part of the superior frontal gyrus (sFG). TS paradigm requires participants to repeat the same task over a variable number of trials, and then to switch to a different task during the trial sequence. A two-sample t -test was carried out to detect differences in cortical responses between groups. Multiple linear regression analysis was used to evaluate the impact of age on the prefrontal neural activity. Elderly participants were significantly slower than young participants in both color- ( p aging. Multivariate regression analysis revealed that the HbO mean concentration of switching task in the PMC ( p = 0.01, β = -0.321) and of shape single-task in the sFG ( p = 0.003, β = 0.342) were the best predictors of age effects. Our findings demonstrated that TS might be a reliable instrument to gather a measure of cognitive resources in older people. Moreover, the fNIRS-related brain activity extracted from frontoparietal cortex might become a useful indicator of aging effects.
Yang, Wenming; Liao, Ningfang; Cheng, Haobo; Li, Yasheng; Bai, Xueqiong; Deng, Chengyang
2018-03-01
Non-invasive blood glucose measurement using near infrared (NIR) spectroscopy relies on wavebands that provide reliable information about spectral absorption. In this study, we investigated wavebands which are informative for blood glucose in the NIR shortwave band (900˜1450 nm) and the first overtone band (1450˜1700 nm) through a specially designed NIR Fourier transform spectrometer (FTS), which featured a test fixture (where a sample or subject's finger could be placed) and all-reflective optics, except for a Michelson structure. Different concentrations of glucose solution and seven volunteers who had undergone oral glucose tolerance tests (OGTT) were studied to acquire transmission spectra in the shortwave band and the first overtone band. Characteristic peaks of glucose absorption were identified from the spectra of glucose aqueous solution by second-order derivative processing. The wavebands linked to blood glucose were successfully estimated through spectra of the middle fingertip of OGTT participants by a simple linear regression and correlation coefficient. The light intensity difference showed that glucose absorption in the first overtone band was much more prominent than it was in the shortwave band. The results of the SLR model established from seven OGTTs in total on seven participants enabled a positive estimation of the glucose-linked wavelength. It is suggested that wavebands with prominent characteristic peaks, a high correlation coefficient between blood glucose and light intensity difference and a relatively low standard deviation of predicted values will be the most informative wavebands for transmission non-invasive blood glucose measurement methods. This work provides a guidance for waveband selection for the development of non-invasive NIR blood glucose measurement.
De Götzen , Amalia; Mion , Luca; Tache , Olivier
2007-01-01
International audience; We call sound algorithms the categories of algorithms that deal with digital sound signal. Sound algorithms appeared in the very infancy of computer. Sound algorithms present strong specificities that are the consequence of two dual considerations: the properties of the digital sound signal itself and its uses, and the properties of auditory perception.
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
Iñiguez, A; García, E; Seabra, R; Bordes, P; Bethencourt, A; Rigla, J
2001-05-01
Despite improvements in the results and techniques of catheter-based revascularization, few studies have evaluated the clinical results of the application of new stent designs. We describe the in-hospital and mid-term outcome of patients undergoing a stent NIR implantation. At least 1 Stent NIR was implanted in 1.004 patients (1.136 lesions) recruited from 50 centers in an international, multicenter, prospective, registry (Spain and Portugal NIR stent registry). Inclusion criteria were objective coronary ischemia related to a severe de novo lesion or first restenosis in native vessels with a reference diameter >= 2.75 mm. The primary end-point was the incidence of major adverse cardiac events within the first 7 months of follow-up. The mean age of the patients was 60 years and 82% were male. Angioplasty was indicated due to unstable angina in 61% of the cases. Stent implantation was successfully achieved in 99.6%. Clinical success (angiographic success without in-hospital major events) was achieved in 98.6% of patients. The rate of angiographic restenosis (> 50% stenosis narrowing) was 16% (CI 95%; 11.7-21.2). The accumulated major cardiac adverse event rate at seven months of follow-up was 8.7%: death (0.9%), acute myocardial infarction (1.2%) and target lesion revascularization (6.6%). In the wide setting of the population included in the ESPORT-NIR registry, stent NIR implantation was a highly effective therapy with a good mid-term clinical and angiographic outcome.
Myllylä, Teemu S.; Sorvoja, Hannu S. S.; Nikkinen, Juha; Tervonen, Osmo; Kiviniemi, Vesa; Myllylä, Risto A.
2011-07-01
Our goal is to provide a cost-effective method for examining human tissue, particularly the brain, by the simultaneous use of functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS). Due to its compatibility requirements, MRI poses a demanding challenge for NIRS measurements. This paper focuses particularly on presenting the instrumentation and a method for the non-invasive measurement of NIR light absorbed in human tissue during MR imaging. One practical method to avoid disturbances in MR imaging involves using long fibre bundles to enable conducting the measurements at some distance from the MRI scanner. This setup serves in fact a dual purpose, since also the NIRS device will be less disturbed by the MRI scanner. However, measurements based on long fibre bundles suffer from light attenuation. Furthermore, because one of our primary goals was to make the measuring method as cost-effective as possible, we used high-power light emitting diodes instead of more expensive lasers. The use of LEDs, however, limits the maximum output power which can be extracted to illuminate the tissue. To meet these requirements, we improved methods of emitting light sufficiently deep into tissue. We also show how to measure NIR light of a very small power level that scatters from the tissue in the MRI environment, which is characterized by strong electromagnetic interference. In this paper, we present the implemented instrumentation and measuring method and report on test measurements conducted during MRI scanning. These measurements were performed in MRI operating rooms housing 1.5 Tesla-strength closed MRI scanners (manufactured by GE) in the Dept. of Diagnostic Radiology at the Oulu University Hospital.
Pinti, Paola; Merla, Arcangelo; Aichelburg, Clarisse; Lind, Frida; Power, Sarah; Swingler, Elizabeth; Hamilton, Antonia; Gilbert, Sam; Burgess, Paul W; Tachtsidis, Ilias
2017-07-15
Recent technological advances have allowed the development of portable functional Near-Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real-world. However, as real-world experiments are designed to mimic everyday life situations, the identification of event onsets can be extremely challenging and time-consuming. Here, we present a novel analysis method based on the general linear model (GLM) least square fit analysis for the Automatic IDentification of functional Events (or AIDE) directly from real-world fNIRS neuroimaging data. In order to investigate the accuracy and feasibility of this method, as a proof-of-principle we applied the algorithm to (i) synthetic fNIRS data simulating both block-, event-related and mixed-design experiments and (ii) experimental fNIRS data recorded during a conventional lab-based task (involving maths). AIDE was able to recover functional events from simulated fNIRS data with an accuracy of 89%, 97% and 91% for the simulated block-, event-related and mixed-design experiments respectively. For the lab-based experiment, AIDE recovered more than the 66.7% of the functional events from the fNIRS experimental measured data. To illustrate the strength of this method, we then applied AIDE to fNIRS data recorded by a wearable system on one participant during a complex real-world prospective memory experiment conducted outside the lab. As part of the experiment, there were four and six events (actions where participants had to interact with a target) for the two different conditions respectively (condition 1: social-interact with a person; condition 2: non-social-interact with an object). AIDE managed to recover 3/4 events and 3/6 events for conditions 1 and 2 respectively. The identified functional events were then corresponded to behavioural data from the video recordings of the movements and actions of the participant. Our results suggest that "brain-first" rather than "behaviour-first" analysis is
International Nuclear Information System (INIS)
Casale, M.; Oliveri, P.; Casolino, C.; Sinelli, N.; Zunin, P.; Armanino, C.; Forina, M.; Lanteri, S.
2012-01-01
Highlights: ► Characterisation of the Italian PDO extra virgin olive oil Chianti Classico. ► Comparison between non-selective (UV–vis, NIR and MIR spectroscopy) and selective (fatty acid composition) analytical techniques. ► Synergy among spectroscopic techniques, by the fusion of the respective spectra. ► Prediction of the content of oleic and linoleic acids in the olive oils. - Abstract: An authentication study of the Italian PDO (protected designation of origin) extra virgin olive oil Chianti Classico was performed; UV–visible (UV–vis), Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopies were applied to a set of samples representative of the whole Chianti Classico production area. The non-selective signals (fingerprints) provided by the three spectroscopic techniques were utilised both individually and jointly, after fusion of the respective profile vectors, in order to build a model for the Chianti Classico PDO olive oil. Moreover, these results were compared with those obtained by the gas chromatographic determination of the fatty acids composition. In order to characterise the olive oils produced in the Chianti Classico PDO area, UNEQ (unequal class models) and SIMCA (soft independent modelling of class analogy) were employed both on the MIR, NIR and UV–vis spectra, individually and jointly, and on the fatty acid composition. Finally, PLS (partial least square) regression was applied on the UV–vis, NIR and MIR spectra, in order to predict the content of oleic and linoleic acids in the extra virgin olive oils. UNEQ, SIMCA and PLS were performed after selection of the relevant predictors, in order to increase the efficiency of both classification and regression models. The non-selective information obtained from UV–vis, NIR and MIR spectroscopy allowed to build reliable models for checking the authenticity of the Italian PDO extra virgin olive oil Chianti Classico.
Energy Technology Data Exchange (ETDEWEB)
Casale, M., E-mail: monica@dictfa.unige.it [Universita degli Studi di Genova, Department of Chemistry and Food and Pharmaceutical Technologies, Via Brigata Salerno 13, I-16147, Genoa (Italy); Oliveri, P.; Casolino, C. [Universita degli Studi di Genova, Department of Chemistry and Food and Pharmaceutical Technologies, Via Brigata Salerno 13, I-16147, Genoa (Italy); Sinelli, N. [Universita degli Studi di Milano, Department of Food Science and Technology, Via Celoria, 2 - I-20133 Milan (Italy); Zunin, P.; Armanino, C.; Forina, M.; Lanteri, S. [Universita degli Studi di Genova, Department of Chemistry and Food and Pharmaceutical Technologies, Via Brigata Salerno 13, I-16147, Genoa (Italy)
2012-01-27
Highlights: Black-Right-Pointing-Pointer Characterisation of the Italian PDO extra virgin olive oil Chianti Classico. Black-Right-Pointing-Pointer Comparison between non-selective (UV-vis, NIR and MIR spectroscopy) and selective (fatty acid composition) analytical techniques. Black-Right-Pointing-Pointer Synergy among spectroscopic techniques, by the fusion of the respective spectra. Black-Right-Pointing-Pointer Prediction of the content of oleic and linoleic acids in the olive oils. - Abstract: An authentication study of the Italian PDO (protected designation of origin) extra virgin olive oil Chianti Classico was performed; UV-visible (UV-vis), Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopies were applied to a set of samples representative of the whole Chianti Classico production area. The non-selective signals (fingerprints) provided by the three spectroscopic techniques were utilised both individually and jointly, after fusion of the respective profile vectors, in order to build a model for the Chianti Classico PDO olive oil. Moreover, these results were compared with those obtained by the gas chromatographic determination of the fatty acids composition. In order to characterise the olive oils produced in the Chianti Classico PDO area, UNEQ (unequal class models) and SIMCA (soft independent modelling of class analogy) were employed both on the MIR, NIR and UV-vis spectra, individually and jointly, and on the fatty acid composition. Finally, PLS (partial least square) regression was applied on the UV-vis, NIR and MIR spectra, in order to predict the content of oleic and linoleic acids in the extra virgin olive oils. UNEQ, SIMCA and PLS were performed after selection of the relevant predictors, in order to increase the efficiency of both classification and regression models. The non-selective information obtained from UV-vis, NIR and MIR spectroscopy allowed to build reliable models for checking the authenticity of the Italian PDO extra virgin olive oil
Steganalysis using logistic regression
Lubenko, Ivans; Ker, Andrew D.
2011-02-01
We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.
SEPARATION PHENOMENA LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Ikaro Daniel de Carvalho Barreto
2014-03-01
Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...
Optimal choice of basis functions in the linear regression analysis
International Nuclear Information System (INIS)
Khotinskij, A.M.
1988-01-01
Problem of optimal choice of basis functions in the linear regression analysis is investigated. Step algorithm with estimation of its efficiency, which holds true at finite number of measurements, is suggested. Conditions, providing the probability of correct choice close to 1 are formulated. Application of the step algorithm to analysis of decay curves is substantiated. 8 refs
Field applications of stand-off sensing using visible/NIR multivariate optical computing
Eastwood, DeLyle; Soyemi, Olusola O.; Karunamuni, Jeevanandra; Zhang, Lixia; Li, Hongli; Myrick, Michael L.
2001-02-01
12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.
Shed a light of wireless technology on portable mobile design of NIRS
Sun, Yunlong; Li, Ting
2016-03-01
Mobile internet is growing rapidly driven by high-tech companies including the popular Apple and Google. The wireless mini-NIRS is believed to deserve a great spread future, while there is sparse report on wireless NIRS device and even for the reported wireless NIRS, its wireless design is scarcely presented. Here we focused on the wireless design of NIRS devices. The widely-used wireless communication standards and wireless communication typical solutions were employed into our NIRS design and then compared on communication efficiency, distance, error rate, low-cost, power consumption, and stabilities, based on the requirements of NIRS applications. The properly-performed wireless communication methods matched with the characteristics of NIRS are picked out. Finally, we realized one recommended wireless communication in our NIRS, developed a test platform on wireless NIRS and tested the full properties on wireless communication. This study elaborated the wireless communication methods specified for NIRS and suggested one implementation with one example fully illustrated, which support the future mobile design on NIRS devices.
Least square regularized regression in sum space.
Xu, Yong-Li; Chen, Di-Rong; Li, Han-Xiong; Liu, Lu
2013-04-01
This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed. We measure the complexity of the sum space by its covering number and demonstrate that the covering number can be bounded by the product of the covering numbers of basic RKHSs. For sum space of RKHSs with Gaussian kernels, by choosing appropriate parameters, we tradeoff the sample error and regularization error, and obtain a polynomial learning rate, which is better than that in any single RKHS. The utility of this method is illustrated with two simulated data sets and five real-life databases.
Tracking time-varying parameters with local regression
DEFF Research Database (Denmark)
Joensen, Alfred Karsten; Nielsen, Henrik Aalborg; Nielsen, Torben Skov
2000-01-01
This paper shows that the recursive least-squares (RLS) algorithm with forgetting factor is a special case of a varying-coe\\$cient model, and a model which can easily be estimated via simple local regression. This observation allows us to formulate a new method which retains the RLS algorithm, bu......, but extends the algorithm by including polynomial approximations. Simulation results are provided, which indicates that this new method is superior to the classical RLS method, if the parameter variations are smooth....
Energy Technology Data Exchange (ETDEWEB)
Berg, Magnus; Wiklund, Sven Erik [AaF-Process AB, Stockholm (Sweden); Karlsson, Mikael; Tryzell, Robert [Bestwood AB, Sundbyberg (Sweden)
2005-07-01
The determination of moisture content of biofuel is of large importance for the energy sector. The used methods for moisture determination are based on fuels samples taken from the bulk followed by drying and weighing. To be able to instead determine the moisture content based on a method with good accuracy and with a short response time would be a large improvement. Both for the fuel sampling and the following analysis there are Swedish standards but concerning the fuel sampling the standards are often not followed. The main reason is the difficulties to sample fuel samples from different depth from a delivery. This is one of the reasons that some plants have installed mechanical samplers but the investment cost for these is relatively high. The aim of this project was to investigate the use of the NIR-method for automatic moisture determination in biofuels. Within the project the NIR-method was used to determine the moisture content on withdrawn fuel samples, in addition the possibility to integrate the NIR-method in an automatic sampling system is also described. A large number of samples, in total over 200 samples, have been evaluated with the NIR-method and compared with the reference method, oven drying and gravimetric determination of moisture content. That the NIR-method can be used to determine moisture content in a number of well defined materials have previously been shown. In this report it has moreover been shown that the method can be used under the conditions at the fuel delivery station and for a large spectrum of biofuels. The accuracy that can be achieved with the NIR-method is in the same magnitude as the standard method, i.e. the reference method used for the measurements. Altogether this shows that the NIR-method is an interesting alternative for integration in an automatic measurement system for determination of fuel moisture content in biofuels. To be able to use the NIR-method for automatic determination of fuel moisture content at the
Online co-regularized algorithms
Ruijter, T. de; Tsivtsivadze, E.; Heskes, T.
2012-01-01
We propose an online co-regularized learning algorithm for classification and regression tasks. We demonstrate that by sequentially co-regularizing prediction functions on unlabeled data points, our algorithm provides improved performance in comparison to supervised methods on several UCI benchmarks
Joux, Antoine
2009-01-01
Illustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.Divided into three parts, the book begins with a short introduction to cryptography and a background chapter on elementary number theory and algebra. It then moves on to algorithms, with each chapter in this section dedicated to a single topic and often illustrated with simple cryptographic applic
FT-NIR: A Tool for Process Monitoring and More.
Martoccia, Domenico; Lutz, Holger; Cohen, Yvan; Jerphagnon, Thomas; Jenelten, Urban
2018-03-30
With ever-increasing pressure to optimize product quality, to reduce cost and to safely increase production output from existing assets, all combined with regular changes in terms of feedstock and operational targets, process monitoring with traditional instruments reaches its limits. One promising answer to these challenges is in-line, real time process analysis with spectroscopic instruments, and above all Fourier-Transform Near Infrared spectroscopy (FT-NIR). Its potential to afford decreased batch cycle times, higher yields, reduced rework and minimized batch variance is presented and application examples in the field of fine chemicals are given. We demonstrate that FT-NIR can be an efficient tool for improved process monitoring and optimization, effective process design and advanced process control.
Near-infrared spectroscopy (NIRS) in a piglet model
DEFF Research Database (Denmark)
Clausen, Nicola Groes; Spielmann, Nelly; Ringer, Simone K.
2017-01-01
Near-infrared spectroscopy (NIRS) in a piglet model: readings are influenced by the colour of the cover Clausen NG1,2, Spielmann N1,3, Weiss M1,3, Ringer SK4 1Children’s Research Center, University Children’s Hospital of Zurich, Switzerland; 2Department of Anaesthesiology and Intensive Care, Odense....... The rSO2 was measured by placing NIRS sensors in the supra glabellar region. In 12 animals sensors were covered with a uni-coloured pink (P) napkin and a turquoise (T) napkin in a random order (Setting A). In further 13 animals sensors were covered with blue-coloured surgical drape (SD) and a napkin...... with a reddish SantaClaus (SC) motive (Setting B). Uncovered (UC) baseline values were captured and measurements obtained for a period of three minutes. During measurements, the animals were kept in normoterm, normotensive, normoglycaemic and normoxic condition. Inspired oxygen fraction and ventilatory settings...
NIR FRET Fluorophores for Use as an Implantable Glucose Biosensor
Directory of Open Access Journals (Sweden)
Majed DWEIK
2008-12-01
Full Text Available Development of an in vivo optical sensor requires the utilization of Near Infra Red (NIR fluorophores due to their ability to operate within the biological tissue window. Alexa Fluor 750 (AF750 and Alexa Fluor 680 (AF680 were examined as potential NIR fluorophores for an in vivo fluorescence resonance energy transfer (FRET glucose biosensor. AF680 and AF750 found to be a FRET pair and percent energy transfer was calculated. Next, the tested dye pair was utilized in a competitive binding assay in order to detect glucose. Concanavalin A (Con A and dextran have binding affinity, but in the presence of glucose, glucose displaces dextran due to its higher affinity to Con A than dextran. Finally, the percent signal transfer through porcine skin was examined. The results showed with approximately 4.0 mm porcine skin thickness, 1.98 % of the fluorescence was transmitted and captured by the detector.
What's next in carbon ion radiotherapy at NIRS?
International Nuclear Information System (INIS)
Kamada, Tadashi
2011-01-01
Since its launch by the National Institute of Radiological Sciences (NIRS) in 1994, cancer therapy using heavy ion beams (carbon ion beams) has been used in approximately 5,500 patients. Accumulated clinical experience has identified certain types of malignant tumors that respond exclusively to this treatment. It has also been made clear that this therapy is capable of treating several other types of cancers safely in a relatively short period of time, effecting remission and/or cure without pain or discomfort in a few days or weeks. We can reasonably state that heavy ion radiotherapy has been established as a safe and effective treatment method. NIRS researchers are continuing to make every effort to develop more effective, efficient, and patient-friendly heavy ion irradiation systems. The result of this research and development is also expected to slash the attendant costs of heavy ion radiotherapy. (author)
Dong, Yanhong; Li, Juan; Zhong, Xiaoxiao; Cao, Liya; Luo, Yang; Fan, Qi
2016-04-15
This paper establishes a novel method to simultaneously predict the tablet weight (TW) and trimethoprim (TMP) content of compound sulfamethoxazole tablets (SMZCO) by near infrared (NIR) spectroscopy with partial least squares (PLS) regression for controlling the uniformity of dosage units (UODU). The NIR spectra for 257 samples were measured using the optimized parameter values and pretreated using the optimized chemometric techniques. After the outliers were ignored, two PLS models for predicting TW and TMP content were respectively established by using the selected spectral sub-ranges and the reference values. The TW model reaches the correlation coefficient of calibration (R(c)) 0.9543 and the TMP content model has the R(c) 0.9205. The experimental results indicate that this strategy expands the NIR application in controlling UODU, especially in the high-throughput and rapid analysis of TWs and contents of the compound pharmaceutical tablets, and may be an important complement to the common NIR on-line analytical method for pharmaceutical tablets. Copyright © 2016 Elsevier B.V. All rights reserved.
Kobayashi, Hisataka
2017-02-01
Near infrared photoimmunotherapy (NIR-PIT) is a new type of molecularly-targeted photo-therapy based on conjugating a near infrared silica-phthalocyanine dye, IR700, to a monoclonal antibody (MAb) targeting target-specific cell-surface molecules. When exposed to NIR light, the conjugate rapidly induces a highly-selective cell death only in receptor-positive, MAb-IR700-bound cells. Current immunotherapies for cancer seek to modulate the balance among different immune cell populations, thereby promoting anti-tumor immune responses. However, because these are systemic therapies, they often cause treatment-limiting autoimmune adverse effects. It would be ideal to manipulate the balance between suppressor and effector cells within the tumor without disturbing homeostasis elsewhere in the body. CD4+CD25+Foxp3+ regulatory T cells (Tregs) are well-known immune-suppressor cells that play a key role in tumor immuno-evasion and have been the target of systemic immunotherapies. We used CD25-targeted NIR-PIT to selectively deplete Tregs, thus activating CD8+ T and NK cells and restoring local anti-tumor immunity. This not only resulted in regression of the treated tumor but also induced responses in separate untreated tumors of the same cell-line derivation. We conclude that CD25-targeted NIR-PIT causes spatially selective depletion of Tregs, thereby providing an alternative approach to cancer immunotherapy that can treat not only local tumors but also distant metastatic tumors.
Directory of Open Access Journals (Sweden)
Daniela Eisenstecken
2015-07-01
Full Text Available The potential of near infrared spectroscopy (NIRS in the wavelength range of 1000–2500 nm for predicting quality parameters such as total soluble solids (TSS, acidity (TA, firmness, and individual sugars (glucose, fructose, sucrose, and xylose for two cultivars of apples (“Braeburn” and “Cripps Pink” was studied during the pre- and post-storage periods. Simultaneously, a qualitative investigation on the capability of NIRS to discriminate varieties, harvest dates, storage periods and fruit inhomogeneity was carried out. In order to generate a sample set with high variability within the most relevant apple quality traits, three different harvest time points in combination with five different storage periods were chosen, and the evolution of important quality parameters was followed both with NIRS and wet chemical methods. By applying a principal component analysis (PCA a differentiation between the two cultivars, freshly harvested vs. long-term stored apples and, notably, between the sun-exposed vs. shaded side of apples could be found. For the determination of quality parameters effective prediction models for titratable acid (TA and individual sugars such as fructose, glucose and sucrose by using partial least square (PLS regression have been developed. Our results complement earlier reports, highlighting the versatility of NIRS as a fast, non-invasive method for quantitative and qualitative studies on apples.
Eisenstecken, Daniela; Panarese, Alessia; Robatscher, Peter; Huck, Christian W; Zanella, Angelo; Oberhuber, Michael
2015-07-24
The potential of near infrared spectroscopy (NIRS) in the wavelength range of 1000-2500 nm for predicting quality parameters such as total soluble solids (TSS), acidity (TA), firmness, and individual sugars (glucose, fructose, sucrose, and xylose) for two cultivars of apples ("Braeburn" and "Cripps Pink") was studied during the pre- and post-storage periods. Simultaneously, a qualitative investigation on the capability of NIRS to discriminate varieties, harvest dates, storage periods and fruit inhomogeneity was carried out. In order to generate a sample set with high variability within the most relevant apple quality traits, three different harvest time points in combination with five different storage periods were chosen, and the evolution of important quality parameters was followed both with NIRS and wet chemical methods. By applying a principal component analysis (PCA) a differentiation between the two cultivars, freshly harvested vs. long-term stored apples and, notably, between the sun-exposed vs. shaded side of apples could be found. For the determination of quality parameters effective prediction models for titratable acid (TA) and individual sugars such as fructose, glucose and sucrose by using partial least square (PLS) regression have been developed. Our results complement earlier reports, highlighting the versatility of NIRS as a fast, non-invasive method for quantitative and qualitative studies on apples.
On Solving Lq-Penalized Regressions
Directory of Open Access Journals (Sweden)
Tracy Zhou Wu
2007-01-01
Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.
[The NIR spectra based variety discrimination for single soybean seed].
Zhu, Da-Zhou; Wang, Kun; Zhou, Guang-Hua; Hou, Rui-Feng; Wang, Cheng
2010-12-01
With the development of soybean producing and processing, the quality breeding becomes more and more important for soybean breeders. Traditional sampling detection methods for soybean quality need to destroy the seed, and does not satisfy the requirement of earlier generation materials sieving for breeding. Near infrared (NIR) spectroscopy has been widely used for soybean quality detection. However, all these applications were referred to mass samples, and they were not suitable for little or single seed detection in breeding procedure. In the present study, the acousto--optic tunable filter (AOTF) NIR spectroscopy was used to measure the single soybean seed. Two varieties of soybean were measured, which contained 60 KENJIANDOU43 seeds and 60 ZHONGHUANG13 seeds. The results showed that NIR spectra combined with soft independent modeling of class analogy (SIMCA) could accurately discriminate the soybean varieties. The classification accuracy for KENJIANDOU43 seeds and ZHONGHUANG13 was 100%. The spectra of single soybean seed were measured at different positions, and it showed that the seed shape has significant influence on the measurement of spectra, therefore, the key point for single seed measurement was how to accurately acquire the spectra and keep their representativeness. The spectra for soybeans with glossy surface had high repeatability, while the spectra of seeds with external defects had significant difference for several measurements. For the fast sieving of earlier generation materials in breeding, one could firstly eliminate the seeds with external defects, then apply NIR spectra for internal quality detection, and in this way the influence of seed shape and external defects could be reduced.
Classification of maize kernels using NIR hyperspectral imaging
DEFF Research Database (Denmark)
Williams, Paul; Kucheryavskiy, Sergey V.
2016-01-01
NIR hyperspectral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and soft. Two approaches, pixel-wise and object-wise, were investigated to group kernels according to hardness. The pixel-wise classification assigned a class to every pixel from individual...... and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale....
Relationship between muscle oxygenation by NIRS and blood lactate
Energy Technology Data Exchange (ETDEWEB)
Xu Guodong [School of Physical Education, Jianghan University, Hubei Wuhan 430056 (China); Mao Zongzhen; Ye Yanjie; Lv Kunru, E-mail: xguodong@wipe.edu.cn [School of Health Sciences, Wuhan Institute of Physical Education, Hubei Wuhan 430079 (China)
2011-01-01
The aim of the study was to investigate the relationship of muscle oxygenation in term of oxy-hemoglobin concentration change ({Delta}HbO{sub 2}) by NIRS and blood lactate (BLA) in local skeletal muscle and evaluate the capability of NIRS in the research of exercise physiology Twenty-three athlete in the national fin-swimming team took the increasing load training on the power bicycle while their {Delta}HbO{sub 2} and BLA were simultaneously recorded. The initial powers used in the training were set as 100 w for males and 40 w for females. During the experiment, the power kept constant for 3 min before each abrupt increment of 30 w until the limit of the athlete's capability. Statistical analysis and data visualization were performed. Following the increasing load training, {Delta}HbO{sub 2} step-likely increased in the phase of aerobic metabolism but linearly decreased in the phase of anaerobic metabolism. The variation tendency of BLA was the same as {Delta}HbO{sub 2} and the concurrency of crucial turning points between {Delta}HbO{sub 2} and BLA was revealed. This relationship between {Delta}HbO{sub 2} and BLA presented in the increasing load training suggested that {Delta}HbO{sub 2} might be capable for taking the place of the invasively measured parameter BLA. Considering that {Delta}HbO{sub 2} can be noninvasively measured by NIRS, {Delta}HbO{sub 2} has the potential in the evaluation of athletes' physiological function and training effect on the athletes and accordingly NIRS can be well used in this field.
Near-infrared (NIR) optogenetics using up-conversion system
Hososhima, Shoko; Yuasa, Hideya; Ishizuka, Toru; Yawo, Hiromu
2015-03-01
Non-invasive remote control technologies designed to manipulate neural functions for a comprehensive and quantitative understanding of the neuronal network in the brain as well as for the therapy of neurological disorders have long been awaited. Recently, it has become possible to optically manipulate the neuronal activity using biological photo-reactive molecules such as channelrhodopsin-2 (ChR2). However, ChR2 and its relatives are mostly reactive to visible light which does not effectively penetrate through biological tissues. In contrast, near-infrared (NIR) light penetrates deep into the tissues because biological systems are almost transparent to light within this so-called `imaging window'. Here we used lanthanide nanoparticles (LNPs), which are composed of rare-earth elements, as luminous bodies to activate channelrhodopsins (ChRs) since they absorb low-energy NIR light to emit high-energy visible light (up-conversion). Neuron-glioma-hybrid ND-7/23 cells were cultured with LNP(NaYF4:Sc/Yb/Er) particles (peak emission, 543 nm) and transfected to express C1V1 (peak absorbance, 539 nm), a chimera of ChR1 and VChR1. The photocurrents were generated in response to NIR laser light (976 nm) to a level comparable to that evoked by a filtered Hg lamp (530-550 nm). NIR light pulses also evoked action potentials in the cultured neurons that expressed C1V1. It is suggested that the green luminescent light emitted from LNPs effectively activated C1V1 to generate the photocurrent. With the optimization of LNPs, acceptor photo-reactive biomolecules and optics, this system could be applied to non-invasively actuate neurons deep in the brain.
Relationship between muscle oxygenation by NIRS and blood lactate
International Nuclear Information System (INIS)
Xu Guodong; Mao Zongzhen; Ye Yanjie; Lv Kunru
2011-01-01
The aim of the study was to investigate the relationship of muscle oxygenation in term of oxy-hemoglobin concentration change (ΔHbO 2 ) by NIRS and blood lactate (BLA) in local skeletal muscle and evaluate the capability of NIRS in the research of exercise physiology Twenty-three athlete in the national fin-swimming team took the increasing load training on the power bicycle while their ΔHbO 2 and BLA were simultaneously recorded. The initial powers used in the training were set as 100 w for males and 40 w for females. During the experiment, the power kept constant for 3 min before each abrupt increment of 30 w until the limit of the athlete's capability. Statistical analysis and data visualization were performed. Following the increasing load training, ΔHbO 2 step-likely increased in the phase of aerobic metabolism but linearly decreased in the phase of anaerobic metabolism. The variation tendency of BLA was the same as ΔHbO 2 and the concurrency of crucial turning points between ΔHbO 2 and BLA was revealed. This relationship between ΔHbO 2 and BLA presented in the increasing load training suggested that ΔHbO 2 might be capable for taking the place of the invasively measured parameter BLA. Considering that ΔHbO 2 can be noninvasively measured by NIRS, ΔHbO 2 has the potential in the evaluation of athletes' physiological function and training effect on the athletes and accordingly NIRS can be well used in this field.
Mo, Changyeun; Kim, Giyoung; Kim, Moon S.; Lim, Jongguk; Lee, Seung Hyun; Lee, Hong-Seok; Cho, Byoung-Kwan
2017-09-01
The rapid detection of biological contaminants such as worms in fresh-cut vegetables is necessary to improve the efficiency of visual inspections carried out by workers. Multispectral imaging algorithms were developed using visible-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging (HSI) techniques to detect worms in fresh-cut lettuce. The optimal wavebands that can detect worms in fresh-cut lettuce were investigated for each type of HSI using one-way ANOVA. Worm-detection imaging algorithms for VNIR and NIR imaging exhibited prediction accuracies of 97.00% (RI547/945) and 100.0% (RI1064/1176, SI1064-1176, RSI-I(1064-1173)/1064, and RSI-II(1064-1176)/(1064+1176)), respectively. The two HSI techniques revealed that spectral images with a pixel size of 1 × 1 mm or 2 × 2 mm had the best classification accuracy for worms. The results demonstrate that hyperspectral reflectance imaging techniques have the potential to detect worms in fresh-cut lettuce. Future research relating to this work will focus on a real-time sorting system for lettuce that can simultaneously detect various defects such as browning, worms, and slugs.
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy....... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.......This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy...
Mixture of Regression Models with Single-Index
Xiang, Sijia; Yao, Weixin
2016-01-01
In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...
The application of near infrared spectroscopy (NIR technique for
Directory of Open Access Journals (Sweden)
Sandor Barabassy
2001-06-01
Full Text Available The production of cow’s milk in Hungary fluctuates by 15-20 % annualy. Surplus milk is dried into powder and can also be converted to modified milk powders using techniques such as ultra filtration. From approximetely 20.000 tonnes, of all milk powder types, 3.000 tonnes, is converted using ultra filtration technology. Multivariable near infrared (NIR calibration was performed on powder mixtures of whole milk, skimmed milk, whey, retenate (protein concentrate and lactose for rapid fat, protein, lactose, water and ash content determination. More than 150 samples were prepared and measured in two NIRS labs (Scottish Agriculture College – SAC – Aberdeen and University of Horticulture and Food Science - UHFS – Budapest. The results obtained from the same samples were compared. The aims of the study were: 1. Rapid quantitative and qualitative determination of mixtures of milk powder products using NIR technique. 2. Comparison of the results achieved in Aberdeen (SAC and Budapest (UHFS institutes. The mass per cent varied between 0.0-2.8% for fat, 0.0-80% for protein, 6.6-100 % for lactose, 0.0-5.0 % for water and 0.0-8.0 % for ash. High correlation coefficients (0.97-0.99 were found for all five components.
Joint attention studies in normal and autistic children using NIRS
Chaudhary, Ujwal; Hall, Michael; Gutierrez, Anibal; Messinger, Daniel; Rey, Gustavo; Godavarty, Anuradha
2011-03-01
Autism is a socio-communication brain development disorder. It is marked by degeneration in the ability to respond to joint attention skill task, from as early as 12 to 18 months of age. This trait is used to distinguish autistic from nonautistic. In this study Near infrared spectroscopy (NIRS) is being applied for the first time to study the difference in activation and connectivity in the frontal cortex of typically developing (TD) and autistic children between 4-8 years of age in response to joint attention task. The optical measurements are acquired in real time from frontal cortex using Imagent (ISS Inc.) - a frequency domain based NIRS system in response to video clips which engenders a feeling of joint attention experience in the subjects. A block design consisting of 5 blocks of following sequence 30 sec joint attention clip (J), 30 sec non-joint attention clip (NJ) and 30 sec rest condition is used. Preliminary results from TD child shows difference in brain activation (in terms of oxy-hemoglobin, HbO) during joint attention interaction compared to the nonjoint interaction and rest. Similar activation study did not reveal significant differences in HbO across the stimuli in, unlike in an autistic child. Extensive studies are carried out to validate the initial observations from both brain activation as well as connectivity analysis. The result has significant implication for research in neural pathways associated with autism that can be mapped using NIRS.
Hougardy, Stefan
2016-01-01
Algorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic graph theory, and numerical questions. In addition, it provides an introduction to programming and demonstrates in detail how to implement algorithms in C++. This textbook is suitable for students who are new to the subject and covers a basic mathematical lecture course, complementing traditional courses on analysis and linear algebra. Both authors have given this "Algorithmic Mathematics" course at the University of Bonn several times in recent years.
Identification of spilled oils by NIR spectroscopy technology based on KPCA and LSSVM
Tan, Ailing; Bi, Weihong
2011-08-01
Oil spills on the sea surface are seen relatively often with the development of the petroleum exploitation and transportation of the sea. Oil spills are great threat to the marine environment and the ecosystem, thus the oil pollution in the ocean becomes an urgent topic in the environmental protection. To develop the oil spill accident treatment program and track the source of the spilled oils, a novel qualitative identification method combined Kernel Principal Component Analysis (KPCA) and Least Square Support Vector Machine (LSSVM) was proposed. The proposed method adapt Fourier transform NIR spectrophotometer to collect the NIR spectral data of simulated gasoline, diesel fuel and kerosene oil spills samples and do some pretreatments to the original spectrum. We use the KPCA algorithm which is an extension of Principal Component Analysis (PCA) using techniques of kernel methods to extract nonlinear features of the preprocessed spectrum. Support Vector Machines (SVM) is a powerful methodology for solving spectral classification tasks in chemometrics. LSSVM are reformulations to the standard SVMs which lead to solving a system of linear equations. So a LSSVM multiclass classification model was designed which using Error Correcting Output Code (ECOC) method borrowing the idea of error correcting codes used for correcting bit errors in transmission channels. The most common and reliable approach to parameter selection is to decide on parameter ranges, and to then do a grid search over the parameter space to find the optimal model parameters. To test the proposed method, 375 spilled oil samples of unknown type were selected to study. The optimal model has the best identification capabilities with the accuracy of 97.8%. Experimental results show that the proposed KPCA plus LSSVM qualitative analysis method of near infrared spectroscopy has good recognition result, which could work as a new method for rapid identification of spilled oils.
Tel, G.
We define the notion of total algorithms for networks of processes. A total algorithm enforces that a "decision" is taken by a subset of the processes, and that participation of all processes is required to reach this decision. Total algorithms are an important building block in the design of
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
International Nuclear Information System (INIS)
Inno, L.; Bono, G.; Buonanno, R.; Genovali, K.; Matsunaga, N.; Caputo, F.; Laney, C. D.; Marconi, M.; Piersimoni, A. M.; Primas, F.; Romaniello, M.
2013-01-01
We present the largest near-infrared (NIR) data sets, JHKs, ever collected for classical Cepheids in the Magellanic Clouds (MCs). We selected fundamental (FU) and first overtone (FO) pulsators, and found 4150 (2571 FU, 1579 FO) Cepheids for Small Magellanic Cloud (SMC) and 3042 (1840 FU, 1202 FO) for Large Magellanic Cloud (LMC). Current sample is 2-3 times larger than any sample used in previous investigations with NIR photometry. We also discuss optical VI photometry from OGLE-III. NIR and optical-NIR Period-Wesenheit (PW) relations are linear over the entire period range (0.0 FU ≤ 1.65) and their slopes are, within the intrinsic dispersions, common between the MCs. These are consistent with recent results from pulsation models and observations suggesting that the PW relations are minimally affected by the metal content. The new FU and FO PW relations were calibrated using a sample of Galactic Cepheids with distances based on trigonometric parallaxes and Cepheid pulsation models. By using FU Cepheids we found a true distance moduli of 18.45 ± 0.02(random) ± 0.10(systematic) mag (LMC) and 18.93 ± 0.02(random) ± 0.10(systematic) mag (SMC). These estimates are the weighted mean over 10 PW relations and the systematic errors account for uncertainties in the zero point and in the reddening law. We found similar distances using FO Cepheids (18.60 ± 0.03(random) ± 0.10(systematic) mag (LMC) and 19.12 ± 0.03(random) ± 0.10(systematic) mag (SMC)). These new MC distances lead to the relative distance, Δμ = 0.48 ± 0.03 mag (FU, log P = 1) and Δμ = 0.52 ± 0.03 mag (FO, log P = 0.5), which agrees quite well with previous estimates based on robust distance indicators.
Directory of Open Access Journals (Sweden)
Helena Pereira
2015-12-01
Full Text Available In this paper, the morphological properties of fiber length (weighted in length and of fiber width of unbleached Kraft pulp of Acacia melanoxylon were determined using TECHPAP Morfi® equipment (Techpap SAS, Grenoble, France, and were used in the calibration development of Near Infrared (NIR partial least squares regression (PLS-R models based on the spectral data obtained for the wood. It is the first time that fiber length and width of pulp were predicted with NIR spectral data of the initial woodmeal, with high accuracy and precision, and with ratios of performance to deviation (RPD fulfilling the requirements for screening in breeding programs. The selected models for fiber length and fiber width used the second derivative and first derivative + multiplicative scatter correction (2ndDer and 1stDer + MSC pre-processed spectra, respectively, in the wavenumber ranges from 7506 to 5440 cm−1. The statistical parameters of cross-validation (RMSECV (root mean square error of cross-validation of 0.009 mm and 0.39 μm and validation (RMSEP (root mean square error of prediction of 0.007 mm and 0.36 μm with RPDTS (ratios of performance to deviation of test set values of 3.9 and 3.3, respectively, confirmed that the models are robust and well qualified for prediction. This modeling approach shows a high potential to be used for tree breeding and improvement programs, providing a rapid screening for desired fiber morphological properties of pulp prediction.
Energy Technology Data Exchange (ETDEWEB)
Wu, Yiling; Li, Yang, E-mail: msliyang@scut.edu.cn; Qin, Xixi; Chen, Ruchun; Wu, Dakun; Liu, Shijian; Qiu, Jianrong, E-mail: qjr@scut.edu.cn
2015-11-15
Recently, long persistent phosphors (LPPs) have been considered to be the most prominent candidates for biomedical applications. However, the LPPs suffer from a dramatic decrease in luminescence intensity after incorporation into the tissue. Therefore, it is very necessary to develop the more competitive LPPs and acquire the reproducible tissue imaging. Here, we propose and experimentally demonstrate an effective bifunctional La{sub 3}Ga{sub 5}GeO{sub 14}: Cr{sup 3+}, Nd{sup 3+} phosphor with the interesting characteristic of near-infrared long persistent phosphorescence and NIR-to-NIR Stokes luminescence. Cr{sup 3+} and Nd{sup 3+} ions are simultaneously selected as the emission centers in order to take advantage of the remarkable phosphorescence properties of Cr{sup 3+}, and the appropriate energy level characteristic of NIR-excitation band (808 nm) and NIR-emission (1064 nm), and the ability as the brilliant auxiliary to create more efficient defects of Nd{sup 3+}. The efficient dual-modal emission is, accordingly utilized to realize the convenient, high-resolution global detection and local imaging. - Highlights: • Dual mode phosphor with NIR long afterglow and NIR-to-NIR Stokes luminescence. • Increasing the persistent duration due to the codoping of Nd. • Avoiding the noteworthy overheating effect due to the strong absorption at 980 nm.
Curtivo, Cátia Panizzon Dal; Funghi, Nathália Bitencourt; Tavares, Guilherme Diniz; Barbosa, Sávio Fujita; Löbenberg, Raimar; Bou-Chacra, Nádia Araci
2015-05-01
In this work, near-infrared spectroscopy (NIRS) method was used to evaluate the uniformity of dosage units of three captopril 25 mg tablets commercial batches. The performance of the calibration method was assessed by determination of Q value (0.9986), standard error of estimation (C-set SEE = 1.956), standard error of prediction (V-set SEP = 2.076) as well as the consistency (106.1%). These results indicated the adequacy of the selected model. The method validation revealed the agreement of the reference high pressure liquid chromatography (HPLC) and NIRS methods. The process evaluation using the NIRS method showed that the variability was due to common causes and delivered predictable results consistently. Cp and Cpk values were, respectively, 2.05 and 1.80. These results revealed a non-centered process in relation to the average target (100% w/w), in the specified range (85-115%). The probability of failure was 21:100 million tablets of captopril. The NIRS in combination with the method of multivariate calibration, partial least squares (PLS) regression, allowed the development of methodology for the uniformity of dosage units evaluation of captopril tablets 25 mg. The statistical process control strategy associated with NIRS method as PAT played a critical role in understanding of the sources and degree of variation and its impact on the process. This approach led towards a better process understanding and provided the sound scientific basis for its continuous improvement.
General regression and representation model for classification.
Directory of Open Access Journals (Sweden)
Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
Present status of the NIRS-ECR ion source for the HIMAC
International Nuclear Information System (INIS)
Kitagawa, A.; Matsushita, H.; Shibuya, S.
1995-01-01
The present status of NIRS-ECR ion source for the Heavy Ion Medical Accelerator in Chiba (HIMAC) at National Institute of Radiological Sciences (NIRS) is reported. The beam intensity of the NIRS-ECR was increased by modifications on the magnetic field structure, chamber cooling system, vacuum conductance and the extraction configuration. The output current of Ar 6+ reached 365 eμA after improvements. The good stability, easy operation, and good reproducibility were realized. (author)
Sparse reduced-rank regression with covariance estimation
Chen, Lisha
2014-12-08
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Sparse reduced-rank regression with covariance estimation
Chen, Lisha; Huang, Jianhua Z.
2014-01-01
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Polynomial regression analysis and significance test of the regression function
International Nuclear Information System (INIS)
Gao Zhengming; Zhao Juan; He Shengping
2012-01-01
In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)
Enhanced surface structuring by ultrafast XUV/NIR dual action
Czech Academy of Sciences Publication Activity Database
Jakubczak, Krzysztof; Mocek, Tomáš; Chalupský, Jaromír; Lee, G.H.; Kim, T.K.; Park, S.B.; Nam, Ch. H.; Hájková, Věra; Toufarová, Martina; Juha, Libor; Rus, Bedřich
2011-01-01
Roč. 13, č. 5 (2011), s. 1-12 ISSN 1367-2630 R&D Projects: GA AV ČR KAN300100702; GA MŠk(CZ) LC528; GA MŠk LA08024; GA ČR GC202/07/J008 Grant - others:AV ČR(CZ) M100100911 Institutional research plan: CEZ:AV0Z10100523 Keywords : XUV beam * ultrafast NIR laser pulses * high-order harmonics * laser-induced periodic surface structures Subject RIV: BH - Optics, Masers, Lasers Impact factor: 4.177, year: 2011 http://iopscience.iop.org/1367-2630/13/5/053049
International Nuclear Information System (INIS)
Graham, David W.; Trippett, Clare; Dodds, Walter K.; O'Brien, Jonathan M.; Banner, Eric B.K.; Head, Ian M.; Smith, Marilyn S.; Yang, Richard K.; Knapp, Charles W.
2010-01-01
Denitrification is a process that reduces nitrogen levels in headwaters and other streams. We compared nirS and nirK abundances with the absolute rate of denitrification, the longitudinal coefficient of denitrification (i.e., K den , which represents optimal denitrification rates at given environmental conditions), and water quality in seven prairie streams to determine if nir-gene abundances explain denitrification activity. Previous work showed that absolute rates of denitrification correlate with nitrate levels; however, no correlation has been found for denitrification efficiency, which we hypothesise might be related to gene abundances. Water-column nitrate and soluble-reactive phosphorus levels significantly correlated with absolute rates of denitrification, but nir-gene abundances did not. However, nirS and nirK abundances significantly correlated with K den , as well as phosphorus, although no correlation was found between K den and nitrate. These data confirm that absolute denitrification rates are controlled by nitrate load, but intrinsic denitrification efficiency is linked to nirS and nirK gene abundances. - Denitrification efficiency best correlated to nirS and nirK gene abundances.
Santagata, Sara; Portella, Luigi; Napolitano, Maria; Greco, Adelaide; D'Alterio, Crescenzo; Barone, Maria Vittoria; Luciano, Antonio; Gramanzini, Matteo; Auletta, Luigi; Arra, Claudio; Zannetti, Antonella; Scala, Stefania
2017-05-31
C-X-C chemokine receptor 4 (CXCR4) is over-expressed in multiple human cancers and correlates with tumor aggressiveness, poor prognosis and increased risk for distant metastases. Imaging agents for CXCR4 are thus highly desirable. We developed a novel CXCR4-targeted near-infrared (NIR) fluorescent probe (Peptide R-NIR750) conjugating the new developed CXCR4 peptidic antagonist Peptide R with the NIR fluorescent dye VivoTag-S750. Specific CXCR4 binding was obtained in cells overexpressing human CXCR4 (B16-hCXCR4 and human melanoma cells PES43), but not in CXCR4 low expressing cells (FB-1). Ex vivo evaluation demonstrated that PepR-NIR750 specifically detects B16-hCXCR4-derived subcutaneous tumors and lung metastases. Fluorescence Molecular Tomography (FMT) in vivo imaging was performed on mice carrying subcutaneous CHO and CHO-CXCR4 tumors. PepR-NIR750 accumulates only in CXCR4-positive expressing subcutaneous tumors. Additionally, an intense NIR fluorescence signal was detected in PES43-derived lung metastases of nude mice injected with PepR-NIR750 versus mice injected with VivoTag-S750. With a therapeutic intent, mice bearing PES43-derived lung metastases were treated with Peptide R. A the dramatic reduction in PES43-derived lung metastases was detected through a decrease of the PepR-NIR750 signal. PepR-NIR750 is a specific probe for non-invasive detection of human high CXCR4-expressing tumors and metastatic lesion and thus a valuable tool for cancer molecular imaging.
Collell, Carles; Gou, Pere; Arnau, Jacint; Muñoz, Israel; Comaposada, Josep
2012-12-01
Three different NIR equipment were evaluated based on their ability to predict superficial water activity (a(w)) and moisture content in two types of fermented sausages (with and without moulds on surface), using partial least squares (PLS) regression models. The instruments differed mainly in wavelength range, resolution and measurement configuration. The most accurate equipment was used in a new experiment to achieve robust models in sausages with different salt contents and submitted to different drying conditions. The models developed showed determination coefficients (R(2)(P)) values of 0.990, 0.910 and 0.984, and RMSEP values of 1.560%, 0.220% and 0.007% for moisture, salt and a(w) respectively. It was demonstrated that NIR spectroscopy could be a suitable non-destructive method for on-line monitoring and control of the drying process in fermented sausages. Copyright © 2012 Elsevier Ltd. All rights reserved.
AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS
Directory of Open Access Journals (Sweden)
Н. Білак
2012-04-01
Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
Fuentes, Mariela; González-Martín, Inmaculada; Hernández-Hierro, Jose Miguel; Hidalgo, Claudia; Govaerts, Bram; Etchevers, Jorge; Sayre, Ken D; Dendooven, Luc
2009-06-30
In the present study the natural abundance of (13)C is quantified in agricultural soils in Mexico which have been submitted to different agronomic practices, zero and conventional tillage, retention of crop residues (with and without) and rotation of crops (wheat and maize) for 17 years, which have influenced the physical, chemical and biological characteristics of the soil. The natural abundance of C13 is quantified by near infrared spectra (NIRS) with a remote reflectance fibre optic probe, applying the probe directly to the soil samples. Discriminate partial least squares analysis of the near infrared spectra allowed to classify soils with and without residues, regardless of the type of tillage or rotation systems used with a prediction rate of 90% in the internal validation and 94% in the external validation. The NIRS calibration model using a modified partial least squares regression allowed to determine the delta(13)C in soils with or without residues, with multiple correlation coefficients 0.81 and standard error prediction 0.5 per thousand in soils with residues and 0.92 and 0.2 per thousand in soils without residues. The ratio performance deviation for the quantification of delta(13)C in soil was 2.5 in soil with residues and 3.8 without residues. This indicated that the model was adequate to determine the delta(13)C of unknown soils in the -16.2 per thousand to -20.4 per thousand range. The development of the NIR calibration permits analytic determinations of the values of delta(13)C in unknown agricultural soils in less time, employing a non-destructive method, by the application of the fibre optic probe of remote reflectance to the soil sample.
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
Physiological Aging Influence on Brain Hemodynamic Activity during Task-Switching: A fNIRS Study
Directory of Open Access Journals (Sweden)
Roberta Vasta
2018-01-01
Full Text Available Task-switching (TS paradigm is a well-known validated tool useful for exploring the neural substrates of cognitive control, in particular the activity of the lateral and medial prefrontal cortex. This work is aimed at investigating how physiological aging influences hemodynamic response during the execution of a color-shape TS paradigm. A multi-channel near infrared spectroscopy (fNIRS was used to measure hemodynamic activity in 27 young (30.00 ± 7.90 years and 11 elderly participants (57.18 ± 9.29 years healthy volunteers (55% male, age range: (19–69 years during the execution of a TS paradigm. Two holders were placed symmetrically over the left/right hemispheres to record cortical activity [oxy-(HbO and deoxy-hemoglobin (HbR concentration] of the dorso-lateral prefrontal cortex (DLPFC, the dorsal premotor cortex (PMC, and the dorso-medial part of the superior frontal gyrus (sFG. TS paradigm requires participants to repeat the same task over a variable number of trials, and then to switch to a different task during the trial sequence. A two-sample t-test was carried out to detect differences in cortical responses between groups. Multiple linear regression analysis was used to evaluate the impact of age on the prefrontal neural activity. Elderly participants were significantly slower than young participants in both color- (p < 0.01, t = −3.67 and shape-single tasks (p = 0.026, t = −2.54 as well as switching (p = 0.026, t = −2.41 and repetition trials (p = 0.012, t = −2.80. Differences in cortical activation between groups were revealed for HbO mean concentration of switching task in the PMC (p = 0.048, t = 2.94. In the whole group, significant increases of behavioral performance were detected in switching trials, which positively correlated with aging. Multivariate regression analysis revealed that the HbO mean concentration of switching task in the PMC (p = 0.01, β = −0.321 and of shape single-task in the sFG (p = 0.003, β = 0
Functional data analysis of generalized regression quantiles
Guo, Mengmeng
2013-11-05
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Functional data analysis of generalized regression quantiles
Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl
2013-01-01
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Advanced statistics: linear regression, part I: simple linear regression.
Marill, Keith A
2004-01-01
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
Infrared and NIR Raman spectroscopy in medical microbiology
Naumann, Dieter
1998-04-01
FTIR and FT-NIR Raman spectra of intact microbial cells are highly specific, fingerprint-like signatures which can be used to (i) discriminate between diverse microbial species and strains, (ii) detect in situ intracellular components or structures such as inclusion bodies, storage materials or endospores, (iii) detect and quantify metabolically released CO2 in response to various different substrate, and (iv) characterize growth-dependent phenomena and cell-drug interactions. The characteristic information is extracted from the spectral contours by applying resolution enhancement techniques, difference spectroscopy, and pattern recognition methods such as factor-, cluster-, linear discriminant analysis, and artificial neural networks. Particularly interesting applications arise by means of a light microscope coupled to the spectrometer. FTIR spectra of micro-colonies containing less than 103 cells can be obtained from colony replica by a stamping technique that transfers micro-colonies growing on culture plates to a special IR-sample holder. Using a computer controlled x, y- stage together with mapping and video techniques, the fundamental tasks of microbiological analysis, namely detection, enumeration, and differentiation of micro- organisms can be integrated in one single apparatus. FTIR and NIR-FT-Raman spectroscopy can also be used in tandem to characterize medically important microorganisms. Currently novel methodologies are tested to take advantage of the complementary information of IR and Raman spectra. Representative examples on medically important microorganisms will be given that highlight the new possibilities of vibrational spectroscopies.
MEMS-based microspectrometer technologies for NIR and MIR wavelengths
International Nuclear Information System (INIS)
Schuler, Leo P; Milne, Jason S; Dell, John M; Faraone, Lorenzo
2009-01-01
Commercially manufactured near-infrared (NIR) instruments became available about 50 years ago. While they have been designed for laboratory use in a controlled environment and boast high performance, they are generally bulky, fragile and maintenance intensive, and therefore expensive to purchase and maintain. Micromachining is a powerful technique to fabricate micromechanical parts such as integrated circuits. It was perfected in the 1980s and led to the invention of micro electro mechanical systems (MEMSs). The three characteristic features of MEMS fabrication technologies are miniaturization, multiplicity and microelectronics. Combined, these features allow the batch production of compact and rugged devices with integrated intelligence. In order to build more compact, more rugged and less expensive NIR instruments, MEMS technology has been successfully integrated into a range of new devices. In the first part of this paper we discuss the UWA MEMS-based Fabry-Perot spectrometer, its design and issues to be solved. MEMS-based Fabry-Perot filters primarily isolate certain wavelengths by sweeping across an incident spectrum and the resulting monochromatic signal is detected by a broadband detector. In the second part, we discuss other microspectrometers including other Fabry-Perot spectrometer designs, time multiplexing devices and mixed time/space multiplexing devices. (topical review)
Use of FT-NIR Spectroscopy for Bovine Colostrum Analysis
Directory of Open Access Journals (Sweden)
P. Navrátilová
2006-01-01
Full Text Available Fourier transformation near infrared spectroscopy (FT-NIR in combination with partial least squares (PLS method were used to determine the content of total solids, fat, non-fatty solids, lactose and proteins in bovine colostrum. Spectra of 90 samples were measured in the reflectance mode with a transflectance cuvette in the 10000-4000 cm-1 spectral ranges with 100 scans. Calibration was performed and statistical values of correlation coefficients (R and standard error of calibration values (SEC were computed for total solids (0.986 and 0.919, respectively, fat (0.997 and 0.285, respectively, non-fatty solids (0.995 and 0.451, respectively, lactose (0.934 and 0.285, respectively and protein (0.999 and 0.149, respectively. The calibration models developed were verified by cross validation. It follows from the study that FT-NIR spectroscopy can be used to determine the components of bovine colostrum.
Multisensor data fusion algorithm development
Energy Technology Data Exchange (ETDEWEB)
Yocky, D.A.; Chadwick, M.D.; Goudy, S.P.; Johnson, D.K.
1995-12-01
This report presents a two-year LDRD research effort into multisensor data fusion. We approached the problem by addressing the available types of data, preprocessing that data, and developing fusion algorithms using that data. The report reflects these three distinct areas. First, the possible data sets for fusion are identified. Second, automated registration techniques for imagery data are analyzed. Third, two fusion techniques are presented. The first fusion algorithm is based on the two-dimensional discrete wavelet transform. Using test images, the wavelet algorithm is compared against intensity modulation and intensity-hue-saturation image fusion algorithms that are available in commercial software. The wavelet approach outperforms the other two fusion techniques by preserving spectral/spatial information more precisely. The wavelet fusion algorithm was also applied to Landsat Thematic Mapper and SPOT panchromatic imagery data. The second algorithm is based on a linear-regression technique. We analyzed the technique using the same Landsat and SPOT data.
Tøgersen, G; Arnesen, J F; Nilsen, B N; Hildrum, K I
2003-04-01
The chemical composition of industrial scale batches of frozen beef was measured on-line during grinding by near infrared (NIR) reflectance spectroscopy. The MM55E filter based non-contact NIR instrument was mounted at the outlet of a meat grinder, and the fat, moisture and protein contents determined from the average of each filter reading throughout the grinding of the batch. The filters were selected from full spectra measurements to be as insensitive to water crystallization as possible. For on-line calibration and prediction, 55 beef batches of 400-800 kg in the range of 7.66-22.91% fat, 59.36-71.48% moisture, and 17.04-20.76% protein, were ground through 4 or 13 mm hole plates. The regression results, presented as root mean square error of cross validation (RMSECV) were between 0.48 and 1.11% for fat, 0.43 and 0.97% for moisture and 0.41 and 0.47% for protein.
Rébufa, Catherine; Pany, Inès; Bombarda, Isabelle
2018-09-30
A rapid methodology was developed to simultaneously predict water content and activity values (a w ) of Moringa oleifera leaf powders (MOLP) using near infrared (NIR) signatures and experimental sorption isotherms. NIR spectra of MOLP samples (n = 181) were recorded. A Partial Least Square Regression model (PLS2) was obtained with low standard errors of prediction (SEP of 1.8% and 0.07 for water content and a w respectively). Experimental sorption isotherms obtained at 20, 30 and 40 °C showed similar profiles. This result is particularly important to use MOLP in food industry. In fact, a temperature variation of the drying process will not affect their available water content (self-life). Nutrient contents based on protein and selected minerals (Ca, Fe, K) were also predicted from PLS1 models. Protein contents were well predicted (SEP of 2.3%). This methodology allowed for an improvement in MOLP safety, quality control and traceability. Published by Elsevier Ltd.
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Visible and NIR image fusion using weight-map-guided Laplacian ...
Indian Academy of Sciences (India)
Ashish V Vanmali
fusion perspective, instead of the conventional haze imaging model. The proposed ... Image dehazing; Laplacian–Gaussian pyramid; multi-resolution fusion; visible–NIR image fusion; weight map. 1. .... Tan's [8] work is based on two assumptions: first, images ... responding colour image, since NIR can penetrate through.
DEFF Research Database (Denmark)
Lai, Xuxin; Zheng, Yiwu; Søndergaard, Ib
2007-01-01
A method for determining the aluminium content of an aluminium hydroxide suspension using near infrared (NIR) transmittance spectroscopy has been developed. Inductively coupled plasma-atomic emission spectroscopy (ICP-AES) was used as reference method. The factors influencing the NIR analysis...... aluminium content in aluminium hydroxide suspension. (c) 2007 Elsevier Ltd. All rights reserved....
A New Framework for the Assessment of Cerebral Hemodynamics Regulation in Neonates Using NIRS
Caicedo, Alexander; Alderliesten, Thomas; Naulaers, Gunnar; Lemmers, Petra; van Bel, Frank; Van Huffel, Sabine
2016-01-01
We present a new framework for the assessment of cerebral hemodynamics regulation (CHR) in neonates using near-infrared spectroscopy (NIRS). In premature infants, NIRS measurements have been used as surrogate variables for cerebral blood flow (CBF) in the assessment of cerebral autoregulation (CA).
The role of cell hydration in realization of biological effects of non-ionizing radiation (NIR).
Ayrapetyan, Sinerik
2015-09-01
The weak knowledge on the nature of cellular and molecular mechanisms of biological effects of NIR such as static magnetic field, infrasound frequency of mechanical vibration, extremely low frequency of electromagnetic fields and microwave serves as a main barrier for adequate dosimetry from the point of Public Health. The difficulty lies in the fact that the biological effects of NIR depend not only on their thermodynamic characteristics but also on their frequency and intensity "windows", chemical and physical composition of the surrounding medium, as well as on the initial metabolic state of the organism. Therefore, only biomarker can be used for adequate estimation of biological effect of NIR on organisms. Because of the absence of such biomarker(s), organizations having the mission to monitor hazardous effects of NIR traditionally base their instruction on thermodynamic characteristics of NIR. Based on the high sensitivity to NIR of both aqua medium structure and cell hydration, it is suggested that cell bathing medium is one of the primary targets and cell hydration is a biomarker for NIR effects on cells and organisms. The purpose of this article is to present a short review of literature and our own experimental data on the effects of NIR on plants' seeds germination, microbe growth and development, snail neurons and heart muscle, rat's brain and heart tissues.
Single seed NIR as a fast method to predict germination ability in Pak Choi
DEFF Research Database (Denmark)
Gislum, René; Deleuran, Lise Christina; Olesen, Merete Halkjær
2012-01-01
Single seed NIR has further been tested to determine the applicability for prediction of seed viability in radish (Raphanus sativus L.) seeds and spinach (Spinacia oleracea L.) seeds. The studies show the possibility of using NIR spectroscopy in a seed separating process in the future, provided...
Directory of Open Access Journals (Sweden)
Qinghu Jiang
2016-09-01
Full Text Available Soil organic carbon (SOC is an essential property for soil function, fertility and sustainability of agricultural systems. It can be measured with visible and near-infrared reflectance (VIS-NIR spectroscopy efficiently based on empirical equations and spectra data for air/oven-dried samples. However, the spectral signal is interfered with by soil moisture content (MC under in situ conditions, which will affect the accuracy of measurements and calibration transfer among different areas. This study aimed to (1 quantify the influences of MC on SOC prediction by VIS-NIR spectroscopy; and (2 explore the potentials of orthogonal signal correction (OSC and generalized least squares weighting (GLSW methods in the removal of moisture interference. Ninety-eight samples were collected from the Jianghan plain, China, and eight MCs were obtained for each sample by a rewetting process. The VIS-NIR spectra of the rewetted soil samples were measured in the laboratory. Partial least squares regression (PLSR was used to develop SOC prediction models. Specifically, three validation strategies, namely moisture level validation, transferability validation and mixed-moisture validation, were designed to test the potentials of OSC and GLSW in removing the MC effect. Results showed that all of the PLSR models generated at different moisture levels (e.g., 50–100, 250–300 g·kg−1 were moderately successful in SOC predictions (r2pre = 0.58–0.85, RPD = 1.55–2.55. These models, however, could not be transferred to soil samples with different moisture levels. OSC and GLSW methods are useful filter transformations improving model transferability. The GLSW-PLSR model (mean of r2pre = 0.77, root mean square error for prediction (RMSEP = 3.08 g·kg−1, and residual prediction deviations (RPD = 2.09 outperforms the OSC-PLSR model (mean of r2pre = 0.67, RMSEP = 3.67 g·kg−1, and RPD = 1.76 when the moisture-mixed protocol is used. Results demonstrated the use of OSC
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Lee, Jessica A; Francis, Christopher A
2017-12-01
Denitrification is a dominant nitrogen loss process in the sediments of San Francisco Bay. In this study, we sought to understand the ecology of denitrifying bacteria by using next-generation sequencing (NGS) to survey the diversity of a denitrification functional gene, nirS (encoding cytchrome-cd 1 nitrite reductase), along the salinity gradient of San Francisco Bay over the course of a year. We compared our dataset to a library of nirS sequences obtained previously from the same samples by standard PCR cloning and Sanger sequencing, and showed that both methods similarly demonstrated geography, salinity and, to a lesser extent, nitrogen, to be strong determinants of community composition. Furthermore, the depth afforded by NGS enabled novel techniques for measuring the association between environment and community composition. We used Random Forests modelling to demonstrate that the site and salinity of a sample could be predicted from its nirS sequences, and to identify indicator taxa associated with those environmental characteristics. This work contributes significantly to our understanding of the distribution and dynamics of denitrifying communities in San Francisco Bay, and provides valuable tools for the further study of this key N-cycling guild in all estuarine systems. © 2017 Society for Applied Microbiology and John Wiley & Sons Ltd.
Purpose: The aim of this study was to develop a technique for the non-destructive and rapid prediction of the moisture content in red pepper powder using near-infrared (NIR) spectroscopy and a partial least squares regression (PLSR) model. Methods: Three red pepper powder products were separated in...
Directory of Open Access Journals (Sweden)
Guangjun Qiu
2018-03-01
Full Text Available The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.
Qiu, Guangjun; Lü, Enli; Lu, Huazhong; Xu, Sai; Zeng, Fanguo; Shui, Qin
2018-03-28
The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000-2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.
Paik, Seung-ho; Kim, Beop-Min
2016-03-01
fNIRS is a neuroimaging technique which uses near-infrared light source in the 700-1000 nm range and enables to detect hemodynamic changes (i.e., oxygenated hemoglobin, deoxygenated hemoglobin, blood volume) as a response to various brain processes. In this study, we developed a new, portable, prefrontal fNIRS system which has 12 light sources, 15 detectors and 108 channels with a sampling rate of 2 Hz. The wavelengths of light source are 780nm and 850nm. ATxmega128A1, 8bit of Micro controller unit (MCU) with 200~4095 resolution along with MatLab data acquisition algorithm was utilized. We performed a simple left and right finger movement imagery tasks which produced statistically significant changes of oxyhemoglobin concentrations in the dorsolateral prefrontal cortex (dlPFC) areas. We observed that the accuracy of the imagery tasks can be improved by carrying out neurofeedback training, during which a real-time feedback signal is provided to a participating subject. The effects of the neurofeedback training was later visually verified using the 3D NIRfast imaging. Our portable fNIRS system may be useful in non-constraint environment for various clinical diagnoses.
Vegetation Removal from Uav Derived Dsms, Using Combination of RGB and NIR Imagery
Skarlatos, D.; Vlachos, M.
2018-05-01
Current advancements on photogrammetric software along with affordability and wide spreading of Unmanned Aerial Vehicles (UAV), allow for rapid, timely and accurate 3D modelling and mapping of small to medium sized areas. Although the importance and applications of large format aerial overlaps cameras and photographs in Digital Surface Model (DSM) production and LIDAR data is well documented in literature, this is not the case for UAV photography. Additionally, the main disadvantage of photogrammetry is the inability to map the dead ground (terrain), when we deal with areas that include vegetation. This paper assesses the use of near-infrared imagery captured by small UAV platforms to automatically remove vegetation from Digital Surface Models (DSMs) and obtain a Digital Terrain Model (DTM). Two areas were tested, based on the availability of ground reference points, both under trees and among vegetation, as well as on terrain. In addition, RGB and near-infrared UAV photography was captured and processed using Structure from Motion (SfM) and Multi View Stereo (MVS) algorithms to generate DSMs and corresponding colour and NIR orthoimages with 0.2 m and 0.25 m as pixel size respectively for the two test sites. Moreover, orthophotos were used to eliminate the vegetation from the DSMs using NDVI index, thresholding and masking. Following that, different interpolation algorithms, according to the test sites, were applied to fill in the gaps and created DTMs. Finally, a statistic analysis was made using reference terrain points captured on field, both on dead ground and under vegetation to evaluate the accuracy of the whole process and assess the overall accuracy of the derived DTMs in contrast with the DSMs.
Analytical robustness of quantitative NIR chemical imaging for Islamic paper characterization
Mahgoub, Hend; Gilchrist, John R.; Fearn, Thomas; Strlič, Matija
2017-07-01
Recently, spectral imaging techniques such as Multispectral (MSI) and Hyperspectral Imaging (HSI) have gained importance in the field of heritage conservation. This paper explores the analytical robustness of quantitative chemical imaging for Islamic paper characterization by focusing on the effect of different measurement and processing parameters, i.e. acquisition conditions and calibration on the accuracy of the collected spectral data. This will provide a better understanding of the technique that can provide a measure of change in collections through imaging. For the quantitative model, special calibration target was devised using 105 samples from a well-characterized reference Islamic paper collection. Two material properties were of interest: starch sizing and cellulose degree of polymerization (DP). Multivariate data analysis methods were used to develop discrimination and regression models which were used as an evaluation methodology for the metrology of quantitative NIR chemical imaging. Spectral data were collected using a pushbroom HSI scanner (Gilden Photonics Ltd) in the 1000-2500 nm range with a spectral resolution of 6.3 nm using a mirror scanning setup and halogen illumination. Data were acquired at different measurement conditions and acquisition parameters. Preliminary results showed the potential of the evaluation methodology to show that measurement parameters such as the use of different lenses and different scanning backgrounds may not have a great influence on the quantitative results. Moreover, the evaluation methodology allowed for the selection of the best pre-treatment method to be applied to the data.
fNIRS suggests increased effort during executive access in ecstasy polydrug users.
Roberts, C A; Montgomery, C
2015-05-01
Ecstasy use is associated with cognitive impairment, believed to result from damage to 5-HT axons. Neuroimaging techniques to investigate executive dysfunction in ecstasy users provide a more sensitive measure of cognitive impairment than behavioural indicators. The present study assessed executive access to semantic memory in ecstasy polydrug users and non-users. Twenty ecstasy polydrug users and 20 non-user controls completed an oral variant of the Chicago Word Fluency Test (CWFT), whilst the haemodynamic response to the task was measured using functional near-infrared spectroscopy (fNIRS). There were no between-group differences in many background measures including measures of sleep and mood state (anxiety, arousal, hedonic tone). No behavioural differences were observed on the CWFT. However, there were significant differences in oxy-Hb level change at several voxels relating to the left dorsolateral prefrontal cortex (DLPFC) and right medial prefrontal cortex (PFC) during the CWFT, indicating increased cognitive effort in ecstasy users relative to controls. Regression analyses showed that frequency of ecstasy use, total lifetime dose and amount used in the last 30 days was significant predictors of oxy-Hb increase at several voxels after controlling for alcohol and cannabis use indices. The results suggest that ecstasy users show increased activation in the PFC as a compensatory mechanism, to achieve equivalent performance to non-users. These findings are in agreement with much of the literature in the area which suggests that ecstasy may be a selective serotonin neurotoxin in humans.
Directory of Open Access Journals (Sweden)
Jiaojiao Wang
2014-02-01
Full Text Available To analyze the nutritional composition of faba bean (Vicia faba L. seed, estimation models were developed for protein, starch, oil, and total polyphenol using near infrared spectroscopy (NIRS. Two hundred and forty-four samples from twelve producing regions were measured in both milled powder and intact seed forms. Partial least squares (PLS regression was applied for model development. The model based on ground seed powder was generally superior to that based on the intact seed. The optimal seed powder-based models for protein, starch, and total polyphenol had coefficients of correlation (r2 of 0.97, 0.93 and 0.89, respectively. The relationship between nutrient contents and twelve producing areas was determined by two-step cluster analysis. Three distinct groupings were obtained with region-constituent features, i.e., Group 1 of high oil, Group 2 of high protein, and Group 3 of high starch as well as total polyphenol. The clustering accuracy was 79.5%. Moreover, the nutrition contents were affected by seeding date, longitude, latitude, and altitude of plant location. Cluster analysis revealed that the differences in the seed were strongly influenced by geographical factors.
A beam energy measurement system at NIRS-930 cyclotron
International Nuclear Information System (INIS)
Hojo, S.; Honma, T.; Sakamoto, Y.; Miyahara, N.; Okada, T.; Komatsu, K.; Tsuji, N.; Yamada, S.
2005-01-01
A beam energy measurement system employing a set of capacitive probes has been developed at NIRS-930 cyclotron. Principle of the measurement is applying a modified-TOF method, so that the two proves are installed at one of the straight section in the beam transport line. Usually they are separated about 5.8 m, which is equivalent to the almost final path length of the beam extracted in the cyclotron. In the measurement, two beam signals are superimposed by adjusting a position of the downstream-probe along the beam direction with watching an oscilloscope screen roughly. In order to determine the beam energy accurately the signals are processed by MCA with suitable electric module. (author)
Rapid NIR determination of alkyl esters in virgin olive oil
International Nuclear Information System (INIS)
Cayuela, J.A.
2017-01-01
The regulation of The European Union for olive oil and olive pomace established the limit of 35 mg·kg-1 for fatty acids ethyl ester contents in extra virgin olive oils, from grinding seasons after 2016. In this work, predictive models have been established for measuring fatty acid ethyl and methyl esters and to measure the total fatty acid alkyl esters based on near infrared spectroscopy (NIRS), and used successfully for this purpose. The correlation coefficients from the external validation exercises carried out with these predictive models ranged from 0.84 to 0.91. Different classification tests using the same models for the thresholds 35 mg·kg-1 for fatty acid ethyl esters and 75 mg·kg-1 for fatty acid alkyl esters provided success percentages from 75.0% to 95.2%. [es
Panel Smooth Transition Regression Models
DEFF Research Database (Denmark)
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Herrera-Vega, Javier; Montero-Hernández, Samuel; Tachtsidis, Ilias; Treviño-Palacios, Carlos G.; Orihuela-Espina, Felipe
2017-11-01
Accurate estimation of brain haemodynamics parameters such as cerebral blood flow and volume as well as oxygen consumption i.e. metabolic rate of oxygen, with funcional near infrared spectroscopy (fNIRS) requires precise characterization of light propagation through head tissues. An anatomically realistic forward model of the human adult head with unprecedented detailed specification of the 5 scalp sublayers to account for blood irrigation in the connective tissue layer is introduced. The full model consists of 9 layers, accounts for optical properties ranging from 750nm to 950nm and has a voxel size of 0.5mm. The whole model is validated comparing the predicted remitted spectra, using Monte Carlo simulations of radiation propagation with 108 photons, against continuous wave (CW) broadband fNIRS experimental data. As the true oxy- and deoxy-hemoglobin concentrations during acquisition are unknown, a genetic algorithm searched for the vector of parameters that generates a modelled spectrum that optimally fits the experimental spectrum. Differences between experimental and model predicted spectra was quantified using the Root mean square error (RMSE). RMSE was 0.071 +/- 0.004, 0.108 +/- 0.018 and 0.235+/-0.015 at 1, 2 and 3cm interoptode distance respectively. The parameter vector of absolute concentrations of haemoglobin species in scalp and cortex retrieved with the genetic algorithm was within histologically plausible ranges. The new model capability to estimate the contribution of the scalp blood flow shall permit incorporating this information to the regularization of the inverse problem for a cleaner reconstruction of brain hemodynamics.
De Leersnyder, Fien; Peeters, Elisabeth; Djalabi, Hasna; Vanhoorne, Valérie; Van Snick, Bernd; Hong, Ke; Hammond, Stephen; Liu, Angela Yang; Ziemons, Eric; Vervaet, Chris; De Beer, Thomas
2018-03-20
A calibration model for in-line API quantification based on near infrared (NIR) spectra collection during tableting in the tablet press feed frame was developed and validated. First, the measurement set-up was optimised and the effect of filling degree of the feed frame on the NIR spectra was investigated. Secondly, a predictive API quantification model was developed and validated by calculating the accuracy profile based on the analysis results of validation experiments. Furthermore, based on the data of the accuracy profile, the measurement uncertainty was determined. Finally, the robustness of the API quantification model was evaluated. An NIR probe (SentroPAT FO) was implemented into the feed frame of a rotary tablet press (Modul™ P) to monitor physical mixtures of a model API (sodium saccharine) and excipients with two different API target concentrations: 5 and 20% (w/w). Cutting notches into the paddle wheel fingers did avoid disturbances of the NIR signal caused by the rotating paddle wheel fingers and hence allowed better and more complete feed frame monitoring. The effect of the design of the notched paddle wheel fingers was also investigated and elucidated that straight paddle wheel fingers did cause less variation in NIR signal compared to curved paddle wheel fingers. The filling degree of the feed frame was reflected in the raw NIR spectra. Several different calibration models for the prediction of the API content were developed, based on the use of single spectra or averaged spectra, and using partial least squares (PLS) regression or ratio models. These predictive models were then evaluated and validated by processing physical mixtures with different API concentrations not used in the calibration models (validation set). The β-expectation tolerance intervals were calculated for each model and for each of the validated API concentration levels (β was set at 95%). PLS models showed the best predictive performance. For each examined saccharine
Measurement of quadriceps endurance by fNIRS
Erdem, Devrim; Şayli, Ömer; Karahan, Mustafa; Akin, A.
2006-02-01
In this paper, the changes in muscle deoxygenation trends during a sustained isometric quadriceps (chair squat/half squat) endurance exercise were evaluated among twelve male subjects and the relationship between muscle oxygenation and endurance times was investigated by means of functional near-infrared spectroscopy (fNIRS). Neuromuscular activation and predictions of muscle performance decrements during extended fatiguing task was investigated by means of surface electromyography (sEMG). The results of the study showed that in the subjects who maintained exercise longer than five minutes (group 1), mean Hb recovery time (33 [sec.]) was 37.4% less than the others (group 2, 52.7 [sec.]). Also mean HbO II decline amplitude (2.53 [a.u.] in group 1 and 2.07 [a.u.] in group 2) and oxy decline amplitude (8.4 [a.u.] in group 1 and 3.04 [a.u.] in group 2) in the beginning of squat exercise are found to be 22.6% and 176.9% bigger in these group. For the EMG parameters, mean slope of MNF and MDF decline are found to be 57.5% and 42.2% bigger in magnitude in group 2 which indicates higher degree of decrement in mean and median frequencies although their mean squat duration time is less. This indicates higher index of fatigue for this group. It is concluded that training leads to altered oxygenation and oxygen extraction capability in the exercising muscle and investigated fNIRS parameters could be used for endurance evaluation.
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
International Nuclear Information System (INIS)
Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei
2007-01-01
Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age
Tumor regression patterns in retinoblastoma
International Nuclear Information System (INIS)
Zafar, S.N.; Siddique, S.N.; Zaheer, N.
2016-01-01
To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)
Directory of Open Access Journals (Sweden)
Ning Wang
2014-01-01
Full Text Available This paper developed a rapid and nondestructive method for quantitative analysis of a cheaper adulterant (wheat flour in oat flour by NIR spectroscopy and chemometrics. Reflectance FT-NIR spectra in the range of 4000 to 12000 cm−1 of 300 oat flour objects adulterated with wheat flour were measured. The doping levels of wheat flour ranged from 5% to 50% (w/w. To ensure the generalization performance of the method, both the oat and the wheat flour samples were collected from different producing areas and an incomplete unbalanced randomized block (IURB design was performed to include the significant variations that may be encountered in future samples. Partial least squares regression (PLSR was used to develop calibration models for predicting the levels of wheat flour. Different preprocessing methods including smoothing, taking second-order derivative (D2, and standard normal variate (SNV transformation were investigated to improve the model accuracy of PLS. The root mean squared error of Monte Carlo cross-validation (RMSEMCCV and root mean squared error of prediction (RMSEP were 1.921 and 1.975 (%, w/w by D2-PLS, respectively. The results indicate that NIR and chemometrics can provide a rapid method for quantitative analysis of wheat flour in oat flour.
Wu, Yan-Wen; Sun, Su-Qin; Zhou, Qun; Leung, Hei-Wun
2008-02-13
Honghua Oil (HHO), a traditional Chinese medicine (TCM) oil preparation, is a mixture of several plant essential oils. In this text, the extended ranges of Fourier transform mid-infrared (FT-MIR) and near infrared (FT-NIR) were recorded for 48 commercially available HHOs of different batches from nine manufacturers. The qualitative and quantitative analysis of three marker components, alpha-pinene, methyl salicylate and eugenol, in different HHO products were performed rapidly by the two vibrational spectroscopic methods, i.e. MIR with horizontal attenuated total reflection (HATR) accessory and NIR with direct sampling technique, followed by partial least squares (PLS) regression treatment of the set of spectra obtained. The results indicated that it was successful to identify alpha-pinene, methyl salicylate and eugenol in all of the samples by simple inspection of the MIR-HATR spectra. Both PLS models established with MIR-HATR and NIR spectral data using gas chromatography (GC) peak areas as calibration reference showed a good linear correlation for each of all three target substances in HHO samples. The above spectroscopic techniques may be the promising methods for the rapid quality assessment/quality control (QA/QC) of TCM oil preparations.
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Celie, Bert M; Boone, Jan; Dumortier, Jasmien; Derave, Wim; De Backer, Tine; Bourgois, Jan G
2016-02-01
The influence of subcutaneous adipose tissue (ATT) and oxygen (O2) delivery has been poorly defined in frequency domain (FD) near-infrared spectroscopy (NIRS). Therefore, the aim of this study was to investigate the possible influence of these variables on all FD NIRS responses using a reliable protocol. Moreover, these influences were also investigated when using relative oxy- and deoxyhemoglobin and -myoglobin (oxy[Hb + Mb] and deoxy[Hb + Mb]) values (in %). A regression analysis was carried out for ATT and maximal-minimum oxy[Hb + Mb], deoxy[Hb + Mb], oxygen saturation (SmO2), and total hemoglobin (totHb) amplitudes during an incremental cyclic contraction protocol (ICCP) in a group of 45 participants. Moreover, the same analysis was carried out between subcutaneous ATT and the relative oxy- and deoxy[Hb + Mb] values (in %). In the second part of this study, a regression analysis was performed for peak forearm blood flow (FBF) during ICCP and the absolute and relative NIRS values in a group of 37 participants. Significant exponential correlation coefficients were found between ATT and deoxy[Hb + Mb] (r = 0.53; P < 0.001), oxy[Hb + Mb] (r = 0.57; P < 0.001), and SmO2 amplitudes (r = 0.57; P < 0.001). No significant relations were found between ATT and relative oxy[Hb + Mb] (r = 0.37; P = 0.07) and deoxy[Hb + Mb] (r = 0.09; P = 0.82). Significant positive correlation coefficients were found between force at exhaustion and maximal FBF (r = 0.66; P < 0.001), maximal differences in deoxy[Hb + Mb] (r = 0.353; P = 0.032) and totHb (r = 0.512; P = 0.002) while no significant correlation coefficients were found between these maximal force values and maximal differences in oxy[Hb + Mb] (r = -0.267; P = 0.111) and SmO2 (r = -0.267; P = 0.111). Significant linear correlation coefficients were found between FBF and deoxy[Hb + Mb] (r = 0.51; P
Hemming, S.; Kempkes, F.; Braak, van der N.; Dueck, T.A.; Marissen, A.
2006-01-01
Wageningen UR investigated the potentials of several NIR-filtering methods to be applied in Dutch horticulture. NIR-filtering can be done by the greenhouse covering or by internal or external moveable screens. The objective of this investigation was to quantify the effect of different NIR-filtering
Regression to Causality : Regression-style presentation influences causal attribution
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...
Regression analysis with categorized regression calibrated exposure: some interesting findings
Directory of Open Access Journals (Sweden)
Hjartåker Anette
2006-07-01
Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a
Directory of Open Access Journals (Sweden)
Hailun Wang
2017-01-01
Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.
Advanced statistics: linear regression, part II: multiple linear regression.
Marill, Keith A
2004-01-01
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
Logic regression and its extensions.
Schwender, Holger; Ruczinski, Ingo
2010-01-01
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.
Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...
International Nuclear Information System (INIS)
Sarrach, D.; Strohner, P.
1986-01-01
The Gauss-Newton algorithm has been used to evaluate tracer binding parameters of RIA by nonlinear regression analysis. The calculations were carried out on the K1003 desk computer. Equations for simple binding models and its derivatives are presented. The advantages of nonlinear regression analysis over linear regression are demonstrated
Independent contrasts and PGLS regression estimators are equivalent.
Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary
2012-05-01
We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.
Optimal regression for reasoning about knowledge and actions
Ditmarsch, van H.; Herzig, Andreas; Lima, de Tiago
2007-01-01
We show how in the propositional case both Reiter’s and Scherl & Levesque’s solutions to the frame problem can be modelled in dynamic epistemic logic (DEL), and provide an optimal regression algorithm for the latter. Our method is as follows: we extend Reiter’s framework by integrating observation
Real-time regression analysis with deep convolutional neural networks
Huerta, E. A.; George, Daniel; Zhao, Zhizhen; Allen, Gabrielle
2018-01-01
We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.
Optimized support vector regression for drilling rate of penetration estimation
Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa
2015-12-01
In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.
International Nuclear Information System (INIS)
Creutz, M.
1987-11-01
A large variety of Monte Carlo algorithms are being used for lattice gauge simulations. For purely bosonic theories, present approaches are generally adequate; nevertheless, overrelaxation techniques promise savings by a factor of about three in computer time. For fermionic fields the situation is more difficult and less clear. Algorithms which involve an extrapolation to a vanishing step size are all quite closely related. Methods which do not require such an approximation tend to require computer time which grows as the square of the volume of the system. Recent developments combining global accept/reject stages with Langevin or microcanonical updatings promise to reduce this growth to V/sup 4/3/
Hu, T C
2002-01-01
Newly enlarged, updated second edition of a valuable text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discusses binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. 153 black-and-white illus. 23 tables.Newly enlarged, updated second edition of a valuable, widely used text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discussed are binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. New to this edition: Chapter 9
Quantile Regression With Measurement Error
Wei, Ying; Carroll, Raymond J.
2009-01-01
. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a
From Rasch scores to regression
DEFF Research Database (Denmark)
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Testing Heteroscedasticity in Robust Regression
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2011-01-01
Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Morales, Esteban; de Leon, John Mark S; Abdollahi, Niloufar; Yu, Fei; Nouri-Mahdavi, Kouros; Caprioli, Joseph
2016-03-01
The study was conducted to evaluate threshold smoothing algorithms to enhance prediction of the rates of visual field (VF) worsening in glaucoma. We studied 798 patients with primary open-angle glaucoma and 6 or more years of follow-up who underwent 8 or more VF examinations. Thresholds at each VF location for the first 4 years or first half of the follow-up time (whichever was greater) were smoothed with clusters defined by the nearest neighbor (NN), Garway-Heath, Glaucoma Hemifield Test (GHT), and weighting by the correlation of rates at all other VF locations. Thresholds were regressed with a pointwise exponential regression (PER) model and a pointwise linear regression (PLR) model. Smaller root mean square error (RMSE) values of the differences between the observed and the predicted thresholds at last two follow-ups indicated better model predictions. The mean (SD) follow-up times for the smoothing and prediction phase were 5.3 (1.5) and 10.5 (3.9) years. The mean RMSE values for the PER and PLR models were unsmoothed data, 6.09 and 6.55; NN, 3.40 and 3.42; Garway-Heath, 3.47 and 3.48; GHT, 3.57 and 3.74; and correlation of rates, 3.59 and 3.64. Smoothed VF data predicted better than unsmoothed data. Nearest neighbor provided the best predictions; PER also predicted consistently more accurately than PLR. Smoothing algorithms should be used when forecasting VF results with PER or PLR. The application of smoothing algorithms on VF data can improve forecasting in VF points to assist in treatment decisions.
Field detection of CO and CH4 by NIR 2f modulation laser spectroscopy
Directory of Open Access Journals (Sweden)
A Khorsandi
2011-12-01
Full Text Available A novel compact fiber-coupled NIR system based on a DFB diode laser source is employed as a portable and sensitive gas sensor for trace detection of combustion pollutant molecules. We demonstrate the performance of such an NIR gas sensor by tracing the absorption lines of CO and CH4 using 2f-WMS technique at moderate temperature of T ~ 600°C in the recuperator channel of an industrial furnace provided by Mobarakeh steel company. This measurement shows the excellent sensitivity of the applied NIR gas sensor to the on-line and in-situ monitoring of such molecular species.
Fluorescence enhancing under UV-NIR simultaneous-excitation in ZnS:Cu,Mn phosphors
Directory of Open Access Journals (Sweden)
L. J. Xie
2012-12-01
Full Text Available The fluorescence properties of a long-lasting phosphor, ZnS:Cu,Mn was studied for the first time under simultaneously excitation of both UV and NIR light. Up to 20% fluorescence enhancement of the phosphor was observed. In the present simultaneously-excitation process, broad-band NIR light was absorbed and converted to visible photons via a single-photon upconversion path. We propose that a novel kind of spectral-conversion material with the unique ability to simultaneously convert both UV and NIR photons can be developed and is promising in the application of enhancing the EQE of solar cells.
The Development of Novel Near-Infrared (NIR Tetraarylazadipyrromethene Fluorescent Dyes
Directory of Open Access Journals (Sweden)
Young-Tae Chang
2013-05-01
Full Text Available Novel structures of an near-infrared (NIR tetraarylazadipyrromethene (aza-BODIPY series have been prepared. We designed the core structure containing two amido groups at the para-position of the aromatic rings. The amido group was incorporated to secure insensitivity to pH and to ensure a bathochromic shift to the NIR region. Forty members of aza-BODIPY compounds were synthesized by substitution of the acetyl group with commercial amines on the alpha bromide. The physicochemical properties and photostability were investigated and the fluorescence emission maxima (745~755 nm were found to be in the near infrared (NIR range of fluorescence.
Variable selection and model choice in geoadditive regression models.
Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard
2009-06-01
Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.
Directory of Open Access Journals (Sweden)
Anna Bourmistrova
2011-02-01
Full Text Available The autodriver algorithm is an intelligent method to eliminate the need of steering by a driver on a well-defined road. The proposed method performs best on a four-wheel steering (4WS vehicle, though it is also applicable to two-wheel-steering (TWS vehicles. The algorithm is based on coinciding the actual vehicle center of rotation and road center of curvature, by adjusting the kinematic center of rotation. The road center of curvature is assumed prior information for a given road, while the dynamic center of rotation is the output of dynamic equations of motion of the vehicle using steering angle and velocity measurements as inputs. We use kinematic condition of steering to set the steering angles in such a way that the kinematic center of rotation of the vehicle sits at a desired point. At low speeds the ideal and actual paths of the vehicle are very close. With increase of forward speed the road and tire characteristics, along with the motion dynamics of the vehicle cause the vehicle to turn about time-varying points. By adjusting the steering angles, our algorithm controls the dynamic turning center of the vehicle so that it coincides with the road curvature center, hence keeping the vehicle on a given road autonomously. The position and orientation errors are used as feedback signals in a closed loop control to adjust the steering angles. The application of the presented autodriver algorithm demonstrates reliable performance under different driving conditions.
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Logistic regression for dichotomized counts.
Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W
2016-12-01
Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.
Gonzalez Viejo, Claudia; Fuentes, Sigfredo; Torrico, Damir; Howell, Kate; Dunshea, Frank R
2018-01-01
Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds are usually time-consuming and costly. This study used rapid methods to evaluate 15 foam and colour-related parameters using a robotic pourer (RoboBEER) and chemical fingerprinting using near infrared spectroscopy (NIR) from six replicates of 21 beers from three types of fermentation. Results from NIR were used to create partial least squares regression (PLS) and artificial neural networks (ANN) models to predict four chemometrics such as pH, alcohol, Brix and maximum volume of foam. The ANN method was able to create more accurate models (R 2 = 0.95) compared to PLS. Principal components analysis using RoboBEER parameters and NIR overtones related to protein explained 67% of total data variability. Additionally, a sub-space discriminant model using the absorbance values from NIR wavelengths resulted in the successful classification of 85% of beers according to fermentation type. The method proposed showed to be a rapid system based on NIR spectroscopy and RoboBEER outputs of foamability that can be used to infer the quality, production method and chemical parameters of beer with minimal laboratory equipment. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.
Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression.
Zhen, Xiantong; Yu, Mengyang; Islam, Ali; Bhaduri, Mousumi; Chan, Ian; Li, Shuo
2017-09-01
Multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis. However, due to the huge image variability and ambiguity, it is fundamentally challenging to handle the highly complex input-target relationship of multioutput regression, especially with indiscriminate high-dimensional representations. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm for multioutput regression, which can establish discriminative and compact feature representations to improve the multivariate estimation performance. The SDL is formulated as generalized low-rank approximations of matrices with a supervised manifold regularization. The SDL is able to simultaneously extract discriminative features closely related to multivariate targets and remove irrelevant and redundant information by transforming raw features into a new low-dimensional space aligned to targets. The achieved discriminative while compact descriptor largely reduces the variability and ambiguity for multioutput regression, which enables more accurate and efficient multivariate estimation. We conduct extensive evaluation of the proposed SDL on both synthetic data and real-world multioutput regression tasks for both computer vision and medical image analysis. Experimental results have shown that the proposed SDL can achieve high multivariate estimation accuracy on all tasks and largely outperforms the algorithms in the state of the arts. Our method establishes a novel SDL framework for multioutput regression, which can be widely used to boost the performance in different applications.
Quantitative interpretations of Visible-NIR reflectance spectra of blood.
Serebrennikova, Yulia M; Smith, Jennifer M; Huffman, Debra E; Leparc, German F; García-Rubio, Luis H
2008-10-27
This paper illustrates the implementation of a new theoretical model for rapid quantitative analysis of the Vis-NIR diffuse reflectance spectra of blood cultures. This new model is based on the photon diffusion theory and Mie scattering theory that have been formulated to account for multiple scattering populations and absorptive components. This study stresses the significance of the thorough solution of the scattering and absorption problem in order to accurately resolve for optically relevant parameters of blood culture components. With advantages of being calibration-free and computationally fast, the new model has two basic requirements. First, wavelength-dependent refractive indices of the basic chemical constituents of blood culture components are needed. Second, multi-wavelength measurements or at least the measurements of characteristic wavelengths equal to the degrees of freedom, i.e. number of optically relevant parameters, of blood culture system are required. The blood culture analysis model was tested with a large number of diffuse reflectance spectra of blood culture samples characterized by an extensive range of the relevant parameters.
Learning word order at birth: A NIRS study
Directory of Open Access Journals (Sweden)
Silvia Benavides-Varela
2017-06-01
Full Text Available In language, the relative order of words in sentences carries important grammatical functions. However, the developmental origins and the neural correlates of the ability to track word order are to date poorly understood. The current study therefore investigates the origins of infants’ ability to learn about the sequential order of words, using near-infrared spectroscopy (NIRS with newborn infants. We have conducted two experiments: one in which a word order change was implemented in 4-word sequences recorded with a list intonation (as if each word was a separate item in a list; list prosody condition, Experiment 1 and one in which the same 4-word sequences were recorded with a well-formed utterance-level prosodic contour (utterance prosody condition, Experiment 2. We found that newborns could detect the violation of the word order in the list prosody condition, but not in the utterance prosody condition. These results suggest that while newborns are already sensitive to word order in linguistic sequences, prosody appears to be a stronger cue than word order for the identification of linguistic units at birth.
2003-2004 ACADEMIC TRAINING PROGRAMME: Y. NIR
Françoise Benz
2004-01-01
ACADEMIC TRAINING Françoise Benz tel. 73127 academic.training@cern.ch 22, 23, 24, 25 and 26 March LECTURE SERIES From 11:00 to 12:00 hrs Main Auditorium bldg. 500 on 22, 24, 25 and 26 March TH Auditorium bldg 4 on 23 March Neutrinos Y. NIR, Weizmann Institute of Science, Rehovot, Israel The Standard Model predicts that the neutrinos are massless and do not mix. Generic extensions of the Standard Model predict that neutrinos are massive (but, very likely, much lighter than the charged fermions). Therefore, the search for neutrino masses and mixing tests the Standard Model and probes new physics. Measurements of various features of the fluxes of atmospheric, solar and, more recently, reactor neutrinos have provided evidence for neutrino oscillations and therefore for neutrino masses and mixing. These results have significant theoretical implications: new physics exists, and its scale can be estimated. There are interesting lessons for grand unified theories and for models of extra dimensions. T...
NIRS report concerning the cyclotron usages. FY 2000
International Nuclear Information System (INIS)
2001-07-01
This report describes the National Institute of Radiological Sciences (NIRS) cyclotron usages concerning of: Operation and improvement-development of the cyclotrons in 2000 (total 1,302 hr operation, development of the insulating septum, renewal of the magnetic channel and radiofrequency (RF) pre-amplifier, and improvement of the magnetic interference); Development of neutron detectors in the cosmic environment (the phoswich detector, low-pressure proportional counter, bubble dosimeter and new type phoswich detector); Development of detectors of charged particle components in the cosmic radiation (Liulin-4J and position sensitive silicon detector); Measurement of the energy and angular distribution of secondary electrons from water vapor with heavy-ion impact; Phase II clinical trials of proton beam therapy for ophthalmological tumors (34 patients in 1996-2000 and survival rate 100% within 3 years); Application of cyclotrons for RI production (mainly, 11 C, 13 N, 15 O and 18 F for basic and clinical PET); Studies on the spread-out peak of proton beam (for radiotherapy); and Related materials to above (details). (N.I.)
Learning word order at birth: A NIRS study.
Benavides-Varela, Silvia; Gervain, Judit
2017-06-01
In language, the relative order of words in sentences carries important grammatical functions. However, the developmental origins and the neural correlates of the ability to track word order are to date poorly understood. The current study therefore investigates the origins of infants' ability to learn about the sequential order of words, using near-infrared spectroscopy (NIRS) with newborn infants. We have conducted two experiments: one in which a word order change was implemented in 4-word sequences recorded with a list intonation (as if each word was a separate item in a list; list prosody condition, Experiment 1) and one in which the same 4-word sequences were recorded with a well-formed utterance-level prosodic contour (utterance prosody condition, Experiment 2). We found that newborns could detect the violation of the word order in the list prosody condition, but not in the utterance prosody condition. These results suggest that while newborns are already sensitive to word order in linguistic sequences, prosody appears to be a stronger cue than word order for the identification of linguistic units at birth. Copyright © 2017. Published by Elsevier Ltd.
New NIR Absorbing DPP-based Polymer for Thick Organic Solar Cells
Oklem, Gulce; Song, Xin; Toppare, Levent; Baran, Derya; Gunbas, Gorkem
2018-01-01
infrared region (NIR) for better photon harvesting in organic solar cells. It has been shown that copolymers compromising diketopyrrolopyrrole based acceptors and simple donors (thiophene or furan) achieve absorption maximum around 800 nm
Steen, Gerrit W.; Wexler, Adam D.; Offerhaus, Herman L.
2014-01-01
Design and optimization of integrated photonic NIR absorbance based sensors for identification and quantification of aqueous electrolytes was performed by simulation in MATLAB and Optodesigner. Ten designs are presented and compared for suitability.
Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research
Kopton, Isabella M.; Kenning, Peter
2014-01-01
Over the last decade, the application of neuroscience to economic research has gained in importance and the number of neuroeconomic studies has grown extensively. The most common method for these investigations is fMRI. However, fMRI has limitations (particularly concerning situational factors) that should be countered with other methods. This review elaborates on the use of functional Near-Infrared Spectroscopy (fNIRS) as a new and promising tool for investigating economic decision making both in field experiments and outside the laboratory. We describe results of studies investigating the reliability of prototype NIRS studies, as well as detailing experiments using conventional and stationary fNIRS devices to analyze this potential. This review article shows that further research using mobile fNIRS for studies on economic decision making outside the laboratory could be a fruitful avenue helping to develop the potential of a new method for field experiments outside the laboratory. PMID:25147517
Near Infrared Spectroscopy (NIRS) for the determination of the milk fat fatty acid profile of goats.
Núñez-Sánchez, N; Martínez-Marín, A L; Polvillo, O; Fernández-Cabanás, V M; Carrizosa, J; Urrutia, B; Serradilla, J M
2016-01-01
Milk fatty acid (FA) composition is important for the goat dairy industry because of its influence on cheese properties and human health. The aim of the present work was to evaluate the feasibility of NIRS reflectance (oven-dried milk using the DESIR method) and transflectance (liquid milk) analysis to predict milk FA profile and groups of fats in milk samples from individual goats. NIRS analysis of milk samples allowed to estimate FA contents and their ratios and indexes in fat with high precision and accuracy. In general, transflectance analysis gave better or similar results than reflectance mode. Interestingly, NIRS analysis allowed direct prediction of the Atherogenicity and Thrombogenicity indexes, which are useful for the interpretation of the nutritional value of goat milk. Therefore, the calibrations obtained in the present work confirm the viability of NIRS as a fast, reliable and effective analytical method to provide nutritional information of milk samples. Copyright © 2015 Elsevier Ltd. All rights reserved.
Rapid analysis of hay attributes using NIRS. Final report, Task II alfalfa supply system
Energy Technology Data Exchange (ETDEWEB)
NONE
1997-10-24
This final report provides technical information on the development of a near infrared reflectance spectroscopy (NIRS) system for the analysis of alfalfa hay. The purpose of the system is to provide consistent quality for processing alfalfa stems for fuel and alfalfa leaf meal products for livestock feed. Project tasks were to: (1) develop an NIRS driven analytical system for analysis of alfalfa hay and processed alfalfa products; (2) assist in hiring a qualified NIRS technician and recommend changes in testing equipment necessary to provide accurate analysis; (3) calibrate the NIRS instrument for accurate analyses; and (4) develop prototype equipment and sampling procedures as a first step towards development of a totally automated sampling system that would rapidly sample and record incoming feedstock and outbound product. An accurate hay testing program was developed, along with calibration equations for analyzing alfalfa hay and sun-cured alfalfa pellets. A preliminary leaf steam calibration protocol was also developed. 7 refs., 11 figs., 10 tabs.
Interference Tolerant Functional Near Infrared Spectrometer (fNIRS) for Cognitive State Monitoring
National Aeronautics and Space Administration — Measuring hemoglobin concentration changes in the brain with Functional Near Infrared Spectroscopy (fNIRS) is a promising technique for monitoring cognitive state...
Acrylamide inverse miniemulsion polymerization: in situ, real-time monitoring using nir spectroscopy
Directory of Open Access Journals (Sweden)
M. M. E. Colmán
2014-12-01
Full Text Available In this work, the ability of on-line NIR spectroscopy for the prediction of the evolution of monomer concentration, conversion and average particle diameter in acrylamide inverse miniemulsion polymerization was evaluated. The spectral ranges were chosen as those representing the decrease in concentration of monomer. An increase in the baseline shift indicated that the NIR spectra were affected by particle size. Multivariate partial least squares calibration models were developed to relate NIR spectra collected by the immersion probe with off-line conversion and polymer particle size data. The results showed good agreement between off-line data and values predicted by the NIR calibration models and these latter were also able to detect different types of operational disturbances. These results indicate that it is possible to monitor variables of interest during acrylamide inverse miniemulsion polymerizations.
PAT: From Western solid dosage forms to Chinese materia medica preparations using NIR-CI.
Zhou, Luwei; Xu, Manfei; Wu, Zhisheng; Shi, Xinyuan; Qiao, Yanjiang
2016-01-01
Near-infrared chemical imaging (NIR-CI) is an emerging technology that combines traditional near-infrared spectroscopy with chemical imaging. Therefore, NIR-CI can extract spectral information from pharmaceutical products and simultaneously visualize the spatial distribution of chemical components. The rapid and non-destructive features of NIR-CI make it an attractive process analytical technology (PAT) for identifying and monitoring critical control parameters during the pharmaceutical manufacturing process. This review mainly focuses on the pharmaceutical applications of NIR-CI in each unit operation during the manufacturing processes, from the Western solid dosage forms to the Chinese materia medica preparations. Finally, future applications of chemical imaging in the pharmaceutical industry are discussed. Copyright © 2015 John Wiley & Sons, Ltd.
[Real-time detection of quality of Chinese materia medica: strategy of NIR model evaluation].
Wu, Zhi-sheng; Shi, Xin-yuan; Xu, Bing; Dai, Xing-xing; Qiao, Yan-jiang
2015-07-01
The definition of critical quality attributes of Chinese materia medica ( CMM) was put forward based on the top-level design concept. Nowadays, coupled with the development of rapid analytical science, rapid assessment of critical quality attributes of CMM was firstly carried out, which was the secondary discipline branch of CMM. Taking near infrared (NIR) spectroscopy as an example, which is a rapid analytical technology in pharmaceutical process over the past decade, systematic review is the chemometric parameters in NIR model evaluation. According to the characteristics of complexity of CMM and trace components analysis, a multi-source information fusion strategy of NIR model was developed for assessment of critical quality attributes of CMM. The strategy has provided guideline for NIR reliable analysis in critical quality attributes of CMM.
Novel self-assembled sandwich nanomedicine for NIR-responsive release of NO
Fan, Jing; He, Qianjun; Liu, Yi; Ma, Ying; Fu, Xiao; Liu, Yijing; Huang, Peng; He, Nongyue; Chen, Xiaoyuan
2015-01-01
A novel sandwich nanomedicine (GO-BNN6) for near-infrared (NIR) light responsive release of nitric oxide (NO) has been constructed by self-assembling of graphene oxide (GO) nanosheets and a NO donor BNN6 through the π-π stacking interaction. GO-BNN6 nanomedicine has an extraordinarily high drug loading capacity (1.2 mg BNN6 per mg GO), good thermal stability, and high NIR responsiveness. The NO release from GO-BNN6 can be easily triggered and effectively controlled by adjusting the switching, irradiation time and power density of NIR laser. The intracellular NIR-responsive release of NO from GO-BNN6 nanomedicine causes a remarkable anti-cancer effect. PMID:26568270
DEFF Research Database (Denmark)
Sood, Mehak; Jindal, Utkarsh; Chowdhury, Shubhajit Roy
2015-01-01
that are also affected by tDCS. An approach may be to use short optode separations to measure systemic hemodynamic fluctuations occurring in the superficial layers which can then be used as regressors to remove the systemic contamination. Here, we demonstrate that temporal artery tap may be used to better...... of neural activity is possible with a measure of cerebral hemoglobin oxygenation using near-infrared spectroscopy (NIRS). In principal accordance, NIRS can capture the hemodynamic response to tDCS but the challenge remains in removing the systemic interference occurring in the superficial layers of the head...... identify systemic interference using this short-separation NIRS. Moreover, NIRS-EEG joint-imaging during anodal tDCS was used to measure changes in mean cerebral haemoglobin oxygen saturation (rSO2) along with changes in the log-transformed mean-power of EEG within 0.5 Hz-11.25 Hz. We found that percent...
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...
Energy-Driven Image Interpolation Using Gaussian Process Regression
Directory of Open Access Journals (Sweden)
Lingling Zi
2012-01-01
Full Text Available Image interpolation, as a method of obtaining a high-resolution image from the corresponding low-resolution image, is a classical problem in image processing. In this paper, we propose a novel energy-driven interpolation algorithm employing Gaussian process regression. In our algorithm, each interpolated pixel is predicted by a combination of two information sources: first is a statistical model adopted to mine underlying information, and second is an energy computation technique used to acquire information on pixel properties. We further demonstrate that our algorithm can not only achieve image interpolation, but also reduce noise in the original image. Our experiments show that the proposed algorithm can achieve encouraging performance in terms of image visualization and quantitative measures.
Extensions of Morse-Smale Regression with Application to Actuarial Science
Farrelly, Colleen M.
2017-01-01
The problem of subgroups is ubiquitous in scientific research (ex. disease heterogeneity, spatial distributions in ecology...), and piecewise regression is one way to deal with this phenomenon. Morse-Smale regression offers a way to partition the regression function based on level sets of a defined function and that function's basins of attraction. This topologically-based piecewise regression algorithm has shown promise in its initial applications, but the current implementation in the liter...
[Induction and analysis for NIR features of frequently-used mineral traditional Chinese medicines].
Chen, Long; Yuan, Ming-Yang; Chen, Ke-Li
2016-10-01
In order to provide theoretical basis for the rapid identification of mineral traditional Chinese medicines(TCM) with near infrared (NIR)diffuse reflectance spectroscopy, Characteristic NIR spectra of 51 kinds of mineral TCMs were generalized and compared on the basis of the previous research, and the characteristic spectral bands were determined and analyzed by referring to mineralogical and geological literatures. It turned out that the NIR features of mineral TCMs were mainly at 8 000-4 000 cm ⁻¹ wavebands, which can be assigned as the absorption of water, -OH and[CO3 ²⁻] and so on. Absorption peaks of water has regularity as follows, the structure water and -OH had a combined peak which was strong and keen-edged around 7 000 cm ⁻¹, the crystal water had two strong peak around 7 000 cm ⁻¹ and 5 100 cm ⁻¹, and water only has a broad peak around 5 100 cm ⁻¹. Due to the differences in the crystal form and the contents of water in mineral TCMs, NIR features of water in mineral TCMs which could be used for identification were different. Mineral TCMs containing sulfate are rich in crystal water, mineral TCMs containing silicate generally had structure water, and mineral TCMs containing carbonate merely had a little of water, so it was reasonable for the use of NIR spectroscopy to classify mineral TCMs with anionic type. In addition, because of the differences in cationic type, impurities, crystal form and crystallinity, mineral TCMs have exclusive NIR features at 4 600-4 000 cm ⁻¹, which can be assigned as Al-OH, Mg-OH, Fe-OH, Si-OH,[CO3 ²⁻] and so on. Calcined mineral TCMs are often associated with water and main composition changes, also changes of the NIR features, which could be used for the monitoring of the processing, and to provide references for the quality control of mineral TCMs. The adaptability and limitation of NIR analysis for mineral TCMs were also discussed:the majority of mineral TCMs had noteworthy NIR features which could be
Energy Technology Data Exchange (ETDEWEB)
Hernandez R, A.; Plascencia C, L. E.; Cordova F, T.; Padilla R, N., E-mail: angelicahr@fisica.ugto.mx [Universidad de Guanajuato, 37150 Leon, Guanajuato (Mexico)
2017-10-15
In addition to the multiple applications of ionizing radiation in clinical diagnosis there is the possibility of using another part of the electromagnetic spectrum such as near infrared (Nir). This paper presents the design and construction of a Nir Biosensor in a range between 800 and 900 nm, which allows the visualization of blood vessels for the venepuncture procedure with the aim of reducing the trauma of venous access to patients of all ages. The possibility that the device is used in the location of venous ulcers as an alternative to veno grams obtained by X-rays is also explored. (Author)
DEFF Research Database (Denmark)
Markham, Annette
This paper takes an actor network theory approach to explore some of the ways that algorithms co-construct identity and relational meaning in contemporary use of social media. Based on intensive interviews with participants as well as activity logging and data tracking, the author presents a richly...... layered set of accounts to help build our understanding of how individuals relate to their devices, search systems, and social network sites. This work extends critical analyses of the power of algorithms in implicating the social self by offering narrative accounts from multiple perspectives. It also...... contributes an innovative method for blending actor network theory with symbolic interaction to grapple with the complexity of everyday sensemaking practices within networked global information flows....
A Matlab program for stepwise regression
Directory of Open Access Journals (Sweden)
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Correlation and simple linear regression.
Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G
2003-06-01
In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.
Regression filter for signal resolution
International Nuclear Information System (INIS)
Matthes, W.
1975-01-01
The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)
Hyperspectral Unmixing with Robust Collaborative Sparse Regression
Directory of Open Access Journals (Sweden)
Chang Li
2016-07-01
Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.
Combination of supervised and semi-supervised regression models for improved unbiased estimation
DEFF Research Database (Denmark)
Arenas-Garía, Jeronimo; Moriana-Varo, Carlos; Larsen, Jan
2010-01-01
In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised and semisupervi......In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised...
Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR for Load Forecasting
Directory of Open Access Journals (Sweden)
Cheng-Wen Lee
2016-10-01
Full Text Available Hybridizing chaotic evolutionary algorithms with support vector regression (SVR to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.
A new network of faint calibration stars from the near infrared spectrometer (NIRS) on the IRTS
Freund, Minoru M.; Matsuura, Mikako; Murakami, Hiroshi; Cohen, Martin; Noda, Manabu; Matsuura, Shuji; Matsumoto, Toshio
1997-01-01
The point source extraction and calibration of the near infrared spectrometer (NIRS) onboard the Infrared Telescope in Space (IRTS) is described. About 7 percent of the sky was observed during a one month mission in the range of 1.4 micrometers to 4 micrometers. The accuracy of the spectral shape and absolute values of calibration stars provided by the NIRS/IRTS were validated.
Energy Technology Data Exchange (ETDEWEB)
De la Roza-Delgado, B.; Modroño, S.; Vicente, F.; Martínez-Fernández, A.; Soldado, A.
2015-07-01
A total of 220 faecal pig and poultry samples, collected from different experimental trials were employed with the aim to demonstrate the suitability of Near Infrared Reflectance Spectroscopy (NIRS) technology for estimation of gross calorific value on faeces as output products in energy balances studies. NIR spectra from dried and grounded faeces samples were analyzed using a Foss NIRSystem 6500 instrument, scanning over the wavelength range 400-2500 nm. Validation studies for quantitative analytical models were carried out to estimate the relevance of method performance associated to reference values to obtain an appropriate, accuracy and precision. The results for prediction of gross calorific value (GCV) of NIRS calibrations obtained for individual species showed high correlation coefficients comparing chemical analysis and NIRS predictions, ranged from 0.92 to 0.97 for poultry and pig. For external validation, the ratio between the standard error of cross validation (SECV) and the standard error of prediction (SEP) varied between 0.73 and 0.86 for poultry and pig respectively, indicating a sufficiently precision of calibrations. In addition a global model to estimate GCV in both species was developed and externally validated. It showed correlation coefficients of 0.99 for calibration, 0.98 for cross-validation and 0.97 for external validation. Finally, relative uncertainty was calculated for NIRS developed prediction models with the final value when applying individual NIRS species model of 1.3% and 1.5% for NIRS global prediction. This study suggests that NIRS is a suitable and accurate method for the determination of GCV in faeces, decreasing cost, timeless and for convenient handling of unpleasant samples.. (Author)
Association of Concurrent fNIRS and EEG Signatures in Response to Auditory and Visual Stimuli.
Chen, Ling-Chia; Sandmann, Pascale; Thorne, Jeremy D; Herrmann, Christoph S; Debener, Stefan
2015-09-01
Functional near-infrared spectroscopy (fNIRS) has been proven reliable for investigation of low-level visual processing in both infants and adults. Similar investigation of fundamental auditory processes with fNIRS, however, remains only partially complete. Here we employed a systematic three-level validation approach to investigate whether fNIRS could capture fundamental aspects of bottom-up acoustic processing. We performed a simultaneous fNIRS-EEG experiment with visual and auditory stimulation in 24 participants, which allowed the relationship between changes in neural activity and hemoglobin concentrations to be studied. In the first level, the fNIRS results showed a clear distinction between visual and auditory sensory modalities. Specifically, the results demonstrated area specificity, that is, maximal fNIRS responses in visual and auditory areas for the visual and auditory stimuli respectively, and stimulus selectivity, whereby the visual and auditory areas responded mainly toward their respective stimuli. In the second level, a stimulus-dependent modulation of the fNIRS signal was observed in the visual area, as well as a loudness modulation in the auditory area. Finally in the last level, we observed significant correlations between simultaneously-recorded visual evoked potentials and deoxygenated hemoglobin (DeoxyHb) concentration, and between late auditory evoked potentials and oxygenated hemoglobin (OxyHb) concentration. In sum, these results suggest good sensitivity of fNIRS to low-level sensory processing in both the visual and the auditory domain, and provide further evidence of the neurovascular coupling between hemoglobin concentration changes and non-invasive brain electrical activity.
NIR techniques create added values for the pellet and biofuel industry.
Lestander, Torbjörn A; Johnsson, Bo; Grothage, Morgan
2009-02-01
A 2(3)-factorial experiment was carried out in an industrial plant producing biofuel pellets with sawdust as feedstock. The aim was to use on-line near infrared (NIR) spectra from sawdust for real time predictions of moisture content, blends of sawdust and energy consumption of the pellet press. The factors varied were: drying temperature and wood powder dryness in binary blends of sawdust from Norway spruce and Scots pine. The main results were excellent NIR calibration models for on-line prediction of moisture content and binary blends of sawdust from the two species, but also for the novel finding that the consumption of electrical energy per unit pelletized biomass can be predicted by NIR reflectance spectra from sawdust entering the pellet press. This power consumption model, explaining 91.0% of the variation, indicated that NIR data contained information of the compression and friction properties of the biomass feedstock. The moisture content model was validated using a running NIR calibration model in the pellet plant. It is shown that the adjusted prediction error was 0.41% moisture content for grinded sawdust dried to ca. 6-12% moisture content. Further, although used drying temperatures influenced NIR spectra the models for drying temperature resulted in low prediction accuracy. The results show that on-line NIR can be used as an important tool in the monitoring and control of the pelletizing process and that the use of NIR technique in fuel pellet production has possibilities to better meet customer specifications, and therefore create added production values.
Measurement of soluble solids content in watermelon by Vis/NIR diffuse transmittance technique*
Tian, Hai-qing; Ying, Yi-bin; Lu, Hui-shan; Fu, Xia-ping; Yu, Hai-yan
2007-01-01
Watermelon is a popular fruit in the world with soluble solids content (SSC) being one of the major characteristics used for assessing its quality. This study was aimed at obtaining a method for nondestructive SSC detection of watermelons by means of visible/near infrared (Vis/NIR) diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer operating over the range 350~1000 nm. Spectra data were analyz...
[Identification of varieties of cashmere by Vis/NIR spectroscopy technology based on PCA-SVM].
Wu, Gui-Fang; He, Yong
2009-06-01
One mixed algorithm was presented to discriminate cashmere varieties with principal component analysis (PCA) and support vector machine (SVM). Cashmere fiber has such characteristics as threadlike, softness, glossiness and high tensile strength. The quality characters and economic value of each breed of cashmere are very different. In order to safeguard the consumer's rights and guarantee the quality of cashmere product, quickly, efficiently and correctly identifying cashmere has significant meaning to the production and transaction of cashmere material. The present research adopts Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere. The near infrared fingerprint of cashmere was acquired by principal component analysis (PCA), and support vector machine (SVM) methods were used to further identify the cashmere material. The result of PCA indicated that the score map made by the scores of PC1, PC2 and PC3 was used, and 10 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99.99%. One hundred cashmere samples were used for calibration and the remaining 75 cashmere samples were used for validation. A one-against-all multi-class SVM model was built, the capabilities of SVM with different kernel function were comparatively analyzed, and the result showed that SVM possessing with the Gaussian kernel function has the best identification capabilities with the accuracy of 100%. This research indicated that the data mining method of PCA-SVM has a good identification effect, and can work as a new method for rapid identification of cashmere material varieties.
Moderation analysis using a two-level regression model.
Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott
2014-10-01
Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.
de Oliveira, Rodrigo Rocha; de Lima, Kássio Michell Gomes; Tauler, Romà; de Juan, Anna
2014-07-01
This study describes two applications of a variant of the multivariate curve resolution alternating least squares (MCR-ALS) method with a correlation constraint. The first application describes the use of MCR-ALS for the determination of biodiesel concentrations in biodiesel blends using near infrared (NIR) spectroscopic data. In the second application, the proposed method allowed the determination of the synthetic antioxidant N,N'-Di-sec-butyl-p-phenylenediamine (PDA) present in biodiesel mixtures from different vegetable sources using UV-visible spectroscopy. Well established multivariate regression algorithm, partial least squares (PLS), were calculated for comparison of the quantification performance in the models developed in both applications. The correlation constraint has been adapted to handle the presence of batch-to-batch matrix effects due to ageing effects, which might occur when different groups of samples were used to build a calibration model in the first application. Different data set configurations and diverse modes of application of the correlation constraint are explored and guidelines are given to cope with different type of analytical problems, such as the correction of matrix effects among biodiesel samples, where MCR-ALS outperformed PLS reducing the relative error of prediction RE (%) from 9.82% to 4.85% in the first application, or the determination of minor compound with overlapped weak spectroscopic signals, where MCR-ALS gave higher (RE (%)=3.16%) for prediction of PDA compared to PLS (RE (%)=1.99%), but with the advantage of recovering the related pure spectral profile of analytes and interferences. The obtained results show the potential of the MCR-ALS method with correlation constraint to be adapted to diverse data set configurations and analytical problems related to the determination of biodiesel mixtures and added compounds therein. Copyright © 2014 Elsevier B.V. All rights reserved.
Laplacian embedded regression for scalable manifold regularization.
Chen, Lin; Tsang, Ivor W; Xu, Dong
2012-06-01
Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical foundations for a large family of SSL algorithms, such as Laplacian support vector machine (LapSVM) and Laplacian regularized least squares (LapRLS). However, most of these algorithms are limited to small scale problems due to the high computational cost of the matrix inversion operation involved in the optimization problem. In this paper, we propose a novel framework called Laplacian embedded regression by introducing an intermediate decision variable into the manifold regularization framework. By using ∈-insensitive loss, we obtain the Laplacian embedded support vector regression (LapESVR) algorithm, which inherits the sparse solution from SVR. Also, we derive Laplacian embedded RLS (LapERLS) corresponding to RLS under the proposed framework. Both LapESVR and LapERLS possess a simpler form of a transformed kernel, which is the summation of the original kernel and a graph kernel that captures the manifold structure. The benefits of the transformed kernel are two-fold: (1) we can deal with the original kernel matrix and the graph Laplacian matrix in the graph kernel separately and (2) if the graph Laplacian matrix is sparse, we only need to perform the inverse operation for a sparse matrix, which is much more efficient when compared with that for a dense one. Inspired by kernel principal component analysis, we further propose to project the introduced decision variable into a subspace spanned by a few eigenvectors of the graph Laplacian matrix in order to better reflect the data manifold, as well as accelerate the calculation of the graph kernel, allowing our methods to efficiently and effectively cope with large scale SSL problems. Extensive experiments on both toy and real
Liu, Chang-hui; Qi, Feng-pei; Wen, Fu-bin; Long, Li-ping; Liu, Ai-juan; Yang, Rong-hua
2018-04-01
Cyanine has been widely utilized as a near infrared (NIR) fluorophore for detection of glutathione (GSH). However, the excitation of most of the reported cyanine-based probes was less than 800 nm, which inevitably induce biological background absorption and lower the sensitivity, limiting their use for detection of GSH in blood samples. To address this issue, here, a heptamethine cyanine probe (DNIR), with a NIR excitation wavelength at 804 nm and a NIR emission wavelength at 832 nm, is employed for the detection of GSH and its oxidized form (GSSG) in blood. The probe displays excellent selectivity for GSH over GSSG and other amino acids, and rapid response to GSH, in particular a good property for indirect detection of GSSG in the presence of enzyme glutathione reductase and the reducing agent nicotinamideadenine dinucleotide phosphate, without further separation prior to fluorescent measurement. To the best of our knowledge, this is the first attempt to explore NIR fluorescent approach for the simultaneous assay of GSH and GSSG in blood. As such, we expect that our fluorescence sensors with both NIR excitation and NIR emission make this strategy suitable for the application in complex physiological systems.
Heterologous expression of the Aspergillus nidulans regulatory gene nirA in Fusarium oxysporum.
Daboussi, M J; Langin, T; Deschamps, F; Brygoo, Y; Scazzocchio, C; Burger, G
1991-12-20
We have isolated strains of Fusarium oxysporum carrying mutations conferring a phenotype characteristic of a loss of function in the regulatory gene of nitrate assimilation (nirA in Aspergillus nidulans, nit-4 in Neurospora crassa). One of these nir- mutants was successfully transformed with a plasmid containing the nirA gene of A. nidulans. The nitrate reductase of the transformants is still inducible, although the maximum activity is lower than in the wild type. Single and multiple integration events were found, as well as a strict correlation between the presence of the nirA gene and the Nir+ phenotype of the F. oxysporum transformants. We also investigated how the A. nidulans structural gene (niaD) is regulated in F. oxysporum. Enzyme assays and Northern experiments show that the niaD gene is subject to nitrate induction and that it responds to nitrogen metabolite repression in a F. oxysporum genetic background. This indicates that both the mechanisms of specific induction, mediated by a gene product isofunctional to nirA, and nitrogen metabolite repression, presumably mediated by a gene product isofunctional to the homologous gene of A. nidulans, are operative in F. oxysporum.
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Regression Models for Repairable Systems
Czech Academy of Sciences Publication Activity Database
Novák, Petr
2015-01-01
Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf
Survival analysis II: Cox regression
Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.
2011-01-01
In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the
Kernel regression with functional response
Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe
2011-01-01
We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.
Directory of Open Access Journals (Sweden)
Garrido, A.
2003-03-01
Full Text Available The objective of this study was to evaluate the potential use of Near-Infrared Spectroscopy (NIRS for the analysis of oil content, moisture and fatty acids composition in intact olive fruit. A total of 287 samples, each from a single plant from an olive breeding program, were scanned by NIRS between 400 and 1700 nm. Partial least squares (PLS regression was used to create calibration models (with 70 % of samples relating laboratory reference values to spectral data (original, first and second derivative spectral data. The best equations obtained were validated (with 30% of samples showing values of r2 of 0.88 % for the moisture, 0.83 % for oil content, 0.77 % for oleic acid content and 0.81 % for linoleic acid content. Therefore a reliable and accurate preselection can be made by using NIRS for both oil content and oleic acid content, with a nondestructive analysis, in a few seconds and without use neither production of chemical reagents.El objetivo de este trabajo es evaluar el potencial de la tecnología NIRS para el análisis del contenido de aceite, humedad y composición de ácidos grasos en aceituna intacta. A un total de 287 muestras de aceituna, cada una de una planta procedente de un programa de mejora de olivo, se les determinó sus datos espectroscópicos mediante reflectancia (400-1700 nm. A partir de los datos espectroscópicos originales, primera y segunda derivadas se obtuvieron diferentes ecuaciones de calibración (con el 70 % de las muestras mediante regresión por mínimos cuadrados parciales (PLS establecidas entre los datos espectroscópicos y los datos de laboratorio de referencia. Las mejores ecuaciones obtenidas fueron validadas (con el 30 % de las muestras mostrando valores de r2 de 0.88 % para la humedad, 0.83 % para contenido graso, 0.77 % para contenido de ácido oleico y 0.81% para contenido de ácido linoleico. Por tanto, la tecnología NIRS puede ser de utilidad para preseleccionar genotipos por su contenido de
PLS-NIR determination of five parameters in different types of Chinese rice wine
Yu, Haiyan; Ying, Yibin; Fu, Xiaping; Lu, Huishan
2005-11-01
To evaluate the applicability of near infrared spectroscopy for determination of the five enological parameters (alcoholic degree, pH value, total acid and amino acid nitrogen, °Brix) of Chinese rice wine, transmission spectra were collected in the spectral range from 12500 to 3800 cm-1 in a 1 mm path length rectangular quartz cuvette with air as reference at room temperature. Five calibration equations for the five parameters were established between the reference data and spectra by partial least squares (PLS) regression, separately. The best calibration results were achieved for the determination of alcoholic degree and °Brix. The RPD (ration of the standard deviation of the samples to the SECV) values of the calibration for both alcoholic degree and °Brix were higher than 3 (4.30 and 7.94, respectively), which demonstrated the robustness and power of the calibration models. The determination coefficients (R2) for alcoholic degree and °Brix were 0.987 and 0.991, respectively. The performance of pH, total acid and amino acid nitrogen was not as good as that of alcoholic degree and °Brix. The RPD values for the three parameters were 1.48, 1.85 and 1.82, respectively, and R2 values were 0.964, 0.970 and 0.971, respectively. In validation step, R2 value of the five parameters are all higher than 0.7, especially for alcoholic degree and °Brix (0.968 and 0.956, respectively). The results demonstrated that NIR spectroscopy could be used to predict the concentration of the five enological parameters in Chinese rice wine.
Directory of Open Access Journals (Sweden)
Shuo Li
2015-05-01
Full Text Available To meet growing food demand with limited land and reduced environmental impact, soil testing and formulated fertilization methods have been widely adopted around the world. However, conventional technology for investigating nitrogen fertilization rates (NFR is time consuming and expensive. Here, we evaluated the use of visible near-infrared shortwave-infrared (VIS-NIR-SWIR: 400–2500 nm spectroscopy for the assessment of NFR to provide necessary information for fast, cost-effective and precise fertilization rating. Over 2000 samples were collected from paddy-rice fields in 10 Chinese provinces; samples were added to the Chinese Soil Spectral Library (CSSL. Two kinds of modeling strategies for NFR, quantitative estimation of soil N prior to classification and qualitative by classification, were employed using partial least squares regression (PLSR, locally weighted regression (LWR, and support vector machine discriminant analogy (SVMDA. Overall, both LWR and SVMDA had moderate accuracies with Cohen’s kappa coefficients of 0.47 and 0.48, respectively, while PLSR had fair accuracy (0.37. We conclude that VIS-NIR-SWIR spectroscopy coupled with the CSSL appears to be a viable, rapid means for the assessment of NFR in paddy-rice soil. Based on qualitative classification of soil spectral data only, it is recommended that the SVMDA be adopted for rapid implementation.
Adaptive kernel regression for freehand 3D ultrasound reconstruction
Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen
2017-03-01
Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.
Directory of Open Access Journals (Sweden)
I. V. Geogdzhayev
2018-01-01
Full Text Available The unique position of the Deep Space Climate Observatory (DSCOVR Earth Polychromatic Imaging Camera (EPIC at the Lagrange 1 point makes an important addition to the data from currently operating low Earth orbit observing instruments. EPIC instrument does not have an onboard calibration facility. One approach to its calibration is to compare EPIC observations to the measurements from polar-orbiting radiometers. Moderate Resolution Imaging Spectroradiometer (MODIS is a natural choice for such comparison due to its well-established calibration record and wide use in remote sensing. We use MODIS Aqua and Terra L1B 1 km reflectances to infer calibration coefficients for four EPIC visible and NIR channels: 443, 551, 680 and 780 nm. MODIS and EPIC measurements made between June 2015 and 2016 are employed for comparison. We first identify favorable MODIS pixels with scattering angle matching temporarily collocated EPIC observations. Each EPIC pixel is then spatially collocated to a subset of the favorable MODIS pixels within 25 km radius. Standard deviation of the selected MODIS pixels as well as of the adjacent EPIC pixels is used to find the most homogeneous scenes. These scenes are then used to determine calibration coefficients using a linear regression between EPIC counts s−1 and reflectances in the close MODIS spectral channels. We present thus inferred EPIC calibration coefficients and discuss sources of uncertainties. The lunar EPIC observations are used to calibrate EPIC O2 absorbing channels (688 and 764 nm, assuming that there is a small difference between moon reflectances separated by ∼ 10 nm in wavelength and provided the calibration factors of the red (680 nm and NIR (780 nm are known from comparison between EPIC and MODIS.
[Determination of Chloride Salt Solution by NIR Spectroscopy].
Zhang, Bin; Chen, Jian-hong; Jiao, Ming-xing
2015-07-01
Determination of chloride salt solution by near infrared spectrum plays a very important role in Biomedicine. The near infrared spectrum analysis of Sodium chloride, potassium chloride, calcium chloride aqueous solution shows that the concentration change of chloride salt can affect hydrogen bond, resulting in the variation of near infrared spectrum of water. The temperature influence on NIR spectrum has been decreased by choosing reasonable wavelength range and the wavelength where the temperature effects are zero (isosbestic point). Chlorine salt prediction model was established based on partial least squares method and used for predicting the concentration of the chlorine ion. The impact on near infrared spectrum of the cation ionic radius, the number of ionic charge, the complex effect of ionic in water has also discussed in this article and the reason of every factor are analysed. Experimental results show that the temperature and concentration will affect the near-infrared spectrum of the solution, It is found that the effect of temperature plays the dominant role at low concentrations of chlorine salt; rather, the ionic dominates at high concentration. Chloride complexes are formed in aqueous solution, It has an effect on hydrogen bond of water combining with the cations in chlorine salt solution, Comparing different chloride solutions at the same concentration, the destruction effects of chloride complexes and catnions on the hydrogen bond of water increases in the sequences: CaCl2 >NaCl>KC. The modeling result shows that the determination coefficients (R2) = 99.97%, the root mean square error of cross validation (RM- SECV) = 4.51, and the residual prediction deviation (RPD) = 62.7, it meets the daily requirements of biochemical detection accuracy.
Cost Effective Process Monitoring using UV-VIS-NIR Spectroscopy
International Nuclear Information System (INIS)
Cipiti, B.; McDaniel, M.; Bryan, S.; Pratt, S.
2015-01-01
UV-VIS-NIR Spectroscopy is a simple and inexpensive measurement technology which has been proposed for process monitoring applications at reprocessing plants. The purpose of this work was to examine if spectroscopy could replace more costly analytical measurements to reduce the safeguards burden to the operator or inspector. Recognizing that the higher measurement uncertainty of spectroscopy makes it unsuited for the accountability tanks, the approach instead was to focus on replacing mass spectrometry for random samples that are taken in a plant. The Interim Inventory Verification and Short Inventory Verification (IIV/SIV) at the Rokkasho Reprocessing Plant utilize random sampling of internal process vessels and laboratory measurement using Isotope Dilution Mass Spectrometry (IDMS) to account for plutonium on a timely basis. These measurements are time-consuming, and the low uncertainty may not always be required. For this work, modelling was used to examine if spectroscopy could be used without adversely affecting the safeguards of the plant. The Separation and Safeguards Performance Model (SSPM), developed at Sandia National Laboratories, was utilized to examine the replacement of IDMS measurements with spectroscopy. Modeling results showed that complete replacement of IDMS with spectroscopy lowered the detection probability for diversion by an unacceptable amount. However, partial replacement (only for samples from vessels with low plutonium content) did not adversely affect the detection probability. This partial replacement covers roughly half of the twenty or so sampling points used for the IIV/SIVA cost-benefit analysis was completed to determine the cost savings that this approach can provide based on lower equipment costs, maintenance, and reduction of analysts' time. This work envisions working with the existing sampling system and performing the spectroscopic measurements in the analytical laboratory, but future work could examine incorporating
Casanova, Henri; Robert, Yves
2008-01-01
""…The authors of the present book, who have extensive credentials in both research and instruction in the area of parallelism, present a sound, principled treatment of parallel algorithms. … This book is very well written and extremely well designed from an instructional point of view. … The authors have created an instructive and fascinating text. The book will serve researchers as well as instructors who need a solid, readable text for a course on parallelism in computing. Indeed, for anyone who wants an understandable text from which to acquire a current, rigorous, and broad vi
DEFF Research Database (Denmark)
Gustavson, Fred G.; Reid, John K.; Wasniewski, Jerzy
2007-01-01
We present subroutines for the Cholesky factorization of a positive-definite symmetric matrix and for solving corresponding sets of linear equations. They exploit cache memory by using the block hybrid format proposed by the authors in a companion article. The matrix is packed into n(n + 1)/2 real...... variables, and the speed is usually better than that of the LAPACK algorithm that uses full storage (n2 variables). Included are subroutines for rearranging a matrix whose upper or lower-triangular part is packed by columns to this format and for the inverse rearrangement. Also included is a kernel...
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
Directional quantile regression in R
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 53, č. 3 (2017), s. 480-492 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : multivariate quantile * regression quantile * halfspace depth * depth contour Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0476587.pdf
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Directory of Open Access Journals (Sweden)
Green HN
2014-11-01
Full Text Available Hadiyah N Green,1,2 Stephanie D Crockett,3 Dmitry V Martyshkin,1 Karan P Singh,2,4 William E Grizzle,2,5 Eben L Rosenthal,2,6 Sergey B Mirov11Department of Physics, Center for Optical Sensors and Spectroscopies, 2Comprehensive Cancer Center, 3Department of Pediatrics, Division of Neonatology, 4Department of Medicine, Division of Preventive Medicine, Biostatistics and Bioinformatics Shared Facility, 5Department of Pathology, 6Department of Surgery, Division of Otolaryngology, Head and Neck Surgery, The University of Alabama at Birmingham, Birmingham, AL, USAPurpose: Nanoparticle (NP-enabled near infrared (NIR photothermal therapy has realized limited success in in vivo studies as a potential localized cancer therapy. This is primarily due to a lack of successful methods that can prevent NP uptake by the reticuloendothelial system, especially the liver and kidney, and deliver sufficient quantities of intravenously injected NPs to the tumor site. Histological evaluation of photothermal therapy-induced tumor regression is also neglected in the current literature. This report demonstrates and histologically evaluates the in vivo potential of NIR photothermal therapy by circumventing the challenges of intravenous NP delivery and tumor targeting found in other photothermal therapy studies.Methods: Subcutaneous Cal 27 squamous cell carcinoma xenografts received photothermal nanotherapy treatments, radial injections of polyethylene glycol (PEG-ylated gold nanorods and one NIR 785 nm laser irradiation for 10 minutes at 9.5 W/cm2. Tumor response was measured for 10–15 days, gross changes in tumor size were evaluated, and the remaining tumors or scar tissues were excised and histologically analyzed.Results: The single treatment of intratumoral nanorod injections followed by a 10 minute NIR laser treatment also known as photothermal nanotherapy, resulted in ~100% tumor regression in ~90% of treated tumors, which was statistically significant in a
Origin of broad NIR photoluminescence in bismuthate glass and Bi-doped glasses at room temperature
Energy Technology Data Exchange (ETDEWEB)
Peng, Mingying; Zollfrank, Cordt; Wondraczek, Lothar [Lehrstuhl fuer Glas und Keramik, WW3, Friedrich Alexander Universitaet Erlangen-Nuernberg, Martensstrasse 5, D-91058 Erlangen (Germany)], E-mail: mingying.peng@ww.uni-erlangen.de, E-mail: lothar.wondraczek@ww.uni-erlangen.de
2009-07-15
Bi-doped glasses with broadband photoluminescence in the near-infrared (NIR) spectral range are presently receiving significant consideration for potential applications in telecommunications, widely tunable fiber lasers and spectral converters. However, the origin of NIR emission remains disputed. Here, we report on NIR absorption and emission properties of bismuthate glass and their dependence on the melting temperature. Results clarify that NIR emission occurs from the same centers as it does in Bi-doped glasses. The dependence of absorption and NIR emission of bismuthate glasses on the melting temperature is interpreted as thermal dissociation of Bi{sub 2}O{sub 3} into elementary Bi. Darkening of bismuthate glass melted at 1300 deg. C is due to the agglomeration of Bi atoms. The presence of Bi nanoparticles is confirmed by transmission electron microscopy, high-resolution energy dispersive x-ray spectroscopy and element distribution mapping. By adding antimony oxide as an oxidation agent to the glass, NIR emission centers can be eliminated and Bi{sup 3+} is formed. By comparing with atomic spectral data, absorption bands at {approx}320 , {approx}500 , 700 , 800 and 1000 nm observed in Bi-doped glasses are assigned to Bi{sup 0} transitions {sup 4}S{sub 3/2}{yields}{sup 2}P{sub 3/2}, {sup 4}S{sub 3/2}{yields}{sup 2}P{sub 1/2}, {sup 4}S{sub 3/2}{yields}{sup 2}D{sub 5/2}, {sup 4}S{sub 3/2}{yields}{sup 2}D{sub 3/2}(2) and {sup 4}S{sub 3/2}{yields}{sup 2}D{sub 3/2}(1), respectively, and broadband NIR emission is assigned to the transition {sup 2}D{sub 3/2}(1){yields}{sup 4}S{sub 3/2}.
Learning theory of distributed spectral algorithms
International Nuclear Information System (INIS)
Guo, Zheng-Chu; Lin, Shao-Bo; Zhou, Ding-Xuan
2017-01-01
Spectral algorithms have been widely used and studied in learning theory and inverse problems. This paper is concerned with distributed spectral algorithms, for handling big data, based on a divide-and-conquer approach. We present a learning theory for these distributed kernel-based learning algorithms in a regression framework including nice error bounds and optimal minimax learning rates achieved by means of a novel integral operator approach and a second order decomposition of inverse operators. Our quantitative estimates are given in terms of regularity of the regression function, effective dimension of the reproducing kernel Hilbert space, and qualification of the filter function of the spectral algorithm. They do not need any eigenfunction or noise conditions and are better than the existing results even for the classical family of spectral algorithms. (paper)
Ishikawa, Daitaro; Nishii, Takashi; Mizuno, Fumiaki; Sato, Harumi; Kazarian, Sergei G; Ozaki, Yukihiro
2013-12-01
This study was carried out to evaluate a new high-speed hyperspectral near-infrared (NIR) camera named Compovision. Quantitative analyses of the crystallinity and crystal evolution of biodegradable polymer, polylactic acid (PLA), and its concentration in PLA/poly-(R)-3-hydroxybutyrate (PHB) blends were investigated using near-infrared (NIR) imaging. This NIR camera can measure two-dimensional NIR spectral data in the 1000-2350 nm region obtaining images with wide field of view of 150 × 250 mm(2) (approximately 100 000 pixels) at high speeds (in less than 5 s). PLA with differing crystallinities between 0 and 50% blended samples with PHB in ratios of 80/20, 60/40, 40/60, 20/80, and pure films of 100% PLA and PHB were prepared. Compovision was used to collect respective NIR spectra in the 1000-2350 nm region and investigate the crystallinity of PLA and its concentration in the blends. The partial least squares (PLS) regression models for the crystallinity of PLA were developed using absorbance, second derivative, and standard normal variate (SNV) spectra from the most informative region of the spectra, between 1600 and 2000 nm. The predicted results of PLS models achieved using the absorbance and second derivative spectra were fairly good with a root mean square error (RMSE) of less than 6.1% and a determination of coefficient (R(2)) of more than 0.88 for PLS factor 1. The results obtained using the SNV spectra yielded the best prediction with the smallest RMSE of 2.93% and the highest R(2) of 0.976. Moreover, PLS models developed for estimating the concentration of PLA in the blend polymers using SNV spectra gave good predicted results where the RMSE was 4.94% and R(2) was 0.98. The SNV-based models provided the best-predicted results, since it can reduce the effects of the spectral changes induced by the inhomogeneity and the thickness of the samples. Wide area crystal evolution of PLA on a plate where a temperature slope of 70-105 °C had occurred was also
Weighted SGD for ℓp Regression with Randomized Preconditioning*
Yang, Jiyan; Chow, Yin-Lam; Ré, Christopher; Mahoney, Michael W.
2018-01-01
In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. SGD methods are easy to implement and applicable to a wide range of convex optimization problems. In contrast, RLA algorithms provide much stronger performance guarantees but are applicable to a narrower class of problems. We aim to bridge the gap between these two methods in solving constrained overdetermined linear regression problems—e.g., ℓ2 and ℓ1 regression problems. We propose a hybrid algorithm named pwSGD that uses RLA techniques for preconditioning and constructing an importance sampling distribution, and then performs an SGD-like iterative process with weighted sampling on the preconditioned system.By rewriting a deterministic ℓp regression problem as a stochastic optimization problem, we connect pwSGD to several existing ℓp solvers including RLA methods with algorithmic leveraging (RLA for short).We prove that pwSGD inherits faster convergence rates that only depend on the lower dimension of the linear system, while maintaining low computation complexity. Such SGD convergence rates are superior to other related SGD algorithm such as the weighted randomized Kaczmarz algorithm.Particularly, when solving ℓ1 regression with size n by d, pwSGD returns an approximate solution with ε relative error in the objective value in 𝒪(log n·nnz(A)+poly(d)/ε2) time. This complexity is uniformly better than that of RLA methods in terms of both ε and d when the problem is unconstrained. In the presence of constraints, pwSGD only has to solve a sequence of much simpler and smaller optimization problem over the same constraints. In general this is more efficient than solving the constrained subproblem required in RLA.For ℓ2 regression, pwSGD returns an approximate solution with ε relative error in the objective value and the solution vector measured in
Delwiche, Stephen R; Reeves, James B
2010-01-01
In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various
NIR Techniques Create Added Values for the Pellet and Biofuel Industry
Energy Technology Data Exchange (ETDEWEB)
Lestander, Torbjoern A. [Swedish Univ of Agricultural Science, Umeaa (Sweden). Unit of Biomass Technology and Chemistry; Johnsson, Bo; Grothage, Morgan [Casco Adhesives AB, Sundsvall (Sweden)
2006-07-15
Pelletizing of biomass as biofuels increases energy density, improves storability and reduces transport costs. This process is a major key factor in the transition from fossil fuels to renewable biomass refined as solid biofuels. The fast growing pellet industry is today producing more than 1.2 Gg wood Pellets in Sweden - one of the leading nations to utilize bioenergy in its energy mix. The multitude of raw biomaterials available for fuel pellet production and their widely different characteristics stress the need for rapid characterization methods. A suitable technique for characterization of variation in biomaterials is near infrared (NIR) spectrometry. NIR radiation interacts with polar molecules and especially with structural groups O-H as in water, C-H as in biomass, but also with C-O bonds and C=C double bonds frequently found in biomass. Biomass contains mostly the atoms C, O and H. This means that transmittance or reflectance in the NIR wavelength region covers most of the covalent bonds in biomass, except for the C-C bonds in carbon chains. The NIR technique is also developed for on-line measurement in harsh industrial conditions. Thus, NIR techniques can be applied for on-line and real time characterization of raw biomass as well as in the refinement process of biomass into standardized solid biofuels. Spectral patterns in the NIR region contain chemical and physical information structure that together with reference parameters can be modeled by multivariate calibration methods to obtain predictions. These predictions can be presented to the operators in real time on screens as charts based on multivariate statistical process controls. This improves the possibilities to overview the raw biomass variation and to control the responses of the treatments the biomass undergo in the pelletizing process. The NIR-technique is exemplified by a 23-factorial experiment that was carried out in a pellet plant using sawdust as raw material to produce wood Pellets as
Optical-NIR dust extinction towards Galactic O stars
Maíz Apellániz, J.; Barbá, R. H.
2018-05-01
Context. O stars are excellent tracers of the intervening ISM because of their high luminosity, blue intrinsic SED, and relatively featureless spectra. We are currently conducting the Galactic O-Star Spectroscopic Survey (GOSSS), which is generating a large sample of O stars with accurate spectral types within several kpc of the Sun. Aims: We aim to obtain a global picture of the properties of dust extinction in the solar neighborhood based on optical-NIR photometry of O stars with accurate spectral types. Methods: We have processed a carefully selected photometric set with the CHORIZOS code to measure the amount [E(4405 - 5495)] and type [R5495] of extinction towards 562 O-type stellar systems. We have tested three different families of extinction laws and analyzed our results with the help of additional archival data. Results: The Maíz Apellániz et al. (2014, A&A, 564, A63) family of extinction laws provides a better description of Galactic dust that either the Cardelli et al. (1989, ApJ, 345, 245) or Fitzpatrick (1999, PASP, 111, 63) families, so it should be preferentially used when analysing samples similar to the one in this paper. In many cases O stars and late-type stars experience similar amounts of extinction at similar distances but some O stars are located close to the molecular clouds left over from their births and have larger extinctions than the average for nearby late-type populations. In qualitative terms, O stars experience a more diverse extinction than late-type stars, as some are affected by the small-grain-size, low-R5495 effect of molecular clouds and others by the large-grain-size, high-R5495 effect of H II regions. Late-type stars experience a narrower range of grain sizes or R5495, as their extinction is predominantly caused by the average, diffuse ISM. We propose that the reason for the existence of large-grain-size, high-R5495 regions in the ISM in the form of H II regions and hot-gas bubbles is the selective destruction of small dust
[Determination of wine original regions using information fusion of NIR and MIR spectroscopy].
Xiang, Ling-Li; Li, Meng-Hua; Li, Jing-Mingz; Li, Jun-Hui; Zhang, Lu-Da; Zhao, Long-Lian
2014-10-01
Geographical origins of wine grapes are significant factors affecting wine quality and wine prices. Tasters' evaluation is a good method but has some limitations. It is important to discriminate different wine original regions quickly and accurately. The present paper proposed a method to determine wine original regions based on Bayesian information fusion that fused near-infrared (NIR) transmission spectra information and mid-infrared (MIR) ATR spectra information of wines. This method improved the determination results by expanding the sources of analysis information. NIR spectra and MIR spectra of 153 wine samples from four different regions of grape growing were collected by near-infrared and mid-infrared Fourier transform spe trometer separately. These four different regions are Huailai, Yantai, Gansu and Changli, which areall typical geographical originals for Chinese wines. NIR and MIR discriminant models for wine regions were established using partial least squares discriminant analysis (PLS-DA) based on NIR spectra and MIR spectra separately. In PLS-DA, the regions of wine samples are presented in group of binary code. There are four wine regions in this paper, thereby using four nodes standing for categorical variables. The output nodes values for each sample in NIR and MIR models were normalized first. These values stand for the probabilities of each sample belonging to each category. They seemed as the input to the Bayesian discriminant formula as a priori probability value. The probabilities were substituteed into the Bayesian formula to get posterior probabilities, by which we can judge the new class characteristics of these samples. Considering the stability of PLS-DA models, all the wine samples were divided into calibration sets and validation sets randomly for ten times. The results of NIR and MIR discriminant models of four wine regions were as follows: the average accuracy rates of calibration sets were 78.21% (NIR) and 82.57% (MIR), and the
Directory of Open Access Journals (Sweden)
M. Kedzierski
2017-08-01
Full Text Available Terrestrial Laser Scanning is currently one of the most common techniques for modelling and documenting structures of cultural heritage. However, only geometric information on its own, without the addition of imagery data is insufficient when formulating a precise statement about the status of studies structure, for feature extraction or indicating the sites to be restored. Therefore, the Authors propose the integration of spatial data from terrestrial laser scanning with imaging data from low-cost cameras. The use of images from low-cost cameras makes it possible to limit the costs needed to complete such a study, and thus, increasing the possibility of intensifying the frequency of photographing and monitoring of the given structure. As a result, the analysed cultural heritage structures can be monitored more closely and in more detail, meaning that the technical documentation concerning this structure is also more precise. To supplement the laser scanning information, the Authors propose using both images taken both in the near-infrared range and in the visible range. This choice is motivated by the fact that not all important features of historical structures are always visible RGB, but they can be identified in NIR imagery, which, with the additional merging with a three-dimensional point cloud, gives full spatial information about the cultural heritage structure in question. The Authors proposed an algorithm that automates the process of integrating NIR images with a point cloud using parameters, which had been calculated during the transformation of RGB images. A number of conditions affecting the accuracy of the texturing had been studies, in particular, the impact of the geometry of the distribution of adjustment points and their amount on the accuracy of the integration process, the correlation between the intensity value and the error on specific points using images in different ranges of the electromagnetic spectrum and the selection
Kedzierski, M.; Walczykowski, P.; Wojtkowska, M.; Fryskowska, A.
2017-08-01
Terrestrial Laser Scanning is currently one of the most common techniques for modelling and documenting structures of cultural heritage. However, only geometric information on its own, without the addition of imagery data is insufficient when formulating a precise statement about the status of studies structure, for feature extraction or indicating the sites to be restored. Therefore, the Authors propose the integration of spatial data from terrestrial laser scanning with imaging data from low-cost cameras. The use of images from low-cost cameras makes it possible to limit the costs needed to complete such a study, and thus, increasing the possibility of intensifying the frequency of photographing and monitoring of the given structure. As a result, the analysed cultural heritage structures can be monitored more closely and in more detail, meaning that the technical documentation concerning this structure is also more precise. To supplement the laser scanning information, the Authors propose using both images taken both in the near-infrared range and in the visible range. This choice is motivated by the fact that not all important features of historical structures are always visible RGB, but they can be identified in NIR imagery, which, with the additional merging with a three-dimensional point cloud, gives full spatial information about the cultural heritage structure in question. The Authors proposed an algorithm that automates the process of integrating NIR images with a point cloud using parameters, which had been calculated during the transformation of RGB images. A number of conditions affecting the accuracy of the texturing had been studies, in particular, the impact of the geometry of the distribution of adjustment points and their amount on the accuracy of the integration process, the correlation between the intensity value and the error on specific points using images in different ranges of the electromagnetic spectrum and the selection of the optimal
Learning Inverse Rig Mappings by Nonlinear Regression.
Holden, Daniel; Saito, Jun; Komura, Taku
2017-03-01
We present a framework to design inverse rig-functions-functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.
DRREP: deep ridge regressed epitope predictor.
Sher, Gene; Zhi, Degui; Zhang, Shaojie
2017-10-03
The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.
Estimation of crosstalk in LED fNIRS by photon propagation Monte Carlo simulation
Iwano, Takayuki; Umeyama, Shinji
2015-12-01
fNIRS (functional near-Infrared spectroscopy) can measure brain activity non-invasively and has advantages such as low cost and portability. While the conventional fNIRS has used laser light, LED light fNIRS is recently becoming common in use. Using LED for fNIRS, equipment can be more inexpensive and more portable. LED light, however, has a wider illumination spectrum than laser light, which may change crosstalk between the calculated concentration change of oxygenated and deoxygenated hemoglobins. The crosstalk is caused by difference in light path length in the head tissues depending on wavelengths used. We conducted Monte Carlo simulations of photon propagation in the tissue layers of head (scalp, skull, CSF, gray matter, and white matter) to estimate the light path length in each layers. Based on the estimated path lengths, the crosstalk in fNIRS using LED light was calculated. Our results showed that LED light more increases the crosstalk than laser light does when certain combinations of wavelengths were adopted. Even in such cases, the crosstalk increased by using LED light can be effectively suppressed by replacing the value of extinction coefficients used in the hemoglobin calculation to their weighted average over illumination spectrum.
A New Platform for Investigating In-Situ NIR Reflectance in Snow
Johnson, M.; Taubenheim, J. R. L.; Stevenson, R.; Eldred, D.
2017-12-01
In-situ near infrared (NIR) reflectance measurements of the snowpack have been shown to have correlations to valuable snowpack properties. To-date many studies take these measurements by digging a pit and setting up a NIR camera to take images of the wall. This setup is cumbersome, making it challenging to investigate things like spatial variability. Over the course of 3 winters, a new device has been developed capable of mitigating some of the downfalls of NIR open pit photography. This new instrument is a NIR profiler capable of taking NIR reflectance measurements without digging a pit, with most measurements taking less than 30 seconds to retrieve data. The latest prototype is built into a ski pole and automatically transfers data wirelessly to the users smartphone. During 2016-2017 winter, the device was used by 37 different users resulting in over 4000 measurements in the Western United States, demonstrating a dramatic reduction in time to data when compared to other methods. Presented here are some initial findings from a full winter of using the ski pole version of this device.
Lisa, Jessica A; Jayakumar, Amal; Ward, Bess B; Song, Bongkeun
2017-12-01
Molecular analysis of dissimilatory nitrite reductase genes (nirS) was conducted using a customized microarray containing 165 nirS probes (archetypes) to identify members of sedimentary denitrifying communities. The goal of this study was to examine denitrifying community responses to changing environmental variables over spatial and temporal scales in the New River Estuary (NRE), NC, USA. Multivariate statistical analyses revealed three denitrifier assemblages and uncovered 'generalist' and 'specialist' archetypes based on the distribution of archetypes within these assemblages. Generalists, archetypes detected in all samples during at least one season, were commonly world-wide found in estuarine and marine ecosystems, comprised 8%-29% of the abundant NRE archetypes. Archetypes found in a particular site, 'specialists', were found to co-vary based on site specific conditions. Archetypes specific to the lower estuary in winter were designated Cluster I and significantly correlated by sediment Chl a and porewater Fe 2+ . A combination of specialist and more widely distributed archetypes formed Clusters II and III, which separated based on salinity and porewater H 2 S respectively. The co-occurrence of archetypes correlated with different environmental conditions highlights the importance of habitat type and niche differentiation among nirS-type denitrifying communities and supports the essential role of individual community members in overall ecosystem function. © 2017 Society for Applied Microbiology and John Wiley & Sons Ltd.
Estimation of Sensory Analysis Cupping Test Arabica Coffee Using NIR Spectroscopy
Safrizal; Sutrisno; Lilik, P. E. N.; Ahmad, U.; Samsudin
2018-05-01
Flavors have become the most important coffee quality parameters now day, many coffee consuming countries require certain taste scores for the coffee to be ordered, the currently used cupping method of appraisal is the method designed by The Specialty Coffee Association Of America (SCAA), from several previous studies was found that Near-Infrared Spectroscopy (NIRS) can be used to detect chemical composition of certain materials including those associated with flavor so it is possible also to be applied to coffee powder. The aim of this research is to get correlation between NIRS spectrum with cupping scoring by tester, then look at the possibility of testing coffee taste sensors using NIRS spectrum. The coffee samples were taken from various places, altitudes and postharvest handling methods, then the samples were prepared following the SCAA protocol, for sensory analysis was done in two ways, with the expert tester and with the NIRS test. The calibration between both found that Without pretreatment using PLS get RMSE cross validation 6.14, using Multiplicative Scatter Correction spectra obtained RMSE cross validation 5.43, the best RMSE cross-validation was 1.73 achieved by de-trending correction, NIRS can be used to predict the score of cupping.
Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.
Khan, Muhammad Jawad; Hong, Keum-Shik
2017-01-01
In this paper, a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG-fNIRS interface.
Hybrid EEG–fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control
Khan, Muhammad Jawad; Hong, Keum-Shik
2017-01-01
In this paper, a hybrid electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain–computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG–fNIRS interface. PMID:28261084
Is there Place for Perfectionism in the NIR Spectral Data Reduction?
Chilingarian, Igor
2017-09-01
"Despite the crucial importance of the near-infrared spectral domain for understanding the star formation and galaxy evolution, NIR observations and data reduction represent a significant challenge. The known complexity of NIR detectors is aggravated by the airglow emission in the upper atmosphere and the water absorption in the troposphere so that up until now, the astronomical community is divided on the issue whether ground based NIR spectroscopy has a future or should it move completely to space (JWST, Euclid, WFIRST). I will share my experience of pipeline development for low- and intermediate-resolution spectrographs operated at Magellan and MMT. The MMIRS data reduction pipeline became the first example of the sky subtraction quality approaching the limit set by the Poisson photon noise and demonstrated the feasibility of low-resolution (R=1200-3000) NIR spectroscopy from the ground even for very faint (J=24.5) continuum sources. On the other hand, the FIRE Bright Source Pipeline developed specifically for high signal-to-noise intermediate resolution stellar spectra proves that systematics in the flux calibration and telluric absorption correction can be pushed down to the (sub-)percent level. My conclusion is that even though substantial effort and time investment is needed to design and develop NIR spectroscopic pipelines for ground based instruments, it will pay off, if done properly, and open new windows of opportunity in the ELT era."
Spontaneous regression of pulmonary bullae
International Nuclear Information System (INIS)
Satoh, H.; Ishikawa, H.; Ohtsuka, M.; Sekizawa, K.
2002-01-01
The natural history of pulmonary bullae is often characterized by gradual, progressive enlargement. Spontaneous regression of bullae is, however, very rare. We report a case in which complete resolution of pulmonary bullae in the left upper lung occurred spontaneously. The management of pulmonary bullae is occasionally made difficult because of gradual progressive enlargement associated with abnormal pulmonary function. Some patients have multiple bulla in both lungs and/or have a history of pulmonary emphysema. Others have a giant bulla without emphysematous change in the lungs. Our present case had treated lung cancer with no evidence of local recurrence. He had no emphysematous change in lung function test and had no complaints, although the high resolution CT scan shows evidence of underlying minimal changes of emphysema. Ortin and Gurney presented three cases of spontaneous reduction in size of bulla. Interestingly, one of them had a marked decrease in the size of a bulla in association with thickening of the wall of the bulla, which was observed in our patient. This case we describe is of interest, not only because of the rarity with which regression of pulmonary bulla has been reported in the literature, but also because of the spontaneous improvements in the radiological picture in the absence of overt infection or tumor. Copyright (2002) Blackwell Science Pty Ltd
Interpretation of commonly used statistical regression models.
Kasza, Jessica; Wolfe, Rory
2014-01-01
A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.
Integrated Multiscale Latent Variable Regression and Application to Distillation Columns
Directory of Open Access Journals (Sweden)
Muddu Madakyaru
2013-01-01
Full Text Available Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions, which are usually estimated using inferential models. Commonly used inferential models include latent variable regression (LVR techniques, such as principal component regression (PCR, partial least squares (PLS, and regularized canonical correlation analysis (RCCA. Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of these models. Multiscale filtering has been shown to be a powerful feature extraction tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR modeling algorithm that integrates modeling and feature extraction. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods.
Directory of Open Access Journals (Sweden)
Elena Tamburini
2015-01-01
Full Text Available Agricultural practices determine the level of food production and, to great extent, the state of the global environment. During the last decades, the indiscriminate recourse to fertilizers as well as the nitrogen losses from land application have been recognized as serious issues of modern agriculture, globally contributing to nitrate pollution. The development of a reliable Near-Infra-Red Spectroscopy (NIRS-based method, for the simultaneous monitoring of nitrogen and chlorophyll in fresh apple (Malus domestica leaves, was investigated on a set of 133 samples, with the aim of estimating the nutritional and physiological status of trees, in real time, cheaply and non-destructively. By means of a FT (Fourier Transform-NIR instrument, Partial Least Squares (PLS regression models were developed, spanning a concentration range of 0.577%–0.817% for the total Kjeldahl nitrogen (TKN content (R2 = 0.983; SEC = 0.012; SEP = 0.028, and of 1.534–2.372 mg/g for the total chlorophyll content (R2 = 0.941; SEC = 0.132; SEP = 0.162. Chlorophyll-a and chlorophyll-b contents were also evaluated (R2 = 0.913; SEC = 0.076; SEP = 0.101 and R2 = 0.899; SEC = 0.059; SEP = 0.101, respectively. All calibration models were validated by means of 47 independent samples. The NIR approach allows a rapid evaluation of the nitrogen and chlorophyll contents, and may represent a useful tool for determining nutritional and physiological status of plants, in order to allow a correction of nutrition programs during the season.
Teixeira, Kelly Sivocy Sampaio; da Cruz Fonseca, Said Gonçalves; de Moura, Luís Carlos Brigido; de Moura, Mario Luís Ribeiro; Borges, Márcia Herminia Pinheiro; Barbosa, Euzébio Guimaraes; De Lima E Moura, Túlio Flávio Accioly
2018-02-05
The World Health Organization recommends that TB treatment be administered using combination therapy. The methodologies for quantifying simultaneously associated drugs are highly complex, being costly, extremely time consuming and producing chemical residues harmful to the environment. The need to seek alternative techniques that minimize these drawbacks is widely discussed in the pharmaceutical industry. Therefore, the objective of this study was to develop and validate a multivariate calibration model in association with the near infrared spectroscopy technique (NIR) for the simultaneous determination of rifampicin, isoniazid, pyrazinamide and ethambutol. These models allow the quality control of these medicines to be optimized using simple, fast, low-cost techniques that produce no chemical waste. In the NIR - PLS method, spectra readings were acquired in the 10,000-4000cm -1 range using an infrared spectrophotometer (IRPrestige - 21 - Shimadzu) with a resolution of 4cm -1 , 20 sweeps, under controlled temperature and humidity. For construction of the model, the central composite experimental design was employed on the program Statistica 13 (StatSoft Inc.). All spectra were treated by computational tools for multivariate analysis using partial least squares regression (PLS) on the software program Pirouette 3.11 (Infometrix, Inc.). Variable selections were performed by the QSAR modeling program. The models developed by NIR in association with multivariate analysis provided good prediction of the APIs for the external samples and were therefore validated. For the tablets, however, the slightly different quantitative compositions of excipients compared to the mixtures prepared for building the models led to results that were not statistically similar, despite having prediction errors considered acceptable in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Drzewiecki Wojciech
2016-12-01
Full Text Available In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques.
Robust mislabel logistic regression without modeling mislabel probabilities.
Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun
2018-03-01
Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.
Learning a Nonnegative Sparse Graph for Linear Regression.
Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung
2015-09-01
Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.
Stellar atmospheric parameter estimation using Gaussian process regression
Bu, Yude; Pan, Jingchang
2015-02-01
As is well known, it is necessary to derive stellar parameters from massive amounts of spectral data automatically and efficiently. However, in traditional automatic methods such as artificial neural networks (ANNs) and kernel regression (KR), it is often difficult to optimize the algorithm structure and determine the optimal algorithm parameters. Gaussian process regression (GPR) is a recently developed method that has been proven to be capable of overcoming these difficulties. Here we apply GPR to derive stellar atmospheric parameters from spectra. Through evaluating the performance of GPR on Sloan Digital Sky Survey (SDSS) spectra, Medium resolution Isaac Newton Telescope Library of Empirical Spectra (MILES) spectra, ELODIE spectra and the spectra of member stars of galactic globular clusters, we conclude that GPR can derive stellar parameters accurately and precisely, especially when we use data preprocessed with principal component analysis (PCA). We then compare the performance of GPR with that of several widely used regression methods (ANNs, support-vector regression and KR) and find that with GPR it is easier to optimize structures and parameters and more efficient and accurate to extract atmospheric parameters.
Energy Technology Data Exchange (ETDEWEB)
Fontana, W.
1990-12-13
In this paper complex adaptive systems are defined by a self- referential loop in which objects encode functions that act back on these objects. A model for this loop is presented. It uses a simple recursive formal language, derived from the lambda-calculus, to provide a semantics that maps character strings into functions that manipulate symbols on strings. The interaction between two functions, or algorithms, is defined naturally within the language through function composition, and results in the production of a new function. An iterated map acting on sets of functions and a corresponding graph representation are defined. Their properties are useful to discuss the behavior of a fixed size ensemble of randomly interacting functions. This function gas'', or Turning gas'', is studied under various conditions, and evolves cooperative interaction patterns of considerable intricacy. These patterns adapt under the influence of perturbations consisting in the addition of new random functions to the system. Different organizations emerge depending on the availability of self-replicators.
Corticospinal excitability changes to anodal tDCS elucidated with NIRS-EEG joint-imaging
DEFF Research Database (Denmark)
Jindal, Utkarsh; Sood, Mehak; Chowdhury, Shubhajit Roy
2015-01-01
Transcranial direct current stimulation (tDCS) has been shown to modulate corticospinal excitability. We used near-infrared spectroscopy (NIRS) - electroencephalography (EEG) joint-imaging during and after anodal tDCS to measure changes in mean cerebral haemoglobin oxygen saturation (rSO2) along...... with changes in the log-transformed mean-power of EEG within 0.5 Hz - 11.25 Hz. In two separate studies, we investigated local post-tDCS alterations from baseline at the site of anodal tDCS using NIRS-EEG/tDCS joint-imaging as well as local post-tDCS alterations in motor evoked potentials (MEP...... that the innovative technologies for portable NIRS-EEG neuroimaging may be leveraged to objectively quantify the progress (e.g., corticospinal excitability alterations) and dose tDCS intervention as an adjuvant treatment during neurorehabilitation....
DEFF Research Database (Denmark)
Houmøller, Lars P.; Kristensen, Dorthe; Rosager, Helle
2007-01-01
The use of near infrared spectroscopy (NIRS) for rapid determination of the degree of interesterification of blends of palm stearin, coconut oil, and rapeseed oil obtained using an immobilized Thermomyces lanuginosa lipase at 70 ◦C was investigated. Interesterification was carried out by applying...... that NIRS could be used to replace the traditional methods for determining FFA and SFC in vegetable oils.It was possible to monitor the activity of the immobilized enzyme for interesterification of margarine oils by predicting the equivalent reaction time in a batch reactor from NIR spectra. Root mean...... square errors of prediction for two different oil blends interesterified for 300 and 170 min were 21 and 12 min, respectively....
Fuji apple storage time rapid determination method using Vis/NIR spectroscopy
Liu, Fuqi; Tang, Xuxiang
2015-01-01
Fuji apple storage time rapid determination method using visible/near-infrared (Vis/NIR) spectroscopy was studied in this paper. Vis/NIR diffuse reflection spectroscopy responses to samples were measured for 6 days. Spectroscopy data were processed by stochastic resonance (SR). Principal component analysis (PCA) was utilized to analyze original spectroscopy data and SNR eigen value. Results demonstrated that PCA could not totally discriminate Fuji apples using original spectroscopy data. Signal-to-noise ratio (SNR) spectrum clearly classified all apple samples. PCA using SNR spectrum successfully discriminated apple samples. Therefore, Vis/NIR spectroscopy was effective for Fuji apple storage time rapid discrimination. The proposed method is also promising in condition safety control and management for food and environmental laboratories. PMID:25874818
A NIR-BODIPY derivative for sensing copper(II) in blood and mitochondrial imaging
He, Shao-Jun; Xie, Yu-Wen; Chen, Qiu-Yun
2018-04-01
In order to develop NIR BODIPY for mitochondria targeting imaging agents and metal sensors, a side chain modified BODIPY (BPN) was synthesized and spectroscopically characterized. BPN has NIR emission at 765 nm when excited at 704 nm. The emission at 765 nm responded differently to Cu2+ and Mn2+ ions, respectively. The BPN coordinated with Cu2+ forming [BPNCu]2+ complex with quenched emission, while Mn2+ induced aggregation of BPN with specific fluorescence enhancement. Moreover, BPN can be applied to monitor Cu2+ in live cells and image mitochondria. Further, BPN was used as sensor for the detection of Cu2+ ions in serum with linear detection range of 0.45 μM-36.30 μM. Results indicate that BPN is a good sensor for the detection of Cu2+ in serum and image mitochondria. This study gives strategies for future design of NIR sensors for the analysis of metal ions in blood.