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.
An, Xin; Xu, Shuo; Zhang, Lu-Da; Su, Shi-Guang
2009-01-01
In the present paper, on the basis of LS-SVM algorithm, we built a multiple dependent variables LS-SVM (MLS-SVM) regression model whose weights can be optimized, and gave the corresponding algorithm. Furthermore, we theoretically explained the relationship between MLS-SVM and LS-SVM. Sixty four broomcorn samples were taken as experimental material, and the sample ratio of modeling set to predicting set was 51 : 13. We first selected randomly and uniformly five weight groups in the interval [0, 1], and then in the way of leave-one-out (LOO) rule determined one appropriate weight group and parameters including penalizing parameters and kernel parameters in the model according to the criterion of the minimum of average relative error. Then a multiple dependent variables quantitative analysis model was built with NIR spectrum and simultaneously analyzed three chemical constituents containing protein, lysine and starch. Finally, the average relative errors between actual values and predicted ones by the model of three components for the predicting set were 1.65%, 6.47% and 1.37%, respectively, and the correlation coefficients were 0.9940, 0.8392 and 0.8825, respectively. For comparison, LS-SVM was also utilized, for which the average relative errors were 1.68%, 6.25% and 1.47%, respectively, and the correlation coefficients were 0.9941, 0.8310 and 0.8800, respectively. It is obvious that MLS-SVM algorithm is comparable to LS-SVM algorithm in modeling analysis performance, and both of them can give satisfying results. The result shows that the model with MLS-SVM algorithm is capable of doing multi-components NIR quantitative analysis synchronously. Thus MLS-SVM algorithm offers a new multiple dependent variables quantitative analysis approach for chemometrics. In addition, the weights have certain effect on the prediction performance of the model with MLS-SVM, which is consistent with our intuition and is validated in this study. Therefore, it is necessary to optimize
Francesco Gregoretti
Full Text Available The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.
[A new algorithm for NIR modeling based on manifold learning].
Hong, Ming-Jian; Wen, Zhi-Yu; Zhang, Xiao-Hong; Wen, Quan
2009-07-01
Manifold learning is a new kind of algorithm originating from the field of machine learning to find the intrinsic dimensionality of numerous and complex data and to extract most important information from the raw data to develop a regression or classification model. The basic assumption of the manifold learning is that the high-dimensional data measured from the same object using some devices must reside on a manifold with much lower dimensions determined by a few properties of the object. While NIR spectra are characterized by their high dimensions and complicated band assignment, the authors may assume that the NIR spectra of the same kind of substances with different chemical concentrations should reside on a manifold with much lower dimensions determined by the concentrations, according to the above assumption. As one of the best known algorithms of manifold learning, locally linear embedding (LLE) further assumes that the underlying manifold is locally linear. So, every data point in the manifold should be a linear combination of its neighbors. Based on the above assumptions, the present paper proposes a new algorithm named least square locally weighted regression (LS-LWR), which is a kind of LWR with weights determined by the least squares instead of a predefined function. Then, the NIR spectra of glucose solutions with various concentrations are measured using a NIR spectrometer and LS-LWR is verified by predicting the concentrations of glucose solutions quantitatively. Compared with the existing algorithms such as principal component regression (PCR) and partial least squares regression (PLSR), the LS-LWR has better predictability measured by the standard error of prediction (SEP) and generates an elegant model with good stability and efficiency.
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.
Regression techniques for estimating soil organic carbon contents from VIS/NIR reflectance spectra
Schwanghart, W.; Jarmer, T.; Bayer, A.; Hoffmann, U.; Hunziker, M.; Kuhn, N. J.; Ehlers, M.
2012-04-01
Soil reflectance spectroscopy is regarded as a promising approach to efficiently obtain densely sampled soil organic carbon (SOC) estimates at various spatial scales. The estimates are usually based on a statistical modeling approach since physical models are mostly not applicable owing to the manifold influences on soil spectra by different soil constituents and properties. Different multivariate statistical methods exist to estimate SOC concentrations in soil samples using visible and near infra-red (VIS/NIR) reflectance spectra. All these techniques face the challenge of generating accurate predictive models with a disproportionate large number of variables compared to the number of observations in such datasets, and in addition highly correlated independent variables. This often results in overfitting and may at the same time reduce the predictive power of such models. In this study, we conduct a rigorous assessment of the predictive ability of different regression techniques (stepwise regression, robust regression with feature selection, lasso, ridge regression, elastic net, principal component (PC) regression, partial least squares (PLS) regression). We apply datasets from different environments to include a wide variety of soils and to investigate the effects of different SOC variances and concentrations on model performance. Our hypothesis is that the predictive ability of regression techniques can be significantly improved by using more advanced techniques such as PLS regression. We discuss our findings with respect to the applicability of SOC estimation from VIS/NIR reflectance spectra in different environments.
NIR Hyperspectral Imaging Measurement of Sugar Content in Peach Using PLS Regression
无
2007-01-01
Near infrared (NIR) hyperspectral imaging measurement of sugar content in peach was introduced. NIR spectral images (650～ 1 000 nm, resolution: 2 nm) of peach samples were captured with developed hyperspectral imaging setup. Partial least square (PLS) regression prediction model was developed to estimate the sugar content in peach; step-wise backward method was utilized to determine optimal wavelength subsets. Experimental results show that the calibration model with optimal wavelength subsets has a correlation coefficient of prediction of 0.97 and a standard error of prediction of 0.19, the prediction accuracy is higher than the calibration model applied over the whole wavelength, which proves that variable selection plays an important role in improving the prediction accuracy of PLS regression model.
Balabin, Roman M; Smirnov, Sergey V
2012-04-07
Modern analytical chemistry of industrial products is in need of rapid, robust, and cheap analytical methods to continuously monitor product quality parameters. For this reason, spectroscopic methods are often used to control the quality of industrial products in an on-line/in-line regime. Vibrational spectroscopy, including mid-infrared (MIR), Raman, and near-infrared (NIR), is one of the best ways to obtain information about the chemical structures and the quality coefficients of multicomponent mixtures. Together with chemometric algorithms and multivariate data analysis (MDA) methods, which were especially created for the analysis of complicated, noisy, and overlapping signals, NIR spectroscopy shows great results in terms of its accuracy, including classical prediction error, RMSEP. However, it is unclear whether the combined NIR + MDA methods are capable of dealing with much more complex interpolation or extrapolation problems that are inevitably present in real-world applications. In the current study, we try to make a rather general comparison of linear, such as partial least squares or projection to latent structures (PLS); "quasi-nonlinear", such as the polynomial version of PLS (Poly-PLS); and intrinsically non-linear, such as artificial neural networks (ANNs), support vector regression (SVR), and least-squares support vector machines (LS-SVM/LSSVM), regression methods in terms of their robustness. As a measure of robustness, we will try to estimate their accuracy when solving interpolation and extrapolation problems. Petroleum and biofuel (biodiesel) systems were chosen as representative examples of real-world samples. Six very different chemical systems that differed in complexity, composition, structure, and properties were studied; these systems were gasoline, ethanol-gasoline biofuel, diesel fuel, aromatic solutions of petroleum macromolecules, petroleum resins in benzene, and biodiesel. Eighteen different sample sets were used in total. General
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.
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...
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...
LIU Tong; BAO Chun-fang; REN Yu-lin
2011-01-01
The modem near-infrared(NIR) spectroscopy analysis is a simple, efficient and nondestructive technique,which has been used in chemical analysis in diverse fields. Shortwave NIR spectroscopy is also a rapid, flexible, and cost-effective method to control product quality in food industry. The method of support vector regression coupled with shortwave NIR spectroscopy was explored for the nondestructive quantitative analysis of the important quality parameters of soy sauce, including amino nitrogen content, total acid content, salt content and color ratio. In this study, the support vector regression(SVR) models based on subtractive spectra and positive spectra were found and compared, the results show that the subtractive spectrum was more excellent than the positive spectrum. Meanwhile,R and RSE were determined, respectively, by means of original spectra and pretreated spectra[standard normal variate (SNV), first-derivative and second-derivative], and the corresponding models were successfully established. The best prediction was achieved by a support vector regression model of the first derivative transformed dataset. In addition,the result obtained by the proposed method was compared with that of Partial Least Squares(PLS), which showed that the generalization performance of the classifier based on SVR was much better than that of PLS. The results demonstrate that shortwave NIR spectroscopy combined with SVR is promising for thc quality control of soy sauce.
Superquantile Regression: Theory, Algorithms, and Applications
2014-12-01
Isabel. I love having you in my arms, and although you are still too young to understand what a hug is, your warmth has given me the strength and...squares and the quantile regression models adjust to changes in the data set, denoted by the red dots. Notice that the observa- tions are moved upwards...model hardly changes. If we change this observation in red even further upwards, we would notice no more changes in the quantile regression function
Finite Algorithms for Robust Linear Regression
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...
Finite Algorithms for Robust Linear Regression
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...
Wang, Jia-Hua; Pan, Lu; Sun, Qian; Li, Peng-Fei; Han, Dong-Hai
2009-03-01
An improved genetic algorithm was used to implement an automated wavelength selection procedure for use in building multivariate calibration models based on partial least squares regression (PLS). The region selecting by genetic algorithms (R-SGA) was applied in building calibration model of soluble solid content (SSC) of Western pear, and the numbers of latent variables used to build calibration model were further reduced. The Fourier transform near infrared reflectance (FT-NIR) spectra were processed by GA after MSC or SNV, and four PLS calibration models were built by using the optimal combinations of these sub-regions. Meanwhile, the full region selecting PLS (Fr-PLS) models were developed. The R-SGA models variables were 434, 496, 310 and 496, for Early Red Comice, Wujiuxiang, Cascade and Kang Buddha, respectively. Despite the complexity of the spectral data, the R-SGA procedure was found to perform well (RMSEP = 0.428, 0.567 for Early Red Comice and Kang Buddha, respectively), leading to calibration models that significantly outperform those based on full-spectrum analyses (RM-SEP = 0.518, 0.633). The prediction precision of GA-PLS models was similar to that of Fr-PLS for Wujiuxiang and Cascade, with RMSEP of 0.696/0.694 and 0.425/0.421 respectively. This work proved that the R-SGA could find optimal values for several disparate variables associated with the calibration model and that the PLS procedure could be integrated into the objective function driving the optimization.
Nishii, Takashi; Genkawa, Takuma; Watari, Masahiro; Ozaki, Yukihiro
2012-01-01
A new selection procedure of an informative near-infrared (NIR) region for regression model building is proposed that uses an online NIR/mid-infrared (mid-IR) dual-region spectrometer in conjunction with two-dimensional (2D) NIR/mid-IR heterospectral correlation spectroscopy. In this procedure, both NIR and mid-IR spectra of a liquid sample are acquired sequentially during a reaction process using the NIR/mid-IR dual-region spectrometer; the 2D NIR/mid-IR heterospectral correlation spectrum is subsequently calculated from the obtained spectral data set. From the calculated 2D spectrum, a NIR region is selected that includes bands of high positive correlation intensity with mid-IR bands assigned to the analyte, and used for the construction of a regression model. To evaluate the performance of this procedure, a partial least-squares (PLS) regression model of the ethanol concentration in a fermentation process was constructed. During fermentation, NIR/mid-IR spectra in the 10000 - 1200 cm(-1) region were acquired every 3 min, and a 2D NIR/mid-IR heterospectral correlation spectrum was calculated to investigate the correlation intensity between the NIR and mid-IR bands. NIR regions that include bands at 4343, 4416, 5778, 5904, and 5955 cm(-1), which result from the combinations and overtones of the C-H group of ethanol, were selected for use in the PLS regression models, by taking the correlation intensity of a mid-IR band at 2985 cm(-1) arising from the CH(3) asymmetric stretching vibration mode of ethanol as a reference. The predicted results indicate that the ethanol concentrations calculated from the PLS regression models fit well to those obtained by high-performance liquid chromatography. Thus, it can be concluded that the selection procedure using the NIR/mid-IR dual-region spectrometer combined with 2D NIR/mid-IR heterospectral correlation spectroscopy is a powerful method for the construction of a reliable regression model.
无
2011-01-01
The modern near-infrared（NIR） spectroscopy analysis is a simple, efficient and nondestructive technique, which has been used in chemical analysis in diverse fields. Shortwave NIR spectroscopy is also a rapid, flexible, and cost-effective method to control product quality in food industry. The method of support vector regression coupled with shortwave NIR spectroscopy was explored for the nondestructive quantitative analysis of the important quality parameters of soy sauce, including amino nitrogen content, total acid content, salt content and color ratio. In this study, the support vector regression（SVR） models based on subtractive spectra and positive spectra were found and compared, the results show that the subtractive spectrum was more excellent than the positive spectrum. Meanwhile, R and RSE were determined, respectively, by means of original spectra and pretreated spectra[standard normal variate （SNV）, first-derivative and second-derivative], and the corresponding models were successfully established. The best prediction was achieved by a support vector regression model of the first derivative transformed dataset. In addition, the result obtained by the proposed method was compared with that of Partial Least Squares（PLS）, which showed that the generalization performance of the classifier based on SVR was much better than that of PLS. The results demonstrate that shortwave NIR spectroscopy combined with SVR is promising for the quality control of soy sauce.
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
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.
Novel algorithm for constructing support vector machine regression ensemble
Li Bo; Li Xinjun; Zhao Zhiyan
2006-01-01
A novel algorithm for constructing support vector machine regression ensemble is proposed. As to regression prediction, support vector machine regression(SVMR) ensemble is proposed by resampling from given training data sets repeatedly and aggregating several independent SVMRs, each of which is trained to use a replicated training set. After training, several independently trained SVMRs need to be aggregated in an appropriate combination manner. Generally, the linear weighting is usually used like expert weighting score in Boosting Regression and it is without optimization capacity. Three combination techniques are proposed, including simple arithmetic mean,linear least square error weighting and nonlinear hierarchical combining that uses another upper-layer SVMR to combine several lower-layer SVMRs. Finally, simulation experiments demonstrate the accuracy and validity of the presented algorithm.
[Study on the Application of NAS-Based Algorithm in the NIR Model Optimization].
Geng, Ying; Xiang, Bing-ren; He, Lan
2015-10-01
In this paper, net analysis signal (NAS)-based concept was introduced to the analysis of multi-component Ginkgo biloba leaf extracts. NAS algorithm was utilized for the preprocessing of spectra, and NAS-based two-dimensional correlation analysis was used for the optimization of NIR model building. Simultaneous quantitative models for three flavonol aglycones: quercetin, keampferol and isorhamnetin were established respectively. The NAS vectors calculated using two algorithms introduced from Lorber and Goicoechea and Olivieri (HLA/GO) were applied in the development of calibration models, the reconstructed spectra were used as input of PLS modeling. For the first time, NAS-based two-dimensional correlation spectroscopy was used for wave number selection. The regions appeared in the main diagonal were selected as useful regions for model building. The results implied that two NAS-based preprocessing methods were successfully used for the analysis of quercetin, keampferol and isorhamnetin with a decrease of factor number and an improvement of model robustness. NAS-based algorithm was proven to be a useful tool for the preprocessing of spectra and for optimization of model calibration. The above research showed a practical application value for the NIRS in the analysis of complex multi-component petrochemical medicine with unknown interference.
Marcelo A. Morgano
2005-03-01
Full Text Available A combinação da espectroscopia no infravermelho próximo (NIR e calibração multivariada (método dos mínimos quadrados parciais - PLS para a determinação do teor de proteína total em amostras de café cru, foi investigada. Os teores de proteína total foram inicialmente determinados usando-se como método de referência o de Kjeldhal, e, posteriormente foram construídos modelos de regressão a partir dos espectros na região do infravermelho próximo das amostras de café cru. Foram coletados 159 espectros das amostras de café cru utilizando um acessório de reflectância difusa, na faixa espectral de 4500 a 10000cm-1. Os espectros originais no NIR sofreram diferentes transformações e pré-tratamento matemático, como a transformação Kubelka-Munk; correção multiplicativa de sinal (MSC; alisamento (SPLINE; derivada primeira; média móvel e o pré-tratamento dos dados escalados pela variância. O método analítico proposto possibilitou a determinação direta, sem destruição da amostra, com obtenção de resultados rápidos e sem o consumo de reagentes químicos de forma a preservar o meio ambiente. O método proposto forneceu resultados com boa capacidade de previsão do teor de proteína total, sendo que os erros médios foram inferiores a 6,7%.The combination of near infrared spectroscopy (NIR and multivariate calibration using the partial least square - PLS method for the determination of the total protein level in raw coffee samples was investigated. The total protein levels were initially determined using the Kjeldhal method as the reference method. Regression models were built from the spectra in the NIR region of the raw coffee samples. Spectra of 159 samples were recorded, using an accessory of diffuse reflectance, in the range of 4500 and 10000cm-1 with 4cm-1 resolution. To the raw spectral data, different transformations and mathematical pretreatment such as Kubelka-Munk transformation; multiplicative sign correction
A Frisch-Newton Algorithm for Sparse Quantile Regression
Roger Koenker; Pin Ng
2005-01-01
Recent experience has shown that interior-point methods using a log barrier approach are far superior to classical simplex methods for computing solutions to large parametric quantile regression problems.In many large empirical applications, the design matrix has a very sparse structure. A typical example is the classical fixed-effect model for panel data where the parametric dimension of the model can be quite large, but the number of non-zero elements is quite small. Adopting recent developments in sparse linear algebra we introduce a modified version of the Frisch-Newton algorithm for quantile regression described in Portnoy and Koenker[28].The new algorithm substantially reduces the storage (memory) requirements and increases computational speed.The modified algorithm also facilitates the development of nonparametric quantile regression methods. The pseudo design matrices employed in nonparametric quantile regression smoothing are inherently sparse in both the fidelity and roughness penalty components. Exploiting the sparse structure of these problems opens up a whole range of new possibilities for multivariate smoothing on large data sets via ANOVA-type decomposition and partial linear models.
An Approach to the Programming of Biased Regression Algorithms.
1978-11-01
Due to the near nonexistence of computer algorithms for calculating estimators and ancillary statistics that are needed for biased regression methodologies, many users of these methodologies are forced to write their own programs. Brute-force coding of such programs can result in a great waste of computer core and computing time, as well as inefficient and inaccurate computing techniques. This article proposes some guides to more efficient programming by taking advantage of mathematical similarities among several of the more popular biased regression estimators.
Wang, Bingqian; Peng, Bangzhu
2017-02-01
This work aims to investigate the potential of fiber-optic Fourier transform-near-infrared (FT-NIR) spectrometry associated with chemometric analysis, which will be applied to monitor time-related changes in residual sugar and alcohol strength during kiwi wine fermentation. NIR calibration models for residual sugar and alcohol strength during kiwi wine fermentation were established on the FT-NIR spectra of 98 samples scanned in a fiber-optic FT-NIR spectrometer, and partial least squares regression method. The results showed that R(2) and root mean square error of cross-validation could achieve 0.982 and 3.81 g/L for residual sugar, and 0.984 and 0.34% for alcohol strength, respectively. Furthermore, crucial process information on kiwi must and wine fermentations provided by fiber-optic FT-NIR spectrometry was found to agree with those obtained from traditional chemical methods, and therefore this fiber-optic FT-NIR spectrometry can be applied as an effective and suitable alternative for analyses and monitoring of those processes. The overall results suggested that fiber-optic FT-NIR spectrometry is a promising tool for monitoring and controlling the kiwi wine fermentation process.
Kohei Arai
2016-10-01
Full Text Available Method for Near Infrared: NIR reflectance estimation with visible camera data based on regression for Normalized Vegetation Index: NDVI estimation is proposed together with its application for insect damage detection of rice paddy fields. Through experiments at rice paddy fields which is situated at Saga Prefectural Agriculture Research Institute SPARI in Saga city, Kyushu, Japan, it is found that there is high correlation between NIR reflectance and Green color reflectance. Therefore, it is possible to estimate NIR reflectance with visible camera data which results in possibility of estimation of NDVI with drone mounted visible camera data. As is well known that the protein content in rice crops is highly correlated with NIR intensity, or reflectance of rice leaves, it is possible to estimate rice crop quality with drone based visible camera data.
A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis
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.
A novel multiobjective evolutionary algorithm based on regression analysis.
Song, Zhiming; Wang, Maocai; Dai, Guangming; Vasile, Massimiliano
2015-01-01
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.
DATA PREORDERING IN GENERALIZED PAV ALGORITHM FOR MONOTONIC REGRESSION
Oleg Burdakov; Anders Grimvall; Oleg Sysoev
2006-01-01
Monotonic regression (MR) is a least distance problem with monotonicity constraints induced by a partially ordered data set of observations. In our recent publication [In Ser.Nonconvex Optimization and Its Applications, Springer-Verlag, (2006) 83, pp. 25-33],the Pool-Adjacent-Violators algorithm (PAV) was generalized from completely to partially ordered data sets (posets). The new algorithm, called GPAV, is characterized by the very low computational complexity, which is of second order in the number of observations.It treats the observations in a consecutive order, and it can follow any arbitrarily chosen topological order of the poset of observations. The GPAV algorithm produces a sufficiently accurate solution to the MR problem, but the accuracy depends on the chosen topological order. Here we prove that there exists a topological order for which the resulted GPAV solution is optimal. Furthermore, we present results of extensive numerical experiments,from which we draw conclusions about the most and the least preferable topological orders.
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
A nonlinear regression model-based predictive control algorithm.
Dubay, R; Abu-Ayyad, M; Hernandez, J M
2009-04-01
This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.
Rogers, David
1991-01-01
G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.
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.
Contributions to "k"-Means Clustering and Regression via Classification Algorithms
Salman, Raied
2012-01-01
The dissertation deals with clustering algorithms and transforming regression problems into classification problems. The main contributions of the dissertation are twofold; first, to improve (speed up) the clustering algorithms and second, to develop a strict learning environment for solving regression problems as classification tasks by using…
Ying, Yibin; Liu, Yande; Fu, Xiaping; Lu, Huishan
2005-11-01
The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.
[Comparative Efficiency of Algorithms Based on Support Vector Machines for Regression].
Kadyrova, N O; Pavlova, L V
2015-01-01
Methods of construction of support vector machines do not require additional a priori information and can be used to process large scale data set. It is especially important for various problems in computational biology. The main set of algorithms of support vector machines for regression is presented. The comparative efficiency of a number of support-vector-algorithms for regression is investigated. A thorough analysis of the study results found the most efficient support vector algorithms for regression. The description of the presented algorithms, sufficient for their practical implementation is given.
Marcelo Antonio Morgano
2008-03-01
Full Text Available A espectroscopia na região do infravermelho próximo (NIR foi usada para determinar o teor de umidade em amostras de café cru. Foram construídos modelos de regressão usando o método dos mínimos quadrados parciais (PLS com diferentes pré-tratamentos de dados e 157 espectros NIR coletados de amostras de café usando um acessório de reflectância difusa, na região entre 4500 e 10000 cm-1. Os espectros originais passaram por diferentes transformações e pré-tratamentos matemáticos, como a transformação Kubelka-Munk; a correção multiplicativa de sinal (MSC; o alisamento com SPLINE e a média móvel, e os dados foram escalados pela variância. O modelo de regressão permitiu determinar o teor de umidade nas amostras de café cru com erro quadrático médio de calibração (SEC de 0,569 g.100 g -1; erro quadrático médio de validação de 0,298 g.100 g -1; coeficiente de correlação (r 0,712 e 0,818 para calibração e validação, respectivamente; e erro relativo médio de 4,1% para amostras de validação.Near infra-red reflectance (NIR spectroscopy was used to measure the moisture content in raw coffee. Different models using partial least squares (PLS with data pre-processing were used. Regression models were built with 157 spectra of the samples of raw coffee collected using a near infrared spectrometer with an accessory of diffuse reflectance, between 4500 and 10000 cm-1. The original NIR spectra went through different transformations and mathematical pre treatments, such as the Kubelka-Munk transformation; multiplicative signal correction (MSC; spline smoothing and movable average, and the data were scaled by variance. The regression model permitted the determination of the moisture content of the raw coffee samples with a standard error of calibration (SEC = 0.569 g.100 g -1; standard error of validation = 0.298 g.100 g -1; correlation coefficient (r 0.712 and 0.818 for calibration and validation, respectively, and average
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.
Outlier detection algorithms for least squares time series regression
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...... theory involves normal distribution results and Poisson distribution results. The theory is applied to a time series data set....
Insausti, Matías; Gomes, Adriano A; Cruz, Fernanda V; Pistonesi, Marcelo F; Araujo, Mario C U; Galvão, Roberto K H; Pereira, Claudete F; Band, Beatriz S F
2012-08-15
This paper investigates the use of UV-vis, near infrared (NIR) and synchronous fluorescence (SF) spectrometries coupled with multivariate classification methods to discriminate biodiesel samples with respect to the base oil employed in their production. More specifically, the present work extends previous studies by investigating the discrimination of corn-based biodiesel from two other biodiesel types (sunflower and soybean). Two classification methods are compared, namely full-spectrum SIMCA (soft independent modelling of class analogies) and SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm). Regardless of the spectrometric technique employed, full-spectrum SIMCA did not provide an appropriate discrimination of the three biodiesel types. In contrast, all samples were correctly classified on the basis of a reduced number of wavelengths selected by SPA-LDA. It can be concluded that UV-vis, NIR and SF spectrometries can be successfully employed to discriminate corn-based biodiesel from the two other biodiesel types, but wavelength selection by SPA-LDA is key to the proper separation of the classes. Copyright © 2012 Elsevier B.V. All rights reserved.
Fernández-Espinosa, Antonio J
2016-01-01
This study presents a systematized method for predicting water content, fat content and free acidity in olive fruits by on-line NIR Spectroscopy combined with chemometric techniques (PCA, LDA and PLSR). Three cultivar varieties of Olea europaea - Hojiblanca cv., Picual cv. and Arbequina cv. - were monitored. Five olive cultivation areas of Southern Spain (Andalucia) and Southern Portugal (Alentejo) were studied in 2011 and 2012. 465 olive samples were collected during the ripening process (non-mature olives) and compared with other 203 samples of mature olives collected at the final ripening stage. NIR spectra were measured directly in the olive fruits in the wavelength region from 1000 to 2300 nm in reflectance mode. The reference analyses were performed on the olive paste by oven drying for the moisture, by mini-Soxhlet extraction for the fat content and by acid titration of the oil extracted from the olive paste. Calibrations and predictive models were developed by Partial Least Square Regression (PLSR) previous Principal Component and Linear Discriminant analyses (PCA and LDA) were employed as exploratory and clean-up tools of data sets. The final models obtained for the total samples showed acceptable statistics of prediction with R(2)=0.88, RMSEV%=4.88 and RMSEP%=4.98 for water content, R(2)=0.76, RMSECV%=19.5 and RMSEP%=20.0 for fat content and R(2)=0.83, RMSECV%=36.8 and RMSEP%=38.8 for free acidity. Regression coefficients were better for only one maturity state (ripe period) than for olive fruit with different composition (ripening period). All models obtained were applied to predict LQPs on a new set of samples with satisfactory results, a good prediction potential of the models.
Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation
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.
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.
Zhang, Hong-guang; Lu, Jian-gang
2016-02-01
Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.
A-optimality orthogonal forward regression algorithm using branch and bound.
Hong, Xia; Chen, Sheng; Harris, Chris J
2008-11-01
In this brief, we propose an orthogonal forward regression (OFR) algorithm based on the principles of the branch and bound (BB) and A-optimality experimental design. At each forward regression step, each candidate from a pool of candidate regressors, referred to as S, is evaluated in turn with three possible decisions: 1) one of these is selected and included into the model; 2) some of these remain in S for evaluation in the next forward regression step; and 3) the rest are permanently eliminated from S . Based on the BB principle in combination with an A-optimality composite cost function for model structure determination, a simple adaptive diagnostics test is proposed to determine the decision boundary between 2) and 3). As such the proposed algorithm can significantly reduce the computational cost in the A-optimality OFR algorithm. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm.
Estimation of biomass in wheat using random forest regression algorithm and remote sensing data
Li'ai Wang; Xudong Zhou; Xinkai Zhu; Zhaodi Dong; Wenshan Guo
2016-01-01
Wheat biomass can be estimated using appropriate spectral vegetation indices. However, the accuracy of estimation should be further improved for on-farm crop management. Previous studies focused on developing vegetation indices, however limited research exists on modeling algorithms. The emerging Random Forest (RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling. The objectives of this study were to (1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass, (2) test the performance of the RF regression model, and (3) compare the performance of the RF algorithm with support vector regression (SVR) and artificial neural network (ANN) machine-learning algorithms for wheat biomass estimation. Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing, booting, and anthesis stages of growth. Fifteen vegetation indices were calculated based on these images. In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition. The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage, and its robustness is as good as SVR but better than ANN. The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China.
Estimation of biomass in wheat using random forest regression algorithm and remote sensing data
Li’ai Wang; Xudong Zhou; Xinkai Zhu; Zhaodi Dong; Wenshan Guo
2016-01-01
Wheat biomass can be estimated using appropriate spectral vegetation indices.However,the accuracy of estimation should be further improved for on-farm crop management.Previous studies focused on developing vegetation indices,however limited research exists on modeling algorithms.The emerging Random Forest(RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling.The objectives of this study were to(1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass,(2) test the performance of the RF regression model,and(3) compare the performance of the RF algorithm with support vector regression(SVR) and artificial neural network(ANN) machine-learning algorithms for wheat biomass estimation.Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing,booting,and anthesis stages of growth.Fifteen vegetation indices were calculated based on these images.In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition.The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage,and its robustness is as good as SVR but better than ANN.The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China.
Computationally efficient algorithm for Gaussian Process regression in case of structured samples
Belyaev, M.; Burnaev, E.; Kapushev, Y.
2016-04-01
Surrogate modeling is widely used in many engineering problems. Data sets often have Cartesian product structure (for instance factorial design of experiments with missing points). In such case the size of the data set can be very large. Therefore, one of the most popular algorithms for approximation-Gaussian Process regression-can be hardly applied due to its computational complexity. In this paper a computationally efficient approach for constructing Gaussian Process regression in case of data sets with Cartesian product structure is presented. Efficiency is achieved by using a special structure of the data set and operations with tensors. Proposed algorithm has low computational as well as memory complexity compared to existing algorithms. In this work we also introduce a regularization procedure allowing to take into account anisotropy of the data set and avoid degeneracy of regression model.
2014-09-01
driving simulation and ecologically valid subject pool to which the simple linear regression algorithm was applied. Table 2 Average squared...Bones PJ, Jones RD. Detection of lapses in responsiveness from the EEG. Journal of Neural Engineering. 2011;8(1):1–15. Perez CA, Palma A, Holzmann
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…
Ortiz-García, E. G.; Salcedo-Sanz, S.; Pérez-Bellido, A. M.; Gascón-Moreno, J.; Portilla-Figueras, A.
In this paper we present the application of a support vector regression algorithm to a real problem of maximum daily tropospheric ozone forecast. The support vector regression approach proposed is hybridized with an heuristic for optimal selection of hyper-parameters. The prediction of maximum daily ozone is carried out in all the station of the air quality monitoring network of Madrid. In the paper we analyze how the ozone prediction depends on meteorological variables such as solar radiation and temperature, and also we perform a comparison against the results obtained using a multi-layer perceptron neural network in the same prediction problem.
Optimal Algorithms for Ridge and Lasso Regression with Partially Observed Attributes
Hazan, Elad
2011-01-01
We consider the problems of ridge (L2-regularized) and lasso (L1-regularized) linear regression in a partial-information setting, in which the learner is allowed to observe only a fixed number of attributes of each example at training time. We present simple and efficient algorithms for both problems, that are optimal (up to logarithmic factors) in the sense that they require to observe the same number of attributes as do full-information algorithms. By that, we answer an open problem recently posed by Cesa-Bianchi et al. (2010), and show their lower bound to be tight.
Lin, Jeng-Wen; Shen, Pu Fun; Wen, Hao-Ping
2015-10-01
The application of a repetitive control mechanism for use in a mechanical control system has been a topic of investigation. The fundamental purpose of repetitive control is to eliminate disturbances in a mechanical control system. This paper presents two different repetitive control laws using individual types of basis function feedback and their combinations. These laws adjust the command given to a feedback control system to eliminate tracking errors, generally resulting from periodic disturbance. Periodic errors can be reduced through linear basis functions using regression and a genetic algorithm. The results illustrate that repetitive control is most effective method for eliminating disturbances. When the data are stabilized, the tracking error of the obtained convergence value, 10-14, is the optimal solution, verifying that the proposed regression and genetic algorithm can satisfactorily reduce periodic errors.
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.
Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms
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. PMID:25110755
LI Gui; LIN Hui; WU Ai-Dong; SONG Gang; WU Yi-Can
2008-01-01
To determine the electron energy spectra for medical accelerator effectively, we investigate a nonlinear programming model with several nonlinear regression algorithms, including Levenberg-Marquardt, Quasi-Newton, Gradient, Conjugate Gradient, Newton, Principal-Axis and NMinimize algorithms. The local relaxation-bound method is also developed to increase the calculation accuracy. The testing results demonstrate that the above methods could reconstruct the electron energy spectra effectively. Especially, further with the local relaxationbound method the Levenberg Marquardt, Newton and N Minimize algorithms could precisely obtain both the electron energy spectra and the photon contamination. Further study shows that ignoring about 4% photon contamination would increase error greatly, and it also inaccurately makes the electron energy spectra 'drift' to the low energy.
A simple and efficient algorithm for gene selection using sparse logistic regression.
Shevade, S K; Keerthi, S S
2003-11-22
This paper gives a new and efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based on the Gauss-Seidel method and is asymptotically convergent. It is simple and extremely easy to implement; it neither uses any sophisticated mathematical programming software nor needs any matrix operations. It can be applied to a variety of real-world problems like identifying marker genes and building a classifier in the context of cancer diagnosis using microarray data. The gene selection method suggested in this paper is demonstrated on two real-world data sets and the results were found to be consistent with the literature. The implementation of this algorithm is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml Supplementary material is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml
Harmonic regression based multi-temporal cloud filtering algorithm for Landsat 8
Joshi, P.
2015-12-01
Landsat data archive though rich is seen to have missing dates and periods owing to the weather irregularities and inconsistent coverage. The satellite images are further subject to cloud cover effects resulting in erroneous analysis and observations of ground features. In earlier studies the change detection algorithm using statistical control charts on harmonic residuals of multi-temporal Landsat 5 data have been shown to detect few prominent remnant clouds [Brooks, Evan B., et al, 2014]. So, in this work we build on this harmonic regression approach to detect and filter clouds using a multi-temporal series of Landsat 8 images. Firstly, we compute the harmonic coefficients using the fitting models on annual training data. This time series of residuals is further subjected to Shewhart X-bar control charts which signal the deviations of cloud points from the fitted multi-temporal fourier curve. For the process with standard deviation σ we found the second and third order harmonic regression with a x-bar chart control limit [Lσ] ranging between [0.5σ HOT), and utilizing the seasonal physical properties of these parameters, we have designed a novel multi-temporal algorithm for filtering clouds from Landsat 8 images. The method is applied to Virginia and Alabama in Landsat8 UTM zones 17 and 16 respectively. Our algorithm efficiently filters all types of cloud cover with an overall accuracy greater than 90%. As a result of the multi-temporal operation and the ability to recreate the multi-temporal database of images using only the coefficients of the fourier regression, our algorithm is largely storage and time efficient. The results show a good potential for this multi-temporal approach for cloud detection as a timely and targeted solution for the Landsat 8 research community, catering to the need for innovative processing solutions in the infant stage of the satellite.
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.
HOS network-based classification of power quality events via regression algorithms
Palomares Salas, José Carlos; González de la Rosa, Juan José; Sierra Fernández, José María; Pérez, Agustín Agüera
2015-12-01
This work compares seven regression algorithms implemented in artificial neural networks (ANNs) supported by 14 power-quality features, which are based in higher-order statistics. Combining time and frequency domain estimators to deal with non-stationary measurement sequences, the final goal of the system is the implementation in the future smart grid to guarantee compatibility between all equipment connected. The principal results are based in spectral kurtosis measurements, which easily adapt to the impulsive nature of the power quality events. These results verify that the proposed technique is capable of offering interesting results for power quality (PQ) disturbance classification. The best results are obtained using radial basis networks, generalized regression, and multilayer perceptron, mainly due to the non-linear nature of data.
Quantitative Recognizing Dissolved Hydrocarbons with Genetic Algorithm-Support Vector Regression
Qu Zhou
2013-09-01
Full Text Available Online monitoring of dissolved fault characteristic hydrocarbon gases, such as methane, ethane, ethylene and acetylene in power transformer oil has significant meaning for condition assessment of transformer. Recently, semiconductor tin oxide based gas sensor array has been widely applied in online monitoring apparatus, while cross sensitivity of the gas sensor array is inevitable due to same compositions and similar structures among the four hydrocarbon gases. Based on support vector regression (SVR with genetic algorithm (GA, a new pattern recognition method was proposed to reduce the cross sensitivity of the gas sensor array and further quantitatively recognize the concentration of dissolved hydrocarbon gases. The experimental data from a certain online monitoring device in China is used to illustrate the performance of the proposed GA-SVR model. Experimental results indicate that the GA-SVR method can effectively decrease the cross sensitivity and the regressed data is much more closed to the real values.
Optimization of Filter by using Support Vector Regression Machine with Cuckoo Search Algorithm
M. İlarslan
2014-09-01
Full Text Available Herein, a new methodology using a 3D Electromagnetic (EM simulator-based Support Vector Regression Machine (SVRM models of base elements is presented for band-pass filter (BPF design. SVRM models of elements, which are as fast as analytical equations and as accurate as a 3D EM simulator, are employed in a simple and efficient Cuckoo Search Algorithm (CSA to optimize an ultra-wideband (UWB microstrip BPF. CSA performance is verified by comparing it with other Meta-Heuristics such as Genetic Algorithm (GA and Particle Swarm Optimization (PSO. As an example of the proposed design methodology, an UWB BPF that operates between the frequencies of 3.1 GHz and 10.6 GHz is designed, fabricated and measured. The simulation and measurement results indicate in conclusion the superior performance of this optimization methodology in terms of improved filter response characteristics like return loss, insertion loss, harmonic suppression and group delay.
Multi-Objective Optimization Algorithms Design based on Support Vector Regression Metamodeling
Qi Zhang
2013-11-01
Full Text Available In order to solve the multi-objective optimization problem in the complex engineering, in this paper a NSGA-II multi-objective optimization algorithms based on Support Vector Regression Metamodeling is presented. Appropriate design parameter samples are selected by experimental design theories, and the response samples are obtained from the experiments or numerical simulations, used the SVM method to establish the metamodels of the objective performance functions and constraints, and reconstructed the original optimal problem. The reconstructed metamodels was solved by NSGA-II algorithm and took the structure optimization of the microwave power divider as an example to illustrate the proposed methodology and solve themulti-objective optimization problem. The results show that this methodology is feasible and highly effective, and thus it can be used in the optimum design of engineering fields.
Wenlong Jing
2016-10-01
Full Text Available Environmental monitoring of Earth from space has provided invaluable information for understanding land–atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation–land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day–night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI, the Digital Elevation Model (DEM, and geolocation (longitude, latitude. Four machine learning regression algorithms, the classification and regression tree (CART, the k-nearest neighbors (k-NN, the support vector machine (SVM, and random forests (RF, were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North China for the purpose of comparison of algorithm performance. The downscaled results were validated based on observations from meteorological stations and were also compared to a previous downscaling algorithm. According to the validation results, the RF-based model produced the results with the highest accuracy. It was followed by SVM, CART, and k-NN, but the accuracy of the downscaled results using SVM relied greatly on residual correction. The downscaled results were well correlated with the observations during the year, but the accuracies were relatively lower in July to September. Downscaling errors increase as monthly total precipitation increases, but the RF model was less affected by this proportional effect between errors and observation compared with the other algorithms. The variable importances of the land surface temperature (LST feature variables were higher than those of NDVI, which indicates the significance of considering the precipitation–land surface temperature
Ling, Steve S H; Nguyen, Hung T
2011-03-01
Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity.
Gayou, Olivier; Das, Shiva K; Zhou, Su-Min; Marks, Lawrence B; Parda, David S; Miften, Moyed
2008-12-01
A given outcome of radiotherapy treatment can be modeled by analyzing its correlation with a combination of dosimetric, physiological, biological, and clinical factors, through a logistic regression fit of a large patient population. The quality of the fit is measured by the combination of the predictive power of this particular set of factors and the statistical significance of the individual factors in the model. We developed a genetic algorithm (GA), in which a small sample of all the possible combinations of variables are fitted to the patient data. New models are derived from the best models, through crossover and mutation operations, and are in turn fitted. The process is repeated until the sample converges to the combination of factors that best predicts the outcome. The GA was tested on a data set that investigated the incidence of lung injury in NSCLC patients treated with 3DCRT. The GA identified a model with two variables as the best predictor of radiation pneumonitis: the V30 (p=0.048) and the ongoing use of tobacco at the time of referral (p=0.074). This two-variable model was confirmed as the best model by analyzing all possible combinations of factors. In conclusion, genetic algorithms provide a reliable and fast way to select significant factors in logistic regression analysis of large clinical studies.
SNPs selection using support vector regression and genetic algorithms in GWAS.
de Oliveira, Fabrízzio Condé; Borges, Carlos Cristiano Hasenclever; Almeida, Fernanda Nascimento; e Silva, Fabyano Fonseca; da Silva Verneque, Rui; da Silva, Marcos Vinicius G B; Arbex, Wagner
2014-01-01
This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels.
Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography.
Camino, Acner; Jia, Yali; Liu, Gangjun; Wang, Jie; Huang, David
2017-06-01
We developed an algorithm to remove decorrelation noise due to bulk motion in optical coherence tomography angiography (OCTA) of the posterior eye. In this algorithm, OCTA B-frames were divided into segments within which the bulk motion velocity could be assumed to be constant. This velocity was recovered using linear regression of decorrelation versus the logarithm of reflectance in axial lines (A-lines) identified as bulk tissue by percentile analysis. The fitting parameters were used to calculate a reflectance-adjusted upper bound threshold for bulk motion decorrelation. Below this threshold, voxels are identified as non-flow tissue, their flow values are set to zeros. Above this threshold, the voxels are identified as flow voxels and bulk motion velocity is subtracted from each using a nonlinear decorrelation-velocity relationship previously established in laboratory flow phantoms. Compared to the simpler median-subtraction method, the regression-based bulk motion subtraction improved angiogram signal-to-noise ratio, contrast, vessel density repeatability, and bulk motion noise cleanup in the foveal avascular zone, while preserving the connectivity of the vascular networks in the angiogram.
欧阳爱国; 谢小强; 周延睿; 刘燕德
2012-01-01
To improve the predictive ability and robustness of the NIR correction model of the soluble solid content (SSC) of apple, the reverse interval partial least squares method, genetic algorithm and the continuous projection method were implemented to select variables of the NIR spectroscopy of the soluble solid content (SSC) of apple, and the partial least squares regression model was established. By genetic algorithm for screening of the 141 variables of the correction model, prediction has the best effect. And compared to the full spectrum correction model, the correlation coefficient increased to 0. 96 from 0. 93, forecast root mean square error decreased from 0. 30°Brix to 0. 23°Brix. This experimental results show that the genetic algorithm combined with partial least squares regression method improved the detection precision of the NIR model of the soluble solid content (SSC) of apple.%为了提高苹果可溶性固形物含量近红外光谱校正模型的预测能力和稳健性,分别采用反向区间偏最小二乘法、遗传算法和连续投影算法,筛选苹果可溶性固形物的近红外光谱变量,并建立了偏最小二乘回归模型.利用遗传算法筛选的141个变量建立的校正模型,预测效果最好,与全谱建立的校正模型比较,预测相关系数,从0.93提高到0.96,预测均方根误差,从0.30°Brix降低到0.23°Brix.实验结果表明遗传算法结合偏最小二乘回归方法,有效地提高了苹果可溶性固形物近红外光谱检测模型的预测精度.
Eken, Cenker; Bilge, Ugur; Kartal, Mutlu; Eray, Oktay
2009-06-03
Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression. ANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic. The study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively. Data mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.
M. Farshad
2013-09-01
Full Text Available This paper presents a novel method based on machine learning strategies for fault locating in high voltage direct current (HVDC transmission lines. In the proposed fault-location method, only post-fault voltage signals measured at one terminal are used for feature extraction. In this paper, due to high dimension of input feature vectors, two different estimators including the generalized regression neural network (GRNN and the random forest (RF algorithm are examined to find the relation between the features and the fault location. The results of evaluation using training and test patterns obtained by simulating various fault types in a long overhead transmission line with different fault locations, fault resistance and pre-fault current values have indicated the efficiency and the acceptable accuracy of the proposed approach.
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.
Application of an Intelligent Fuzzy Regression Algorithm in Road Freight Transportation Modeling
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.
G.Keerthi Lakshmi
2012-03-01
Full Text Available Performing regression testing on a pre production environment is often viewed by software practitioners as a daunting task since often the test execution shall by-pass the stipulated downtime or the test coverage would be non linear. Choosing the exact test cases to match this type of complexity not only needs prior knowledge of the system, but also a right use of calculations to set the goals right. On systems that are just entering the production environment after getting promoted from the staging phase, trade-offs are often needed to between time and the test coverage to ensure the maximum test cases are covered within the stipulated time. There arises a need to refine the test cases to accommodate the maximum test coverage it makes within the stipulated period of time since at most of the times, the most important test cases are often not deemed to qualify under the sanity test suite and any bugs that creped in them would go undetected until it is found out by the actual user at firsthand. Hence An attempt has been made in the paper to layout a testing framework to address the process of improving the regression suite by adopting a modified version of the Ant Colony Algorithm over and thus dynamically injecting dependency over the best route encompassed by the ant colony.
Bioucas-Dias, José M
2010-01-01
Convex optimization problems are common in hyperspectral unmixing. Examples are the constrained least squares (CLS) problem used to compute the fractional abundances in a linear mixture of known spectra, the constrained basis pursuit (CBP) to find sparse (i.e., with a small number of terms) linear mixtures of spectra, selected from large libraries, and the constrained basis pursuit denoising (CBPDN), which is a generalization of BP to admit modeling errors. In this paper, we introduce two new algorithms to efficiently solve these optimization problems, based on the alternating direction method of multipliers, a method from the augmented Lagrangian family. The algorithms are termed SUnSAL (sparse unmixing by variable splitting and augmented Lagrangian) and C-SUnSAL (constrained SUnSAL). C-SUnSAL solves the CBP and CBPDN problems, while SUnSAL solves CLS as well as a more general version thereof, called constrained sparse regression} (CSR). C-SUnSAL and SUnSAL are shown to outperform off-the-shelf methods in te...
Garcia-Magariños, Manuel; Antoniadis, Anestis; Cao, Ricardo; Gonzãlez-Manteiga, Wenceslao
2010-01-01
Statistical methods generating sparse models are of great value in the gene expression field, where the number of covariates (genes) under study moves about the thousands while the sample sizes seldom reach a hundred of individuals. For phenotype classification, we propose different lasso logistic regression approaches with specific penalizations for each gene. These methods are based on a generalized soft-threshold (GSoft) estimator. We also show that a recent algorithm for convex optimization, namely, the cyclic coordinate descent (CCD) algorithm, provides with a way to solve the optimization problem significantly faster than with other competing methods. Viewing GSoft as an iterative thresholding procedure allows us to get the asymptotic properties of the resulting estimates in a straightforward manner. Results are obtained for simulated and real data. The leukemia and colon datasets are commonly used to evaluate new statistical approaches, so they come in useful to establish comparisons with similar methods. Furthermore, biological meaning is extracted from the leukemia results, and compared with previous studies. In summary, the approaches presented here give rise to sparse, interpretable models that are competitive with similar methods developed in the field.
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
A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data
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.
Yao, Zizhen; Ruzzo, Walter L
2006-03-20
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. 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. 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 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.
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.
Chang Li
2014-01-01
Full Text Available Much of the previous work in D-optimal design for regression models with correlated errors focused on polynomial models with a single predictor variable, in large part because of the intractability of an analytic solution. In this paper, we present a modified, improved simulated annealing algorithm, providing practical approaches to specifications of the annealing cooling parameters, thresholds, and search neighborhoods for the perturbation scheme, which finds approximate D-optimal designs for 2-way and 3-way polynomial regression for a variety of specific correlation structures with a given correlation coefficient. Results in each correlated-errors case are compared with traditional simulated annealing algorithm, that is, the SA algorithm without our improvement. Our improved simulated annealing results had generally higher D-efficiency than traditional simulated annealing algorithm, especially when the correlation parameter was well away from 0.
QSAR study of prolylcarboxypeptidase inhibitors by genetic algorithm: Multiple linear regressions
Eslam Pourbasheer; Saadat Vahdani; Reza Aalizadeh; Alireza Banaei; Mohammad Reza Ganjali
2015-07-01
The predictive analysis based on quantitative structure activity relationships (QSAR) on benzim-idazolepyrrolidinyl amides as prolylcarboxypeptidase (PrCP) inhibitors was performed. Molecules were represented by chemical descriptors that encode constitutional, topological, geometrical, and electronic structure features. The hierarchical clustering method was used to classify the dataset into training and test subsets. The important descriptors were selected with the aid of the genetic algorithm method. The QSAR model was constructed, using the multiple linear regressions (MLR), and its robustness and predictability were verified by internal and external cross-validation methods. Furthermore, the calculation of the domain of applicability defines the area of reliable predictions. The root mean square errors (RMSE) of the training set and the test set for GA-MLR model were calculated to be 0.176, 0.279 and the correlation coefficients (R2) were obtained to be 0.839, 0.923, respectively. The proposed model has good stability, robustness and predictability when verified by internal and external validation.
Yao Weixiong; Yang Yi; Zeng Bin
2009-01-01
High pressure die casting (HPDC) is a versatile material processing method for mass-production of metal parts with complex geometries,and this method has been widely used in manufacturing various products of excellent dimensional accuracy and productivity. In order to ensure the quality of the components,a number of variables need to be properly set. A novel methodology for high pressure die casting process optimization was developed,validated and applied to selection of optimal parameters,which incorporate design of experiment (DOE),Gaussian process (GP) regression technique and genetic algorithms (GA). This new approach was applied to process optimization for cast magnesium alloy notebook shell. After being trained,using data generated by PROCAST (FEM-based simulation software),the GP model approximated well with the simulation by extracting useful information from the simulation results. With the help of MATLAB,the GP/GA based approach has achieved the optimum solution of die casting process condition settings.
Prediction of hourly O 3 concentrations using support vector regression algorithms
Ortiz-García, E. G.; Salcedo-Sanz, S.; Pérez-Bellido, Á. M.; Portilla-Figueras, J. A.; Prieto, L.
2010-11-01
In this paper we present an application of the Support Vector Regression algorithm (SVMr) to the prediction of hourly ozone values in Madrid urban area. In order to improve the training capacity of SVMrs, we have used a recently proposed approach, based on reductions of the SVMr hyper-parameters search space. Using the modified SVMr, we study different influences which may modify the ozone prediction, such as previous ozone measurements in a given station, measurements in neighbors stations, and the influence of meteorologic variables. We use statistical tests to verify the significance of incorporating different variables into the SVMr. A comparison with the results obtained using a neural network (multi-layer perceptron) is also carried out. This study has been carried out in 5 different stations of the air pollution monitoring network of Madrid, so the conclusions raised are backed by real data. The final result of the work is a robust and powerful software for tropospheric ozone prediction in Madrid. Also, the prediction tool based on SVMr is flexible enough to incorporate any other prediction variable, such as city models, or traffic patters, which may improve the prediction obtained with the SVMr.
Zhang, Yan-jun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong
2015-10-01
According to the high precision extracting characteristics of scattering spectrum in Brillouin optical time domain reflection optical fiber sensing system, this paper proposes a new algorithm based on flies optimization algorithm with adaptive mutation and generalized regression neural network. The method takes advantages of the generalized regression neural network which has the ability of the approximation ability, learning speed and generalization of the model. Moreover, by using the strong search ability of flies optimization algorithm with adaptive mutation, it can enhance the learning ability of the neural network. Thus the fitting degree of Brillouin scattering spectrum and the extraction accuracy of frequency shift is improved. Model of actual Brillouin spectrum are constructed by Gaussian white noise on theoretical spectrum, whose center frequency is 11.213 GHz and the linewidths are 40-50, 30-60 and 20-70 MHz, respectively. Comparing the algorithm with the Levenberg-Marquardt fitting method based on finite element analysis, hybrid algorithm particle swarm optimization, Levenberg-Marquardt and the least square method, the maximum frequency shift error of the new algorithm is 0.4 MHz, the fitting degree is 0.991 2 and the root mean square error is 0.024 1. The simulation results show that the proposed algorithm has good fitting degree and minimum absolute error. Therefore, the algorithm can be used on distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can improve the fitting of Brillouin scattering spectrum and the precision of frequency shift extraction effectively.
Di, Nur Faraidah Muhammad; Satari, Siti Zanariah
2017-05-01
Outlier detection in linear data sets has been done vigorously but only a small amount of work has been done for outlier detection in circular data. In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. Clustering technique basically utilizes distance measure to define distance between various data points. Here, we introduce the similarity distance based on Euclidean distance for circular model and obtain a cluster tree using the single linkage clustering algorithm. Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. We classify the cluster group that exceeds the stopping rule as potential outlier. Our aim is to demonstrate the effectiveness of proposed algorithms with the similarity distances in detecting the outliers. It is found that the proposed methods are performed well and applicable for circular regression model.
van Gaans, P. F. M.; Vriend, S. P.
Application of ridge regression in geoscience usually is a more appropriate technique than ordinary least-squares regression, especially in the situation of highly intercorrelated predictor variables. A FORTRAN 77 program RIDGE for ridged multiple linear regression is presented. The theory of linear regression and ridge regression is treated, to allow for a careful interpretation of the results and to understand the structure of the program. The program gives various parameters to evaluate the extent of multicollinearity within a given regression problem, such as the correlation matrix, multiple correlations among the predictors, variance inflation factors, eigenvalues, condition number, and the determinant of the predictors correlation matrix. The best method for the optimum choice of the ridge parameter with ridge regression has not been established yet. Estimates of the ridge bias, ridged variance inflation factors, estimates, and norms for the ridge parameter therefore are given as output by RIDGE and should complement inspection of the ridge traces. Application within the earth sciences is discussed.
GOAL PROGRAMMING ALGORITHM FOR A TYPE OF LEAST ABSOLUTE VALUE REGRESSION PROBLEM
SHI Kuiran; XIAO Tiaojun; ZHANG Weirong
2004-01-01
This paper develops goal programming algorithm to solve a type of least absolute value (LAV) problem. Firstly, we simplify the simplex algorithm by proving the existence of solutions of the problem. Then, we present a goal programming algorithm on the basis of the original techniques. Theoretical analysis and numerical results indicate that the new method contains a lower number of deviation variables and consumes less computational time as compared to current LAV methods.
Bril, Andrey; Maksyutov, Shamil; Belikov, Dmitry; Oshchepkov, Sergey; Yoshida, Yukio; Deutscher, Nicholas M.; Griffith, David; Hase, Frank; Kivi, Rigel; Morino, Isamu; Notholt, Justus; Pollard, David F.; Sussmann, Ralf; Velazco, Voltaire A.; Warneke, Thorsten
2017-03-01
This paper presents a novel retrieval algorithm for the rapid retrieval of the carbon dioxide total column amounts from high resolution spectra in the short wave infrared (SWIR) range observations by the Greenhouse gases Observing Satellite (GOSAT). The algorithm performs EOF (Empirical Orthogonal Function)-based decomposition of the measured spectral radiance and derives the relationship of limited number of the decomposition coefficients in terms of the principal components with target gas amount and a priori data such as airmass, surface pressure, etc. The regression formulae for retrieving target gas amounts are derived using training sets of collocated GOSAT and ground-based observations. The precision/accuracy characteristics of the algorithm are analyzed by the comparison of the retrievals with those from the Total Carbon Column Observing Network (TCCON) measurements and with the modeled data, and appear similar to those achieved by full-physics retrieval algorithms.
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.
[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.
Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm
Fengkai Yang
2014-01-01
normal distribution, we developed the expectation maximization (EM algorithm to estimate the position of mean change-point. We investigated the performance of the algorithm through different simulations, finding that our methods is robust to the distributions of errors and is effective to estimate the position of mean change-point. Finally, we applied our method to the classical Holbert data and detected a change-point.
Benjamin W. Y. Lo
2016-01-01
Conclusions: A clinically useful classification 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.
Petrenko, B.; Ignatov, A.; Kramar, M.; Kihai, Y.
2016-05-01
Multichannel regression algorithms are widely used to retrieve sea surface temperature (SST) from infrared observations with satellite radiometers. Their theoretical foundations were laid in the 1980s-1990s, during the era of the Advanced Very High Resolution Radiometers which have been flown onboard NOAA satellites since 1981. Consequently, the multi-channel and non-linear SST algorithms employ the bands centered at 3.7, 11 and 12 μm, similar to available in AVHRR. More recent radiometers carry new bands located in the windows near 4 μm, 8.5 μm and 10 μm, which may also be used for SST. Involving these bands in SST retrieval requires modifications to the regression SST equations. The paper describes a general approach to constructing SST regression equations for an arbitrary number of radiometric bands and explores the benefits of using extended sets of bands available with the Visible Infrared Imager Radiometer Suite (VIIRS) flown onboard the Suomi National Polar-orbiting Partnership (SNPP) and to be flown onboard the follow-on Joint Polar Satellite System (JPSS) satellites, J1-J4, to be launched from 2017-2031; Moderate Resolution Imaging Spectroradiometers (MODIS) flown onboard Aqua and Terra satellites; and the Advanced Himawari Imager (AHI) flown onboard the Japanese Himawari-8 satellite (which in turn is a close proxy of the Advanced Baseline Imager (ABI) to be flown onboard the future Geostationary Operational Environmental Satellites - R Series (GOES-R) planned for launch in October 2016.
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.
Feng Wu; Hao Zhou; Tao Ren; Ligang Zheng; Kefa Cen [Zhejiang University, Hangzhou (China). State Key Laboratory of Clean Energy Utilization
2009-10-15
Support vector regression (SVR) was employed to establish mathematical models for the NOx emissions and carbon burnout of a 300 MW coal-fired utility boiler. Combined with the SVR models, the cellular genetic algorithm for multi-objective optimization (MOCell) was used for multi-objective optimization of the boiler combustion. Meanwhile, the comparison between MOCell and the improved non-dominated sorting genetic algorithm (NSGA-II) shows that MOCell has superior performance to NSGA-II regarding the problem. The field experiments were carried out to verify the accuracy of the results obtained by MOCell, the results were in good agreement with the measurement data. The proposed approach provides an effective tool for multi-objective optimization of coal combustion performance, whose feasibility and validity are experimental validated. A time period of less than 4 s was required for a run of optimization under a PC system, which is suitable for the online application. 19 refs., 8 figs., 2 tabs.
Li, Weixuan; Lin, Guang; Li, Bing
2016-09-01
A well-known challenge in uncertainty quantification (UQ) is the "curse of dimensionality". However, many high-dimensional UQ problems are essentially low-dimensional, because the randomness of the quantity of interest (QoI) is caused only by uncertain parameters varying within a low-dimensional subspace, known as the sufficient dimension reduction (SDR) subspace. Motivated by this observation, we propose and demonstrate in this paper an inverse regression-based UQ approach (IRUQ) for high-dimensional problems. Specifically, we use an inverse regression procedure to estimate the SDR subspace and then convert the original problem to a low-dimensional one, which can be efficiently solved by building a response surface model such as a polynomial chaos expansion. The novelty and advantages of the proposed approach is seen in its computational efficiency and practicality. Comparing with Monte Carlo, the traditionally preferred approach for high-dimensional UQ, IRUQ with a comparable cost generally gives much more accurate solutions even for high-dimensional problems, and even when the dimension reduction is not exactly sufficient. Theoretically, IRUQ is proved to converge twice as fast as the approach it uses seeking the SDR subspace. For example, while a sliced inverse regression method converges to the SDR subspace at the rate of $O(n^{-1/2})$, the corresponding IRUQ converges at $O(n^{-1})$. IRUQ also provides several desired conveniences in practice. It is non-intrusive, requiring only a simulator to generate realizations of the QoI, and there is no need to compute the high-dimensional gradient of the QoI. Finally, error bars can be derived for the estimation results reported by IRUQ.
Detecting outlying studies in meta-regression models using a forward search algorithm.
Mavridis, Dimitris; Moustaki, Irini; Wall, Melanie; Salanti, Georgia
2017-06-01
When considering data from many trials, it is likely that some of them present a markedly different intervention effect or exert an undue influence on the summary results. We develop a forward search algorithm for identifying outlying and influential studies in meta-analysis models. The forward search algorithm starts by fitting the hypothesized model to a small subset of likely outlier-free studies and proceeds by adding studies into the set one-by-one that are determined to be closest to the fitted model of the existing set. As each study is added to the set, plots of estimated parameters and measures of fit are monitored to identify outliers by sharp changes in the forward plots. We apply the proposed outlier detection method to two real data sets; a meta-analysis of 26 studies that examines the effect of writing-to-learn interventions on academic achievement adjusting for three possible effect modifiers, and a meta-analysis of 70 studies that compares a fluoride toothpaste treatment to placebo for preventing dental caries in children. A simple simulated example is used to illustrate the steps of the proposed methodology, and a small-scale simulation study is conducted to evaluate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Polak, Adam G
2011-02-01
Many patients undergo long-term artificial ventilation and their respiratory system mechanics should be monitored to detect changes in the patient's state and to optimize ventilator settings. In this work the most popular algorithms for tracking variations of respiratory resistance (R(rs)) and elastance (E(rs)) over a ventilatory cycle were analysed in terms of systematic and random errors. Additionally, a new approach was proposed and compared to the previous ones. It takes into account an exact description of flow integration by volume-dependent lung compliance. The results of analyses showed advantages of this new approach and enabled to form several suggestions. Algorithms including R(rs) and E(rs) dependencies on airflow and lung volume can be effectively applied only at low levels of noise present in measurement data, otherwise the use of the simplest model with constant parameters is preferable. Additionally, one should avoid including the resistance dependence on airflow alone, since this considerably destroys the retrieved trace of R(rs). Finally, the estimated cyclic trajectories of R(rs) and E(rs) are more sensitive to noise present in pressure than in the flow signal, and the elastance traces are estimated more accurately than the resistance ones.
Lentka Łukasz
2015-09-01
Full Text Available This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.
Do regression-based computer algorithms for determining the ventilatory threshold agree?
Ekkekakis, Panteleimon; Lind, Erik; Hall, Eric E; Petruzzello, Steven J
2008-07-01
The determination of the ventilatory threshold has been a persistent problem in research and clinical practice. Several computerized methods have been developed to overcome the subjectivity of visual methods but it remains unclear whether different computerized methods yield similar results. The purpose of this study was to compare nine regression-based computerized methods for the determination of the ventilatory threshold. Two samples of young and healthy volunteers (n = 30 each) participated in incremental treadmill protocols to volitional fatigue. The ventilatory data were averaged in 20-s segments and analysed with a computer program. Significant variance among methods was found in both samples (Sample 1: F = 11.50; Sample 2: F = 11.70, P < 0.001 for both). The estimates of the ventilatory threshold ranged from 2.47 litres.min(-1) (71% VO2max) to 3.13 litres.min(-1) (90% VO2max) in Sample 1 and from 2.37 litres.min(-1) (67% VO2max) to 3.03 litres.min(-1) (83% VO2max) in Sample 2. The substantial differences between methods challenge the practice of relying on any single computerized method. A standardized protocol, likely based on a combination of methods, might be necessary to increase the methodological consistency in both research and clinical practice.
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.
Qin, Jing; Garcia, Tanya P; Ma, Yanyuan; Tang, Ming-Xin; Marder, Karen; Wang, Yuanjia
2014-01-01
In certain genetic studies, clinicians and genetic counselors are interested in estimating the cumulative risk of a disease for individuals with and without a rare deleterious mutation. Estimating the cumulative risk is difficult, however, when the estimates are based on family history data. Often, the genetic mutation status in many family members is unknown; instead, only estimated probabilities of a patient having a certain mutation status are available. Also, ages of disease-onset are subject to right censoring. Existing methods to estimate the cumulative risk using such family-based data only provide estimation at individual time points, and are not guaranteed to be monotonic, nor non-negative. In this paper, we develop a novel method that combines Expectation-Maximization and isotonic regression to estimate the cumulative risk across the entire support. Our estimator is monotonic, satisfies self-consistent estimating equations, and has high power in detecting differences between the cumulative risks of different populations. Application of our estimator to a Parkinson's disease (PD) study provides the age-at-onset distribution of PD in PARK2 mutation carriers and non-carriers, and reveals a significant difference between the distribution in compound heterozygous carriers compared to non-carriers, but not between heterozygous carriers and non-carriers.
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.
Wu, W.; Chen, G. Y.; Kang, R.; Xia, J. C.; Huang, Y. P.; Chen, K. J.
2017-07-01
During slaughtering and further processing, chicken carcasses are inevitably contaminated by microbial pathogen contaminants. Due to food safety concerns, many countries implement a zero-tolerance policy that forbids the placement of visibly contaminated carcasses in ice-water chiller tanks during processing. Manual detection of contaminants is labor consuming and imprecise. Here, a successive projections algorithm (SPA)-multivariable linear regression (MLR) classifier based on an optimal performance threshold was developed for automatic detection of contaminants on chicken carcasses. Hyperspectral images were obtained using a hyperspectral imaging system. A regression model of the classifier was established by MLR based on twelve characteristic wavelengths (505, 537, 561, 562, 564, 575, 604, 627, 656, 665, 670, and 689 nm) selected by SPA , and the optimal threshold T = 1 was obtained from the receiver operating characteristic (ROC) analysis. The SPA-MLR classifier provided the best detection results when compared with the SPA-partial least squares (PLS) regression classifier and the SPA-least squares supported vector machine (LS-SVM) classifier. The true positive rate (TPR) of 100% and the false positive rate (FPR) of 0.392% indicate that the SPA-MLR classifier can utilize spatial and spectral information to effectively detect contaminants on chicken carcasses.
Jingyan Song
2011-07-01
Full Text Available The star centroid estimation is the most important operation, which directly affects the precision of attitude determination for star sensors. This paper presents a theoretical study of the systematic error introduced by the star centroid estimation algorithm. The systematic error is analyzed through a frequency domain approach and numerical simulations. It is shown that the systematic error consists of the approximation error and truncation error which resulted from the discretization approximation and sampling window limitations, respectively. A criterion for choosing the size of the sampling window to reduce the truncation error is given in this paper. The systematic error can be evaluated as a function of the actual star centroid positions under different Gaussian widths of star intensity distribution. In order to eliminate the systematic error, a novel compensation algorithm based on the least squares support vector regression (LSSVR with Radial Basis Function (RBF kernel is proposed. Simulation results show that when the compensation algorithm is applied to the 5-pixel star sampling window, the accuracy of star centroid estimation is improved from 0.06 to 6 × 10−5 pixels.
Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
Tramontana, Gianluca; Jung, Martin; Schwalm, Christopher R.; Ichii, Kazuhito; Camps-Valls, Gustau; Ráduly, Botond; Reichstein, Markus; Altaf Arain, M.; Cescatti, Alessandro; Kiely, Gerard; Merbold, Lutz; Serrano-Ortiz, Penelope; Sickert, Sven; Wolf, Sebastian; Papale, Dario
2016-07-01
Spatio-temporal fields of land-atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2 0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.
Near Infrared (NIR) spectroscopy has been found to be a useful technique to characterize raw materials and finished textile products, and NIR methods and techniques continue to find increasingly diverse and wide-ranging quantitative and qualitative applications in the textile industry. NIR methods ...
Harrison, R. J.; Feinberg, J. M.
2007-12-01
First-order reversal curves (FORCs) are a powerful method for characterizing the magnetic hysteresis properties of natural and synthetic materials, and are rapidly becoming a standard tool in rock magnetic and paleomagnetic investigations. Here we describe a modification to existing algorithms for the calculation of FORC diagrams using locally-weighted regression smoothing (often referred to as loess smoothing). Like conventional algorithms, the FORC distribution is calculated by fitting a second degree polynomial to a region of FORC space defined by a smoothing factor, N. Our method differs from conventional algorithms in two ways. Firstly, rather than a square of side (2N+1) centered on the point of interest, the region of FORC space used for fitting is defined as a span of arbitrary shape encompassing the (2N+1)2 data points closest to the point of interest. Secondly, data inside the span are given a weight that depends on their distance from the point being evaluated: data closer to the point being evaluated have higher weights and have a greater effect on the fit. Loess smoothing offers two advantages over current methods. Firstly, it allows the FORC distribution to be calculated using a constant smoothing factor all the way to the Hc = 0 axis. This eliminates possible distortions to the FORC distribution associated with reducing the smoothing factor close to the Hc = 0 axis, and does not require use of the extended FORC formalism and the reversible ridge, which swamps the low-coercivity signal. Secondly, it allows finer control over the degree of smoothing applied to the data, enabling automated selection of the optimum smoothing factor for a given FORC measurement, based on an analysis of the standard deviation of the fit residuals. The new algorithm forms the basis for FORCinel, a new suite of FORC analysis tools for Igor Pro (www.wavemetrics.com), freely available on request from the authors.
Schönbichler, S A; Bittner, L K H; Weiss, A K H; Griesser, U J; Pallua, J D; Huck, C W
2013-08-01
The aim of this study was to evaluate the ability of near-infrared chemical imaging (NIR-CI), near-infrared (NIR), Raman and attenuated-total-reflectance infrared (ATR-IR) spectroscopy to quantify three polymorphic forms (I, II, III) of furosemide in ternary powder mixtures. For this purpose, partial least-squares (PLS) regression models were developed, and different data preprocessing algorithms such as normalization, standard normal variate (SNV), multiplicative scatter correction (MSC) and 1st to 3rd derivatives were applied to reduce the influence of systematic disturbances. The performance of the methods was evaluated by comparison of the standard error of cross-validation (SECV), R(2), and the ratio performance deviation (RPD). Limits of detection (LOD) and limits of quantification (LOQ) of all methods were determined. For NIR-CI, a SECVcorr-spec and a SECVsingle-pixel corrected were calculated to assess the loss of accuracy by taking advantage of the spatial information. NIR-CI showed a SECVcorr-spec (SECVsingle-pixel corrected) of 2.82% (3.71%), 3.49% (4.65%), and 4.10% (5.06%) for form I, II, III. NIR had a SECV of 2.98%, 3.62%, and 2.75%, and Raman reached 3.25%, 3.08%, and 3.18%. The SECV of the ATR-IR models were 7.46%, 7.18%, and 12.08%. This study proves that NIR-CI, NIR, and Raman are well suited to quantify forms I-III of furosemide in ternary mixtures. Because of the pressure-dependent conversion of form II to form I, ATR-IR was found to be less appropriate for an accurate quantification of the mixtures. In this study, the capability of NIR-CI for the quantification of polymorphic ternary mixtures was compared with conventional spectroscopic techniques for the first time. For this purpose, a new way of spectra selection was chosen, and two kinds of SECVs were calculated to achieve a better comparability of NIR-CI to NIR, Raman, and ATR-IR.
Li, Zhongwei; Xin, Yuezhen; Wang, Xun; Sun, Beibei; Xia, Shengyu; Li, Hui; Zhu, Hu
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.
Estimation of soil parameters using VIS-NIR spectroscopy - Challenges and Chances
Grau, Thomas; Werban, Ulrike; Zacharias, Steffen; Dietrich, Peter
2013-04-01
Recently VIS-NIR spectroscopy has become a popular method for the prediction and mapping of soil parameters. It is a rapid, cost and time effective method to measure quantities like soil organic carbon (SOC), clay content or pH which are important information for e.g. precision agriculture or digital soil mapping. However, there is a need to calibrate measured field spectra against laboratory determined soil properties. In this regard, it is common to use data mining algorithms for calibration, statistical tools that discover empirically the relation between spectra and soil properties. However, each time a site-specific calibration model is required, which clearly limits the operational application of VIS-NIR calibration methods. Therefore, the incorporation of physical understanding regarding the relationship is desirable. So far three field campaigns have been conducted in Germany in order to collect soil data and connected spectral information. VIS-NIR data were measured using a mobile VIS-NIR spectrometer (Veris Technologies). Furthermore at each field campaign the geophysical methods Electromagnetic Induction and Gamma Ray Spectroscopy have been used to gather additional soil related information. The soil samples were analyzed in the laboratory for SOC, texture and pH. Using common calibration methods like partial least square regression and site specific calibration models the specific soil parameters have been spatially mapped. This work will present results of the field campaigns and resulting recommendations. Additionally a conceptual framework will be presented how physical knowledge can be incorporated into the VIS-NIR calibration process.
Pear quality characteristics by Vis / NIR spectroscopy.
Machado, Nicácia P; Fachinello, José C; Galarça, Simone P; Betemps, Débora L; Pasa, Mateus S; Schmitz, Juliano D
2012-09-01
Recently, non-destructive techniques such as the Vis / NIR spectroscopy have been used to evaluate the characteristics of maturation and quality of pears. The study aims to validate the readings by the Vis / NIR spectroscopy as a non-destructive way to assess the qualitative characteristics of pear cultivars 'Williams', 'Packams' and 'Carrick', produced according to Brazilian conditions. The experiment was conducted at the Pelotas Federal University, UFPel, in Pelotas / RS, and the instrument used to measure the fruit quality in a non-destructive way was the NIR- Case spectrophotometer (SACMI, Imola, Italy). To determine pears' soluble solids (SS) and pulp firmness (PF), it was established calibration equations for each variety studied, done from the evaluations obtained by a non-destructive method (NIR-Case) and a destructive method. Further on, it was tested the performance of these readings by linear regressions. The results were significant for the soluble solids parameter obtained by the Vis / NIR spectroscopy; however, it did not achieve satisfactory results for the pear pulp firmness of these cultivars. It is concluded that the Vis / NIR spectroscopy, using linear regression, allows providing reliable estimates of pears' quality levels, especially for soluble solids.
Vasiliu, Daniel; Clamons, Samuel; McDonough, Molly; Rabe, Brian; Saha, Margaret
2015-01-01
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.
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.
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.
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.
Mozaffari, Ahmad; Gorji-Bandpy, Mofid; Samadian, Pendar
2013-01-01
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......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...
Monitoring of whey quality with NIR spectroscopy
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...... processes and had a large variability for most of the parameters. Partial Least Squares (PLS) regression was used to make the prediction models based on NIR spectra taken at 30 and 40 °C. Using proper wavelength range allowed to get models for prediction of fat, protein and amount of total solids with very...
Pear quality characteristics by Vis / NIR spectroscopy
Nicácia P. Machado
2012-09-01
Full Text Available Recently, non-destructive techniques such as the Vis / NIR spectroscopy have been used to evaluate the characteristics of maturation and quality of pears. The study aims to validate the readings by the Vis / NIR spectroscopy as a non-destructive way to assess the qualitative characteristics of pear cultivars 'Williams', 'Packams' and 'Carrick', produced according to Brazilian conditions. The experiment was conducted at the Pelotas Federal University, UFPel, in Pelotas / RS, and the instrument used to measure the fruit quality in a non-destructive way was the NIR- Case spectrophotometer (SACMI, Imola, Italy. To determine pears' soluble solids (SS and pulp firmness (PF, it was established calibration equations for each variety studied, done from the evaluations obtained by a non-destructive method (NIR-Case and a destructive method. Further on, it was tested the performance of these readings by linear regressions. The results were significant for the soluble solids parameter obtained by the Vis / NIR spectroscopy; however, it did not achieve satisfactory results for the pear pulp firmness of these cultivars. It is concluded that the Vis / NIR spectroscopy, using linear regression, allows providing reliable estimates of pears' quality levels, especially for soluble solids.Recentemente, técnicas não destrutivas como a espectroscopia Vis/NIR têm sido utilizadas para avaliar as características de maturação e qualidade das peras. O trabalho tem como objetivo validar as leituras por espectroscopia Vis/NIR, como forma não destrutiva de avaliar as características qualitativas em peras das cultivares Williams, Packams e Carrick produzidas em condições brasileiras. O experimento foi realizado na Universidade Federal de Pelotas, UFPel, Pelotas/RS e o instrumento utilizado para determinar a qualidade dos frutos de forma não destrutiva foi o espectrofotômetro NIR-Case (SACMI, Imola, Itália. Para a determinação de sólidos solúveis (SS e firmeza
谈爱玲; 赵勇; 王思远; 陈静雯
2016-01-01
The rapid and accurate detection of milk electrical conductivity is of great significance to the healthy development of the dairy farm.A novel rapid detection method using near infrared spectroscopy combined with chemometrics was proposed in this paper. For the NIR spectral data of 90 farm milk samples, the support vector regression models were built by using genetic algorithm and particle swarm algorithm respectively, the results show that the PSO-SVR model has better performance and higher prediction preci-sion compared with the GA-SVR model and the traditional PLS model.The near infrared model based on PSO-SVR algorithm can be applied to the rapid and accurate measurement of the milk electrical conductivity.%牛乳电导率值是判断奶牛是否感染乳腺炎的重要依据，其快速、准确测量对奶牛养殖业的健康发展具有重要意义。本文提出一种近红外光谱结合化学计量学快速测量牛乳电导率的新方法。针对90个牛乳样本的近红外光谱，建立电导率值的支持向量机回归模型，分别采用遗传算法和粒子群算法进行模型参数寻优，结果表明基于粒子群寻优算法所建立的牛乳电导率值预测模型，相比GA-SVR模型和传统PLS模型具有更好的性能指标，预测精度更高，可以应用于牛乳电导率的快速、准确测量。
Diversity of nitrite reductase (nirK and nirS) gene fragments in forested upland and wetland soils
Priemé, Anders; Braker, Gesche; Tiedje, James M.
2002-01-01
The genetic heterogeneity of nitrite reductase gene (nirK and nirS) fragments from denitrifying prokaryotes in forested upland and marsh soil was investigated using molecular methods. nirK gene fragments could be amplified from both soils, whereas nirS gene fragments could be amplified only from...... the marsh soil. PCR products were cloned and screened by restriction fragment length polymorphism (RFLP), and representative fragments were sequenced. The diversity of nirK clones was lower than the diversity of nirS clones. Among the 54 distinct nirK RFLP patterns identified in the two soils, only one...... marsh clones and all upland clones. Only a few of the nirK clone sequences branched with those of known denitrifying bacteria. The nirS clones formed two major clusters with several subclusters, but all nirS clones showed less than 80% identity to nirS sequences from known denitrifying bacteria. Overall...
关于切换回归的集成模糊聚类算法 GFC%An Integrated Fuzzy Clustering Algorithm GFC for Switching Regressions
王士同; 江海峰; 陆宏钧
2002-01-01
已经有多个方法可用于解决切换回归问题.根据所提出的基于Newton引力定理的引力聚类算法GC,结合模糊聚类算法,进一步提出了新的集成模糊聚类算法 GFC.理论分析表明GFC 能收敛到局部最小.实验结果表明GFC在解决切换回归问题时,比标准模糊聚类算法更有效,特别在收敛速度方面.%In order to solve switching regression problems, many approaches have been investigated. In this paper, anintegrated fuzzy clustering algorithm GFC that combines gravity-based clustering algorithm GC with fuzzy clustering is presented. GC, as a new hard clustering algorithm presented here, is based on the well-known Newton's Gravity Law. The theoretic analysis shows that GFC can conve rge to a local minimum of the object function. Experimental results show that GFC for switching regression problems has better performance than standard fuzzy clustering algorithms, especially in terms of convergence speed.
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 e...
2013-01-01
The periplasmic cytochrome cd 1 nitrite reductase NirS occurring in denitrifying bacteria such as the human pathogen Pseudomonas aeruginosa contains the essential tetrapyrrole cofactors haem c and haem d 1. Whereas the haem c is incorporated into NirS by the cytochrome c maturation system I, nothing is known about the insertion of the haem d 1 into NirS. Here, we show by co-immunoprecipitation that NirS interacts with the potential haem d 1 insertion protein NirN in vivo. This NirS–NirN inter...
Naqvi, Rizwan Ali; Park, Kang Ryoung
2016-06-01
Gaze tracking systems are widely used in human-computer interfaces, interfaces for the disabled, game interfaces, and for controlling home appliances. Most studies on gaze detection have focused on enhancing its accuracy, whereas few have considered the discrimination of intentional gaze fixation (looking at a target to activate or select it) from unintentional fixation while using gaze detection systems. Previous research methods based on the use of a keyboard or mouse button, eye blinking, and the dwell time of gaze position have various limitations. Therefore, we propose a method for discriminating between intentional and unintentional gaze fixation using a multimodal fuzzy logic algorithm applied to a gaze tracking system with a near-infrared camera sensor. Experimental results show that the proposed method outperforms the conventional method for determining gaze fixation.
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.
Knüppel Sven
2012-01-01
Full Text Available Abstract Background Genome-wide association studies (GWAS provide an increasing number of single nucleotide polymorphisms (SNPs associated with diseases. Our aim is to exploit those closely spaced SNPs in candidate regions for a deeper analysis of association beyond single SNP analysis, combining the classical stepwise regression approach with haplotype analysis to identify risk haplotypes for complex diseases. Methods Our proposed multi-locus stepwise regression starts with an evaluation of all pair-wise SNP combinations and then extends each SNP combination stepwise by one SNP from the region, carrying out haplotype regression in each step. The best associated haplotype patterns are kept for the next step and must be corrected for multiple testing at the end. These haplotypes should also be replicated in an independent data set. We applied the method to a region of 259 SNPs from the epidermal differentiation complex (EDC on chromosome 1q21 of a German GWAS using a case control set (1,914 individuals and to 268 families with at least two affected children as replication. Results A 4-SNP haplotype pattern with high statistical significance in the case control set (p = 4.13 × 10-7 after Bonferroni correction could be identified which remained significant in the family set after Bonferroni correction (p = 0.0398. Further analysis revealed that this pattern reflects mainly the effect of the well-known FLG gene; however, a FLG-independent haplotype in case control set (OR = 1.71, 95% CI: 1.32-2.23, p = 5.6 × 10-5 and family set (OR = 1.68, 95% CI: 1.18-2.38, p = 2.19 × 10-3 could be found in addition. Conclusion Our approach is a useful tool for finding allele combinations associated with diseases beyond single SNP analysis in chromosomal candidate regions.
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.
线性回归模型的Boosting变量选择方法∗%Boosting Variable Selection Algorithm for Linear Regression Models
李毓; 张春霞; 王冠伟
2015-01-01
针对线性回归模型的变量选择问题，本文基于遗传算法提出了一种新的Boosting学习方法。该方法对每一训练个体赋予权重，以遗传算法作为Boosting的基学习算法，将带有权重分布的训练集作为遗传算法的输入进行变量选择。同时，根据前一次变量选择效果的好坏更新训练集上的权重分布。重复上述步骤多次，最后以加权融合方式合并多次变量选择的结果。基于模拟和实际数据的试验结果表明，本文新提出的Boosting方法能显著提高传统遗传算法用于变量选择的质量，准确识别出与响应变量相关的协变量，这为线性回归模型的变量选择提供了一种有效的新方法。%With respect to variable selection for linear regression models, this paper proposes a novel Boosting learning method based on genetic algorithm. In the novel algorithm, all train-ing examples are firstly assigned equal weights and a traditional genetic algorithm is adopted as the base learning algorithm of Boosting. Then, the training set associated with a weight distribution is taken as the input of genetic algorithm to do variable selection. Subsequently, the weight distribution is updated according to the quality of the previous variable selection results. Through repeating the above steps for multiple times, the results are then fused via a weighted combination rule. The performance of the proposed Boosting method is investigated on some simulated and real-world data. The experimental results show that our method can significantly improve the variable selection performance of traditional genetic algorithm and accurately identify the relevant variables. Thus, the novel Boosting method can be deemed as an effective technique for handling variable selection problems in linear regression models.
Stephen Eyije Abechi
2016-04-01
Full Text Available Aim: To develop good and rational Quantitative Structure Activity Relationship (QSAR mathematical models that can predict to a significant level the anti-tyrosinase and anti-Candida Albicans Minimum inhibitory concentration (MIC of ketone and tetra- etone derivatives. Place and Duration of Study: Department of Chemistry (Mathieson Laboratory (3-Physical Chemistry unit, Ahmadu Bello University, Zaria, Nigeria, between December 2015 and March 2016. Methodology: A set of 44 ketone and tetra-ketone derivatives with their anti-tyrosinase and anti-Candida Albicans activities in terms of minimum inhibitory concentration (MIC against the gram-positive fungal and hyperpigmentation were selected for 1D-3D quantitative structure activity relationship (QSAR analysis using the parameterization method 6 (PM6 basis set. The computed descriptors were correlated with their experimental MIC. Genetic Function Approximation (GFA method and Multi-Linear Regression analysis (MLR were used to derive the most statistically significant QSAR model. Results: The result obtained indicates that the most statistically significant QSAR model was a five- parametric linear equation with the squared correlation coefficient (R2 value of 0.9914, adjusted squared correlation coefficient (R 2 adj value of 0.9896 and Leave one out (LOO cross validation coefficient (Q2 value of 0.9853. An external set was used for confirming the predictive power of the model, its R2 pred = 0.9618 and rm^2 = 0.8981. Conclusion: The QSAR results reveal that molecular mass, atomic mass, polarity, electronic and topological predominantly influence the anti-tyrosinase and anti-Candida Albicans activity of the complexes. The wealth of information in this study will provide an insight to designing novel bioactive ketones and tetra-ketones compound that will curb the emerging trend of multi-drug resistant strain of fungal and hyperpigmentation
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.
Systematization method for distinguishing plastic groups by using NIR spectroscopy.
Kaihara, Mikio; Satoh, Minami; Satoh, Minoru
2007-07-01
A systematic classification method for polymers is not yet available in case of using near infrared spectra (NIR). That is why we have been searching for a systematic method. Because raw NIR spectra usually have few obvious peaks, NIR spectra have been pretreated by 2nd derivation for taking well modulated spectra. After the pretreatment, we applied classification and regression trees (CART) to the discrimination between the spectra and the species of polymers. As a result, we obtained a relatively simple classification tree. Judging from the obtained splitting conditions and the classified polymers, we concluded that obtained knowledge on the chemical function groups estimated by the important wavelength regions is not always applicable to this classification tree. However, we clarified the splitting rules for polymer species from the NIR spectral point of view.
Fast Twin Support Vector Regression Algorithm in Primal Space%快速原空间孪生支持向量回归算法
彭新俊; 王翼飞
2011-01-01
Twin support vector regression (TSVR) efficiently determines its objective regression function by optimizing a pair of smaller sized SVM-type problems. The objective functions of TSVR in the primal space are directly optimized by introducing the well-known Newton algorithm. This method effectively overcomes the shortcoming of TSVR that its regressor is aplroximated by the dual quadratic programming problems. Numerical studies show that the proposed method provides good performance and obtains less learning time compared with TSVR.%孪生支持向量回归(TSVR)通过快速优化--对较小规模的支持向量机问题获得回归函数.文中提出在原始输入空间中采用Newton法直接优化TSVR的目标函数,从而有效克服TSVR通过对偶二次规划问题求得近似最优解导致性能上的损失.数值模拟实验表明该方法不仅能提高TSVR的性能,并且可降低学习时间.
ERLS Algorithm for Linear Regression Model with Missing Response Variable%响应变量缺失下线性回归模型的ERLS算法
刘力军
2012-01-01
针对线性回归模型,提出了一个新的期望递归最小二乘算法（Expectation Recursive Least Square,ERLS）。在响应变量数据存在部分缺失的条件下,ERLS取响应变量的期望值代替缺失值,基于该期望值与自变量数据,实现自适应的递归估计回归系数,避免了高维数据相关矩阵的求逆困难。ERLS算法充分利用了全部有效数据,实现了在线回归估计。数值实验结果表明,在观测数据存在野值时,通过引入非线性抑制函数,ERLS算法优于LS方法。%A novel Expectation Least Square(ERLS) algorithm is proposed for linear regression model.Under the condition that response is partly missing,ERLS uses expectation value of the response instead of the missing value.Based on the expectation value and the data of independent variable,ERLS adaptively estimates the regression coefficients,which avoids the difficulty of inversion operation to the correlation matrix of high-dimensional data.ERLS makes fully use of the available data and sovles the regression problem in an online manner.Numerical expriments show that,by introducing a nonlinear function of supression,ERLS is superior to LS solution under the existence of wild data points.
The incidence of nirS and nirK and their genetic heterogeneity in cultivated denitrifiers.
Heylen, Kim; Gevers, Dirk; Vanparys, Bram; Wittebolle, Lieven; Geets, Joke; Boon, Nico; De Vos, Paul
2006-11-01
Gene sequence analysis of nirS and nirK, both encoding nitrite reductases, was performed on cultivated denitrifiers to assess their incidence in different bacterial taxa and their taxonomical value. Almost half of the 227 investigated denitrifying strains did not render an nir amplicon with any of five previously described primers. NirK and nirS were found to be prevalent in Alphaproteobacteria and Betaproteobacteria, respectively, nirK was detected in the Firmicutes and Bacteroidetes and nirS and nirK with equal frequency in the Gammaproteobacteria. These observations deviated from the hitherto reported incidence of nir genes in bacterial taxa. NirS gene phylogeny was congruent with the 16S rRNA gene phylogeny on family or genus level, although some strains did group within clusters of other bacterial classes. Phylogenetic nirK gene sequence analysis was incongruent with the 16S rRNA gene phylogeny. NirK sequences were also found to be significantly more similar to nirK sequences from the same habitat than to nirK sequences retrieved from highly related taxa. This study supports the hypothesis that horizontal gene transfer events of denitrification genes have occurred and underlines that denitrification genes should not be linked with organism diversity of denitrifiers in cultivation-independent studies.
Selection of efficient wavelengths in NIR spectrum for determination of dry matter in kiwi fruit
Cai Jianrong
2010-04-01
Full Text Available The feasibility of using efficient wavelengths in the near-infrared (NIR spectrum for the rapid determination of the dry matter (DM in kiwi fruit was investigated. Partial least squares (PLS, synergy interval PLS (siPLS and genetic algorithm siPLS (GA-siPLS were comparatively performed to calibrate regression models. The number of wavelengths and the number of PLS components were optimised as per the root mean square error of cross-validation (RMSECV in the calibration set. The performance of the final model was evaluated by the root mean square error of prediction (RMSEP and the correlation coefficient (r in the prediction set. Results indicate that the performance of GA-siPLS model is the best one compared to PLS and siPLS models. The optimal model was achieved with r = 0.9020 and RMSEP = 0.5315 in the prediction set. This work shows that it is feasible to determine DM in kiwi fruit using NIR spectroscopy and that GA-siPLS algorithm is most suitable in solving the problem of selection of efficient wavelengths.
Scaled Sparse Linear Regression
Sun, Tingni
2011-01-01
Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual squares and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs nearly nothing beyond the computation of a path of the sparse regression estimator for penalty levels above a threshold. For the scaled Lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression coefficients and noise level. Under mild regularity conditions, we prove that the method yields simultaneously an estimator for the noise level and an estimated coefficient vector in the Lasso path satisfying certain oracle inequalities for the estimation of the noise level, prediction, and the estimation of regression coefficients. These oracle inequalities provide sufficient conditions for the consistency and asymptotic...
Time-adaptive quantile regression
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
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...... 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...... 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....
华媛媛; 陈蕾
2012-01-01
The thesis studied the algorithms of single-frequency GPS precise point positioning, including the regression equation of single-frequency precise point positioning and the Kalman filter for single frequency precise point positioning. The observation equation and the equation of state of the Kalman filter were explored, and the state transition matrix and the system noise matrix were provided. Through the numerical example,the thesis analyzed the system performance characteristics and the positioning accuracy that reached a decimeter-level in 1 second sampling rate.%研究了单频GPS精密单点定位的算法,包括单频精密单点定位的回归方程及卡尔曼滤波用于单频精密单点定位,探讨卡尔曼滤波的观测方程和状态方程,给出了状态转移矩阵及系统噪声矩阵.通过算例验证了在1s采样率的情况下,定位达到了分米级的精度.
Ghaedi, M; Dashtian, K; Ghaedi, A M; Dehghanian, N
2016-05-11
The aim of this work is the study of the predictive ability of a hybrid model of support vector regression with genetic algorithm optimization (GA-SVR) for the adsorption of malachite green (MG) onto multi-walled carbon nanotubes (MWCNTs). Various factors were investigated by central composite design and optimum conditions was set as: pH 8, 0.018 g MWCNTs, 8 mg L(-1) dye mixed with 50 mL solution thoroughly for 10 min. The Langmuir, Freundlich, Temkin and D-R isothermal models are applied to fitting the experimental data, and the data was well explained by the Langmuir model with a maximum adsorption capacity of 62.11-80.64 mg g(-1) in a short time at 25 °C. Kinetic studies at various adsorbent dosages and the initial MG concentration show that maximum MG removal was achieved within 10 min of the start of every experiment under most conditions. The adsorption obeys the pseudo-second-order rate equation in addition to the intraparticle diffusion model. The optimal parameters (C of 0.2509, σ(2) of 0.1288 and ε of 0.2018) for the SVR model were obtained based on the GA. For the testing data set, MSE values of 0.0034 and the coefficient of determination (R(2)) values of 0.9195 were achieved.
无
2008-01-01
This paper describes a robust support vector regression (SVR) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique (SVR-GA) for efficient tuning of SVR mcta-parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters.
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.
Hao, Lingxin
2007-01-01
Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao
Online process analysis by NIRS; Online Prozessanalyse mit NIRS
Andree, Helga [TENIRS GmbH, Kiel (Germany)
2009-07-01
Nearinfrared-spectroscopy (NIRS) is a quick optical measurement system. It is meanwhile state of the art in many laboratory applications, such as feed analysis. Beyond that, NIRS is well suited as a multivariate inline-analysis-system for in-situ process-monitoring in biogas plants. First TENIRS pilot applications in full scale biogas plants deliver continuously DM, ODM, acetic-acid-equivalent, buffer-capacity, pH, ammonium and nitrogen. Advantages of NIRS as an integrated process-analysis-system derive from the contact-less, permanent measurement directly in the sample-stream, which allows representative complete monitoring of the entire process, simultaneous measurement of a wide range of parameters and the concurrent monitoring of multiple substrate streams. (orig.)
Mapping of TBARS distribution in frozen-thawed pork using NIR hyperspectral imaging.
Wu, Xiang; Song, Xinglin; Qiu, Zhengjun; He, Yong
2016-03-01
In this study, NIR hyperspectral imaging technology was applied to determine the distribution of TBARS in frozen-thawed pork. A total of 240 fresh pork samples were assigned to 4 treatment groups (0, 1, 3, 5 frozen-thawed cycles). For each sample, a hyperspectral image (874-1734nm) was collected, followed by chemical TBARS analysis. Successive projection algorithm (SPA) was applied to choose effective wavelengths (EWs). The selected 13 EWs of the calibration set and relevant TBARS value were used as inputs of partial least squares regression (PLSR) model, yielding correlation coefficient of prediction of 0.81 and root mean square error of prediction of 0.33. The developed PLSR model were applied pixel-wise to produce chemical maps of TBARS for 24 selected samples in the prediction set. The results indicated that NIR hyperspectral imaging combined with image processing has the potential to visualize TBARS distribution in frozen-thawed pork. This technique could be useful in real-time quality monitoring in meat industry.
Classification and quantification of palm oil adulteration via portable NIR spectroscopy
Basri, Katrul Nadia; Hussain, Mutia Nurulhusna; Bakar, Jamilah; Sharif, Zaiton; Khir, Mohd Fared Abdul; Zoolfakar, Ahmad Sabirin
2017-02-01
Short wave near infrared spectroscopy (NIR) method was used to detect the presence of lard adulteration in palm oil. MicroNIR was set up in two different scan modes to study the effect of path length to the performance of spectral measurement. Pure and adulterated palm oil sample were classified using soft independent modeling class analogy (SIMCA) algorithm with model accuracy more than 0.95 reported for both transflectance and transmission modes. Additionally, by employing partial least square (PLS) regression, the coefficient of determination (R2) of transflectance and transmission were 0.9987 and 0.9994 with root mean square error of calibration (RMSEC) of 0.5931 and 0.6703 respectively. In order to remove the uninformative variables, variable selection using cumulative adaptive reweighted sampling (CARS) has been performed. The result of R2 and RMSEC after variable selection for transflectance and transmission were improved significantly. Based on the result of classification and quantification analysis, the transmission mode has yield better prediction model compared to the transflectance mode to distinguish the pure and adulterated palm oil.
Hoshi, Y
Near-infrared spectroscopy (NIRS) was originally designed for clinical monitoring of tissue oxygenation, and it has also been developed into a useful tool in neuroimaging studies, with the so-called functional NIRS (fNIRS). With NIRS, cerebral activation is detected by measuring the cerebral hemoglobin (Hb), where however, the precise correlation between NIRS signal and neural activity remains to be fully understood. This can in part be attributed to the situation that NIRS signals are inherently subject to contamination by signals arising from extracerebral tissue. In recent years, several approaches have been investigated to distinguish between NIRS signals originating in cerebral tissue and signals originating in extracerebral tissue. Selective measurements of cerebral Hb will enable a further evolution of fNIRS. This chapter is divided into six sections: first a summary of the basic theory of NIRS, NIRS signals arising in the activated areas, correlations between NIRS signals and fMRI signals, correlations between NIRS signals and neural activities, and the influence of a variety of extracerebral tissue on NIRS signals and approaches to this issue are reviewed. Finally, future prospects of fNIRS are described. © 2016 Elsevier B.V. All rights reserved.
Application of NIR spectroscopy for firmness evaluation of peaches
Xia-ping FU; Yi-bin YING; Ying ZHOU; Li-juan XIE; Hui-rong XU
2008-01-01
The use of near infrared (NIR) spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800～2500 nm by lnGaAs detector, was used to evaluate the firmness of peaches. Anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts (including three latitudes and three longitudes) were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes (i,ii, iii) and latitudes (A, B, C). For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. Partial least square regression (PLSR) models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction (MSC) pretreatment, with correlation coefficient of cross-validation rcv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of cali-bration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance.
Forage nutritive value (i.e., forage quality) impacts livestock health and performance, but determining the quality of forages for grazing animals is difficult. In the 1970s, development and application of bench-top near-infrared spectroscopy (NIRS) techniques to assess forage quality proved to be ...
Kahane, Leo H
2007-01-01
Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition Offers greater coverage of simple panel-data estimation:
A career in agriculture and NIR
Having spent the last three decades in research and most of that dealing with the application of near-infrared (NIR) spectroscopy (NIRS) to animal agriculture has led to observations about NRS in general and career decisions in particular. For example, over the last two decades NIRS has moved from ...
Constrained Sparse Galerkin Regression
Loiseau, Jean-Christophe
2016-01-01
In this work, we demonstrate the use of sparse regression techniques from machine learning to identify nonlinear low-order models of a fluid system purely from measurement data. In particular, we extend the sparse identification of nonlinear dynamics (SINDy) algorithm to enforce physical constraints in the regression, leading to energy conservation. The resulting models are closely related to Galerkin projection models, but the present method does not require the use of a full-order or high-fidelity Navier-Stokes solver to project onto basis modes. Instead, the most parsimonious nonlinear model is determined that is consistent with observed measurement data and satisfies necessary constraints. The constrained Galerkin regression algorithm is implemented on the fluid flow past a circular cylinder, demonstrating the ability to accurately construct models from data.
Application of on-line NIR spectroscopy in fuel pellet production
Grothage, Morgan; Svensson, Elin; Johnsson, Bo [Casco Adhesives AB, Sundsvall (Sweden); Lestander, Torbjoern A. [Swedish Univ of Agricultural Science, Umeaa (Sweden). Unit of Biomass Technology and Chemistry
2006-07-15
Different possibilities of installation of on-line NIR spectrometers in various process environments in the fuel Pellets process were investigated using moisture content on dried wood raw material as the model analyte. Scanning NIR instruments of different types and to some extent diode-array instrument were used. Real time predictions of moisture content from partial least squares regression models were presented to the process operators using dedicated software.
[Potential Applicability of Fecal NIRs: A Review].
Yan, Xu; Du, Zhou-he; Bai, Shi-qie; Zuo, Yan-chun; Zhou, Xiao-kang; Kou, Jing; Yan, Jia-jun; Zhang, Jian-bo; Li, Ping; You, Ming-hong; Zhang, Yu; Li, Da-xu; Zhang, Chang-bing; Zhang, Jin
2015-12-01
Near-infrared reflectance spectroscopy (NIRS) is an inexpensive, rapid, environment-friendly and non-invasive analytical technique that has been extensively applied in the analysis of the dietary attributes and the animal products. Acquisition of dietary attributes is essential for nutritional diagnoses to provide animals with reasonable diet. Traditionally, the calibration equations for the prediction of dietary attributes (e. g. crude protein) are developed from feed NIR spectra and the results of conventional chemical analysis (i. e. reference data). It is difficult to obtain the NIR spectra of forages consumed by grazing animals, so the method of this calibration is inappropriate for free-grazing herbivores. Feces, as the animal's metabolites, contain the information about both the animal's diet and the animal itself. Recently, Fecal-NIRS (F. NIRS) has been directly used to monitor diet information (botanical composition, chemical composition and digestibility), based on correlation between reference data and fecal NIR profile. Subsequently, some additional application (such as sex and species discrimination, reproductive and parasite status) of F. NIRS also is outlined. In the last, application of NIRS in animal manure is summarized. NIRS was shown to be an alternative to conventional wet chemical methods for analyzing some nutrient concentrations in animal manure rapidly. Overall, this paper proves that F. NIRS is a rapid and valid tool for the determination of the dietary attributes and of the physiological status of animal, although more efforts need to be done to improve the accuracy of the F. NIRS technique. Several researchers in English have reviewed the applications of F. NIRS. In China, however, there is a paucity of research and application regarding F. NIRS. We expect that this paper in Chinese will be helpful to the development of F. NIRS in China. At the same time, we propose NIRS as a simple and rapid analytical method for predicting the main
Generating passive NIR images from active LIDAR
Hagstrom, Shea; Broadwater, Joshua
2016-05-01
Many modern LIDAR platforms contain an integrated RGB camera for capturing contextual imagery. However, these RGB cameras do not collect a near-infrared (NIR) color channel, omitting information useful for many analytical purposes. This raises the question of whether LIDAR data, collected in the NIR, can be used as a substitute for an actual NIR image in this situation. Generating a LIDAR-based NIR image is potentially useful in situations where another source of NIR, such as satellite imagery, is not available. LIDAR is an active sensing system that operates very differently from a passive system, and thus requires additional processing and calibration to approximate the output of a passive instrument. We examine methods of approximating passive NIR images from LIDAR for real-world datasets, and assess differences with true NIR images.
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…
Nick, Todd G; Campbell, Kathleen M
2007-01-01
The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as "statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable." Logistic regression models are used to study effects of predictor variables on categorical outcomes and normally the outcome is binary, such as presence or absence of disease (e.g., non-Hodgkin's lymphoma), in which case the model is called a binary logistic model. When there are multiple predictors (e.g., risk factors and treatments) the model is referred to as a multiple or multivariable logistic regression model and is one of the most frequently used statistical model in medical journals. In this chapter, we examine both simple and multiple binary logistic regression models and present related issues, including interaction, categorical predictor variables, continuous predictor variables, and goodness of fit.
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....
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...
Cotton Micronaire Measurements Using Small Portable Near-Infrared (NIR) Analyzers.
Zumba, Jimmy; Rodgers, James
2016-05-01
A key quality and processing parameter for cotton fiber is micronaire, which is a function of the fiber's maturity and fineness. Near-infrared (NIR) spectroscopy has previously shown the ability to measure micronaire, primarily in the laboratory and using large, research-grade laboratory NIR instrumentation. International interest has been expressed by the industry in the measurement of fiber micronaire using small, portable NIR spectroscopy instruments for both laboratory and outside the laboratory (e.g., field or greenhouse) locations. New, very small NIR micro-spectrometers have been commercialized that offer the potential advantages of smaller size and lower weight, lower cost, and increased portability over current portable units. A program was implemented to determine the feasibility of a small NIR micro-spectrometer to measure fiber micronaire both in the laboratory and outside the laboratory, with initial emphasis on laboratory measurements prior to moving to field evaluations. In the laboratory, distinct spectral differences with increasing micronaire were observed. Optimal sampling and instrumental procedures and protocols for two units (different spectral wavelength capabilities) were established. Comparative evaluations established very good method micronaire agreement between the micro-spectrometer and a standard portable spectrometer, with high Regression (R) value, low residuals, and few outliers (less than 20%). The NIR micro-spectrometer measurements were fast (NIR micro-spectrometer was demonstrated.
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...
无
2007-01-01
To compare mid-infrared (MIR) and near-infrared (NIR) spectroscopies for the determination of the fat and protein contents in milk, the same sample sets with varying concentrations of fat and protein were measured in the MIR range of 3 200-700 cm-1 and NIR range of 9 000-4 000 cm- 1. The spectral features in the two regions were analyzed. The MIR spectra of milk were characteristic due to the MIR inherent molecular specificity, whereas the NIR spectra were relatively characterless due to the NIR low selectivity. Partial least squares (PLS) regression models for fat and protein were developed by using both MIR and NIR spectra. MIR data with no pretreatment gave better results than NIR data. The square correlation coefficient ( R2) and the root mean square error of prediction (RMSEP) were 0.98 and 0.10 g/dL for fat and 0.97 and 0.11 g/dL for protein. With NIR techniques, satisfactory results were not obtained with raw data. However, NIR data after pretreatment gave similarly good results to the ones using MIR method. This paper indicates that either of the MIR and NIR spectral methods is reliable for the determination of the fat and protein contents.
Patil, S.G.; Mandal, S.; Hegde, A.V.
, number of hidden layers and neurons by trial and error, which is time consuming. To overcome the problems inherent in ANN training procedures Jeng et al., [19] adopted the concept of genetic algorithm based training of ANN models, which provided...-fuzzy inference system (ANFIS), which is a five-layer feed-forward neural network, which includes fuzzification layer, rule layer, normalization layer, defuzzification layer and a single summation neuron . It is a hybrid neuro-fuzzy technique that brings...
Combining Alphas via Bounded Regression
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.
Maleki, Afshin; Daraei, Hiua; Alaei, Loghman; Faraji, Aram
2014-01-01
Four stepwise multiple linear regressions (SMLR) and a genetic algorithm (GA) based multiple linear regressions (MLR), together with artificial neural network (ANN) models, were applied for quantitative structure-activity relationship (QSAR) modeling of dissociation constants (Kd) of 62 arylsulfonamide (ArSA) derivatives as human carbonic anhydrase II (HCA II) inhibitors. The best subsets of molecular descriptors were selected by SMLR and GA-MLR methods. These selected variables were used to generate MLR and ANN models. The predictability power of models was examined by an external test set and cross validation. In addition, some tests were done to examine other aspects of the models. The results show that for certain purposes GA-MLR is better than SMLR and for others, ANN overcomes MLR models.
Using Massive Multivariate NIRS Data in Ryegrass
Edriss, Vahid; Greve-Pedersen, Morten; Jensen, Christian S;
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...
NIR- and SWIR-based on-orbit vicarious calibrations for satellite ocean color sensors.
Wang, Menghua; Shi, Wei; Jiang, Lide; Voss, Kenneth
2016-09-05
The near-infrared (NIR) and shortwave infrared (SWIR)-based atmospheric correction algorithms are used in satellite ocean color data processing, with the SWIR-based algorithm particularly useful for turbid coastal and inland waters. In this study, we describe the NIR- and two SWIR-based on-orbit vicarious calibration approaches for satellite ocean color sensors, and compare results from these three on-orbit vicarious calibrations using satellite measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). Vicarious calibration gains for VIIRS spectral bands are derived using the in situ normalized water-leaving radiance nLw(λ) spectra from the Marine Optical Buoy (MOBY) in waters off Hawaii. The SWIR vicarious gains are determined using VIIRS measurements from the South Pacific Gyre region, where waters are the clearest and generally stable. Specifically, vicarious gain sets for VIIRS spectral bands of 410, 443, 486, 551, and 671 nm derived from the NIR method using the NIR 745 and 862 nm bands, the SWIR method using the SWIR 1238 and 1601 nm bands, and the SWIR method using the SWIR 1238 and 2257 nm bands are (0.979954, 0.974892, 0.974685, 0.965832, 0.979042), (0.980344, 0.975344, 0.975357, 0.965531, 0.979518), and (0.980820, 0.975609, 0.975761, 0.965888, 0.978576), respectively. Thus, the NIR-based vicarious calibration gains are consistent with those from the two SWIR-based approaches with discrepancies mostly within ~0.05% from three data processing methods. In addition, the NIR vicarious gains (745 and 862 nm) derived from the two SWIR methods are (0.982065, 1.00001) and (0.981811, 1.00000), respectively, with the difference ~0.03% at the NIR 745 nm band. This is the fundamental basis for the NIR-SWIR combined atmospheric correction algorithm, which has been used to derive improved satellite ocean color products over open oceans and turbid coastal/inland waters. Therefore, a unified
Huttunen, Jani; Kokkola, Harri; Mielonen, Tero; Esa Juhani Mononen, Mika; Lipponen, Antti; Reunanen, Juha; Vilhelm Lindfors, Anders; Mikkonen, Santtu; Erkki Juhani Lehtinen, Kari; Kouremeti, Natalia; Bais, Alkiviadis; Niska, Harri; Arola, Antti
2016-07-01
In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the look-up table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during
任媛媛; 姚宏亮
2013-01-01
随着我国金融行业的快速发展，大量繁杂的金融数据需要快速有效的处理，而通过最小二乘法来估计参数的多元线性回归算法处理金融数据，难以得到准确的结果。因此，基于岭回归基本原理，针对深证指数数据，运用岭回归分析和处理金融数据，通过与最小二乘法模型的对比分析，显示基于岭回归的金融数据分析方法能够有效地避免多重共线性对于金融数据的影响，克服了传统的最小二乘法进行回归分析所带来的模型失真问题。%With the rapid development of China's financial industry,a large number of financial data are complicated to be treated quickly and effectively,and by using the least square method to estimate the parameters of the multivariate linear regression algorithm to deal with the financial data,it is difficult to obtain accurate results. Therefore,the ridge regression based on the basic principles,the Shenzhen stock index data,using the ridge regression analysis and processing of financial data,by comparing the model and the method of least squares analysis,showed that the financial data of ridge regression analysis method can effectively avoid the multicollinearity effect on financial data based on least square method,has overcome the traditional was brought about by regression analysis model distortion.
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 est
Using Massive Multivariate NIRS Data in Ryegrass
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...
无
2007-01-01
A novel near infrared (NIR) modeling method-Laplacian regularized least squares regression (LapRLSR) was presented,which can take the advantage of many unlabeled spectra to promote the prediction performance of the model even if there are only few calibration samples. Using LapRLSR modeling, NIR spectral analysis was applied to the online monitoring of the concentration of salvia acid B in the column separation of Salvianolate. The results demonstrated that LapRLSR outperformed partial least squares (PLS) significantly, and NIR online analysis was applicable.(C) 2007 Guo An Luo. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved.
Lopes, Marta B.; Gonçalves, Geisa A. L.; Felício-Silva, Daniel; Prather, Kristala L. J.; Monteiro, Gabriel; Prazeres, Duarte M. F.; Calado, Cecília Ribeiro da Cruz
2015-01-01
BACKGROUNDWhile the pharmaceutical industry keeps an eye on plasmid DNA production for new generation gene therapies, real-time monitoring techniques for plasmid bioproduction are as yet unavailable. This work shows the possibility of in situ monitoring of plasmid production in Escherichia coli cultures using a near infrared (NIR) fiber optic probe. RESULTSPartial least squares (PLS) regression models based on the NIR spectra were developed for predicting bioprocess critical variables su...
孙文胜; 胡玲敏
2011-01-01
Concerning the common problem of tag collision in Radio Frequency Identification (RFID) system, an improved anti-collision algorithm for multi-branch tree was proposed based on the regressive-style search algorithm.According to the characteristics of the tags collision, the presented algorithm adopted the dormancy count, and took quad tree structure when continuous collision appeared, which had the ability to choose the number of forks dynamically during the searching process, reduced the search range and improved the identification efficiency.The performance analysis results show that the system efficiency of the proposed algorithm is about 76.5％; moreover, with the number of tags increased, the superiority of the performance is more obvious.%针对无线射频识别(RFID)系统中常见的标签防碰撞问题,在后退式搜索算法的基础上提出了一种改进的多叉树防碰撞算法.根据标签碰撞的特点,采用休眠计数的方法,以及遇到连续碰撞位时进行四叉树分裂的策略,使得在搜索过程中能够动态选择分叉数量,缩短了标签识别时间,有效地提高了算法的搜索效率.性能分析表明,该算法的系统识别效率达76.5%,且随着标签数目的增多,优越性更加明显.
基于稳健回归技术的软件成本估计方法%Software Cost Estimation Based on the Robust Regression Algorithm
孙士兵; 马莉
2008-01-01
随着软件系统规模的不断扩大和复杂程度的日益加大,从20世纪60年代末期开始,出现了以大量软件项目进度延期、预算超支和质量缺陷为典型特征的软件危机.在对软件项目进行估算时,通常情况下能得到相关软件组织或软件产品的某些历史数据,充分利用这些历史数据对预测与估算软件项目是很有帮助的.稳健回归分析(RRA),就是这样一种相当常用与有效的数据驱动方法.在比较、回顾一些稳健回归分析研究成果的基础上,重点解决了软件成本估算数据用传统回归分析存在的问题,并有效地解决了由于异常数据存在而产生的掩蔽效应.同时尝试提出在软件成本数据估算中运用稳健回归方法进行系统而全面的仿真实验分析,发现该方法能有效地解决异常数据的掩蔽效应,得到比较满意的结果.%Along with the unceasing expansion and the complex degree daily enlarging of the software system scale,it appears the typical software crisis such as the massive software project progress extension,the budget overspending and the quality flaw from the 20th century 60s last stages.The correlated software organization or the software product certain historical data can obtain carry on the estimation of the software project in the usual situation,and it is helpful to take advantage of these data to forecast the future software projects.The robust regression analysis (RRA) is such one kind quite commonly used and the effective data actuation method.Based on some retrospective studies of RRA,focuses on some problems of the software cost estimation data with ordinary methods and tries to propose the RRA methods to analysis of the software development cost estimation data and effectively solutes to the problem of masking effects when the outliers exist.The results are found that this method could solve effectively the masking effects by outliers and obtained better results.
ORDINAL REGRESSION FOR INFORMATION RETRIEVAL
无
2008-01-01
This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effectiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM significantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.
NIR reflectance method to determine moisture content in food products
Kandala, C. V. K.; Konda Naganathan, G.; Subbiah, J.
2008-08-01
Moisture content (MC) is an important quality factor that is measured and monitored, at various stages of processing and storage, in the food industry. There are some commercial instruments available that use near infrared (NIR) radiation measurements to determine the moisture content of a variety of grain products, such as wheat and corn, with out the need of any sample grinding or preparation. However, to measure the MC of peanuts with these instruments the peanut kernels have to be chopped into smaller pieces and filled into the measuring cell. This is cumbersome, time consuming and destructive. An NIR reflectance method is presented here by which the average MC of about 100 g of whole kernels could be determined rapidly and nondestructively. The MC range of the peanut kernels tested was between 8% and 26%. Initially, NIR reflectance measurements were made at 1 nm intervals in the wave length range of 1000 nm to 1800 nm and the data was modeled using partial least squares regression (PLSR). The predicted values of the samples tested in the above range were compared with the values determined by the standard air-oven method. The predicted values agreed well with the air-oven values with an R2 value of 0.96 and a standard error of prediction (SEP) of 0.83. Using the PLSR beta coefficients, five key wavelengths were identified and using multiple linear regression (MLR) method MC predictions were made. The R2 and SEP values of the MLR model were 0.84 and 1.62, respectively. Both methods performed satisfactorily and being rapid, nondestructive, and non-contact, may be suitable for continuous monitoring of MC of grain and peanuts as they move on conveyor belts during their processing.
Pourbasheer, Eslam; Riahi, Siavash; Ganjali, Mohammad Reza; Norouzi, Parviz
2010-12-01
A linear quantitative structure-activity relationship (QSAR) model is presented for the modelling and prediction for the interleukin-1 receptor associated kinase 4 (IRAK-4) inhibition activity of amides and imidazo[1,2-α] pyridines. The model was produced using the multiple linear regression (MLR) technique on a database that consisted of 65 recently discovered amides and imidazo[1,2- α] pyridines. Among the different constitutional, topological, geometrical, electrostatic and quantum-chemical descriptors that were considered as inputs to the model, seven variables were selected using the genetic algorithm subset selection method (GA). The accuracy of the proposed MLR model was illustrated using the following evaluation techniques: cross-validation, validation through an external test set, and Y-randomisation. The predictive ability of the model was found to be satisfactory and could be used for designing a similar group of compounds.
Enhanced piecewise regression based on deterministic annealing
ZHANG JiangShe; YANG YuQian; CHEN XiaoWen; ZHOU ChengHu
2008-01-01
Regression is one of the important problems in statistical learning theory. This paper proves the global convergence of the piecewise regression algorithm based on deterministic annealing and continuity of global minimum of free energy w.r.t temperature, and derives a new simplified formula to compute the initial critical temperature. A new enhanced piecewise regression algorithm by using "migration of prototypes" is proposed to eliminate "empty cell" in the annealing process. Numerical experiments on several benchmark datasets show that the new algo-rithm can remove redundancy and improve generalization of the piecewise regres-sion model.
Logistic Regression for Evolving Data Streams Classification
YIN Zhi-wu; HUANG Shang-teng; XUE Gui-rong
2007-01-01
Logistic regression is a fast classifier and can achieve higher accuracy on small training data. Moreover,it can work on both discrete and continuous attributes with nonlinear patterns. Based on these properties of logistic regression, this paper proposed an algorithm, called evolutionary logistical regression classifier (ELRClass), to solve the classification of evolving data streams. This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier, to keep this classifier if its performance is deteriorated by the reason of bursting noise, or to construct a new classifier if a major concept drift is detected. The intensive experimental results demonstrate the effectiveness of this algorithm.
基于蚁群算法的GUI软件回归测试用例集优化%Ant Algorithm-Based Regression Test Suite Optimization for GUI Software
于长钺; 张萌萌; 窦平安; 于秀山
2012-01-01
针对GUI(Graphical User Interface)软件输入/输出图形化、事件驱动、事件触发随机性所带来的回归测试用例数量巨大的难题,在GUI事件模型图基础上,构建了GUI软件回归测试用例集优化数学模型,给出了目标函数和约束条件,提出了一种基于蚁群算法的求解方法,制定了蚂蚁信息素更新规则和蚂蚁路径选择规则.仿真结果表明,该方法在保证覆盖效果的前提下,可以有效减少回归测试用例的数量和长度.%Aimed at the large number of regression test cases caused by the features of graphical input/output, event driven, random event trigger in GUI (Graphical User Interface) software, and on the basis of GUI event model, a mathematical model of regression test suite optimization for GUI software is constructed. The objective function and constraints in the model are given. And an ant algorithm is presented to solve the problem. Ant pheromone update rules and ant path selection rules in the algorithm are set. Simulation results show that under the premise that coverage is guaranteed, this method can reduce both the number and length of test case effectively.
VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity
Fernández, Roemi; Montes, Héctor; Salinas, Carlota
2015-01-01
Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR), Short-Wave InfraRed (SWIR) and Long-Wave InfraRed (LWIR) imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. To evaluate the feasibility and capabilities of the presented approach, several experimental tests were carried out in a sandy-loam terrain. The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation. Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations. PMID:26083227
VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity
Roemi Fernández
2015-06-01
Full Text Available Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR, Short-Wave InfraRed (SWIR and Long-Wave InfraRed (LWIR imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. To evaluate the feasibility and capabilities of the presented approach, several experimental tests were carried out in a sandy-loam terrain. The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation. Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations.
李艳肖; 黄晓玮; 邹小波; 赵杰文; 石吉勇; 张小磊
2015-01-01
Optimization of Near infrared (NIR) spectroscopy for quantitative analysis of the anthocyanin content in scented tea was discussed by selecting the optimal spectra intervals from the whole NIR spectroscopy using two variable models: Ant colony optimization interval partial least squares (ACO-iPLS) and Genetic Algorithm interval partial least squares (GA-iPLS). The ACO-iPLS full-spectrum was split into 12 intervals. The optimal intervals selected were the 1st interval, 9th interval and 10th interval. The calibration and prediction correlation coefficient of ACO-iPLS model were 0.901 3 and 0.864 2, in which the root mean square error of cross validation (RMSECV) of 0.160 0 mg/g and the root mean square error of prediction (RMSEP) of 0.206 0 mg/g were achieved.As in the GA-iPLS model, the data set was split into 15 intervals for optimization where 1st and 5th intervals were selected. The calibration and prediction correlation coefficient of GA-iPLS model were 0.901 3 and 0.864 2, and the RMSECV and RMSEP of GA-iPLS models based on these intervals were 0.156 0 mg/g and 0.206 0 mg/g, respectively. The results showed that both ACO-iPLS and GA-iPLS models could efficiently select spectrum intervals for quantitative analysis of anthocyanin in scented tea. The optimal GA-iPLS model had better performance with higher accuracy.%以建立花茶花青素含量的最优近红外光谱模型为目标，对比研究了蚁群算法（Ant Colony Optimization, ACO）和遗传算法（Genetic Algorithm, GA）优化近红外光谱谱区的效果。ACO-iPLS将全光谱划分为12个子区间时，优选出第1、9、10共3个子区间，所建的校正集和预测集相关系数分别为0.9013和0.8642；交互验证均方根误差（RMSECV）和预测均方根误差（RMSEP）分别为0.1600 mg/g和0.2020 mg/g；GA-iPLS将全光谱划分为15个子区间时，优选出第1、5共2个子区间，所建模型的校正集和预测集相关系数分别为0.9063和0.8793，交互验证均
Monitoring of whey quality with NIR spectroscopy--a feasibility study.
Kucheryavskiy, Sergey; Lomborg, Carina Juel
2015-06-01
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 processes and had a large variability for most of the parameters. Partial Least Squares (PLS) regression was used to make the prediction models based on NIR spectra taken at 30 and 40°C. Using proper wavelength range allowed to get models for prediction of fat, protein and amount of total solids with very high precision and accuracy. The lactose was found to be the most challenging parameter.
Chang, Tinghong; Lai, Xuxin; Zhang, Hong
2005-01-01
This work demonstrates the application of FT-IR and FT-NIR spectroscopy to monitor the enzymatic interesterification process for bulky fat modification. The reaction was conducted between palm stearin and coconut oil (70/30, w/w) with the catalysis of Lipozyme TL IM at 70°C in a batch reactor...... (PLS) regression. High correlations (r > 0.96) were obtained from cross validations of the data estimated by FT-IR, FT-NIRand above-mentioned conventional analytical methods, except for correlations (r = 0.90-0,95) between FT-IR and SFC profiles. Overall, FT-NIR spectroscopy coupled with transmission...
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
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.
Francis, C.A.; Jackson, G.A.; Ward, B.B.
2008-01-01
total RNA extracts) targets were hybridized to the same array to compare the profiles of community composition at the DNA (relative abundance) and mRNA (gene expression) levels. Only the three dominant denitrifying groups (in terms of relative strength of DNA hybridization signal) were detected at the m......A functional gene microarray was used to investigate denitrifier community composition and nitrite reductase (nirS) gene expression in sediments along the estuarine gradient in Chesapeake Bay, USA. The nirS oligonucleotide probe set was designed to represent a sequence database containing 539...
SHEN Heng-sheng; CHEN Jun-chen; ZHAO Wu-shan; NI De-bin; TANG Bao-sha; Valdes E V
2002-01-01
The silicification of rice straw is a factor that affects the grain production and straw nutritive quality. The procedure of chemical analysis for silicon in straw is, however, time and labor consuming, and slightly poor in accuracy. The study has attempted to apply near infrared reflectance spectroscopy (NIRS)technique as an advanced alternative to predict the fiber composition and silicification in rice straw. Ninetytwo samples from different seasons and varieties were collected over the Fujian Province. Their chemical analyses were carried on the aspects of hemicellulose, cellulose, lignin, extractable and non-extractable silicon,and the results were used as a database for NIRS analyses. The prediction model was developed through modified partial least square regression (MPLS) for a calibration program. The factors that may affect the calibration, cross-validation and the prediction for the application of NIRS on rice straw were also discussed.
Determination of Protein Content by NIR Spectroscopy in Protein Powder Mix Products.
Ingle, Prashant D; Christian, Roney; Purohit, Piyush; Zarraga, Veronica; Handley, Erica; Freel, Keith; Abdo, Saleem
2016-01-01
Protein is a principal component in commonly used dietary supplements and health food products. The analysis of these products, within the consumer package form, is of critical importance for the purpose of ensuring quality and supporting label claims. A rapid test method was developed using near-infrared (NIR) spectroscopy as a compliment to current protein determination by the Dumas combustion method. The NIR method was found to be a rapid, low-cost, and green (no use of chemicals and reagents) complimentary technique. The protein powder samples analyzed in this study were in the range of 22-90% protein. The samples were prepared as mixtures of soy protein, whey protein, and silicon dioxide ingredients, which are common in commercially sold protein powder drink-mix products in the market. A NIR regression model was developed with 17 samples within the constituent range and was validated with 20 independent samples of known protein levels (85-88%). The results show that the NIR method is capable of predicting the protein content with a bias of ±2% and a maximum bias of 3% between NIR and the external Dumas method.
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).
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.
Boyer, Chantal; Gaudin, Karen; Kauss, Tina; Gaubert, Alexandra; Boudis, Abdelhakim; Verschelden, Justine; Franc, Mickaël; Roussille, Julie; Boucher, Jacques; Olliaro, Piero; White, Nicholas J.; Millet, Pascal; Dubost, Jean-Pierre
2012-01-01
Near infrared spectroscopy (NIRS) methods were developed for the determination of analytical content of an antimalarial-antibiotic (artesunate and azithromycin) co-formulation in hard gelatin capsule (HGC). The NIRS consists of pre-processing treatment of spectra (raw spectra and first-derivation of two spectral zones), a unique principal component analysis model to ensure the specificity and then two partial least-squares regression models for the determination content of each active pharmaceutical ingredient. The NIRS methods were developed and validated with no reference method, since the manufacturing process of HGC is basically mixed excipients with active pharmaceutical ingredients. The accuracy profiles showed β-expectation tolerance limits within the acceptance limits (±5%). The analytical control approach performed by reversed phase (HPLC) required two different methods involving two different preparation and chromatographic methods. NIRS offers advantages in terms of lower costs of equipment and procedures, time saving, environmentally friendly. PMID:22579599
Boyer, Chantal; Gaudin, Karen; Kauss, Tina; Gaubert, Alexandra; Boudis, Abdelhakim; Verschelden, Justine; Franc, Mickaël; Roussille, Julie; Boucher, Jacques; Olliaro, Piero; White, Nicholas J; Millet, Pascal; Dubost, Jean-Pierre
2012-01-01
Near infrared spectroscopy (NIRS) methods were developed for the determination of analytical content of an antimalarial-antibiotic (artesunate and azithromycin) co-formulation in hard gelatin capsule (HGC). The NIRS consists of pre-processing treatment of spectra (raw spectra and first-derivation of two spectral zones), a unique principal component analysis model to ensure the specificity and then two partial least-squares regression models for the determination content of each active pharmaceutical ingredient. The NIRS methods were developed and validated with no reference method, since the manufacturing process of HGC is basically mixed excipients with active pharmaceutical ingredients. The accuracy profiles showed β-expectation tolerance limits within the acceptance limits (±5%). The analytical control approach performed by reversed phase (HPLC) required two different methods involving two different preparation and chromatographic methods. NIRS offers advantages in terms of lower costs of equipment and procedures, time saving, environmentally friendly.
Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks.
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.
Prediction of Dynamical Systems by Symbolic Regression
Quade, Markus; Shafi, Kamran; Niven, Robert K; Noack, Bernd R
2016-01-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting a...
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.
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.
张艳莉
2014-01-01
顾客抱怨作为消费行为研究中的一个重要课题，已成为营销研究的热点。有效的抱怨管理机制对于化解顾客不满情绪，提升企业服务水平，维持长期顾客关系等具有十分重要的意义。针对这一问题，本文详细描述了企业应如何加强抱怨管理流程，优化抱怨管理方法，改造企业组织结构，妥善处理顾客抱怨和开发抱怨的各种有用价值，并通过Logistic回归算法模型对数据进行实例分析，通过仿真结果表明，本文所提出的Logistic回归算法模型，能够有效地加速运算的速度，缩短运算的时间，对于构建企业顾客抱怨管理机制具有很好的作用。%Customer complaints as a consumer behavior research is an important topic, has become a marketing research hotspot. Effective complaints management mechanism for resolving customer dissatisfaction, enhance service levels, maintaining long-term customer relationships has very important significance. To solve this problem, this paper describes in detail how an entity should strengthen management processes complain, complain management optimization method, the transformation of enterprise organizational structure, and properly handle customer complaints and developers complain about a variety of useful value and Logistic Regression algorithm through the data model instance analysis, the simulation results show that the proposed algorithm Logistic regression model can effectively accelerate the speed of operation, reduce the computation time required to build enterprise customer complaints management mechanism has a good effect.
Sparén, Anders; Hartman, Madeleine; Fransson, Magnus; Johansson, Jonas; Svensson, Olof
2015-05-01
Raman spectroscopy can be an alternative to near-infrared spectroscopy (NIR) for nondestructive quantitative analysis of solid pharmaceutical formulations. Compared with NIR spectra, Raman spectra have much better selectivity, but subsampling was always an issue for quantitative assessment. Raman spectroscopy in transmission mode has reduced this issue, since a large volume of the sample is measured in transmission mode. The sample matrix, such as particle size of the drug substance in a tablet, may affect the Raman signal. In this work, matrix effects in transmission NIR and Raman spectroscopy were systematically investigated for a solid pharmaceutical formulation. Tablets were manufactured according to an experimental design, varying the factors particle size of the drug substance (DS), particle size of the filler, compression force, and content of drug substance. All factors were varied at two levels plus a center point, except the drug substance content, which was varied at five levels. Six tablets from each experimental point were measured with transmission NIR and Raman spectroscopy, and their concentration of DS was determined for a third of those tablets. Principal component analysis of NIR and Raman spectra showed that the drug substance content and particle size, the particle size of the filler, and the compression force affected both NIR and Raman spectra. For quantitative assessment, orthogonal partial least squares regression was applied. All factors varied in the experimental design influenced the prediction of the DS content to some extent, both for NIR and Raman spectroscopy, the particle size of the filler having the largest effect. When all matrix variations were included in the multivariate calibrations, however, good predictions of all types of tablets were obtained, both for NIR and Raman spectroscopy. The prediction error using transmission Raman spectroscopy was about 30% lower than that obtained with transmission NIR spectroscopy.
Photoswitchable NIR-Emitting Gold Nanoparticles.
Bonacchi, Sara; Cantelli, Andrea; Battistelli, Giulia; Guidetti, Gloria; Calvaresi, Matteo; Manzi, Jeannette; Gabrielli, Luca; Ramadori, Federico; Gambarin, Alessandro; Mancin, Fabrizio; Montalti, Marco
2016-09-05
Photo-switching of the NIR emission of gold nanoparticles (GNP) upon photo-isomerization of azobenzene ligands, bound to the surface, is demonstrated. Photophysical results confirm the occurrence of an excitation energy transfer process from the ligands to the GNP that produces sensitized NIR emission. Because of this process, the excitation efficiency of the gold core, upon excitation of the ligands, is much higher for the trans form than for the cis one, and t→c photo-isomerization causes a relevant decrease of the GNP NIR emission. As a consequence, photo-isomerization can be monitored by ratiometric detection of the NIR emission upon dual excitation. The photo-isomerization process was followed in real-time through the simultaneous detection of absorbance and luminescence changes using a dedicated setup. Surprisingly, the photo-isomerization rate of the ligands, bound to the GNP surface, was the same as measured for the chromophores in solution. This outcome demonstrated that excitation energy transfer to gold assists photo-isomerization, rather than competing with it. These results pave the road to the development of new, NIR-emitting, stimuli-responsive nanomaterials for theranostics.
NIRS in clinical neurology - a 'promising' tool?
Obrig, Hellmuth
2014-01-15
Near-infrared spectroscopy (NIRS) has become a relevant research tool in neuroscience. In special populations such as infants and for special tasks such as walking, NIRS has asserted itself as a low resolution functional imaging technique which profits from its ease of application, portability and the option to co-register other neurophysiological and behavioral data in a 'near natural' environment. For clinical use in neurology this translates into the option to provide a bed-side oximeter for the brain, broadly available at comparatively low costs. However, while some potential for routine brain monitoring during cardiac and vascular surgery and in neonatology has been established, NIRS is largely unknown to clinical neurologists. The article discusses some of the reasons for this lack of use in clinical neurology. Research using NIRS in three major neurologic diseases (cerebrovascular disease, epilepsy and headache) is reviewed. Additionally the potential to exploit the established position of NIRS as a functional imaging tool with regard to clinical questions such as preoperative functional assessment and neurorehabilitation is discussed.
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.…
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.…
Prediction of mixed hardwood lignin and carbohydrate content using ATR-FTIR and FT-NIR.
Zhou, Chengfeng; Jiang, Wei; Via, Brian K; Fasina, Oladiran; Han, Guangting
2015-05-05
This study used Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Fourier transform near-infrared (FT-NIR) spectroscopy with principal component regression (PCR) and partial least squares regression (PLS) to build hardwood prediction models. Wet chemistry analysis coupled with high performance liquid chromatography (HPLC) was employed to obtain the chemical composition of these samples. Spectra loadings were studied to identify key wavenumber in the prediction of chemical composition. NIR-PLS and FTIR-PLS performed the best for extractives, lignin and xylose, whose residual predictive deviation (RPD) values were all over 3 and indicates the potential for either instrument to provide superior prediction models with NIR performing slightly better. During testing, it was found that more accurate determination of holocellulose content was possible when HPLC was used. Independent chemometric models, for FT-NIR and ATR-FTIR, identified similar functional groups responsible for the prediction of chemical composition and suggested that coupling the two techniques could strengthen interpretation and prediction.
Huang, Kang; Wang, Hui-jun; Xu, Hui-rong; Wang, Jian-ping; Ying, Yi-bin
2009-04-01
The application of least square support vector machines (LS-SVM) regression method based on statistics study theory to the analysis with near infrared (NIR) spectra of tomato juice was introduced in the present paper. In this method, LS-SVM was used for establishing model of spectral analysis, and was applied to predict the sugar contents (SC) and available acid (VA) in tomato juice samples. NIR transmission spectra of tomato juice were measured in the spectral range of 800-2,500 nm using InGaAs detector. The radial basis function (RBF) was adopted as a kernel function of LS-SVM. Sixty seven tomato juice samples were used as calibration set, and thirty three samples were used as validation set. The results of the method for sugar contents (SC) and available acid (VA) prediction were: a high correlation coefficient of 0.9903 and 0.9675, and a low root mean square error of prediction (RMSEP) of 0.0056 degree Brix and 0.0245, respectively. And compared to PLS and PCR methods, the performance of the LSSVM method was better. The results indicated that it was possible to built statistic models to quantify some common components in tomato juice using near-infrared (NIR) spectroscopy and least square support vector machines (LS-SVM) regression method as a nonlinear multivariate calibration procedure, and LS-SVM could be a rapid and accurate method for juice components determination based on NIR spectra.
吴启凡
2015-01-01
我国人口老龄化问题日趋明显，现阶段对人口老龄化的模型研究依然存在问题，在对我国人口老龄化情况的研究过程中，单纯运用多元回归的方法需考虑多重共线性问题，为避免此问题则要优选变量，但在逐步回归过程中又会将对其可能造成显著性影响的偏相关扰动项忽略，而且单纯运用回归模型进行预测将在长时间序列中造成较大误差，为此，结合年龄移算法对回归因子进行单项细度预测，再运用回归方程进行宏观计算，将大幅提高预测的精度。本文以男性人口、女性人口、城市人口、乡村人口等因素进行动态研究，先根据相关性分析，初步筛选影响因素，再通过多元线性回归找到人口老龄化与人口结构中相关因素的数量关系，这里通过逐步回归出恰好出现了偏相关扰动项无法接受检验的情况，我们运用两种标准化方法结合Mann-Whitney U检验进行验证分析，最终运用年龄移算模型和回归矩阵预测人口老龄化发展趋势，并根据预测结果进行相关分析，给出相应评价。%The problem of our aging population has become more evident ,the model for the study of population aging is still a problem at this stage. In the case of China’s aging population of the study,the issues of using a simple method (multiple re-gression multicollinearity) is to be considered,To avoid this problem may lead to the Multicollinearity,however they will be the likely cause of a significant impact which can be easily ignored. And use the simple regression model to predict the result in the long sequence may also give rise to more errors,so we need to combined with age-shift algorithm to return the individu-al factors fineness forecast,then use the macro regression equation to calculate,which will significantly improve the prediction accuracy. In this paper,According to correlation analysis,initial screening factors from
RGB-NIR color image fusion: metric and psychophysical experiments
Hayes, Alex E.; Finlayson, Graham D.; Montagna, Roberto
2015-01-01
In this paper, we compare four methods of fusing visible RGB and near-infrared (NIR) images to produce a color output image, using a psychophysical experiment and image fusion quality metrics. The results of the psychophysical experiment show that two methods are significantly preferred to the original RGB image, and therefore RGB-NIR image fusion may be useful for photographic enhancement in those cases. The Spectral Edge method is the most preferred method, followed by the dehazing method of Schaul et al. We then investigate image fusion metrics which give results correlated with the psychophysical experiment results. We extend several existing metrics from 2 to 1 to M to N channel image fusion, as well as introducing new metrics based on output image colorfulness and contrast, and test them on our experimental data. While none of the individual metrics gives a ranking of the algorithms which exactly matches that of the psychophysical experiment, through a combination of two metrics we accurately rank the two leading fusion methods.
Development and testing of a VisNIR penetrometer for in situ soil characterization
Bricklemyer, R.; Poggio, M.; Brown, D. J.
2012-12-01
Heterogenous agricultural fields must be partitioned into zones with similar soil properties for effective site-specific management. However, standard soil surveys do not generally provide the necessary spatial resolution for this application, and it is expensive and time consuming to generate high-resolution soil maps using standard soil sampling and analysis techniques. Visible and Near-Infrared (VisNIR) spectroscopy is an established method for rapidly and inexpensively estimating soil properties when applied to dried and sieved samples in a laboratory setting. However, this technique still requires that samples be extracted, transported and processed using standard methods. To reduce soil analysis costs further (and allow more dense spatial sampling), it would be ideal to interrogate soils in situ with a field-portable VisNIR spectrometer and foreoptic. The goal of this study was to design and test a VisNIR penetrometer capable of simultaneously collecting soil spectra and insertion force, in situ. Our design allows the use of field-deployable spectrometers that employ signal delivery via fiber optics (e.g. ASD Agrispec) and hydraulic push-type soil coring rig (ex. Giddings). We first compared the quality of VisNIR spectra collected using the penetrometer fore-optic with the spectrometer manufacturer's contact probe foreoptic in a laboratory setting for dried and sieved (2 mm) Palouse soils (eastern WA and northern ID, USA.) Partial least squares regression (PLSR) was used calibrate and validate VisNIR models predicting soil clay and organic carbon content. The VisNIR penetrometer was then deployed for in situ soil characterization at ten fields in the states of Washington, Oregon and Idaho selected to capture broad ranges of soil characteristics (ex. parent material, soil organic C, soil inorganic C, clay content, clay mineralogy). To calibrate VisNIR PLSR models, intact soil cores were collected adjacent to location probed with the VisNIR penetrometer and
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
2013-12-07
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 (Ln(3+)), nanoscopic host materials doped with Ln(3+), e.g. Y2O3:Er(3+),Yb(3+), are promising candidates for NIR-NIR bioimaging. Ln(3+)-doped gadolinium-based inorganic nanostructures, such as Gd2O3:Er(3+),Yb(3+), 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.
NIR optimerer produktionen af gammeldags modnede sild
Svensson, Vibeke Tølbøl; 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....
XRA image segmentation using regression
Jin, Jesse S.
1996-04-01
Segmentation is an important step in image analysis. Thresholding is one of the most important approaches. There are several difficulties in segmentation, such as automatic selecting threshold, dealing with intensity distortion and noise removal. We have developed an adaptive segmentation scheme by applying the Central Limit Theorem in regression. A Gaussian regression is used to separate the distribution of background from foreground in a single peak histogram. The separation will help to automatically determine the threshold. A small 3 by 3 widow is applied and the modal of the local histogram is used to overcome noise. Thresholding is based on local weighting, where regression is used again for parameter estimation. A connectivity test is applied to the final results to remove impulse noise. We have applied the algorithm to x-ray angiogram images to extract brain arteries. The algorithm works well for single peak distribution where there is no valley in the histogram. The regression provides a method to apply knowledge in clustering. Extending regression for multiple-level segmentation needs further investigation.
Fernández-Novales, Juan; López, María-Isabel; González-Caballero, Virginia; Ramírez, Pilar; Sánchez, María-Teresa
2011-06-01
Volumic mass-a key component of must quality control tests during alcoholic fermentation-is of great interest to the winemaking industry. Transmitance near-infrared (NIR) spectra of 124 must samples over the range of 200-1,100-nm were obtained using a miniature spectrometer. The performance of this instrument to predict volumic mass was evaluated using partial least squares (PLS) regression and multiple linear regression (MLR). The validation statistics coefficient of determination (r(2)) and the standard error of prediction (SEP) were r(2) = 0.98, n = 31 and r(2) = 0.96, n = 31, and SEP = 5.85 and 7.49 g/dm(3) for PLS and MLR equations developed to fit reference data for volumic mass and spectral data. Comparison of results from MLR and PLS demonstrates that a MLR model with six significant wavelengths (P alcoholic fermentation, and that a low-cost NIR instrument can be used for this purpose.
Martelo-Vidal, M J; Vázquez, M
2014-09-01
Spectral analysis is a quick and non-destructive method to analyse wine. In this work, trans-resveratrol, oenin, malvin, catechin, epicatechin, quercetin and syringic acid were determined in commercial red wines from DO Rías Baixas and DO Ribeira Sacra (Spain) by UV-VIS-NIR spectroscopy. Calibration models were developed using principal component regression (PCR) or partial least squares (PLS) regression. HPLC was used as reference method. The results showed that reliable PLS models were obtained to quantify all polyphenols for Rías Baixas wines. For Ribeira Sacra, feasible models were obtained to determine quercetin, epicatechin, oenin and syringic acid. PCR calibration models showed worst reliable of prediction than PLS models. For red wines from mencía grapes, feasible models were obtained for catechin and oenin, regardless the geographical origin. The results obtained demonstrate that UV-VIS-NIR spectroscopy can be used to determine individual polyphenolic compounds in red wines.
Mohamed, Amir Ibrahim; Ahmed, Osama A A; Amin, Suzan; Elkadi, Omar Anwar; Kassem, Mohamed A
2015-10-15
The purpose of this study was to use near-infrared (NIR) transmission spectroscopic technique to determine clindamycin plasma concentration after oral administration of clindamycin loaded GMO-alginate microspheres using rabbits as animal models. Lyophilized clindamycin-plasma standard samples at a concentration range of 0.001-10 μg/ml were prepared and analyzed by NIR and HPLC as a reference method. NIR calibration model was developed with partial least square (PLS) regression analysis. Then, a single dose in-vivo evaluation was carried out and clindamycin-plasma concentration was estimated by NIR. Over 24 h time period, the pharmacokinetic parameters of clindamycin were calculated for the clindamycin loaded GMO-alginate microspheres (F3) and alginate microspheres (F2), and compared with the plain drug (F1). PLS calibration model with 7-principal components (PC), and 8000-9200 cm(-1) spectral range shows a good correlation between HPLC and NIR values with root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP), and calibration coefficient (R(2)) values of 0.245, 1.164, and 0.9753, respectively, which suggests that NIR transmission technique can be used for drug-plasma analysis without any extraction procedure. F3 microspheres exhibited controlled and prolonged absorption Tmax of 4.0 vs. 1.0 and 0.5 h; Cmax of 2.37±0.3 vs. 3.81±0.8 and 5.43±0.7 μg/ml for F2 and F1, respectively. These results suggest that the combination of GMO and alginate (1:4 w/w) could be successfully employed for once daily clindamycin microspheres formulation which confirmed by low Cmax and high Tmax values.
DEVELOPMENT OF NIRS CALIBRATION MODELS FOR MINIMIZATION OF Eucalyptus spp WOOD ANALYSIS
Leonardo Chagas de Sousa
2011-09-01
Full Text Available The Kennard-Stone algorithm was used to select Eucalyptus spp. wood samples for development of NIRS (Near-Infrared Spectroscopy calibration models aiming to minimize number of samples but maintaining the model precisions. A large number of Eucalyptus spp. wood samples (3369 samples were used to develop NIRS calibration models for the wood basic density, the lignin content and the ethanol-toluene extractives. The models developed with the total number of samples were compared with models developed using only 1000, 500, 200 and 100 samples, which were selected using the Kennard-Stone algorithm. Analysis of the models statistics parameters confirmed the similarity of all models, with exception of the 100 sample models, demonstrating the possibility of substantial savings in time and costs for wood laboratory analysis.
Unsupervised defect segmentation of patterned materials under NIR illumination
Millan, Maria S; Escofet, Jaume [Departament d' Optica y Optometria, Universitat Politecnica de Catalunya, Campus Terrassa. 08222 Terrassa, Barcelona (Spain); Rallo, Miquel, E-mail: millan@oo.upc.edu [Departament de Matematica Aplicada III, Universitat Politecnica de Catalunya, Campus Terrassa. 08222 Terrassa, Barcelona (Spain)
2011-01-01
An unsupervised detection method for automatic flaw segmentation in patterned materials (textile, non-woven, paper) that has no need of any defect-free references or a training stage is presented in this paper. Printed materials having a pattern of colored squares, bands, etc. superimposed to the background texture can be advantageously analyzed using NIR illumination and a camera with enough sensitivity to this region of the spectrum. The contrast reduction of the pattern in the NIR image facilitates material inspection and defect segmentation. Underdetection and misdetection errors can be reduced in comparison with the inspection performed under visible illumination. For woven fabrics, with periodic structure, the algorithm is based on the structural feature extraction of the weave repeat from the Fourier transform of the sample image. These features are used to define a set of multiresolution bandpass filters adapted to the fabric structure that operate in the Fourier domain. Inverse Fourier transformation, binarization and merging of the information obtained at different scales lead to the output image that contains flaws segmented from the fabric background. For non-woven and random textured materials, the algorithm combines the multiresolution Gabor analysis of the sample image with a statistical analysis of the wavelet coefficients corresponding to each detail. The information of all the channels is merged in a single binary output image where the defect appears segmented from the background. The method is applicable to random, non-periodic, and periodic textures. Since all the information to inspect a sample is obtained from the sample itself, the method is proof against heterogeneities between different samples of the material, in-plane positioning errors, scale variations and lack of homogeneous illumination. Experimental results are presented for a variety of materials and defects.
Agricultural applications of NIR reflectance and transmittance
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...
Wu, Zhisheng; Sui, Chenglin; Xu, Bing; Ai, Lu; Ma, Qun; Shi, Xinyuan; Qiao, Yanjiang
2013-04-15
A methodology is proposed to estimate the multivariate detection limits (MDL) of on-line near-infrared (NIR) model in Chinese Herbal Medicines (CHM) system. In this paper, Lonicera japonica was used as an example, and its extraction process was monitored by on-line NIR spectroscopy. Spectra of on-line NIR could be collected by two fiber optic probes designed to transmit NIR radiation by a 2mm-flange. High performance liquid chromatography (HPLC) was used as a reference method to determine the content of chlorogenic acid in the extract solution. Multivariate calibration models were carried out including partial least squares regression (PLS) and interval partial least-squares (iPLS). The result showed improvement of model performance: compared with PLS model, the root mean square errors of prediction (RMSEP) of iPLS model decreased from 0.111mg to 0.068mg, and the R(2) parameter increased from 0.9434 to 0.9801. Furthermore, MDL values were determined by a multivariate method using the type of errors and concentration ranges. The MDL of iPLS model was about 14ppm, which confirmed that on-line NIR spectroscopy had the ability to detect trace amounts of chlorogenic acid in L. japonica. As a result, the application of on-line NIR spectroscopy for monitoring extraction process in CHM could be very encouraging and reliable.
Kriegs, Stefanie; Buddenbaum, Henning; Rogge, Derek; Steffens, Markus
2015-04-01
Laboratory imaging Vis-NIR spectroscopy of soil profiles is a novel technique in soil science that can determine quantity and quality of various chemical soil properties with a hitherto unreached spatial resolution in undisturbed soil profiles. We have applied this technique to soil cores in order to get quantitative proof of redoximorphic processes under two different tree species and to proof tree-soil interactions at microscale. Due to the imaging capabilities of Vis-NIR spectroscopy a spatially explicit understanding of soil processes and properties can be achieved. Spatial heterogeneity of the soil profile can be taken into account. We took six 30 cm long rectangular soil columns of adjacent Luvisols derived from quaternary aeolian sediments (Loess) in a forest soil near Freising/Bavaria using stainless steel boxes (100×100×300 mm). Three profiles were sampled under Norway spruce and three under European beech. A hyperspectral camera (VNIR, 400-1000 nm in 160 spectral bands) with spatial resolution of 63×63 µm² per pixel was used for data acquisition. Reference samples were taken at representative spots and analysed for organic carbon (OC) quantity and quality with a CN elemental analyser and for iron oxides (Fe) content using dithionite extraction followed by ICP-OES measurement. We compared two supervised classification algorithms, Spectral Angle Mapper and Maximum Likelihood, using different sets of training areas and spectral libraries. As established in chemometrics we used multivariate analysis such as partial least-squares regression (PLSR) in addition to multivariate adaptive regression splines (MARS) to correlate chemical data with Vis-NIR spectra. As a result elemental mapping of Fe and OC within the soil core at high spatial resolution has been achieved. The regression model was validated by a new set of reference samples for chemical analysis. Digital soil classification easily visualizes soil properties within the soil profiles. By combining
Bárta, Jiří; Tahovská, Karolina; Kaåa, Jiří; Antrå¯Čková, Hana Å.
2010-05-01
The denitrification is the main biotic process leading to loses of fixed nitrogen as well as removal of excess of nitrate (NO3-) from the soil environment. The reduction of NO2- to nitric oxide (NO) distinguishes the 'true' denitrifiers from other nitrate-respiring bacteria. This reaction is catalyzed by two different types of nitrite reductases, either a cytochrome cd1 encoded by nirS gene (nirS denitrifiers) or a Cu-containing enzyme encoded by nirK gene (nirK denitrifiers). The nirS denitrifiers are located mostly in rhizosphere, while the nirK denitrifiers are more abundant in bulk soil. These two groups can be also classified as markers of denitrification. Glutamine synthetase is one of the main bacterial NH4+ assimilating enzymes; it is coded by glnI gene. Glutamine synthetase is mostly active when N is the limiting factor for bacterial growth. There is recent evidence that the activity may be affected by the presence of alternative N source (i.e. NO3-). However, in anaerobic condition NO3- can be used also by the denitrifying bacteria so there may be strong competition for this nutrient. The laboratory experiment was performed to evaluate the effect of nitrates (NO3-) on abundance of nirK, nirS and gln gene copy numbers. The amount of NO3- corresponded to the actual atmospheric depositions on experimental sites in the Bohemian Forest. Litter organic layer (0-5cm of soil) was used for laboratory incubation experiment. Four replicates of control (no addition of NO3-), and NO3-addition were incubated anaerobically for one month. After the incubation DNA was extracted and the number of nirK, nirS and gln gene copies was determined using qPCR (SYBRGreen methodology). Results showed that the addition of NO3- significantly increased the number of nirK and nirS denitrifiers from 5.9x106 to 1.1x107 and from not detectable amount to 1.4x106, respectively. The gln gene copy number was also higher after NO3-addition. However, the difference was not statistically
Nondestructive Quantitative Analysis of Cofrel Medicines by Double ANN-NIR Spectroscopy
Ming Yang LIU; Yu MENG; Jun Feng LI; Hai Tao ZHANG; Hong Yan WANG
2006-01-01
In this paper, a double artificial neural network (DANN) algorithm was used to parse near infrared (NIR) reflectance spectrum of Cofrel medicines. The contents of benproperine phosphate, which is the effective ingredient in Cofrel medicines, were accurately nondestructive quantitatively predicted. Compared the results with those of HPLC, the relative errors (RE %)were less than 0.18%. The analytical results could be applied to qualitative control of Cofrel medicines.
Clustered regression with unknown clusters
Barman, Kishor
2011-01-01
We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion relationships are unknown. The regression relation- ships define the experiment clusters, and in general, the predictor and response variables may not exhibit any clus- tering. We call this prediction problem clustered regres- sion with unknown clusters (CRUC) and in this paper we focus on linear regression. We study and compare several methods for CRUC, demonstrate their applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in- vestigate an associated mathematical model. CRUC is at the crossroads of many prior works and we study several prediction algorithms with diverse origins: an adaptation of the expectation-maximization algorithm, an approach in- spired by K-means clustering, the singular value threshold- ing approach to matrix rank minimization u...
Regression analysis by example
Chatterjee, Samprit; Hadi, Ali S
2012-01-01
.... The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression...
Braker, G.; Witzel, K.P. [Max-Planck-Inst. fuer Limnologie, Ploen (Germany); Fesefeldt, A. [Univ. Kiel (Germany). Inst. fuer Allgemeine Mikrobiologie
1998-10-01
A system was developed for the detection of denitrifying bacteria by the application of specific nitrite reductase gene fragments with PCR. Primer sequences were found for the amplification of fragments from both nitrite reductase genes (nirK and nirS) after comparative sequence analysis. Whenever amplification was tried with these primers, the known nir type of denitrifying laboratory cultures could be confirmed. Likewise, the method allowed a determination of the nir type of five laboratory strains. The nirK gene could be amplified from Blastobacter denitrificans, Alcaligenes xylosoxidans, and Alcaligenes sp. (DSM 30128); the nirS gene was amplified from Alcaligenes eutrophus DSM 530 and from the denitrifying isolate IFAM 3698. For each of the two genes, at least one primer combination amplified successfully for all of the test strains. Specific amplification products were not obtained wit h nondenitrifying bacteria or with strains of the other nir type. The specificity of the amplified products was confirmed by subsequent sequencing. These results suggest the suitability of the method for the qualitative detection of denitrifying bacteria in environmental samples. This was shown by applying the generally amplifying primer combination for each nir gene developed in this study to total DNA preparations from aquatic habitats.
NIR-red spectral space based new method for soil moisture monitoring
ZHAN ZhiMing; QIN QiMing; GHULAN Abduwasit; WANG DongDong
2007-01-01
Drought is a complex natural disaster that occurs frequently. Soil moisture has been the main issue in remote monitoring of drought events as the most direct and important variable describing the drought. Spatio-temporal distribution and variation of soil moisture evidently affect surface evapotranspiration, agricultural water demand, etc. In this paper, a new simple method for soil moisture monitoring is developed using near-infrared versus red (NIR-red) spectral reflectance space. First, NIR-red spectral reflectance space is established using atmospheric and geometric corrected ETM+ data, which is manifested by a triangle shape, in which different surface covers have similar spatial distribution rules. Next, the model of soil moisture monitoring by remote sensing (SMMRS) is developed on the basis of the distribution characteristics of soil moisture in the NIR-red spectral reflectance space. Then, the SMMRS model is validated by comparison with field measured soil moisture data at different depths. The results showed that satellite estimated soil moisture by SMMRS is highly accordant with field measured data at 5 cm soil depth and average soil moisture at 0―20 cm soil depths, correlation coefficients are 0.80 and 0.87, respectively. This paper concludes that, being simple and effective, the SMMRS model has great potential to estimate surface moisture conditions.
NIR-red spectral space based new method for soil moisture monitoring
GHULAN; Abduwasit
2007-01-01
Drought is a complex natural disaster that occurs frequently. Soil moisture has been the main issue in remote monitoring of drought events as the most direct and important variable describing the drought. Spatio-temporal distribution and variation of soil moisture evidently affect surface evapotranspiration, agricultural water demand, etc. In this paper, a new simple method for soil moisture monitoring is de- veloped using near-infrared versus red (NIR-red) spectral reflectance space. First, NIR-red spectral reflectance space is established using atmospheric and geometric corrected ETM+ data, which is manifested by a triangle shape, in which different surface covers have similar spatial distribution rules. Next, the model of soil moisture monitoring by remote sensing (SMMRS) is developed on the basis of the distribution characteristics of soil moisture in the NIR-red spectral reflectance space. Then, the SMMRS model is validated by comparison with field measured soil moisture data at different depths. The results showed that satellite estimated soil moisture by SMMRS is highly accordant with field measured data at 5 cm soil depth and average soil moisture at 0―20 cm soil depths, correlation coef- ficients are 0.80 and 0.87, respectively. This paper concludes that, being simple and effective, the SMMRS model has great potential to estimate surface moisture conditions.
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...
束茹欣; 孙平; 杨凯; 张建平; 刘太昂
2011-01-01
To accurately identify the growing area of flue - cured tobacco, the contents of chemical components, including total sugar, reducing sugar, total nitrogen, nicotine, total chlorine and total potassium, in 402 cured tobacco samples collected from Yunnan, Henan, Anhui, Fujian, Guizhou and Jilin Provinces in 2010 were tested, and the samples were scanned by near infrared spectrometer. The near infrared spectra (NIR) pattern recognition models of growing area were developed by principal component analysis (PCA) and support vector machine (SVM) algorithms, and the growing areas of the samples were recognized. The results indicated that: 1) The prediction accuracy recognized by NIR-PCA-SVM models reached 97%, while that by chemical component-SVM and NIR-SVM models were lower. 2) The NIR - PCA - SVM, and chemical component - SVM models all offered better recoginition for Yunnan tobacco samples. NIR-PCA-SVM model could be applied to pattern recognition of flue-cured tobacco samples of different origins.%为了更准确地对烟叶样品进行产地模式识别,检测了云南、河南、安徽、福建、贵州、吉林6省2010年生产的402个初烤烟叶样品的总糖、还原糖、总氮、烟碱、总氯、总钾含量,同时进行了近红外( NIR)光谱扫描,利用主成分分析( PCA)法和支持向量机算法(SVM)建立了烟叶产地模式识别模型,并对云南、河南、安徽、福建、贵州、吉林6省烟叶样品进行了产地模式识别.结果表明:①NIR-PCA-SVM模型对6省烟叶样品识别的预报正确率高达97％,而化学成分-SVM模型和NIR-SVM模型对6省烟叶产地的识别效果差；②NIR-PCA-SVM、化学成分-SVM和NIR-SVM 3个模型对云南省烟叶都有着较好的识别效果.NIR-PCA-SVM模型可用于不同烟叶样品产地的模式识别.
Zhan, Xue-yan; Zhao, Na; Lin, Zhao-zhou; Wu, Zhi-sheng; Yuan, Rui-juan; Qiao, Yan-jiang
2014-12-01
The appropriate algorithm for calibration set selection was one of the key technologies for a good NIR quantitative model. There are different algorithms for calibration set selection, such as Random Sampling (RS) algorithm, Conventional Selection (CS) algorithm, Kennard-Stone(KS) algorithm and Sample set Portioning based on joint x-y distance (SPXY) algorithm, et al. However, there lack systematic comparisons between two algorithms of the above algorithms. The NIR quantitative models to determine the asiaticoside content in Centella total glucosides were established in the present paper, of which 7 indexes were classified and selected, and the effects of CS algorithm, KS algorithm and SPXY algorithm for calibration set selection on the accuracy and robustness of NIR quantitative models were investigated. The accuracy indexes of NIR quantitative models with calibration set selected by SPXY algorithm were significantly different from that with calibration set selected by CS algorithm or KS algorithm, while the robustness indexes, such as RMSECV and |RMSEP-RMSEC|, were not significantly different. Therefore, SPXY algorithm for calibration set selection could improve the predicative accuracy of NIR quantitative models to determine asiaticoside content in Centella total glucosides, and have no significant effect on the robustness of the models, which provides a reference to determine the appropriate algorithm for calibration set selection when NIR quantitative models are established for the solid system of traditional Chinese medcine.
ASHISH V VANMALI; VIKRAM M GADRE
2017-07-01
Image visibility is affected by the presence of haze, fog, smoke, aerosol, etc. Image dehazing using either single visible image or visible and near-infrared (NIR) image pair is often considered as a solution to improve the visual quality of such scenes. In this paper, we address this problem from a visible–NIR image fusion perspective, instead of the conventional haze imaging model. The proposed algorithm uses a Laplacian–Gaussian pyramid based multi-resolution fusion process, guided by weight maps generated using local entropy,local contrast and visibility as metrics that control the fusion result. The proposed algorithm is free from any human intervention, and produces results that outperform the existing image-dehazing algorithms both visually as well as quantitatively. The algorithm proves to be efficient not only for the outdoor scenes with or without haze, but also for the indoor scenes in improving scene visibility.
Determination of persimmon leaf chloride contents using near-infrared spectroscopy (NIRS).
de Paz, José Miguel; Visconti, Fernando; Chiaravalle, Mara; Quiñones, Ana
2016-05-01
Early diagnosis of specific chloride toxicity in persimmon trees requires the reliable and fast determination of the leaf chloride content, which is usually performed by means of a cumbersome, expensive and time-consuming wet analysis. A methodology has been developed in this study as an alternative to determine chloride in persimmon leaves using near-infrared spectroscopy (NIRS) in combination with multivariate calibration techniques. Based on a training dataset of 134 samples, a predictive model was developed from their NIR spectral data. For modelling, the partial least squares regression (PLSR) method was used. The best model was obtained with the first derivative of the apparent absorbance and using just 10 latent components. In the subsequent external validation carried out with 35 external data this model reached r(2) = 0.93, RMSE = 0.16% and RPD = 3.6, with standard error of 0.026% and bias of -0.05%. From these results, the model based on NIR spectral readings can be used for speeding up the laboratory determination of chloride in persimmon leaves with only a modest loss of precision. The intermolecular interaction between chloride ions and the peptide bonds in leaf proteins through hydrogen bonding, i.e. N-H···Cl, explains the ability for chloride determinations on the basis of NIR spectra.
Tai, Kelly; Chau, Tom
2009-11-09
Corporeal machine interfaces (CMIs) are one of a few available options for restoring communication and environmental control to those with severe motor impairments. Cognitive processes detectable solely with functional imaging technologies such as near-infrared spectroscopy (NIRS) can potentially provide interfaces requiring less user training than conventional electroencephalography-based CMIs. We hypothesized that visually-cued emotional induction tasks can elicit forehead hemodynamic activity that can be harnessed for a CMI. Data were collected from ten able-bodied participants as they performed trials of positively and negatively-emotional induction tasks. A genetic algorithm was employed to select the optimal signal features, classifier, task valence (positive or negative emotional value of the stimulus), recording site, and signal analysis interval length for each participant. We compared the performance of Linear Discriminant Analysis and Support Vector Machine classifiers. The latency of the NIRS hemodynamic response was estimated as the time required for classification accuracy to stabilize. Baseline and activation sequences were classified offline with accuracies upwards of 75.0%. Feature selection identified common time-domain discriminatory features across participants. Classification performance varied with the length of the input signal, and optimal signal length was found to be feature-dependent. Statistically significant increases in classification accuracy from baseline rates were observed as early as 2.5 s from initial stimulus presentation. NIRS signals during affective states were shown to be distinguishable from baseline states with classification accuracies significantly above chance levels. Further research with NIRS for corporeal machine interfaces is warranted.
Effect of ultrasonic homogenization on the Vis/NIR bulk optical properties of milk.
Aernouts, Ben; Van Beers, Robbe; Watté, Rodrigo; Huybrechts, Tjebbe; Jordens, Jeroen; Vermeulen, Daniel; Van Gerven, Tom; Lammertyn, Jeroen; Saeys, Wouter
2015-02-01
The size of colloidal particles in food products has a considerable impact on the product's physicochemical, functional and sensory characteristics. Measurement techniques to monitor the size of suspended particles could, therefore, help to further reduce the variability in production processes and promote the development of new food products with improved properties. Visible and near-infrared (Vis/NIR) spectroscopy is already widely used to measure the composition of agricultural and food products. However, this technology can also be consulted to acquire microstructure-related scattering properties of food products. In this study, the effect of the fat globule size on the Vis/NIR bulk scattering properties of milk was investigated. Variability in fat globule size distribution was created using ultrasonic homogenization of raw milk. Reduction of the fat globule size resulted in a higher wavelength-dependency of both the Vis/NIR bulk scattering coefficient and the scattering anisotropy factor. Moreover, the anisotropy factor and the bulk scattering coefficients for wavelengths above 600 nm were reduced and were dominated by Rayleigh scattering. Additionally, the bulk scattering properties could be well (R(2) ≥ 0.990) estimated from measured particle size distributions by consulting an algorithm based on the Mie solution. Future research could aim at the inversion of this model to estimate the particle size distributions from Vis/NIR spectroscopic measurements.
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
González-Martín, Inmaculada; Hernández-Hierro, José Miguel
2008-09-15
The additives (urea, biuret and poultry litter) present in alfalfa, which contribute non-proteic nitrogen, were analysed using near infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe. We used 75 samples of known alfalfa without additives and 75 samples with each of the additives, urea (0.01-10%), biuret (0.01-10%) and poultry litter (1-25%). Using the discriminant partial least squares (DPLS) algorithm, the presence or absence of the additives urea, biuret and poultry litter is classified and predicted with a high prediction rate of 96.9%, 100% and 100%, obtaining the equations of discrimination for each additive. The regression method employed for the quantification was modified partial least squares (MPLS). The equations were developed using the fibre-optic probe to determine the content of urea, biuret and poultry litter with multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP (C)) of 0.990, 0.28% for urea, 0.991, 0.29% for biuret and 0.925, 2.08% for poultry litter. The work permits the instantaneous and simultaneous prediction and determination of urea, biuret and poultry litter in alfalfas, applying the fibre-optic directly on the ground samples of alfalfa.
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.
NIR sensitivity analysis with the VANE
Carrillo, Justin T.; Goodin, Christopher T.; Baylot, Alex E.
2016-05-01
Near infrared (NIR) cameras, with peak sensitivity around 905-nm wavelengths, are increasingly used in object detection applications such as pedestrian detection, occupant detection in vehicles, and vehicle detection. In this work, we present the results of simulated sensitivity analysis for object detection with NIR cameras. The analysis was conducted using high performance computing (HPC) to determine the environmental effects on object detection in different terrains and environmental conditions. The Virtual Autonomous Navigation Environment (VANE) was used to simulate highresolution models for environment, terrain, vehicles, and sensors. In the experiment, an active fiducial marker was attached to the rear bumper of a vehicle. The camera was mounted on a following vehicle that trailed at varying standoff distances. Three different terrain conditions (rural, urban, and forest), two environmental conditions (clear and hazy), three different times of day (morning, noon, and evening), and six different standoff distances were used to perform the sensor sensitivity analysis. The NIR camera that was used for the simulation is the DMK firewire monochrome on a pan-tilt motor. Standoff distance was varied along with environment and environmental conditions to determine the critical failure points for the sensor. Feature matching was used to detect the markers in each frame of the simulation, and the percentage of frames in which one of the markers was detected was recorded. The standoff distance produced the biggest impact on the performance of the camera system, while the camera system was not sensitive to environment conditions.
fNIRS-based online deception decoding
Hu, Xiao-Su; Hong, Keum-Shik; Ge, Shuzhi Sam
2012-04-01
Deception involves complex neural processes in the brain. Different techniques have been used to study and understand brain mechanisms during deception. Moreover, efforts have been made to develop schemes that can detect and differentiate deception and truth-telling. In this paper, a functional near-infrared spectroscopy (fNIRS)-based online brain deception decoding framework is developed. Deploying dual-wavelength fNIRS, we interrogate 16 locations in the forehead when eight able-bodied adults perform deception and truth-telling scenarios separately. By combining preprocessed oxy-hemoglobin and deoxy-hemoglobin signals, we develop subject-specific classifiers using the support vector machine. Deception and truth-telling states are classified correctly in seven out of eight subjects. A control experiment is also conducted to verify the deception-related hemodynamic response. The average classification accuracy is over 83.44% from these seven subjects. The obtained result suggests that the applicability of fNIRS as a brain imaging technique for online deception detection is very promising.
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.
Screening of grated cheese authenticity by nir spectroscopy
Chiara Cevoli
2013-09-01
Full Text Available Parmigiano–Reggiano (PR cheese is one of the oldest traditional cheeses produced in Europe, and it is still one of the most valuable Protected Designation of Origin (PDO cheeses of Italy. The denomination of origin is extended to the grated cheese when manufactured exclusively from whole Parmigiano-Reggiano cheese wheels that respond to the production standard. The grated cheese must be matured for a period of at least 12 months and characterized by a rind content not over 18%. In this investigation the potential of near infrared spectroscopy (NIR, coupled to different statistical methods, were used to estimate the authenticity of grated Parmigiano Reggiano cheese PDO. Cheese samples were classified as: compliance PR, competitors, non-compliance PR (defected PR, and PR with rind content greater then 18%. NIR spectra were obtained using a spectrophotometer Vector 22/N (Bruker Optics, Milan, Italy in the diffuse reflectance mode. Instrument was equipped with a rotating integrating sphere. Principal Component Analysis (PCA was conducted for an explorative spectra analysis, while the Artificial Neural Networks (ANN were used to classify spectra, according to different cheese categories. Subsequently the rind percentage and month of ripening were estimated by a Partial Least Squares regression (PLS. Score plots of the PCA show a clear separation between compliance PR samples and the rest of the sample was observed. Competitors samples and the defected PR samples were grouped together. The classification performance for all sample classes, obtained by ANN analysis, was higher of 90%, in test set validation. Rind content and month of ripening were predicted by PLS a with a determination coefficient greater then 0.95 (test set. These results showed that the method can be suitable for a fast screening of grated cheese authenticity.
Lin, Zhidan; Wang, Yubing; Wang, Rujing; Liu, Jing; Lu, Cuiping; Wang, Liusan
2015-10-01
The on-line measurement of the main component contents is essential for production, detection and identification of compound fertilizer. Using developed VIS-NIR sensors for on-line measurement of the main component contents in compound fertilizer, primary results about nitrogen (N), phosphorus pentoxide (P2O5) and potassium oxide (K2O) were reported. A visible (VIS) and near infrared (NIR) spectrophotometer (Ocean Optics), with a measurement range of 360.18-2221.53 nm was used to measure fertilizer spectra in reflectance mode. By using principal component analysis (PCA) and mahalanobis distance method, 3 outlier samples were detected and eliminated from 174 samples firstly. Then these models of three components with the 124 samples in calibration set were established using principal component regress (PCR) and partial least squares regression (PLS) coupled respectively with the full cross-validation technique after preprocessing the original spectrum with different methods. These models were used to estimate the contents of N, P2O5 and K2O of the other 47 samples in predicted set. The research results showed that the method could be applied to rapid measurement to the main component contents in compound fertilizer. Compared with the traditional analysis method, the on-line measurement could do it rapidly, inexpensively and pollution-freely. It suggested the potential use of the VIS-NIR sensing system for on-line measurement in the production, detection and identification process of compound fertilizer.
Sultan, E.; Manseta, K.; Khwaja, A.; Najafizadeh, L.; Gandjbakhche, A.; Pourrezaei, K.; Daryoush, A. S.
2011-02-01
Fiber based functional near infra-red (fNIR) spectroscopy has been considered as a cost effective imaging modality. To achieve a better spatial resolution and greater accuracy in extraction of the optical parameters (i.e., μa and μ's), broadband frequency modulated systems covering multi-octave frequencies of 10-1000MHz is considered. A helmet mounted broadband free space fNIR system is considered as significant improvement over bulky commercial fiber fNIR realizations that are inherently uncomfortable and dispersive for broadband operation. Accurate measurements of amplitude and phase of the frequency modulated NIR signals (670nm, 795nm, and 850nm) is reported here using free space optical transmitters and receivers realized in a small size and low cost modules. The tri-wavelength optical transmitter is based on vertical cavity semiconductor lasers (VCSEL), whereas the sensitive optical receiver is based on either PIN or APD photodiodes combined with transimpedance amplifiers. This paper also has considered brain phantoms to perform optical parameter extraction experiments using broadband modulated light for separations of up to 5cm. Analytical models for predicting forward (transmittance) and backward (reflectance) scattering of modulated photons in diffused media has been modeled using Diffusion Equation (DE). The robustness of the DE modeling and parameter extraction algorithm was studied by experimental verification of multi-layer diffused media phantoms. In particular, comparison between analytical and experimental models for narrow band and broadband has been performed to analyze the advantages of our broadband fNIR system.
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Stability Analysis for Regularized Least Squares Regression
Rudin, Cynthia
2005-01-01
We discuss stability for a class of learning algorithms with respect to noisy labels. The algorithms we consider are for regression, and they involve the minimization of regularized risk functionals, such as L(f) := 1/N sum_i (f(x_i)-y_i)^2+ lambda ||f||_H^2. We shall call the algorithm `stable' if, when y_i is a noisy version of f*(x_i) for some function f* in H, the output of the algorithm converges to f* as the regularization term and noise simultaneously vanish. We consider two flavors of...
NIR-NIR fluorescence: A new genre of fingermark visualisation techniques.
King, Roberto S P; Hallett, Peter M; Foster, Doug
2016-05-01
A preliminary study reveals that finely divided cuprorivaite powder may be used to efficiently develop and subsequently image latent fingermarks across a range of highly patterned, coloured non-porous and semi-porous substrates using near infrared illumination and imaging. Problematic multi-coloured backgrounds provide very little interference under the illumination conditions used, and invoked fluorescence observed, when using this material. This is the first reported example of a NIR-NIR fluorophore for use within latent fingermark visualisation and offers the potential for application at the scene and in the laboratory.
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
NIR fingerprint screening for early control of non-conformity at feed mills.
Fernández Pierna, Juan Antonio; Abbas, Ouissam; Lecler, Bernard; Hogrel, Patrick; Dardenne, Pierre; Baeten, Vincent
2015-12-15
The objective of this work was to devise a complete procedure based on chemometrics and the use NIR spectroscopy at the entrance of a feed mill to provide early evidence of non-conformity and unusual ingredients and thus help to achieve cost-savings. The procedure was validated at laboratory level and was adapted for application at the Cargill Animal Nutrition feed mill. The study focused on the characterisation of pure soybean meal with the aim of creating an early control system for detecting and quantifying any unusual ingredient that might be present in the soybean meal, such as melamine, cyanuric acid or whey powder (milk serum). The study results showed that the use of NIR, combined with some simple chemometric tools based on distances and residuals from regression equations, is appropriate for authenticating important feed products (in this case, soybean meal) and detecting the presence of abnormal samples or impurities in both the laboratory and at the feed mill.
NIRS as an alternative to conventional soil analysis for Greenland soils (focus on SOC)
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...
Unitary Response Regression Models
Lipovetsky, S.
2007-01-01
The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…
Flexible survival regression modelling
Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben
2009-01-01
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
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 m...... treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work....
Effect of Palagonite Dust Deposition on the Automated Detection of Carbonate Vis/NIR Spectra
Gilmore, Martha S.; Merrill, Matthew D.; Castano, Rebecca; Bornstein, Benjamin; Greenwood, James
2004-01-01
Currently Mars missions can collect more data than can be returned. Future rovers of increased mission lifetime will benefit from onboard autonomous data processing systems to guide the selection, measurement and return of scientifically important data. One approach is to train a neural net to recognize spectral reflectance characteristics of minerals of interest. We have developed a carbonate detector using a neural net algorithm trained on 10,000 synthetic Vis/NIR (350-2500 nm) spectra. The detector was able to correctly identify carbonates in the spectra of 30 carbonate and noncarbonate field samples with 100% success. However, Martian dust coatings strongly affect the spectral characteristics of surface rocks potentially masking the underlying substrate rock. In this experiment, we measure Vis/NIR spectra of calcite coated with different thicknesses of palagonite dust and evaluate the performance of the carbonate detector.
The NIRS Analysis Package: noise reduction and statistical inference.
Fekete, Tomer; Rubin, Denis; Carlson, Joshua M; Mujica-Parodi, Lilianne R
2011-01-01
Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.
Saunders, N F; Ferguson, S J; Baker, S C
2000-02-01
The gene for cytochrome cd1 nitrite reductase of Paracoccus pantotrophus, a protein of known crystal structure, is nirS. This gene is shown to be flanked by genes previously recognized in other organisms to encode proteins involved in the control of its transcription (nirI) and the biosynthesis of the d1 cofactor (nirE). Northern blot analysis has established under anaerobic conditions that a monocistronic transcript is produced from nirS, in contrast to observations with other denitrifying bacteria in which arrangement of flanking genes is different and the messages produced are polycistronic. The lack of a transcript under aerobic conditions argues against a role for cytochrome cd1 in the previously proposed aerobic denitrification pathway in Pa. pantotrophus. A putative rho-independent transcription termination sequence immediately following nirS, and preceding nirE, can be identified. The independent transcription of nirS and nirE indicates that it should be possible to produce site-directed mutants of nirS borne on a plasmid in a nirS deletion mutant. The transcript start point for nirS has been determined by two complementary techniques, 5'-RACE (Rapid amplification of cDNA 5' ends) and primer extension. It is 29 bp upstream of the AUG of nirS. An anaerobox, which presumably binds Nnr, is centred a further 41.5 bp upstream of the transcript start. No standard sigma70 DNA sequence motifs can be identified, but a conserved sequence (T-T-GIC-C-G/C-G/C) can be found in approximately the same position (-16) upstream of the transcript starts of nirS and nirI, whose products are both involved in the conversion of nitrite to nitric oxide.
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.
Incremental Support Vector Learning for Ordinal Regression.
Gu, Bin; Sheng, Victor S; Tay, Keng Yeow; Romano, Walter; Li, Shuo
2015-07-01
Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR. Recently, an interesting accurate on-line algorithm was proposed for training ν -support vector classification (ν-SVC), which can handle a quadratic formulation with a pair of equality constraints. In this paper, we first present a modified SVOR formulation based on a sum-of-margins strategy. The formulation has multiple constraints, and each constraint includes a mixture of an equality and an inequality. Then, we extend the accurate on-line ν-SVC algorithm to the modified formulation, and propose an effective incremental SVOR algorithm. The algorithm can handle a quadratic formulation with multiple constraints, where each constraint is constituted of an equality and an inequality. More importantly, it tackles the conflicts between the equality and inequality constraints. We also provide the finite convergence analysis for the algorithm. Numerical experiments on the several benchmark and real-world data sets show that the incremental algorithm can converge to the optimal solution in a finite number of steps, and is faster than the existing batch and incremental SVOR algorithms. Meanwhile, the modified formulation has better accuracy than the existing incremental SVOR algorithm, and is as accurate as the sum-of-margins based formulation of Shashua and Levin.
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.
Naghshpour, Shahdad
2012-01-01
Regression analysis is the most commonly used statistical method in the world. Although few would characterize this technique as simple, regression is in fact both simple and elegant. The complexity that many attribute to regression analysis is often a reflection of their lack of familiarity with the language of mathematics. But regression analysis can be understood even without a mastery of sophisticated mathematical concepts. This book provides the foundation and will help demystify regression analysis using examples from economics and with real data to show the applications of the method. T
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.
Food quality assessment by NIR hyperspectral imaging
Whitworth, Martin B.; Millar, Samuel J.; Chau, Astor
2010-04-01
Near infrared reflectance (NIR) spectroscopy is well established in the food industry for rapid compositional analysis of bulk samples. NIR hyperspectral imaging provides new opportunities to measure the spatial distribution of components such as moisture and fat, and to identify and measure specific regions of composite samples. An NIR hyperspectral imaging system has been constructed for food research applications, incorporating a SWIR camera with a cooled 14 bit HgCdTe detector and N25E spectrograph (Specim Ltd, Finland). Samples are scanned in a pushbroom mode using a motorised stage. The system has a spectral resolution of 256 pixels covering a range of 970-2500 nm and a spatial resolution of 320 pixels covering a swathe adjustable from 8 to 300 mm. Images are acquired at a rate of up to 100 lines s-1, enabling samples to be scanned within a few seconds. Data are captured using SpectralCube software (Specim) and analysed using ENVI and IDL (ITT Visual Information Solutions). Several food applications are presented. The strength of individual absorbance bands enables the distribution of particular components to be assessed. Examples are shown for detection of added gluten in wheat flour and to study the effect of processing conditions on fat distribution in chips/French fries. More detailed quantitative calibrations have been developed to study evolution of the moisture distribution in baguettes during storage at different humidities, to assess freshness of fish using measurements of whole cod and fillets, and for prediction of beef quality by identification and separate measurement of lean and fat regions.
Binary mixtures of waxy wheat and conventional wheat as measured by NIR reflectance.
Delwiche, Stephen R; Graybosch, Robert A
2016-01-01
Waxy wheat contains very low concentration (generally industries seek to have a rapid technique to ensure the purity of identity preserved waxy wheat lots. Near infrared (NIR) reflectance spectroscopy, a technique widely used in the cereals industry for proximate analysis, is a logical candidate for measuring contamination level and thus is the subject of this study. Two sets of wheat samples, harvested, prepared and scanned one year apart, were used to evaluate the NIR concept. One year consisted of nine pairs of conventional:waxy preparations, with each preparation consisting of 29 binary mixtures ranging in conventional wheat fraction (by weight) of 0-100% (261 spectral samples). The second year was prepared in the same fashion, with 12 preparations, thus producing 348 spectral samples. One year's samples were controlled for protein content and moisture level between pair components in order to avoid the basis for the conventional wheat fraction models being caused by something other than spectral differences attributed to waxy and nonwaxy endosperm. Likewise the second year was controlled by selection of conventional wheat for mixture preparation based on either protein content or cluster analysis of principal components of candidate spectra. Partial least squares regression, one and two-term linear regression, and support vector machine regression models were examined. Validation statistics arising from sets within the same year or across years were remarkably similar, as were those among the three regression types. A single wavelength on second derivative transformed spectra, namely 2290 nm, was effective at estimating the mixture level by weight, with standard errors of performance in the 6-9% range. Thus, NIR spectroscopy may be used for measuring conventional hard wheat 'contamination' in waxy wheat at mixture levels above 10% w/w.
Balabin, Roman M; Smirnov, Sergey V
2011-07-15
Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular-for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1 ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1 ppm (0.76±0.11 ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm-partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)-are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis.
[The new method monitoring crop water content based on NIR-Red spectrum feature space].
Cheng, Xiao-juan; Xu, Xin-gang; Chen, Tian-en; Yang, Gui-jun; Li, Zhen-hai
2014-06-01
Moisture content is an important index of crop water stress condition, timely and effective monitoring of crop water content is of great significance for evaluating crop water deficit balance and guiding agriculture irrigation. The present paper was trying to build a new crop water index for winter wheat vegetation water content based on NIR-Red spectral space. Firstly, canopy spectrums of winter wheat with narrow-band were resampled according to relative spectral response function of HJ-CCD and ZY-3. Then, a new index (PWI) was set up to estimate vegetation water content of winter wheat by improveing PDI (perpendicular drought index) and PVI (perpendicular vegetation index) based on NIR-Red spectral feature space. The results showed that the relationship between PWI and VWC (vegetation water content) was stable based on simulation of wide-band multispectral data HJ-CCD and ZY-3 with R2 being 0.684 and 0.683, respectively. And then VWC was estimated by using PWI with the R2 and RMSE being 0.764 and 0.764, 3.837% and 3.840%, respectively. The results indicated that PWI has certain feasibility to estimate crop water content. At the same time, it provides a new method for monitoring crop water content using remote sensing data HJ-CCD and ZY-3.
On the terminology of the spectral vegetation index (NIR – SWIR)/(NIR + SWIR)
Ji, Lel; Zhang, Li; Wylie, Bruce K.; Rover, Jennifer R.
2011-01-01
The spectral vegetation index (ρNIR – ρSWIR)/(ρNIR + ρSWIR), where ρNIR and ρSWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), normalized difference moisture index (NDMI), land surface water index (LSWI), and normalized burn ratio (NBR), etc. After reviewing each term’s definition, associated sensors, and channel specifications, we found that the index consists of three variants, differing only in the SWIR region (1.2–1.3 µm, 1.55–1.75 µm, or 2.05–2.45 µm). Thus, three terms are sufficient to represent these three SWIR variants; other names are redundant and therefore unnecessary. Considering the spectral representativeness, the term’s popularity, and the “rule of priority” in scientific nomenclature, NDWI, NDII, and NBR, each corresponding to the three SWIR regions, are more preferable terms.
Sun, Jian-ying; Li, Min-zan; Zheng, Li-hua; Hu, Yong-guang; Zhang, Xi-jie
2006-03-01
The grey-brown alluvial soil in northern China was selected as research object, and the feasibility and possibility of real-time analyzing soil para-fueter with NIR spectroscopic techniques were explored. One hundred fifty samples were collected from a winter wheat farm. NIR absorbance spectra were rapidly measured under their original conditions by a Nicolet Antaris FT-NIR analyzer. Three soil parameters, namely soil moisture, SOM (soil organic matter) and TN (total nitrogen) content, were analyzed. For soil moisture content, a linear regression model was available, using 1920 nm wavelength with correlation coefficient of 0.937, so that the results obtained could be directly used to real-time evaluate soil moisture. SOM content and TN content were estimated with a muviaiple linear regression model, 1870 and 1378 nm wavelengths were selected in the SOM estimate model, and 2262 and 1888 nrameter wavelengths were selected in the TN estimate model. The results showed that soil SOM and TN contents can be evaluated by using NIR absorbance spectra of soil samples.
Multiple Kernel Spectral Regression for Dimensionality Reduction
Bing Liu
2013-01-01
Full Text Available Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL into SR for dimensionality reduction. The proposed approach (termed MKL-SR seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.
Jayakumar, D.A.; Francis, C.A.; Naqvi, S.W.A.; Ward, B.B.
and 1 sequence from Sample G840. Cluster VIII contained the majority (23 of 26) of the sequences from Sample V400. Many of the se- quences in Cluster VIII were virtually identical to the nirS sequence of the cultivated denitrifier Pseudo- monas... environmental clone, exhibiting close identity to a cul- tured denitrifier species, was also observed in a recent study of the River Colne estuary sediments, in which virtually identical nirS sequences were obtained from a Flavobacterium isolate and from 2 RT...
Autistic epileptiform regression.
Canitano, Roberto; Zappella, Michele
2006-01-01
Autistic regression is a well known condition that occurs in one third of children with pervasive developmental disorders, who, after normal development in the first year of life, undergo a global regression during the second year that encompasses language, social skills and play. In a portion of these subjects, epileptiform abnormalities are present with or without seizures, resembling, in some respects, other epileptiform regressions of language and behaviour such as Landau-Kleffner syndrome. In these cases, for a more accurate definition of the clinical entity, the term autistic epileptifom regression has been suggested. As in other epileptic syndromes with regression, the relationships between EEG abnormalities, language and behaviour, in autism, are still unclear. We describe two cases of autistic epileptiform regression selected from a larger group of children with autistic spectrum disorders, with the aim of discussing the clinical features of the condition, the therapeutic approach and the outcome.
Advanced MEMS spectral sensor for the NIR
Antila, Jarkko E.; Kantojärvi, Uula; Mäkynen, Jussi; Tammi, Matti; Suhonen, Janne
2015-02-01
Near Infrared (NIR) spectrometers are widely used in many fields to measure material content, such as moisture, fat and protein in grains, foodstuffs and pharmaceutical powders. These fields include applications where only highly miniaturized and robust NIR sensors can be used due to small usable space, weight requirements and/or hostile working environment. Handheld devices for material inspection, online process automation and automotive industry introduce requirements for size, robustness and cost, which is currently difficult to meet. In this paper we present an advanced spectral sensor based on a tunable Microelectromechanical (MEMS) Fabry-Perot Interferometer. The sensor is fibercoupled, weighs 125 grams and fits to an envelope of 25x55x55 mm3. Three types of sensors cover the wavelength ranges from 1.35-1.7 μm, 1.55-2.0 μm and 1.7-2.2 μm, utilizing only a single pixel extended InGaAs detector, avoiding the expensive linear array detectors. We describe the design, principle of operation and calibration methods together with the control schemes. Some environmental tests are described and their results and finally application measurement results are presented along with discussion and conclusions.
Bond, T C; Cole, G D; Goddard, L L; Behymer, E
2007-07-03
We report on a novel sensing technique combining photonics and microelectromechanical systems (MEMS) for the detection and monitoring of gas emissions for critical environmental, medical, and industrial applications. We discuss how MEMS-tunable vertical-cavity surface-emitting lasers (VCSELs) can be exploited for in-situ detection and NIR spectroscopy of several gases, such as O{sub 2}, N{sub 2}O, CO{sub x}, CH{sub 4}, HF, HCl, etc., with estimated sensitivities between 0.1 and 20 ppm on footprints {approx}10{sup -3} mm{sup 3}. The VCSELs can be electrostatically tuned with a continuous wavelength shift up to 20 nm, allowing for unambiguous NIR signature determination. Selective concentration analysis in heterogeneous gas compositions is enabled, thus paving the way to an integrated optical platform for multiplexed gas identification by bandgap and device engineering. We will discuss here, in particular, our efforts on the development of a 760 nm AlGaAs based tunable VCSEL for O{sub 2} detection.
Prediction of dynamical systems by symbolic regression
Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.
2016-07-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.
Rolling Regressions with Stata
Kit Baum
2004-01-01
This talk will describe some work underway to add a "rolling regression" capability to Stata's suite of time series features. Although commands such as "statsby" permit analysis of non-overlapping subsamples in the time domain, they are not suited to the analysis of overlapping (e.g. "moving window") samples. Both moving-window and widening-window techniques are often used to judge the stability of time series regression relationships. We will present an implementation of a rolling regression...
Guijun YANG; Lu LIN; Runchu ZHANG
2007-01-01
Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.
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
Weisberg, Sanford
2005-01-01
Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: ""I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."" -Technometrics, February 1987 ""Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."" -American Scientist, May-June 1987
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
A New NIR Flareof the QSO PMNJ2301-0157
Carrasco, L.; Miramon, J.; Recillas, E.; Porras, A.; Chavushyan, V.; Mayya, D. Y.
2016-12-01
We report on the new NIR flare of the high redshift QSO PMNJ2301-0157 (z=0.778), cross identified with the source BZQJ 2301-1058. On November 13th,2016 (MJD 2457705.722), we found the source with the following flux in the NIR band: H = 14.287 +/- 0.03.
NIR Flare of the AGN Candidate PMNJ0107+0333
Carrasco, L.; Miramon, J.; Recillas, E.; Porras, A.; Chavushyan, V.; Mayya, D. Y.
2016-12-01
We report on the new NIR flare of the AGN candidate PMNJ0107+0333, cross identified with the X-ray source 1RXS J010729.5+033341. On November 13th,2016 (MJD 2457705.699), we found the source with the following flux in the NIR band: H = 14.657 +/- 0.05.
Cotton micronaire measurements by small portable near infrared (nir) analyzers
A key quality and processing parameter for cotton fiber is micronaire, which is a function of the fiber’s maturity and fineness. Near Infrared (NIR) spectroscopy has previously shown the ability to measure micronaire, primarily in the laboratory and using large, research-grade laboratory NIR instru...
TMS: a navigator for NIRS of the primary motor cortex?
Koenraadt, K.L.; Munneke, M.; Duysens, J.E.J.; Keijsers, N.L.W.
2011-01-01
Near-infrared spectroscopy (NIRS) is a non-invasive optical imaging technique, which is increasingly used to measure hemodynamic responses in the motor cortex. The location at which the NIRS optodes are placed on the skull is a major factor in measuring the hemodynamic responses optimally. In this s
Gerber, Samuel [Univ. of Utah, Salt Lake City, UT (United States); Rubel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Whitaker, Ross T. [Univ. of Utah, Salt Lake City, UT (United States)
2012-01-19
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.
Detection of starch adulteration in onion powder by FT-NIR and FT-IR spectroscopy.
Lohumi, Santosh; Lee, Sangdae; Lee, Wang-Hee; Kim, Moon S; Mo, Changyeun; Bae, Hanhong; Cho, Byoung-Kwan
2014-09-24
Adulteration of onion powder with cornstarch was identified by Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy. The reflectance spectra of 180 pure and adulterated samples (1-35 wt % starch) were collected and preprocessed to generate calibration and prediction sets. A multivariate calibration model of partial least-squares regression (PLSR) was executed on the pretreated spectra to predict the presence of starch. The PLSR model predicted adulteration with an R(p)2 of 0.98 and a standard error of prediction (SEP) of 1.18% for the FT-NIR data and an R(p)2 of 0.90 and SEP of 3.12% for the FT-IR data. Thus, the FT-NIR data were of greater predictive value than the FT-IR data. Principal component analysis on the preprocessed data identified the onion powder in terms of added starch. The first three principal component loadings and β coefficients of the PLSR model revealed starch-related absorption. These methods can be applied to rapidly detect adulteration in other spices.
Chang, Tinghong; Lai, Xuxin; Zhang, Hong
2005-01-01
This work demonstrates the application of FT-IR and FT-NIR spectroscopy to monitor the enzymatic interesterification process for bulky fat modification. The reaction was conducted between palm stearin and coconut oil (70/30, w/w) with the catalysis of Lipozyme TL IM at 70°C in a batch reactor...... (PLS) regression. High correlations (r > 0.96) were obtained from cross validations of the data estimated by FT-IR, FT-NIRand above-mentioned conventional analytical methods, except for correlations (r = 0.90-0,95) between FT-IR and SFC profiles. Overall, FT-NIR spectroscopy coupled with transmission....... The blends and interesterified fats samples in liquid form were measured by attenuated total reflectance (ATR) based FT-IR (spectra region: 1516-781 cm-1) and transmission mode based FT-NIR (spectra region: 5369-4752 cm-1) with temperature both controlled at 70°C. The samples in solid form were also measured...
[Detection of benzoyl peroxide in wheat flour by NIR diffuse reflectance spectroscopy technique].
Zhang, Zhi-yong; Li, Gang; Liu, Hai-xue; Lin, Ling; Zhang, Bao-ju; Wu, Xiao-rong
2011-12-01
Adding benzoyl peroxide (BPO) into wheat flour was prohibited by the relevant government departments since May 1, 2011. And it is of great importance to detect BPO additive amount in wheat flour quickly and accurately. Part of BPO which was added into wheat flour will be deoxidized into benzoic acid, and this make it complex to detect the original BPO additive amount. The objective of the present research is to investigate the potential of NIR diffuse reflectance spectroscopy as a way for measurement of BPO original adding amount in wheat flour. A total of 133 wheat flour samples were prepared by adding different content of BPO into pure wheat flour. Spectra data were obtained by NIR spectrometer and then denoised by wavelet transform. Ninety seven samples were taken as calibration set and other 36 samples as prediction set. Partial least squares regression (PLSR) was applied to establish the calibration model between BPO original adding contents and the spectra data. The determination coefficient of model for the calibration set is 0.8901, and root mean squared error of calibration (RMSEC) is 40.85 mg x kg(-1). The determination coefficient for the prediction set is 0.8865, and root mean squared error of prediction (RMSEP) is 44.69 mg x kg(-1). The result indicates that it is feasible to detect the BPO adding contents in wheat flour by NIR diffuse reflectance spectroscopy technique and this technique has the potential to measure some other additives in food.
Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy
António J. A. Santos
2015-08-01
Full Text Available Aim of the study: The ability of NIR spectroscopy for predicting the ISO brightness was studied on unbleached Kraft pulps of Acacia melanoxylon R. Br. Area of study: Sites covering littoral north, mid interior north and centre interior of Portugal. Materials and methods: The samples were Kraft pulped in standard identical conditions targeted to a kappa number of 15. A Near Infrared (NIR partial least squares regression (PLSR model was developed for the ISO brightness prediction using 75 pulp samples with a variation range of 18.9 to 47.9 %. Main results: Very good correlations between NIR spectra and ISO brightness were obtained. Ten methods were used for PLS analysis (cross validation with 48 samples, and a test set validation was made with 27 samples. The 1stDer pre-processed spectra coupling two wavenumber ranges from 9404 to 7498 cm-1 and 4605 to 4243 cm-1 allowed the best model with a root mean square error of ISO brightness prediction of 0.5 % (RMSEP, a r2 of 99.5 % with a RPD of 14.7. Research highlights: According to AACC Method 39-00, the present model is sufficiently accurate to be used for process control (RPD ≥ 8
Quantification of MBM adulteration in compound fertilizers and composts by NIRS
Han L.
2009-01-01
Full Text Available The objective of this study was to demonstrate the feasibility of using near infrared reflectance spectroscopy (NIRS to determine MBM content in compound fertilizers and composts. One hundred fourty adulterated compound fertilizer samples were prepared in the laboratory by mixing 4 types of compound fertilizers with 3 types of MBM randomly at different levels of 0.1%-10.0% (w/w. One hundred twenty adulterated compost samples were obtained by mixing 41 compost samples with 28 MBM at different levels of 3.0%-24.0% (w/w. NIRS calibration models were developed using the partial least squares (PLS regression method. Results showed that the coefficients of determination for calibration (R2 and validation (r2 were 0.996 and 0.622, 0.988 and 0.722 for adulterated compound fertilizers and composts respectively. The ratios of prediction to deviation (RPD were 8.84 and 1.87 for them respectively. These results indicated that NIRS could be used to quantify the adulteration of banned MBM in compound fertilizers with high prediction accuracy, and be insufficient to determine the content of MBM in composts due to low prediction accuracy.
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests 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...... 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...
Real-time state estimation in a flight simulator using fNIRS.
Thibault Gateau
Full Text Available Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot's instantaneous mental state (not-on-task vs. on-task. It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load. These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot's mental state matched significantly better than chance with the pilot's real state (62% global accuracy, 58% specificity, and 72% sensitivity. The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development.
Real-time state estimation in a flight simulator using fNIRS.
Gateau, Thibault; Durantin, Gautier; Lancelot, Francois; Scannella, Sebastien; Dehais, Frederic
2015-01-01
Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot's instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot's mental state matched significantly better than chance with the pilot's real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development.
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.
Towett, Erick K; Alex, Merle; Shepherd, Keith D; Polreich, Severin; Aynekulu, Ermias; Maass, Brigitte L
2013-01-01
There is uncertainty on how generally applicable near-infrared reflectance spectroscopy (NIRS) calibrations are across genotypes and environments, and this study tests how well a single calibration performs across a wide range of conditions. We also address the optimization of NIRS to perform the analysis of crude protein (CP) content in a variety of cowpea accessions (n = 561) representing genotypic variation as well as grown in a wide range of environmental conditions in Tanzania and Uganda. The samples were submitted to NIRS analysis and a predictive calibration model developed. A modified partial least-squares regression with cross-validation was used to evaluate the models and identify possible spectral outliers. Calibration statistics for CP suggests that NIRS can predict this parameter in a wide range of cowpea leaves from different agro-ecological zones of eastern Africa with high accuracy (R (2)cal = 0.93; standard error of cross-validation = 0.74). NIRS analysis improved when a calibration set was developed from samples selected to represent the range of spectral variability. We conclude from the present results that this technique is a good alternative to chemical analysis for the determination of CP contents in leaf samples from cowpea in the African context, as one of the main advantages of NIRS is the large number of compounds that can be measured at once in the same sample, thus substantially reducing the cost per analysis. The current model is applicable in predicting the CP content of young cowpea leaves for human nutrition from different agro-ecological zones and genetic materials, as cowpea leaves are one of the popular vegetables in the region.
A review of NIR dyes in cancer targeting and imaging.
Luo, Shenglin; Zhang, Erlong; Su, Yongping; Cheng, Tianmin; Shi, Chunmeng
2011-10-01
The development of multifunctional agents for simultaneous tumor targeting and near infrared (NIR) fluorescence imaging is expected to have significant impact on future personalized oncology owing to the very low tissue autofluorescence and high tissue penetration depth in the NIR spectrum window. Cancer NIR molecular imaging relies greatly on the development of stable, highly specific and sensitive molecular probes. Organic dyes have shown promising clinical implications as non-targeting agents for optical imaging in which indocyanine green has long been implemented in clinical use. Recently, significant progress has been made on the development of unique NIR dyes with tumor targeting properties. Current ongoing design strategies have overcome some of the limitations of conventional NIR organic dyes, such as poor hydrophilicity and photostability, low quantum yield, insufficient stability in biological system, low detection sensitivity, etc. This potential is further realized with the use of these NIR dyes or NIR dye-encapsulated nanoparticles by conjugation with tumor specific ligands (such as small molecules, peptides, proteins and antibodies) for tumor targeted imaging. Very recently, natively multifunctional NIR dyes that can preferentially accumulate in tumor cells without the need of chemical conjugation to tumor targeting ligands have been developed and these dyes have shown unique optical and pharmaceutical properties for biomedical imaging with superior signal-to-background contrast index. The main focus of this article is to provide a concise overview of newly developed NIR dyes and their potential applications in cancer targeting and imaging. The development of future multifunctional agents by combining targeting, imaging and even therapeutic routes will also be discussed. We believe these newly developed multifunctional NIR dyes will broaden current concept of tumor targeted imaging and hold promise to make an important contribution to the diagnosis
Kapper, C.; Klont, R.E.; Verdonk, J.M.A.J.; Urlings, H.A.P.
2012-01-01
The objective was to study prediction of pork quality by near infrared spectroscopy (NIRS) technology in the laboratory. A total of 131 commercial pork loin samples were measured with NIRS. Predictive equations were developed for drip loss %, colour L*, a*, b* and pH ultimate (pHu). Equations with
Mallick, Abhijit; Oh, Juwon; Kim, Dongho; Rath, Harapriya
2016-06-06
Two hitherto unknown planar aromatic [30] fused heterocyclic macrocycles (1.1.0.1.1.0), with NIR absorption in free-base form and protonation-induced enhanced NIR emission, have been synthesized from easy to make precursors. The induced correspondence of fusion on the macrocyclic structure, electronic absorption, and emission spectra have been highlighted.
Near Infrared (nir) Imaging for Nde
Diamond, G. G.; Pallav, P.; Hutchins, D. A.
2008-02-01
A novel application of near infrared (NIR) signals is presented, which can be used to provide images of many different materials and objects. It is effectively a very low cost non-ionising alternative to many applications currently being investigated using electromagnetic waves at other frequencies, such as THz and X-ray imaging. This alternative technique can be realised by very simple and inexpensive electronics and is inherently far more portable and easy to use. Transmission imaging results from this technique are presented from examples industrial quality control, food inspection and various security applications, and the results compared to existing techniques. In addition, this technique can be used in through-transmission mode on biological and medical samples, and images are presented that differentiate between not only flesh and bone, but also various types of soft tissue.
Jacobi, H Fabian; Ohl, Susanne; Thiessen, Eiko; Hartung, Eberhard
2012-01-01
The aim of this study was to apply near-infrared spectroscopy (NIRS), available biogas plant data and lumped degradation kinetics to predict biogas production (BPr) of maize silage. A full-scale agricultural biogas plant was equipped with NIRS-metrology at the feeding station. Continuously NIR-spectra were collected for 520 d. Substrate samples were analyzed by means of feedstuff analysis. Biogas potential of the samples was calculated from the laboratory analysis results and for a sample-subset practically assessed by "Hohenheim biogas tests". NIRS-regression-models for all mentioned parameters were calibrated. Continuously gathered spectra, NIRS-models, actual plant-feeding data and degradation kinetics were used to calculate time-series of theoretically expectable BPr. Results were validated against measured gas quantity. Determination coefficients between calculated and measured BPr were up to 58.2%. This outcome was mainly due to the positive correlation between BPr and input amount since the substrate was very homogeneous. The use of NIRS seems more promising for plants with stronger substrate heterogeneity.
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.
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 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 °Brix(QL); 0.58 °Brix (ZC)], low RMSEC [0.48 °Brix (QL); 0.34 °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 nondestructive way.
Absolute Radiation Thermometry in the NIR
Bünger, L.; Taubert, R. D.; Gutschwager, B.; Anhalt, K.; Briaudeau, S.; Sadli, M.
2017-04-01
A near infrared (NIR) radiation thermometer (RT) for temperature measurements in the range from 773 K up to 1235 K was characterized and calibrated in terms of the "Mise en Pratique for the definition of the Kelvin" (MeP-K) by measuring its absolute spectral radiance responsivity. Using Planck's law of thermal radiation allows the direct measurement of the thermodynamic temperature independently of any ITS-90 fixed-point. To determine the absolute spectral radiance responsivity of the radiation thermometer in the NIR spectral region, an existing PTB monochromator-based calibration setup was upgraded with a supercontinuum laser system (0.45 μm to 2.4 μm) resulting in a significantly improved signal-to-noise ratio. The RT was characterized with respect to its nonlinearity, size-of-source effect, distance effect, and the consistency of its individual temperature measuring ranges. To further improve the calibration setup, a new tool for the aperture alignment and distance measurement was developed. Furthermore, the diffraction correction as well as the impedance correction of the current-to-voltage converter is considered. The calibration scheme and the corresponding uncertainty budget of the absolute spectral responsivity are presented. A relative standard uncertainty of 0.1 % (k=1) for the absolute spectral radiance responsivity was achieved. The absolute radiometric calibration was validated at four temperature values with respect to the ITS-90 via a variable temperature heatpipe blackbody (773 K ...1235 K) and at a gold fixed-point blackbody radiator (1337.33 K).
NIR autofluorescence and OCT imaging of biotissues
Chorvat, Dusan, Jr.; Smolka, Jozef; Mateasik, Anton; Hrin, Lubos
2003-10-01
Optical coherence tomography (OCT) is one of the most promising recently developed methods for non-invasive in vivo characterization of biological highly scattering tissues. However, one of the drawbacks of the pure OCT imaging is that it is not sensitive to changes in metabolism. This may impact derived information and consecutive diagnostics, because pathological changes of tissue structure are accompanied with changes in metabolic activity or functional state in these areas even if there is not yet strongly evident structural change. Therefore, it is desirable to combine early detection of tissue malformations by OCT with other techniques, capable to detect and evaluate their functional state. One of suitable candidates for such non-invasive optical functional imaging is detection of laser induced autofluorescence, which could provide information about rate of biological and chemical processes in living cells. As an example, the cells in proliferative state (with increased metabolic activity or during mitosis) show more intensive NIR fluorescence than the cells that are not proliferative, because of increased concentration of free porphyrins. In presented study we used OCT and laser induced NIR autofluorescence imaging for detection and evaluation of changes in areas of naevus and injuries in group of volunteers. The fusion of information on structural and functional state of biotissues provided by the two mentioned complementary methods may enhance the diagnostics power of their prospective clinical use. Firstly the fluorescence of area of naevus and injuries, excited by 630 nm was taken by CCD camera and then was naevus scanned by OCT. The findings of both methods were compared and correlated. In the case of naevus the obtained results were reviewed with histological treatment of the same area.
NIRS-based noninvasive cerebrovascular regulation assessment
Miller, S.; Richmond, I.; Borgos, J.; Mitra, K.
2016-03-01
Alterations to cerebral blood flow (CBF) have been implicated in diverse neurological conditions, including mild traumatic brain injury, microgravity induced intracranial pressure (ICP) increases, mild cognitive impairment, and Alzheimer's disease. Near infrared spectroscopy (NIRS)-measured regional cerebral tissue oxygen saturation (rSO2) provides an estimate of oxygenation of the interrogated cerebral volume that is useful in identifying trends and changes in oxygen supply to cerebral tissue and has been used to monitor cerebrovascular function during surgery and ventilation. In this study, CO2-inhalation-based hypercapnic breathing challenges were used as a tool to simulate CBF dysregulation, and NIRS was used to monitor the CBF autoregulatory response. A breathing circuit for the selective administration of CO2-compressed air mixtures was designed and used to assess CBF regulatory responses to hypercapnia in 26 healthy young adults using non-invasive methods and real-time sensors. After a 5 or 10 minute baseline period, 1 to 3 hypercapnic challenges of 5 or 10 minutes duration were delivered to each subject while rSO2, partial pressure of end tidal CO2 (PETCO2), and vital signs were continuously monitored. Change in rSO2 measurements from pre- to intrachallenge (ΔrSO2) detected periods of hypercapnic challenges. Subjects were grouped into three exercise factor levels (hr/wk), 1: 0, 2:>0 and 10. Exercise factor level 3 subjects showed significantly greater ΔrSO2 responses to CO2 challenges than level 2 and 1 subjects. No significant difference in ΔPETCO2 existed between these factor levels. Establishing baseline values of rSO2 in clinical practice may be useful in early detection of CBF changes.
Diseno y construccion de un espectrometro NIR; Design and construction of a NIR spectrometer
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.
Tai Kelly
2009-11-01
Full Text Available Abstract Background Corporeal machine interfaces (CMIs are one of a few available options for restoring communication and environmental control to those with severe motor impairments. Cognitive processes detectable solely with functional imaging technologies such as near-infrared spectroscopy (NIRS can potentially provide interfaces requiring less user training than conventional electroencephalography-based CMIs. We hypothesized that visually-cued emotional induction tasks can elicit forehead hemodynamic activity that can be harnessed for a CMI. Methods Data were collected from ten able-bodied participants as they performed trials of positively and negatively-emotional induction tasks. A genetic algorithm was employed to select the optimal signal features, classifier, task valence (positive or negative emotional value of the stimulus, recording site, and signal analysis interval length for each participant. We compared the performance of Linear Discriminant Analysis and Support Vector Machine classifiers. The latency of the NIRS hemodynamic response was estimated as the time required for classification accuracy to stabilize. Results Baseline and activation sequences were classified offline with accuracies upwards of 75.0%. Feature selection identified common time-domain discriminatory features across participants. Classification performance varied with the length of the input signal, and optimal signal length was found to be feature-dependent. Statistically significant increases in classification accuracy from baseline rates were observed as early as 2.5 s from initial stimulus presentation. Conclusion NIRS signals during affective states were shown to be distinguishable from baseline states with classification accuracies significantly above chance levels. Further research with NIRS for corporeal machine interfaces is warranted.
Identification of fine wool and cashmere by Vis/NIR spectroscopy technology
Wu, Guifang; He, Yong
2008-03-01
As a rapid and non-destructive methodology, near infrared spectroscopy technique has been paid much attention recently. This paper presents an automatic recognition scheme for the fine wool fiber and cashmere fiber by Vis/NIR spectroscopy technique, aim at the characteristics of Vis/NIR spectra on cashmere and fine wool. One mixed algorithm was presented to discriminate cashmere and fine wool with principal component analysis (PCA) and Artificial Neural Network (ANN). Preliminary qualitative analysis model has been built: We adopt Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere and fine wool, two kinds of data pretreatment methods were applied: the standard normal variate (SNV) was used as scatter correction. Savitzky-Golay with the segment size 3 was used as the smoothing way to decrease the noise processed. Followed the pretreatment, spectral data were processed using principal component analysis, 6 principal components (PCs) were selected based on the reliabilities of PCs of 99.8%, the scores of these 6 PCs would be taken as the input of the three-layer back-propagation (BP) artificial neural network (BP-ANN). Trained the BP-ANN with samples in calibration collection and predicted the samples in prediction collection. Experiments demonstrate that the system works quickly and effectively, and has remarkable advantages in comparison with the previous systems, The result indicted a model had been built to discriminate cashmere from fine wool using Vis/NIR spectra method combined with PCA-BP technology. The model works well, which indicates that this kind of approach is effective and promising, can raise resolution of cashmere and fine wool.
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-
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
Xiao, Li; Wei, Hui; Himmel, Michael E; Jameel, Hasan; Kelley, Stephen S
2014-01-01
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. This review
Transductive Ordinal Regression
Seah, Chun-Wei; Ong, Yew-Soon
2011-01-01
Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, are often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely Transductive Ordinal Regression (TOR). The key challenge of the present study lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive setting...
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
Ernesto A Restaino
2009-12-01
Full Text Available The aim of this study was to investigate the use of near infrared reflectance (NIRS spectroscopy to predict the nutritive value of silages from pastures and to assess the effect of silage structure type (e.g. bunker and bag silos on the NIRS predictions. Samples (n = 120 were sourced from commercial farms and analyzed in a NIRS monochromator instrument (NIR Systems, Silver Spring, Maryland, USA using wavelengths between 400 and 2500 nm in reflectance. Calibration models were developed between chemical and NIRS spectral data using partial least squares (PLS regression. The coefficients of determination in calibration (R² and the standard error in cross validation (SECV were 0.73 (SECV: 1.2%, 0.81 (SECV: 2.0%, 0.75 (SECV: 6.6%, 0.80 (SECV: 6.7%, 0.80 (SECV: 4.0%, 0.60 (SECV: 3.6% and 0.70 (SECV: 0.34 for ash, crude protein (CP, neutral detergent fiber (NDF, dry matter (DM, acid detergent fiber (ADF, in vitro dry matter digestibility (IVDMD and pH, respectively. The results showed the potential of NIRS to analyze DM, ADF and CP in silage samples from pastures.El objetivo de este trabajo fue investigar el uso de la espectrofotometría de reflectancia en el infrarrojo cercano (NIRS para predecir el valor nutritivo en ensilaje de pasturas y evaluar el tipo de estructura de silo (silo bolsa y trinchera en las predicciones NIRS. Muestras (n = 120 provenientes de granjas comerciales fueron leídas en un equipo monocromador NIRS (NIR Systems, Silver Spring, Maryland, USA en el rango de longitudes de onda de 400 a 2500 nm, en reflectancia. Modelos de calibración entre los datos químicos y los espectros NIRS fueron desarrollados usando el método de los cuadrados mínimos parciales. Los coeficientes de determinación en calibración (R² y el error estándar de la validación cruzada (SEVC fueron 0,73 (SECV: 1,2%, 0,81 (SECV: 2,0%, 0,75 (SECV: 6,6%, 0,80 (SECV: 6,7%, 0,80 (SECV: 4,0%, 0,60 (SECV: 3,6% and 0,70 (SECV: 0,34 para cenizas, proteína cruda
A Gibbs Sampler for Multivariate Linear Regression
Mantz, Adam B
2015-01-01
Kelly (2007, hereafter K07) described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modeled by a flexible mixture of Gaussians rather than assumed to be uniform. Here I extend the K07 algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Second, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamica...
NIR fluorescent dyes: versatile vehicles for marker and probe applications
Patonay, Gabor; Chapman, Gala; Beckford, Garfield; Henary, Maged
2013-02-01
The use of the NIR spectral region (650-900 nm) is advantageous due to the inherently lower background interference and the high molar absorptivities of NIR chromophores. Near-Infrared (NIR) dyes are increasingly used in the biological and medical field. The binding characteristics of NIR dyes to biomolecules are possibly controlled by several factors, including hydrophobicity, size and charge just to mention a few parameters. Binding characteristics of symmetric carbocyanines and found that the hydrophobic nature of the NIR dye is only partially responsible for the binding strength. Upon binding to biomolecules significant fluorescence enhancement can be observed for symmetrical carbocyanines. This fluorescence amplification facilitates the detection of the NIR dye and enhances its utility as NIR reporter. This manuscript discusses some probe and marker applications of such NIR fluorescent dyes. One application discussed here is the use of NIR dyes as markers. For labeling applications the fluorescence intensity of the NIR fluorescent label can significantly be increased by enclosing several dye molecules in nanoparticles. To decrease self quenching dyes that have relatively large Stokes' shift needs to be used. This is achieved by substituting meso position halogens with amino moiety. This substitution can also serve as a linker to covalently attach the dye molecule to the nanoparticle backbone. We report here on the preparation of NIR fluorescent silica nanoparticles. Silica nanoparticles that are modified with aminoreactive moieties can be used as bright fluorescent labels in bioanalytical applications. A new bioanalytical technique to detect and monitor the catalytic activity of the sulfur assimilating enzyme using NIR dyes is reported as well. In this spectroscopic bioanalytical assay a family of Fischer based n-butyl sulfonate substituted dyes that exhibit distinct variation in absorbance and fluorescence properties and strong binding to serum albumin as its
Lavin, Shana R; Sullivan, Kathleen E; Wooley, Stuart C; Stone, Koni; Russell, Scott; Valdes, Eduardo V
2015-11-01
Iron overload disorder has been described in a number of zoo-managed species, and it has been recommended to increase the tannin composition of the diet as a safe way to minimize iron absorption in these iron-sensitive species. The goal of this study was to examine the potential of near infrared reflectance spectroscopy (NIRS) as a rapid and simple screening tool to assess willow (Salix caroliniana) nutrient composition (crude protein: CP; acid detergent fiber: ADF; neutral detergent fiber: NDF; lignin, gross energy: GE) and condensed tannin (CT) concentrations. Calibration equations were developed by regression of the lab values from 2 years using partial least squares on n = 144 NIRS spectra to predict n = 20 independent validation samples. Using the full 2-year dataset, good prediction statistics were obtained for CP, ADF, NDF, and GE in plant leaves and stems (r(2 ) > 0.75). NIRS did not predict lignin concentrations reliably (leaves r(2) = 0.52, stems r(2) = 0.33); however, CTs were predicted moderately well (leaves r(2) = 0.72, stems r(2) = 0.67). These data indicate that NIRS can be used to quantify several key nutrients in willow leaves and stems including concentrations of plant secondary compounds which, depending on the bioactivity of the compound, may be targeted to feed iron-sensitive browsing animals.
Wu, Zhi-Sheng; Zhou, Lu-Wei; Dai, Sheng-Yun; Shi, Xin-Yuan; Qiao, Yan-Jiang
2015-04-01
It has been reported that hyperspectral data could be employed to qualitatively elucidate the spatial composition of tablets of Chinese medicinal plants. To gain more insights into this technology, a quantitative profile provided by near infrared (NIR) spectromicroscopy was further studied by determining the glycyrrhizic acid content in licorice, Glycyrrhiza uralensis. Thirty-nine samples from twenty-four different origins were analyzed using NIR spectromicroscopy. Partial least squares, interval partial least square (iPLS), and least squares support vector regression (LS-SVR) methods were used to develop linear and non-linear calibration models, with optimal calibration parameters (number of interval numbers, kernel parameter, etc.) being explored. The root mean square error of prediction (RMSEP) and the coefficient of determination (R(2)) of the iPLS model were 0.717 7% and 0.936 1 in the prediction set, respectively. The RMSEP and R(2) of LS-SVR model were 0.515 5% and 0.951 4 in the prediction set, respectively. These results demonstrated that the glycyrrhizic acid content in licorice could barely be analyzed by NIR spectromicroscopy, suggesting that good quality quantitative data are difficult to obtain from microscopic NIR spectra for complicated Chinese medicinal plant materials.
Zhang, Man; Liu, Xu-Hua; He, Xiong-Kui; Zhang, Lu-Da; Zhao, Long-Lian; Li, Jun-Hui
2010-05-01
In the present paper, taking 66 wheat samples for testing materials, ridge regression technology in near-infrared (NIR) spectroscopy quantitative analysis was researched. The NIR-ridge regression model for determination of protein content was established by NIR spectral data of 44 wheat samples to predict the protein content of the other 22 samples. The average relative error was 0.015 18 between the predictive results and Kjeldahl's values (chemical analysis values). And the predictive results were compared with those values derived through partial least squares (PLS) method, showing that ridge regression method was deserved to be chosen for NIR spectroscopy quantitative analysis. Furthermore, in order to reduce the disturbance to predictive capacity of the quantitative analysis model resulting from irrelevant information, one effective way is to screen the wavelength information. In order to select the spectral information with more content information and stronger relativity with the composition or the nature of the samples to improve the model's predictive accuracy, ridge regression was used to select wavelength information in this paper. The NIR-ridge regression model was established with the spectral information at 4 wavelength points, which were selected from 1 297 wavelength points, to predict the protein content of the 22 samples. The average relative error was 0.013 7 and the correlation coefficient reached 0.981 7 between the predictive results and Kjeldahl's values. The results showed that ridge regression was able to screen the essential wavelength information from a large amount of spectral information. It not only can simplify the model and effectively reduce the disturbance resulting from collinearity information, but also has practical significance for designing special NIR analysis instrument for analyzing specific component in some special samples.
刘勇; 王萱; 邹慧挺
2014-01-01
为提高传统非线性预测模型的预测精度，提出一种基于改进果蝇优化算法优化广义回归神经网络的预测方法，将果蝇群体分两部分分别进行迭代寻优，从而改进了果蝇优化算法的寻优性能，进而避免了在寻优过程中陷入局部最优。该方法利用改进果蝇优化算法优化广义回归神经网络的径向基函数扩展参数，然后用训练好的广义回归神经网络预测模型进行预测，最后通过订单预测算例进行实证研究。实证研究结果显示，该方法在解决订单预测问题中与未改进的果蝇优化算法优化广义回归神经网络和传统的广义回归神经网络方法对比，具有更高的预测精度和更好的非线性拟合能力。%In order to improve the prediction accuracy of traditional nonlinear prediction model,an improved forecasting method of optimized general regression neural network based on fruit fly optimization algorithm(FOA)was proposed.In this modified FOA algorithm,the fruit flies were divided into two parts iterative optimization to avoid the optimization process into a local optimum,which improved FOA optimizing performance.The modified FOA was used to optimize the speed of radial basis function expansion of generalized regression neural network,and then using the trained general regression neural net-work prediction model to forecast,finally,an order forecasting numerical example is used to empirical research.The empiri-cal results show that the method in solving the order forecasting problems has higher prediction accuracy and better nonlin-ear fitting than general regression neural network based on fruit fly optimization algorithm and traditional generalized regres-sion neural network.
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.
刘仁明; 刘瑞明; 武延春; 柳振全; 张德清; 自兴发; 司民真
2011-01-01
Investigations on near infrared surface-enhanced Raman scattering (NIR-SERS) spectra of the serum for patients with liver cancer and healthy persons based on Ag nanofilms prepared by using electrostatic self-assembly are reported. Analysis indicates NIR-SERS spectra of the sera between healthy persons and liver cancer patients are different. Firstly, Raman bands at 630, 720, 812 and 1578 cm-1 become weaker (even disappear) in NIR-SERS spectra of the sera for cancer liver patients but stronger in NIR-SERS spectra of the sera for healthy persons.Secondly, Raman bands at 1130 cm-1 and 1204 cm-1 in NIR-SERS spectra of the sera for healthy persons have blue shifts to 1135 cm-1 and 1269 cm 1 in NIR-SERS spectra of sera for cancer liver patients, respectively. Meanwhile, a new Raman band at 558 cm-1 appears in NIR-SERS spectra of liver cancer patients. Additionally, striking spectral differences are abvious in NIR-SERS spectra in the intensity ratios at 630/300, 1130/300, and 1578/300 cm-1.These three peak-intensity ratios of liver cancer patients with values of 0. 848±0. 042, 1. 094~ 0. 118, 0. 914 ± 0. 070, respectively, are more notable (mean ~ S. D., n = 15, P ＜ 0.01 ) compared with those of healthy persons (1.985t0. 487, 1.568±0.286, 1. 189±0. 108, respectively). The results show that the intensity relative peak-ratios at 630/300, 1130/300, and 1578/300 cm-1 can be used to discriminate liver cancer patients from healthy persons, which indicate these three intensity ratios can be served as N1R-SERS spectral criteria for the diagnosis of cancer liver.%基于一种新型、高效、生物兼容性近红外表面增强拉曼散射(NIR-SERS)基底,采用便携式近红外拉曼光谱仪分别对健康人和肝癌病患者的血清进行了NIR-SERS光谱研究.实验发现,健康人与肝癌患者的血清NIR-SERS光谱存在显著差异:1)健康人血清NIR-SERS光谱中位于630、720、812和1578 cm-1附近的谱峰在肝癌患者血清NIR-SERS光
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.
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.
Gutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, Maria P.
2016-01-01
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. PMID:26891304
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.
Hegazy, Maha A; Lotfy, Hayam M; Mowaka, Shereen; Mohamed, Ekram Hany
2016-07-05
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.
[Understanding logistic regression].
El Sanharawi, M; Naudet, F
2013-10-01
Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge.
Lívia Cássia Viana
2009-12-01
Full Text Available This work aimed to apply the near infrared spectroscopy technique (NIRS for fast prediction of basic density and morphological characteristics of wood fibers in Eucalyptus clones. Six Eucalyptus clones aged three years were used, obtained from plantations in Cocais, Guanhães, Rio Doce and Santa Bárbara, in Minas Gerais state. The morphological characteristics of the fibers and basic density of the wood were determined by conventional methods and correlated with near infrared spectra using partial least square regression (PLS regression. Best calibration correlations were obtained in basic density prediction, with values 0.95 for correlation coefficient of cross validation (Rcv and 3.4 for ratio performance deviation (RPD, in clone 57. Fiber length can be predicted by models with Rcv ranging from 0.61 to 0.89 and standard error (SECV ranging from 0.037 to 0.079 mm. The prediction model for wood fiber width presented higher Rcv (0.82 and RPD (1.9 values in clone 1046. Best fits to estimate lumen diameter and fiber wall thickness were obtained with information from clone 1046. In some clones, the NIRS technique proved efficient to predict the anatomical properties and basic density of wood in Eucalyptus clones.
Practical Session: Logistic Regression
Clausel, M.; Grégoire, G.
2014-12-01
An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.
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.
fNIRS measurements in migraine
Akin, Ata; Emir, Uzay E.; Bilensoy, Didem; Erdogan, Gulin; Candansyar, Selcuk; Bolay, Hayrunnisa
2005-04-01
Migraine is a complex chronic neurovascular disorder in which the interictal changes in neuronal excitability and vascular reactivity in the cerebral cortex were detected. The extent and direction of the changes in cerebral blood flow that affect cerebral hemodynamics during attacks, however, are still a matter of debate. This may have been due to the logistic and technical problems posed by the different techniques to determine cerebral blood flow during migraine attacks and the different definitions of patient populations. In this study, we have investigated hypercapnia challenges by breath holding task on subjects with and without migraine by using functional near infrared spectroscopy (fNIRS). Measurements of the relative changes in concentration of deoxy-hemoglobin [Hb] and oxy-hemoglobin [HbO2] are performed on four healthy subjects during three breath holdings of 30 seconds (s.) interleaved with 90 s. of normal breathing. We have observed [Hb]increase during breath holding interval in subject without migraine whereas in subject with migraine [Hb] decreases during breath holding interval. The result of our study suggest that hypercapnia effect on cerebral hemodynamic of subject with migraine and without migraine could be due to different vascular reactivity to PCO2 (carbon dioxide partial pressure) in arteries.
Compensation techniques in NIRS proton beam radiotherapy
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.
Hot electron induced NIR detection in CdS films.
Sharma, Alka; Kumar, Rahul; Bhattacharyya, Biplab; Husale, Sudhir
2016-03-11
We report the use of random Au nanoislands to enhance the absorption of CdS photodetectors at wavelengths beyond its intrinsic absorption properties from visible to NIR spectrum enabling a high performance visible-NIR photodetector. The temperature dependent annealing method was employed to form random sized Au nanoparticles on CdS films. The hot electron induced NIR photo-detection shows high responsivity of ~780 mA/W for an area of ~57 μm(2). The simulated optical response (absorption and responsivity) of Au nanoislands integrated in CdS films confirms the strong dependence of NIR sensitivity on the size and shape of Au nanoislands. The demonstration of plasmon enhanced IR sensitivity along with the cost-effective device fabrication method using CdS film enables the possibility of economical light harvesting applications which can be implemented in future technological applications.
Identification of transgenic foods using NIR spectroscopy: A review
Alishahi, A.; Farahmand, H.; Prieto, N.; Cozzolino, D.
2010-01-01
The utilization of chemometric methods in the quantitative and qualitative analysis of feeds, foods, medicine and so on has been accompanied with the great evolution in the progress and in the near infrared spectroscopy (NIRS). Hence, recently the application of NIR spectroscopy has extended on the context of genetics and transgenic products. The aim of this review was to investigate the application of NIR spectroscopy to identificate transgenic products and to compare it with the traditional methods. The results of copious researches showed that the application of NIRS technology was successful to distinguish transgenic foods and it has advantages such as fast, avoiding time-consuming, non-destructive and low cost in relation to the antecedent methods such as PCR and ELISA.
Water-soluble pyrrolopyrrole cyanine (PPCy) NIR fluorophores.
Wiktorowski, Simon; Rosazza, Christelle; Winterhalder, Martin J; Daltrozzo, Ewald; Zumbusch, Andreas
2014-05-11
Water-soluble derivatives of pyrrolopyrrole cyanines (PPCys) have been synthesized by a post-synthetic modification route. In highly polar media, these dyes are excellent NIR fluorophores. Labeling experiments show how these novel dyes are internalized into mammalian cells.
NIR emitting ytterbium chelates for colourless luminescent solar concentrators.
Sanguineti, Alessandro; Monguzzi, Angelo; Vaccaro, Gianfranco; Meinardi, Franco; Ronchi, Elisabetta; Moret, Massimo; Cosentino, Ugo; Moro, Giorgio; Simonutti, Roberto; Mauri, Michele; Tubino, Riccardo; Beverina, Luca
2012-05-14
A new oxyiminopyrazole-based ytterbium chelate enables NIR emission upon UV excitation in colorless single layer luminescent solar concentrators for building integrated photovoltaics. This journal is © the Owner Societies 2012
NIRS Characterization of Paper Pulps to Predict Kappa Number
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.
[Identification of fine wool and cashmere by using Vis/NIR spectroscopy technology].
Wu, Gui-fang; Zhu, Deng-sheng; He, Yong
2008-06-01
As a rapid and non-destructive methodology, near infrared spectroscopy technique has been attracting much attention recently. The present study applied Vis/NIR spectra to the identification of cashmere and fine wool fiber. Cashmere and fine wool are resemble in superficies, but they differs in diameter, height, thickness, angle of inclination, and marginal morphology of surface scale. Although researchers both at home and abroad did a lot researches and experiments to distinguish fine wool from cashmere, the resolution of cashmere and fine wool is still not satisfactory, and it is always a challenging task to differentiate and recognize fine wool and cashmere. This paper presents an automatic recognition scheme for the fine wool fiber and cashmere fiber by Vis/NIR spectroscopy technique, aiming at the characteristics of Vis/NIR spectra of cashmere and fine wool. One mixed algorithm was presented to discriminate cashmere and fine wool with principal component analysis (PCA) and artificial neural network (ANN). Preliminary qualitative analysis model has been built: Vis/NIRS spectroscopy diffuse techniques were used to collect the spectral data of cashmere and fine wool, and two kinds of data pretreatment methods were applied: the standard normal variate (SNV) was used for scatter correction. Savitzky-Golay with the segment size 3 was used as the smoothing way to decrease the noise processed. Following the pretreatment, spectral data were processed using principal component analysis, 6 principal components (PCs) were selected based on the reliabilities of PCs of 99.8%, and the scores of these 6 PCs would be taken as the input of the three-layer back-propagation (BP) artificial neural network (BP-ANN). The BP-ANN was trained with samples in calibration collection and predicted the samples in prediction collection were predicted. Experiments demonstrate that the system works quickly and effectively, and has remarkable advantages in comparison with the previous systems
NIR Electrofluorochromic Properties of Aza-Boron-dipyrromethene Dyes
2016-01-01
The photophysical properties of near-infrared (NIR) emissive aza-boron-dipyrromethene (aza-BDP) dyes incorporating nitrofluorene and alkoxy decorations were intensively investigated. Their highly reversible one-electron reduction process showed characteristic electrofluorochromic (EF) properties in the NIR range, depending on the substituents. The nitrofluorene ethynyl-substituted (Type I) dyes showed smaller EF effects than the alkoxy-containing (Type II) dyes because of the difference in th...
Brain Functional Connectivity in MS: An EEG-NIRS Study
2015-10-01
oxygen- based ( near -infrared spectroscopy (NIRS), functional MRI (fMRI)) signals, and to use the results to help optimize BOLD fMRI analyses of brain...2. Keywords BOLD – blood oxygen level dependent EEG – electroencephalography NIRS – near -infrared spectroscopy fMRI – functional MRI MS...INTRODUCTION TO ELECTROENCEPHALOGRAPHY AND NEAR -INFRARED SPECTROSCOPY NEUROIMAGING MEASUREMENT AND ANALYSIS P.40LO GlACO~lETTT 1. COURSE O VERVIEW T he
Cerebral blood volume in humans by NIRS and PET
Pott, Frank; Knudsen, Gitte M.; Rostrup, Egill; Ide, Kojiro; Secher, Niels H.; Paulson, Olaf B.
1998-01-01
Near infrared spectroscopy (NIRS) determined changes in the cerebral blood volume (CBV) were compared to those obtained by positron emission tomography (PET) in five healthy volunteers (2 females). Two NIRS optodes were placed on the left forehead and NIRS-CBV was derived from the sum of oxyhemoglobin and deoxyhemoglobin. CBV changes were induced by hyperventilation and inhalation of 6% CO2. After 2 min inhalation of labeled carbon monoxide, data were sampled during 8 min for both PET- and NIRS-CBV as well as for the arterial carbon dioxide tension (PaCO2). The region of interest for PET-CBV was `banana-shaped' with boundaries corresponding to the position of the NIRS optodes on the transmission scan and to a depth of approximately 2 cm. During hyperventilation, PaCO2 decreased from 5.2 (4.6 - 5.8) to 4.6 (4.2 - 4.9) kPa and equally PET-CBV (from 3.9 (2.5 - 5.2) to 3.6 (3.0 - 4.8) ml (DOT) 100 g-1) and NIRS-CBV were reduced (by -0.14 [-0.38 - 0.50] ml (DOT) 100 g-1). During hypercapnia PaCO2 increased to 6.0 (5.9 - 7.0) kPa accompanied by parallel changes in PET- (to 4.5 (3.9 - 4.9) ml (DOT) 100 g-1) and NIRS-CBV (by 0.04 [-0.02 - 0.30] ml (DOT) 100 g-1) and the two variables were correlated (r equals 0.78, p arterial carbon dioxide tension, the cerebral blood volumes determined by near infrared spectroscopy and by positron emission tomography change in parallel but the change in NIRS-CBV is small compared to that obtained by PET.
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.
Emerging Multifunctional NIR Photothermal Therapy Systems Based on Polypyrrole Nanoparticles
Mozhen Wang
2016-01-01
Near-infrared (NIR)-light-triggered therapy platforms are now considered as a new and exciting possibility for clinical nanomedicine applications. As a promising photothermal agent, polypyrrole (PPy) nanoparticles have been extensively studied for the hyperthermia in cancer therapy due to their strong NIR light photothermal effect and excellent biocompatibility. However, the photothermal application of PPy based nanomaterials is still in its preliminary stage. Developing PPy based multifuncti...
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.
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.
Adaptive metric kernel regression
Goutte, Cyril; Larsen, Jan
2000-01-01
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...
Software Regression Verification
2013-12-11
of recursive procedures. Acta Informatica , 45(6):403 – 439, 2008. [GS11] Benny Godlin and Ofer Strichman. Regression verifica- tion. Technical Report...functions. Therefore, we need to rede - fine m-term. – Mutual termination. If either function f or function f ′ (or both) is non- deterministic, then their
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.
Novel INHAT repressor (NIR) is required for early lymphocyte development.
Ma, Chi A; Pusso, Antonia; Wu, Liming; Zhao, Yongge; Hoffmann, Victoria; Notarangelo, Luigi D; Fowlkes, B J; Jain, Ashish
2014-09-23
Novel inhibitor of histone acetyltransferase repressor (NIR) is a transcriptional corepressor with inhibitor of histone acetyltransferase activity and is a potent suppressor of p53. Although NIR deficiency in mice leads to early embryonic lethality, lymphoid-restricted deletion resulted in the absence of double-positive CD4(+)CD8(+) thymocytes, whereas bone-marrow-derived B cells were arrested at the B220(+)CD19(-) pro-B-cell stage. V(D)J recombination was preserved in NIR-deficient DN3 double-negative thymocytes, suggesting that NIR does not affect p53 function in response to physiologic DNA breaks. Nevertheless, the combined deficiency of NIR and p53 provided rescue of DN3L double-negative thymocytes and their further differentiation to double- and single-positive thymocytes, whereas B cells in the marrow further developed to the B220(+)CD19(+) pro-B-cell stage. Our results show that NIR cooperate with p53 to impose checkpoint for the generation of mature B and T lymphocytes.
Lanthanide NIR luminescence for telecommunications, bioanalyses and solar energy conversion
Jean-Claude; G.; Bünzli; V.
2010-01-01
Present-day advanced technologies heavily rely on the exciting magnetic and spectroscopic properties of lanthanide ions. In particular, their ability to generate well-characterized and intense near-infrared (NIR) luminescence is exploited in any modern fiber-optic telecommunication network. In this feature article, we first summarize the whereabouts underlying the design of highly luminescent NIR molecular edifices and materials. We then focus on describing the main trends in three applications related to this spectral range: telecommunications, biosciences, and solar energy conversion. In telecommunications, efforts concentrate presently on getting easily processable polymer-based waveguide amplifiers. Upconversion nanophosphors emitting in the visible after NIR excitation are now ubiquitous in many bioanalyses while their application to bio-imaging is still in its early stages; however, highly sensitive NIR-NIR systems start to be at hand for both in vitro and in vivo imaging, as well as dual probes combining magnetic resonance and optical imaging. Finally, both silicon-based and dye-sensitized solar cells benefit from the downconversion and upconversion capabilities of lanthanide ions to harvest UV and NIR solar light and to boost the overall quantum efficiency of these next-generation devices.
A Clinical Tissue Oximeter Using NIR Time-Resolved Spectroscopy.
Fujisaka, Shin-ichi; Ozaki, Takeo; Suzuki, Tsuyoshi; Kamada, Tsuyoshi; Kitazawa, Ken; Nishizawa, Mitsunori; Takahashi, Akira; Suzuki, Susumu
2016-01-01
The tNIRS-1, a new clinical tissue oximeter using NIR time-resolved spectroscopy (TRS), has been developed. The tNIRS-1 measures oxygenated, deoxygenated and total hemoglobin and oxygen saturation in living tissues. Two-channel TRS measurements are obtained using pulsed laser diodes (LD) at three wavelengths, multi-pixel photon counters (MPPC) for light detection, and time-to-digital converters (TDC) for time-of-flight photon measurements. Incorporating advanced semiconductor devices helped to make the design of this small-size, low-cost and low-power TRS instrument possible. In order to evaluate the correctness and reproducibility of measurement data obtained with the tNIRS-1, a study using blood phantoms and healthy volunteers was conducted to compare data obtained from a conventional SRS device and data from an earlier TRS system designed for research purposes. The results of the study confirmed the correctness and reproducibility of measurement data obtained with the tNIRS-1. Clinical evaluations conducted in several hospitals demonstrated a high level of usability in clinical situations and confirmed the efficacy of measurement data obtained with the tNIRS-1.
党文新; 卢晓宇; 龚红菊
2011-01-01
The effect of selecting calibration sample sets on the NIR predictive model for 1000- grain weight of paddy was investigated. NIR models were developed by using partial least square regression in the wavelength region from 600 nm to 1100 nm under the conditions of different calibration sets with various paddy quantities, different ratios of the calibration set to the validation set, and different methods for selecting the calibration sets. The developed NIR models were evaluated according to determination coefficients for cress- validation (Rv2) and for prediction (Rp2), and standard errors for cross- validation (SECV) and for prediction (SEP). The results showed that the quantity of paddy sample, the ratio of the calibration set to the validation set and the method for selecting calibration set all had significant influences upon the NIR model for 1000 - grain weight of paddy. The 7:3 was the optimal ratio of the calibration set to the validation set for the development of NIR model. The NIR model developed based on the calibration set which was selected with K - S algorithm had better predictive ability for 1000 - grain weight of paddy than that developed based on the calibration sets which were selected with the gradient and the random methods.%为研究样本集选择方法对稻谷千粒重NIR模型的影响,分别采用不同数量样品,不同定标集、验证集比例以及不同定标集选择方法,选出建模的定标集,在600～1100nm的波长区间,用偏最小二乘法建立稻谷千粒重的近红外光谱预测模型,根据内部交叉验证决定系数(Rv2)、外部验证决定系数(Rp2)、内部交叉验证误差(SECV)和预测误差(SEP)比较模型的预测能力.结果显示,样品数量、定标集和验证集比例以及定标集选择方法均对稻谷千粒重的NIR模型有明显影响.采用合适数量的样品可以得到较佳的NIR模型,以7∶3的比例分割定标集与验证集,得到的稻谷千粒重NIR模型具有相对
Maximum likelihood polynomial regression for robust speech recognition
LU Yong; WU Zhenyang
2011-01-01
The linear hypothesis is the main disadvantage of maximum likelihood linear re- gression （MLLR）. This paper applies the polynomial regression method to model adaptation and establishes a nonlinear model adaptation algorithm using maximum likelihood polyno
Highly diverse nirK genes comprise two major clades that harbour ammonium-producing denitrifiers
2016-01-01
Background Copper dependent nitrite reductase, NirK, catalyses the key step in denitrification, i.e. nitrite reduction to nitric oxide. Distinct structural NirK classes and phylogenetic clades of NirK-type denitrifiers have previously been observed based on a limited set of NirK sequences, however, their environmental distribution or ecological strategies are currently unknown. In addition, environmental nirK-type denitrifiers are currently underestimated in PCR-dependent surveys due to prime...
NIR spectroscopy of the Sun and HD20010 - Compiling a new linelist in the NIR
Andreasen, D T; Mena, E Delgado; Santos, N C; Tsantaki, M; Rojas-Ayala, B; Neves, V
2016-01-01
Context: Effective temperature, surface gravity, and metallicity are basic spectroscopic stellar parameters necessary to characterize a star or a planetary system. Reliable atmospheric parameters for FGK stars have been obtained mostly from methods that relay on high resolution and high signal-to-noise optical spectroscopy. The advent of a new generation of high resolution near-IR spectrographs opens the possibility of using classic spectroscopic methods with high resolution and high signal-to-noise in the NIR spectral window. Aims: We aim to compile a new iron line list in the NIR from a solar spectrum to derive precise stellar atmospheric parameters, comparable to the ones already obtained from high resolution optical spectra. The spectral range covers 10 000 {\\AA} to 25 000 {\\AA}, which is equivalent to the Y, J, H, and K bands. Methods: Our spectroscopic analysis is based on the iron excitation and ionization balance done in LTE. We use a high resolution and high signal-to-noise ratio spectrum of the Sun ...
A modified perpendicular drought index in NIR-Red reflectance space
Zhe, Li; Debao, Tan
2014-03-01
Soil moisture and vegetation index provides valuable information for surface water content and drought assessment with remotely sensed data. In this paper, a new drought monitoring method (MPDI1) with soil moisture and vegetation index is constructed in NIR-Red reflectance space. The relationship between MPDI1 and soil moisture is explored using satellite image and field measure data, and a comparison among MPDI1, the Perpendicular Drought Index (PDI), and the Modified Perpendicular Drought Index (MPDI) is also evaluated. Results indicate that the MPDI1 is highly accordant with the in-situ ground observation with the coefficient of determination (R2=0.4905) between MPDI1 and 5-20 cm mean soil moisture. Moreover, PDI, MPDI and MPDI1 provide quite similar spatial patterns for bare soil or lower vegetated surface, but MPDI1 demonstrates a better performance in measuring densely vegetated surface. This paper concludes that MPDI1 is a useful tool for surface drought estimation under complex underlying conditions.
Fonteyne, Margot; Arruabarrena, Julen; de Beer, Jacques; Hellings, Mario; Van Den Kerkhof, Tom; Burggraeve, Anneleen; Vervaet, Chris; Remon, Jean Paul; De Beer, Thomas
2014-11-01
This study focuses on the thorough validation of an in-line NIR based moisture quantification method in the six-segmented fluid bed dryer of a continuous from-powder-to-tablet manufacturing line (ConsiGma™ 25, GEA Pharma Systems nv, Wommelgem, Belgium). The moisture assessment ability of an FT-NIR spectrometer (Matrix™-F Duplex, Bruker Optics Ltd, UK) equipped with a fiber-optic Lighthouse Probe™ (LHP, GEA Pharma Systems nv, Wommelgem, Belgium) was investigated. Although NIR spectroscopy is a widely used technique for in-process moisture determination, a minority of NIR spectroscopy methods is thoroughly validated. A moisture quantification PLS model was developed. Twenty calibration experiments were conducted, during which spectra were collected at-line and then regressed versus the corresponding residual moisture values obtained via Karl Fischer measurements. The developed NIR moisture quantification model was then validated by calculating the accuracy profiles on the basis of the analysis results of independent in-line validation experiments. Furthermore, as the aim of the NIR method is to replace the destructive, time-consuming Karl Fischer titration, it was statistically demonstrated that the new NIR method performs at least as good as the Karl Fischer reference method.
Polat, Esra; Gunay, Suleyman
2013-10-01
One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.
Comparative NIR Detector Characterization for NGST
Greenhouse, Matthew (Technical Monitor); Figer, Donald
2004-01-01
List of publications for final perfomance report are: Detectors for the JWST Near-Infrared Spectrometer Rauscher, B.J., Strada, P., Regan, M.W., Figer, D.F., Jakobsen, P., Moseley, H.S., & Boeker, T. 2004, SPIE Detectors for the JWST Near-Infrared Spectrometer Rauscher, B.J., Strada, P., Regan, M.W., Figer, D.F., Jakobsen, P., Moseley, H.S., & Boeker, T. 2004, AAS, 203, 124.07 Independent Testing of JWST Detector Prototypes Figer, D.F., Rauscher, B. J., Regan, M. W., Morse, E., Balleza, J., Bergeron, L., & Stockman, H. S. 2003 , SPIE, 5 167 The Independent Detector Testing Laboratory and the NGST Detector Program Figer, D.F., Agronin, M., Balleza, J., Barkhouser, R., Bergeron, L., Greene, G. R., McCandliss, S. R., Rauscher, B. J., Reeves, T., Regan, M. W., Sharma, U., Stockman, H. S. 2003, SPIE, 4850,981 Intra-Pixel Sensitivity in NIR Detectors for NGST Sharma, U., Figer, D.F., Sivaramakrishnan, A., Agronin, M., Balleza, J., Barkhouser, R., Bergeron, L., Greene, G. R., McCandliss, S. R., Rauscher, B. J., Reeves, T., Regan, M. W., Stockman, H. S. 2003, SPIE, 4850,1001 NIRCAM Image Simulations for NGST Wavefiont SensinglPS A. Sivaramakrishnan, D. Figer, H. Bushouse, H. S. Stockman (STScI),C. Ohara , D. Redding (JPL), M. Im (IPAC), & J. Offenberg (Raytheon) 2003, SPIE, 4850,388 Ultra-Low Background Operation of Near-Infrared Detectors for NGS Rauscher, B. J., Figer, D. F., Agronin, M., Balleza, J., Barkhouser, R., Bergeron, L., Greene, G. R., McCandliss, S. R., Reeves, T., Regan, M. W., Sharma, U., Stockman, H. S. 2003, SPIE, 4850,962 The Independent Detector Testing Laboratory and the JWST Detector Program Figer, D.F. et a1.2003, AAS201, #131.05
Low rank Multivariate regression
Giraud, Christophe
2010-01-01
We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting estimator. We also investigate the easier case where the variance of the noise is known and outline that the penalties appearing in our criterions are minimal (in some sense). These penalties involve the expected value of the Ky-Fan quasi-norm of some random matrices. These quantities can be evaluated easily in practice and upper-bounds can be derived from recent results in random matrix theory.
Barrett, Maria; Fenton, Owen; Ibrahim, Tristan G.; O'Flaherty, Vincent; Healey, Mark G.
2014-05-01
Biological denitrification in soil is the main producer of nitrous oxide (N2O) emissions. Denitrifying soil microbes are capable of reducing nitrate (NO3-) to nitrite (NO2-) to N2O and di-nitrogen gas (N2). One third of these denitrifers possess a truncated functional gene pathway, which may lack the nosZ gene and emit N2O as a final emission product instead of the more benign N2. A carbon rich environment, specific to certain types of carbon sources, has been shown to foster an anaerobic environment, which positively impacts microbial denitrification rates. The present study examined the effect of varying carbon sources in laboratory-scale denitrification bioreactors on NO3- removal and also correlated performance with the abundance of the denitrifying microbial consortia possessing the denitrifying functional genes nirK, nirS and nosZ in each bioreactor. The bioreactors comprised either lodgepole pine woodchips (LPW), lodgepole pine needles (LPN), barley straw (BBS), or cardboard (CCB), each mixed with soil in a 1:1 ratio (by volume) and subject to sequentially increasing hydraulic loading rates of 3, 5 and 10 cm d-1 for a total operation period of up to 744 days. A reactor containing soil only (CSO) was used as the study control. The abundance of denitrifers was determined by targeting nirK, nirS, nosZ functional genes and the overall microbial population was determined by targeting bacterial and archaeal 16sRNA genes. Nitrate removal from all bioreactors was > 99.7%, but when pollution swapping was considered, this ranged from 67% for LPW to 95% for the CCB ; this was also mirrored in the average nirk/nirS/nosZ gene abundance (CCB, c. 94% (c. 108); LPN, 75% (c. 107); BBS, c. 74% (c. 106/107); LPW, 70% (c. 105). Bacterial 16sRNA gene abundance was similar in all reactors including the control (P=0.0362). The abundance of nosZ genes and the genetic potential for N2 emissions varied in all reactors in comparison to the control CSO, BBS (P=0.0051); CCB (P=0
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.
Hansen, Henrik; Tarp, Finn
2001-01-01
. There are, however, decreasing returns to aid, and the estimated effectiveness of aid is highly sensitive to the choice of estimator and the set of control variables. When investment and human capital are controlled for, no positive effect of aid is found. Yet, aid continues to impact on growth via...... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes....
Robust Nonstationary Regression
1993-01-01
This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed which allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities and they belong to the same ...
Wiggins, Ian M; Anderson, Carly A; Kitterick, Pádraig T; Hartley, Douglas E H
2016-09-01
Functional near-infrared spectroscopy (fNIRS) is a silent, non-invasive neuroimaging technique that is potentially well suited to auditory research. However, the reliability of auditory-evoked activation measured using fNIRS is largely unknown. The present study investigated the test-retest reliability of speech-evoked fNIRS responses in normally-hearing adults. Seventeen participants underwent fNIRS imaging in two sessions separated by three months. In a block design, participants were presented with auditory speech, visual speech (silent speechreading), and audiovisual speech conditions. Optode arrays were placed bilaterally over the temporal lobes, targeting auditory brain regions. A range of established metrics was used to quantify the reproducibility of cortical activation patterns, as well as the amplitude and time course of the haemodynamic response within predefined regions of interest. The use of a signal processing algorithm designed to reduce the influence of systemic physiological signals was found to be crucial to achieving reliable detection of significant activation at the group level. For auditory speech (with or without visual cues), reliability was good to excellent at the group level, but highly variable among individuals. Temporal-lobe activation in response to visual speech was less reliable, especially in the right hemisphere. Consistent with previous reports, fNIRS reliability was improved by averaging across a small number of channels overlying a cortical region of interest. Overall, the present results confirm that fNIRS can measure speech-evoked auditory responses in adults that are highly reliable at the group level, and indicate that signal processing to reduce physiological noise may substantially improve the reliability of fNIRS measurements.
TWO REGRESSION CREDIBILITY MODELS
Constanţa-Nicoleta BODEA
2010-03-01
Full Text Available In this communication we will discuss two regression credibility models from Non – Life Insurance Mathematics that can be solved by means of matrix theory. In the first regression credibility model, starting from a well-known representation formula of the inverse for a special class of matrices a risk premium will be calculated for a contract with risk parameter θ. In the next regression credibility model, we will obtain a credibility solution in the form of a linear combination of the individual estimate (based on the data of a particular state and the collective estimate (based on aggregate USA data. To illustrate the solution with the properties mentioned above, we shall need the well-known representation theorem for a special class of matrices, the properties of the trace for a square matrix, the scalar product of two vectors, the norm with respect to a positive definite matrix given in advance and the complicated mathematical properties of conditional expectations and of conditional covariances.
Wang, Yong; Mei, Minghua; Ni, Yongnian; Kokot, Serge
2014-09-15
A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, Iuicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95-100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method.
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
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
Stochastic Approximation Methods for Latent Regression Item Response Models
von Davier, Matthias; Sinharay, Sandip
2010-01-01
This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates…
Herrero Latorre, C; Peña Crecente, R M; García Martín, S; Barciela García, J
2013-12-15
In this work, information contained in near infrared (NIR) spectra of honeys with protected geographical indication (PGI) "Mel de Galicia" was processed by means of different chemometric techniques to develop an authentication system for this high quality food product. Honey spectra were obtained in a fast and single way, and they were pretreated by means of standard normal variate transformation in order to remove the influence of particle size, scattering and other factors, and prior to their use as input data. As the first step in chemometric study, display techniques such as principal component analysis and cluster analysis were applied in order to demonstrate that the NIR data contained useful information to develop a pattern recognition classification system to authenticate honeys with PGI. The second step consisted in the application of different pattern recognition techniques (such as D-PLS: Discriminant partial least squares regression; SIMCA: Soft independent modelling of class analogy; KNN: K-nearest neighbours; and MLF-NN: Multilayer feedforward neural networks) to derive diverse models for PGI-honey class with the objective of detecting possible falsification of these high-quality honeys. Amongst all the classification chemometric procedures, SIMCA achieved to be the best PGI-model with 93.3% of sensitivity and 100% of specificity. Therefore, the combination of NIR information data with SIMCA developed a single and fast method in order to differentiate between genuine PGI-Galician honey samples and other commercial honey samples from other origins that, due to their lower price, could be used as substrates for falsification of genuine PGI ones. Copyright © 2013 Elsevier Ltd. All rights reserved.
Magalhães, Luís M; Machado, Sandia; Segundo, Marcela A; Lopes, João A; Páscoa, Ricardo N M J
2016-01-15
Spent coffee grounds (SCGs) are a great source of bioactive compounds with interest to pharmaceutical and cosmetic industries. Phenolics and methylxanthines are the main health related compounds present in SCG samples. Content estimation of these compounds in SCGs is of upmost importance in what concerns their profitable use by waste recovery industries. In the present work, near-infrared spectroscopy (NIRS) was proposed as a rapid and non-destructive technique to assess the content of three main phenolics (caffeic acid, (+)-catechin and chlorogenic acid) and three methylxanthines (caffeine, theobromine and theophylline) in SCG samples obtained from different coffee brands and diverse coffee machines. The content of these compounds was determined for 61 SCG samples by HPLC coupled with diode-array detection. Partial least squares (PLS) regression based models were calibrated to correlate diffuse reflectance NIR spectra against the reference data for the six parameters obtained by HPLC. Spectral wavelength selection and number of latent variables were optimized by minimizing the cross-validation error. PLS models showed good linearity with a coefficient of determination for the prediction set (Rp(2)) of 0.95, 0.92, 0.88, 071 and 0.84 for caffeine, caffeic acid, (+)-catechin, chlorogenic acid and theophylline, respectively. The range error ratio (RER) was higher for caffeine (17.8) when compared to other compounds (12.0, 10.1, 7.6 and 9.2, respectively for caffeic acid, (+)-catechin, chlorogenic acid and theophylline). Moreover, the content of caffeine could be used to predict the antioxidant properties of SCG samples (R=0.808, n=61), despite not presenting this property itself. The results obtained confirmed that NIRS is a suitable technique to screen SCG samples unveiling those with high content of bioactive compounds, which are interesting for subsequent extraction procedures.
杨兴兴; 陈学萍; 刘冬秀; 沈洁; 陆永生
2014-01-01
通过基因工程手段增加厌氧氨氧化菌亚硝酸盐还原酶(nitrite reductase, nirS)的表达量,运用质粒载体pGEM-T克隆nirS基因。琼脂糖凝胶电泳检测显示, nirS基因重组工程菌在440 bp处有明显的目的条带； nirS基因重组工程菌扩大培养7~8h后即达到生长曲线稳定期,引入外加氮源后,菌体生长情况更优。通过不同菌液投加量以及处理不同初始浓度的亚硝酸钠溶液,检测nirS基因重组工程菌的性能。结果表明,当nirS基因重组工程菌投加30 mL(细菌数为2.3×107个∕mL),亚硝酸盐初始质量浓度为40 mg∕L时,亚硝酸盐去除率达到90%以上。nirS基因重组工程菌可适用于亚硝酸盐废水的处理。%In order to improve the expression quantity of nitrite reductase (nirS) in ANAMMOX bacteria through bioengineering means, nirS gene was cloned using the plasmid vector pGEM-T. A target band of 440 bp PCR products from the recombinant genetic engineering bacter was observed by agarose gel electrophoresis. The nirS recombinant genetic engineering bacteria reached stationary phase after 7-8 hours incubation, the addition of nitrogen source was advantageous to the growth of bacteria significantly. The performance of nirS recombinant genetic engineering bacteria was tested by adding different dosages of bacteria and treating sodium nitrite solu‐tion with different initial concentrations. The results showed that, when 30 mL of nirS recombinant genetic engi‐neering bacteria(2.3 × 107 cells/mL) inoculates was added to the solution with 40 mg/L of initial mass concentra‐tion of nitrite, the removal rate of nitrite reached above 90%. It was indicated that nirS recombinant genetic en‐gineering bacteria could be applied for nitrite-containing wastewater treatment in the future.
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.
Multiple-Instance Regression with Structured Data
Wagstaff, Kiri L.; Lane, Terran; Roper, Alex
2008-01-01
We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.
NIRS monitoring of muscle contraction to control a prosthetic device
Bianchi, Thomas; Zambarbieri, Daniela; Beltrami, Giorgio; Verni, Gennaro
1999-01-01
The fitting of upper-extremity amputees requires special efforts, and its significance has been increased by the development of the myoelectrically controlled prosthetic arm. This solution is not free of problems due to the nature of the amputation, to the electromagnetic noise affecting the myelectrical signal and to the perspiration due to the contact between socket and the residual limb. Starting from the fact that NIRS and electromyographic signals are similar during a muscle contraction, we have first studied the NIRS signal during forearm muscle contractions in normal and amputee subjects. Then a new system to interface the NIRS unit and the myoelectrical prosthetic hand has been developed. The NIRS unit has been used as optical sensor and all the operations (I/O and signal processing) are performed via software. This system has been tested on normal and amputee subjects performing hand grasping using a visual biofeedback control scheme. All the subjects have been able to perform these operations demonstrating the NIRS technique. This could represent an alternative solution for controlling a prosthetic device.
Estimation of Anthocyanin Content of Berries by NIR Method
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.
Activation detection in fNIRS by wavelet coherence
Zhang, Xin; Niu, Haijing; Song, Yan; Fan, Yong
2012-03-01
Functional near infrared spectroscopy (fNIRS) is an optical technique measuring hemoglobin oxygenation and deoxygenation concentrations of the brain cortex with higher temporal resolution than current alternative techniques. The high temporal resolution enables collecting abundant brain functional information. However, the information collected by fNIRS is correlated and mixed with a variety of physiological signals. Due to the mixture effect, activation detection is one of challenges in fNIRS based studies of the brain functional activities. To achieve a better detection of activated brain regions from the complicated information measures, we present a multi-scale analysis method based on a wavelet coherence measure. In particular, the paradigm of an experiment is used as the reference signal. The coherence of the signal with data measured by fNIRS at each channel is calculated and summed up to evaluate the activation level. Experiments on simulated and real data have demonstrated that the proposed method is efficient and effective to detect activated brain regions covered by the fNIRS probe.
潘文超
2011-01-01
In recent years, influenced by european debt, bankruptcy or debt-raising risk occurs in many enterprises at Taiwan,sometime,even settlement default might occur at the stock market. Therefore, the manager level of an enterprise really has to inspect the financial situation of an en- terprise well. In this article, financial five forces are followed to collect the financial ratio data from enterprises, in the mean time, grey relational analysis is performed on financial five forces, then the analysis results are ranked according to grey relational grade so as to understand the op- erating performance ranking of each enterprise; then fruit fly optimization algorithm optimized general regression neural network,general regression neural network and multiple regression are used to construct respectively operating performance of enterprises model. From the analytical re- sult,we have found that in operating performance of enterprises model,the RMSE value of fruit fly optimization algorithm optimized general regression neural network model has very good con- vergent result and classification forecast capability.%近年来,台湾受到美国次贷风暴及欧洲债信的影响,许多大型企业瓦解的事件陆续发生,因此,公司管理阶层有必要好好地检视公司的财务状况,及早防范公司可能面临的经营风险。文章按照财务五力搜集台湾企业财务比率资料,根据活动力、稳定力与收益力进行灰关联分析,再将分析结果按照灰关联度进行排序,以了解各企业的经营绩效排名;然后采用果蝇优化算法优化广义回归神经网络、一般广义回归神经网络与多元回归模型,进行企业经营绩效侦测模型的建构,以供研究人员及公司管理阶层参考。分析结果显示,应用果蝇优化算法优化广义回归神经网络在企业经营绩效侦测模型的预测误差有很好的收敛结果,也有很好的分类预测能力。
On Solving Lq-Penalized Regressions
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.
Tracking time-varying parameters with local regression
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....
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.
Barbin, Douglas Fernandes; Valous, Nektarios A; Dias, Adriana Passos; Camisa, Jaqueline; Hirooka, Elisa Yoko; Yamashita, Fabio
2015-11-01
There is an increasing interest in the use of polysaccharides and proteins for the production of biodegradable films. Visible and near-infrared (VIS-NIR) spectroscopy is a reliable analytical tool for objective analyses of biological sample attributes. The objective is to investigate the potential of VIS-NIR spectroscopy as a process analytical technology for compositional characterization of biodegradable materials and correlation to their mechanical properties. Biofilms were produced by single-screw extrusion with different combinations of polybutylene adipate-co-terephthalate, whole oat flour, glycerol, magnesium stearate, and citric acid. Spectral data were recorded in the range of 400-2498nm at 2nm intervals. Partial least square regression was used to investigate the correlation between spectral information and mechanical properties. Results show that spectral information is influenced by the major constituent components, as they are clustered according to polybutylene adipate-co-terephthalate content. Results for regression models using the spectral information as predictor of tensile properties achieved satisfactory results, with coefficients of prediction (R(2)C) of 0.83, 0.88 and 0.92 (calibration models) for elongation, tensile strength, and Young's modulus, respectively. Results corroborate the correlation of NIR spectra with tensile properties, showing that NIR spectroscopy has potential as a rapid analytical technology for non-destructive assessment of the mechanical properties of the films.
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.
Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P.; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping
2013-10-01
Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non
曹文哲; 应俊; 陈广飞; 周丹
2016-01-01
目的：应用随机森林算法和Logistic回归算法，分析2型糖尿病并发视网膜病变的关联因素并构建风险预测模型。方法采用2011~2013年中国人民解放军总医院2型糖尿病住院患者的电子病历信息，主要利用其中的糖尿病诊断数据、糖尿病糖化数据以及糖尿病生化检查数据，应用Logistic回归和随机森林算法，根据ROC曲线下面积比较两种模型的预测效果。结果在随机森林模型的39个变量重要性评分中，糖化血红蛋白、空腹血糖、尿素、肌酐、尿酸、年龄、冠心病和慢性肾病得分较高且具有临床意义，Logistic回归模型最终纳入性别、血糖控制情况（糖化血红蛋白浓度）、慢性肾病、冠心病、心梗和癌症6个因素，ROC曲线下面积提示随机森林模型预测效果优于Logistic回归模型。结论本次研究随机森林算法分析结果给出了各个因素指标的重要性评分，为2型糖尿病并发视网膜病变的早期诊断以及优化诊断流程提供了一定的依据。%Objective To analyze the relevant factors of type 2 diabetes mellitus complicated with retinopathy and to construct the risk prediction model based on machine learning, the random forest algorithm, and the Logistic regression algorithm based on the epidemiological design.Methods To analyze the data from the electronic medical record of patients with type 2 diabetes mellitus complicated with retinopathy in the General Hospital of PLA during 2011-2013. The main focus was on the diagnostic data of diabetes mellitus, the glycosylated data, and biochemical examination data. The prediction effect of the two models were compared with the Logistic regression algorithm and random forest algorithm according the area under the ROC curve.Results Among the 39 variables in the the random forest models, blood glucose control (HbAlc), fasting glucose, urea, creatinine, uric acid, age, coronary heart disease (CHD), and
Hu, Xingyue; He, Yong; Garcla Pereira, Annia; Hernandez Gomez, Antihus
2005-01-01
The objective of this study was to establish the relationship between Vis/NIR spectral and the major physiological properties of tomato-soluble solids content (SSC), acidity (pH) and fruit firmness. A total of 200 tomatoes were hand harvested and analyzed the spectra features using spectrophotometer. Principal component regression (PCR) and partial least squares (PLS) were used to develop the prediction models. The models for SSC (r= 0.90) of standard error of prediction (SEP) 0.19 Brix with three factors; pH r= 0.83) of SEP 0.09 with four factors; compression force(r= 0.81) of SEP 16.017N with six factors, and puncture force (r= 0.83) of SEP 1.18N with three factors, showed the excellent prediction performance. The Vis/NIR spectroscopy technique had significantly greater accuracy in determining SSC. It was concluded that it is possible to assess the quality characteristics of tomato.
Rapid Analysis of Deoxynivalenol in Durum Wheat by FT-NIR Spectroscopy
De Girolamo, Annalisa; Cervellieri, Salvatore; Visconti, Angelo; Pascale, Michelangelo
2014-01-01
Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%–90% and 3%–7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation. PMID:25384107
Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration.
Xu, Lu; Zhou, Yan-Ping; Tang, Li-Juan; Wu, Hai-Long; Jiang, Jian-Hui; Shen, Guo-Li; Yu, Ru-Qin
2008-06-01
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.
A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series.
Keshmiri, Soheil; Sumioka, Hidenobu; Yamazaki, Ryuji; Ishiguro, Hiroshi
2017-01-01
We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females.
Fast Discrimination of Bamboo Species Using VIS/NIR Spectroscopy
Wang, Y. Z.; Dong, W. Y.; Kouba, A. J.
2016-11-01
The potential of visible/near-infrared (Vis/NIR) spectroscopy to discriminate different bamboo species was investigated. Vis/NIR spectra were collected on three bamboo species, Bashania fargesii, Fargesia qinlingensis, and Phyllostachys glauca, in the wavelength range of 350-2500 nm. The range of 425-2400 nm was chosen for the spectra modeling. Multiplicative signal correction, standard normal variate with detrending, and 1st and 2nd derivatives were used to preprocess the raw spectral data, and the results were compared. Soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) methods were applied for building discriminant models. The recognition ratio of 30 samples in the validation set was 100% by both SIMCA and PLSDA models. These results indicate that Vis/NIR spectroscopy may provide a fast and nondestructive technique to discriminate different bamboo species in the field.
NIRS - Near infrared spectroscopy - investigations in neurovascular diseases
Schytz, Henrik Winther
2015-01-01
in cerebral blood flow (CBF), the first study investigated a multi-source detector separation configuration and indocyanine green (ICG) as a tracer to calculate a corrected blood flow index (BFI) value. The study showed no correlation between CBF changes measured by 133Xenon single photon emission computer......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...
NIR triggered observations of Sgr A* at 43 GHz
Rauch, C.; Ros, E.; Krichbaum, T. P.; Eckart, A.; Zensus, J. A.; Lu, R.-S.; Shahzamanian, B.; Mužić, K.; Peißker, F.
2017-01-01
The compact radio and near-infrared (NIR) source Sagittarius A* has been observed in the context of two NIR triggered global VLT and VLBA campaigns at 43 GHz (7 mm) on May 16-18 2012 and October 4 2014. While on October 4 2014 Sgr A* remained in a quiescent state, a NIR flare on May 17 2012 is accompanied by an increase in flux density of 0.22 Jy at 7 mm delayed by 4.5+/-0.5 h. Additionally, Sgr A* seems to develop a weak secondary radio off-core component of 0.02 Jy at a position angle of 140° and an angular distance of 1.5 mas shortly before the peak of the flare. This spatial extension and the time delay are in the range of expected values for events casually connected by adiabatic expansion.
Hardersen IRTF Asteroid NIR Reflectance Spectra V1.0
Hardersen, P. S.
2016-06-01
This dataset includes average near-infrared (NIR) reflectance spectra for 68 main-belt asteroids that were observed at the NASA Infrared Telescope Facility (IRTF), Mauna Kea, Hawaii, from April 2001 to January 2015. Raw NIR spectral data were obtained under mostly uniform instrumental conditions and include observations of the asteroids, extinction stars, and solar analog stars that were necessary for data reduction and production of the final average asteroid NIR reflectance spectra. SpecPR and Spextool were used during data reduction to produce the final spectra and both programs utilize similar functions that include sky background subtraction, telluric corrections, channel shifting, and averaging routines. The set of asteroids observed include a wide variety of taxonomic types and include V-, S-, M-, X-types that correspond to a wide variety of surface mineralogies, rock types, and potential meteorite analogs.
Emerging Multifunctional NIR Photothermal Therapy Systems Based on Polypyrrole Nanoparticles
Mozhen Wang
2016-10-01
Full Text Available Near-infrared (NIR-light-triggered therapy platforms are now considered as a new and exciting possibility for clinical nanomedicine applications. As a promising photothermal agent, polypyrrole (PPy nanoparticles have been extensively studied for the hyperthermia in cancer therapy due to their strong NIR light photothermal effect and excellent biocompatibility. However, the photothermal application of PPy based nanomaterials is still in its preliminary stage. Developing PPy based multifunctional nanomaterials for cancer treatment in vivo should be the future trend and object for cancer therapy. In this review, the synthesis of PPy nanoparticles and their NIR photothermal conversion performance were first discussed, followed by a summary of the recent progress in the design and implementation on the mulitifunctionalization of PPy or PPy based therapeutic platforms, as well as the introduction of their exciting biomedical applications based on the synergy between the photothermal conversion effect and other stimulative responsibilities.
Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS
Lu, Q.; Fan, Y.; Peng, Z.; Ding, H.; Gao, H.
2012-07-01
A new approach for the nondestructive discrimination between genuine wild ginsengs and the counterfeit ones by near infrared spectroscopy (NIRS) was developed. Both discriminant analysis and back propagation artificial neural network (BP-ANN) were applied to the model establishment for discrimination. Optimal modeling wavelengths were determined based on the anomalous spectral information of counterfeit samples. Through principal component analysis (PCA) of various wild ginseng samples, genuine and counterfeit, the cumulative percentages of variance of the principal components were obtained, serving as a reference for principal component (PC) factor determination. Discriminant analysis achieved an identification ratio of 88.46%. With sample' truth values as its outputs, a three-layer BP-ANN model was built, which yielded a higher discrimination accuracy of 100%. The overall results sufficiently demonstrate that NIRS combined with BP-ANN classification algorithm performs better on ginseng discrimination than discriminant analysis, and can be used as a rapid and nondestructive method for the detection of counterfeit wild ginsengs in food and pharmaceutical industry.
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.
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).
Karim Hardani*
2012-05-01
Full Text Available A 10-month-old baby presented with developmental delay. He had flaccid paralysis on physical examination.An MRI of the spine revealed malformation of the ninth and tenth thoracic vertebral bodies with complete agenesis of the rest of the spine down that level. The thoracic spinal cord ends at the level of the fifth thoracic vertebra with agenesis of the posterior arches of the eighth, ninth and tenth thoracic vertebral bodies. The roots of the cauda equina appear tightened down and backward and ended into a subdermal fibrous fatty tissue at the level of the ninth and tenth thoracic vertebral bodies (closed meningocele. These findings are consistent with caudal regression syndrome.
fNIRS-based brain-computer interfaces: a review.
Naseer, Noman; Hong, Keum-Shik
2015-01-01
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 (ICA), 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 (SVM), hidden Markov model (HMM), 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.
fNIRS-based brain-computer interfaces: a review
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.
Deep NIR Photometry of HI Galaxies Behind the Milky Way
Williams, Wendy L; Kraan-Korteweg, Renee C
2011-01-01
Current studies of the peculiar velocity flow field in the Local Universe are limited by the lack of detection of galaxies behind the Milky Way. The contribution of the largely unknown mass distribution in this "Zone of Avoidance" (ZoA) to the dynamics of the Local group remains contraversial. We have undertaken a near infrared (NIR) survey of HI detected galaxies in the ZoA. The photomety derived here will be used in the NIR Tully-Fisher (TF) relation to derive the peculiar velocities of this sample of galaxies in the ZoA.
NIR brightening of the Quasar PKS0735+17
Carrasco, L.; Porras, A.; Escobedo, G.; Recillas, E.; Chabushyan, V.; Carraminana, A.; Mayya, D.
2014-01-01
We report on the NIR brightening of the intermediate redshift quasar PKS0735+17 (z=0.424), also known as CGRaBSJ04738+1742, associated with the gamma-ray source 2FGL0738.0+1742. Our NIR photometry for this source shows that, on Jan 7th,2014 (JD2456664.848838), the object brightness corresponded to J = 13.39 +/- 0.04, H = 12.582 +/- 0.03 and Ks = 11.826 +/- 0.03. These values are about 0.5 magnitud brighter than our previous photometry, obtained on JD2456306, for this field.
NIR brightening of the Blazar BZBJ1059-1134
Carrasco, L.; Porras, A.; Escobedo, G.; Recillas, E.; Chabushyan, V.; Carraminana, A.; Mayya, D.
2014-01-01
We report on the NIR brightening of the blazar BZBJ1059-1134, also known as PKSB1056-113 associated with the gamma-ray source 2FGL1059.3-1132. Our NIR photometry for this source shows that, on Jan 6th,2014 (JD2456663.9615), the object brightness corresponded to J = 13.804 +/- 0.04, H = 13.069 +/- 0.04 and Ks = 12.352 +/- 0.04. These values are about 1.1 magnitud brighter than our previous photometry, obtained on JD2456464.6, for this field.
António J.A. Santos
2014-09-01
Full Text Available A total of 120 Acacia melanoxylon R. Br. (Australian blackwood stem discs, belonging to 20 trees from four sites in Portugal, were used in this study. The samples were kraft pulped under standard identical conditions targeted to a Kappa number of 15. A Near Infrared (NIR partial least squares regression (PLSR model was developed for the Kappa number prediction using 75 pulp samples with a narrow Kappa number variation range of 10 to 17. Very good correlations between NIR spectra of A. melanoxylon pulps and Kappa numbers were obtained. Besides the raw spectra, also pre-processed spectra with ten methods were used for PLS analysis (cross validation with 48 samples, and a test set validation was made with 27 samples. The first derivative spectra in the wavenumber range from 6110 to 5440 cm-1 yielded the best model with a root mean square error of prediction of 0.4 units of Kappa number, a coefficient of determination of 92.1%, and two PLS components, with the ratios of performance to deviation (RPD of 3.6 and zero outliers. The obtained NIR-PLSR model for Kappa number determination is sufficiently accurate to be used in screening programs and in quality control.
Pan, Xiaoning; Li, Yang; Wu, Zhisheng; Zhang, Qiao; Zheng, Zhou; Shi, Xinyuan; Qiao, Yanjiang
2015-04-14
Model performance of the partial least squares method (PLS) alone and bagging-PLS was investigated in online near-infrared (NIR) sensor monitoring of pilot-scale extraction process in Fructus aurantii. High-performance liquid chromatography (HPLC) was used as a reference method to identify the active pharmaceutical ingredients: naringin, hesperidin and neohesperidin. Several preprocessing methods and synergy interval partial least squares (SiPLS) and moving window partial least squares (MWPLS) variable selection methods were compared. Single quantification models (PLS) and ensemble methods combined with partial least squares (bagging-PLS) were developed for quantitative analysis of naringin, hesperidin and neohesperidin. SiPLS was compared to SiPLS combined with bagging-PLS. Final results showed the root mean square error of prediction (RMSEP) of bagging-PLS to be lower than that of PLS regression alone. For this reason, an ensemble method of online NIR sensor is here proposed as a means of monitoring the pilot-scale extraction process in Fructus aurantii, which may also constitute a suitable strategy for online NIR monitoring of CHM.
Xiaoning Pan
2015-04-01
Full Text Available Model performance of the partial least squares method (PLS alone and bagging-PLS was investigated in online near-infrared (NIR sensor monitoring of pilot-scale extraction process in Fructus aurantii. High-performance liquid chromatography (HPLC was used as a reference method to identify the active pharmaceutical ingredients: naringin, hesperidin and neohesperidin. Several preprocessing methods and synergy interval partial least squares (SiPLS and moving window partial least squares (MWPLS variable selection methods were compared. Single quantification models (PLS and ensemble methods combined with partial least squares (bagging-PLS were developed for quantitative analysis of naringin, hesperidin and neohesperidin. SiPLS was compared to SiPLS combined with bagging-PLS. Final results showed the root mean square error of prediction (RMSEP of bagging-PLS to be lower than that of PLS regression alone. For this reason, an ensemble method of online NIR sensor is here proposed as a means of monitoring the pilot-scale extraction process in Fructus aurantii, which may also constitute a suitable strategy for online NIR monitoring of CHM.
Determination of moisture content of lyophilized allergen vaccines by NIR spectroscopy.
Zheng, Yiwu; Lai, Xuxin; Bruun, Susanne Wrang; Ipsen, Henrik; Larsen, Jørgen Nedergaard; Løwenstein, Henning; Søndergaard, Ib; Jacobsen, Susanne
2008-02-13
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 into the specifications of a pharmaceutical company.
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.
Mirschel, Gabriele; Heymann, Katja; Savchuk, Olesya; Genest, Beatrix; Scherzer, Tom
2012-07-01
In this work, it is demonstrated that the coating weight of printed layers can be determined in-line in a running printing press by near-infrared (NIR) reflection spectroscopy assisted by chemometric methods. Three different unpigmented lacquer systems, i.e., a conventional oil-based printing lacquer, an ultraviolet (UV)-curable formulation, and a water-based dispersion varnish, were printed on paper with coating weights between about 0.5 and 7 g m(-2). NIR spectra for calibration were recorded with a special metal reflector simulating the mounting conditions of the probe head at the printing press. Calibration models were developed on the basis of the partial least squares (PLS) algorithm and evaluated by independent test samples. The prediction performance of the developed models was examined at a sheet-fed offset printing press at line speeds between 90 and 180 m min(-1). Results show an excellent correlation of data predicted in-line from the NIR spectra with reference values obtained off-line by gravimetry. The prediction errors were found to be ≤ 0.2 g m(-2), which confirms the suitability of the developed spectroscopic method for process control in technical printing processes.
Frankhuizen, R.; Munsteren, van A.J.; Veen, van der N.G.; Herstel, H.
1987-01-01
Met behulp van een research Nabij Infrarood Reflectie Spectrometer (NIRS) (Technicon Infra-Alyzer 500) is oriënterend onderzoek uitgevoerd naar de bepaling van het gehalte aan glucosinolaten in raapzaad met behulp van NIRS.
Ma, Dandan; Xu, Xiang; Hu, Min; Wang, Jing; Zhang, Zhenxi; Yang, Jian; Meng, Lingjie
2016-04-05
Multifunctional NaGdF4 :Yb(3+),Er(3+),Nd(3+) @NaGdF4 :Nd(3+) core-shell nanoparticles (called Gd:Yb(3+),Er(3+),Nd(3+) @Gd:Nd(3+) NPs) with simultaneously enhanced near-infrared (NIR)-visible (Vis) and NIR-NIR dual-conversion (up and down) luminescence (UCL/DCL) properties were successfully synthesized. The resulting core-shell NPs simultaneously emitted enhanced UCL at 522, 540, and 660 nm and DCL at 980 and 1060 nm under the excitation of a 793 nm laser. The enhanced UCL and DCL can be explained by complex energy-transfer processes, Nd(3+) →Yb(3+) →Er(3+) and Nd(3+) →Yb(3+) , respectively. The effects of Nd(3+) concentration and shell thickness on the UCL/DCL properties were systematically investigated. The UCL and DCL properties of NPs were observed under the optimal conditions: a shell Nd(3+) content of 20 % and a shell thickness of approximately 5 nm. Moreover, the Gd:Yb(3+) ,Er(3+) ,Nd(3+) @Gd:20 % Nd(3+) NPs exhibited remarkable magnetic resonance imaging (MRI) properties similar to that of a clinical agent, Omniscan. Thus, the core-shell NPs with excellent UCL/DCL/magnetic resonance imaging (MRI) properties have great potential for both in vitro and in vivo multimodal bioimaging.
Saleh-Lakha, Saleema; Shannon, Kelly E; Henderson, Sherri L; Zebarth, Bernie J; Burton, David L; Goyer, Claudia; Trevors, Jack T
2009-08-01
Nitrate acts as an electron acceptor in the denitrification process. The effect of nitrate in the range of 0 to 1,000 mg/liter on Pseudomonas mandelii nirS, cnorB, and nosZ gene expression was studied, using quantitative reverse transcription-quantitative PCR. Denitrification activity was measured by using the acetylene blockage method and gas chromatography. The effect of acetylene on gene expression was assessed by comparing denitrification gene expression in P. mandelii culture grown in the presence or absence of acetylene. The higher the amount of NO(3)(-) present, the greater the induction and the longer the denitrification genes remained expressed. nirS gene expression reached a maximum at 2, 4, 4, and 6 h in cultures grown in the presence of 0, 10, 100, and 1,000 mg of KNO(3)/liter, respectively, while induction of nirS gene ranged from 12- to 225-fold compared to time zero. cnorB gene expression also followed a similar trend. nosZ gene expression did not respond to NO(3)(-) treatment under the conditions tested. Acetylene decreased nosZ gene expression but did not affect nirS or cnorB gene expression. These results showed that nirS and cnorB responded to nitrate concentrations; however, significant denitrification activity was only observed in culture with 1,000 mg of KNO(3)/liter, indicating that there was no relationship between gene expression and denitrification activity under the conditions tested.
Wang, Dong; Pan, Li-Gang; Wang, Ji-Hua; Li, An; Jin, Xin-Xin; Zhu, Ye-Wei; Ma, Zhi-Hong
2014-11-01
In the present paper, the micro-NIR spectrometer with the splitter of linear variable filter was used to develop the recognition models of the West Lake Longjing tea and the ordinary flat tea of the year 2012 and 2013. The NIR spectral data of different years and different storage times were decomposed by PCA algorithm. The PLS-DA models were developed by the representative samples selected by the mathematical characteristics of PCA-scores' distribution in order to analyze the reason for the inadaptability of the models according to mathematical principles and find out the solution for its correction. Being examined by the external validation set, the adaptability of the authenticity identification model was enhanced effectively. The result of this research indicated that, for the West Lake Longjing tea and the ordinary flat tea, the correct recognition rate of the model developed by all different-year samples' NIR spectral data would be enhanced effectively. The model developed by the NIR spectral data of different storage time samples indicated that the physicochemical properties of the ordinary flat tea have changed remarkably after cryopreservation for 3 months, while the physicochemical properties of the West Lake Longjing tea are relatively stable. The model adaptabilities for different years and different storage times were studied according to the mathematical perspective of the principal component characteristics of spectral data. After the authenticity identification model of West Lake Longjing tea was developed, the prediction accuracy was enhanced effectively. This research would provide reference for not only the application of NIR spectroscopy in quality grading and safety of agricultural products, but also the enhancement of the prediction accuracy of the NIR grading models for agricultural products.
Tan, Ailing; Zhao, Yong; Wang, Siyuan
2016-10-01
Quantitative analysis of the simulated complex oil spills was researched based on PSO-LS-SVR method. Forty simulated mixture oil spills samples were made with different concentration proportions of gasoline, diesel and kerosene oil, and their near infrared spectra were collected. The parameters of least squares support vector machine were optimized by particle swarm optimization algorithm. The optimal concentration quantitative models of three-component oil spills were established. The best regularization parameter C and kernel parameter σ of gasoline, diesel and kerosene model were 48.1418 and 0.1067, 53.2820 and 0.1095, 59.1689 and 0.1000 respectively. The decision coefficient R2 of the prediction model were 0.9983, 0.9907 and 0.9942 respectively. RMSEP values were 0.0753, 0.1539 and 0.0789 respectively. For gasoline, diesel fuel and kerosene oil models, the mean value and variance value of predict absolute error were -0.0176±0.0636 μL/mL, -0.0084+/-0.1941 μL/mL, and 0.00338+/-0.0726 μL/mL respectively. The results showed that each component's concentration of the oil spills samples could be detected by the NIR technology combined with PSO-LS-SVR regression method, the predict results were accurate and reliable, thus this method can provide effective means for the quantitative detection and analysis of complex marine oil spills.
Huber, Silvia; Tagesson, Håkan Torbern; Fensholt, Rasmus
2014-01-01
off-nadir observation angles but for observations of large off-nadir angles highest values were found in the morning or evening hours (both forward and backward scatter direction). Anisotropy factors corresponding to MODIS, SPOT and SEVIRI red, near-infrared (NIR) and shortwave-infrared (SWIR) sensor...... collected with the DAFIS system for monitoring of plant spectro-directional behavior in semi-arid African savanna for quantitative evaluation of satellite or airborne remote sensing data or development of new Earth Observation (EO) based indices and algorithms to monitor vegetation status or stress....
NIR Electrofluorochromic Properties of Aza-Boron-dipyrromethene Dyes.
Lim, Hanwhuy; Seo, Seogjae; Pascal, Simon; Bellier, Quentin; Rigaut, Stéphane; Park, Chihyun; Shin, Haijin; Maury, Olivier; Andraud, Chantal; Kim, Eunkyoung
2016-01-06
The photophysical properties of near-infrared (NIR) emissive aza-boron-dipyrromethene (aza-BDP) dyes incorporating nitrofluorene and alkoxy decorations were intensively investigated. Their highly reversible one-electron reduction process showed characteristic electrofluorochromic (EF) properties in the NIR range, depending on the substituents. The nitrofluorene ethynyl-substituted (Type I) dyes showed smaller EF effects than the alkoxy-containing (Type II) dyes because of the difference in their intrinsic fluorescence contrast between the neutral and reduced states (radical anion). In addition, the Type II chromophores showed a larger diffusion coefficient for ion transport, which enhanced the EF contrast and the response time for the fluorescence change at a given step potential. With an optimized condition, the NIR EF ON/OFF ratio reached a value of 6.1 and a long cyclability over 1000 EF cycles between -0.4 V and +0.4 V switching potentials, with approximately 20% loss of the initial ON/OFF switching ratio. The NIR EF switching was visually observed through a visible light cut-off filter, featuring high fluorescence contrast.
A small-molecule dye for NIR-II imaging.
Antaris, Alexander L; Chen, Hao; Cheng, Kai; Sun, Yao; Hong, Guosong; Qu, Chunrong; Diao, Shuo; Deng, Zixin; Hu, Xianming; Zhang, Bo; Zhang, Xiaodong; Yaghi, Omar K; Alamparambil, Zita R; Hong, Xuechuan; Cheng, Zhen; Dai, Hongjie
2016-02-01
Fluorescent imaging of biological systems in the second near-infrared window (NIR-II) can probe tissue at centimetre depths and achieve micrometre-scale resolution at depths of millimetres. Unfortunately, all current NIR-II fluorophores are excreted slowly and are largely retained within the reticuloendothelial system, making clinical translation nearly impossible. Here, we report a rapidly excreted NIR-II fluorophore (∼90% excreted through the kidneys within 24 h) based on a synthetic 970-Da organic molecule (CH1055). The fluorophore outperformed indocyanine green (ICG)-a clinically approved NIR-I dye-in resolving mouse lymphatic vasculature and sentinel lymphatic mapping near a tumour. High levels of uptake of PEGylated-CH1055 dye were observed in brain tumours in mice, suggesting that the dye was detected at a depth of ∼4 mm. The CH1055 dye also allowed targeted molecular imaging of tumours in vivo when conjugated with anti-EGFR Affibody. Moreover, a superior tumour-to-background signal ratio allowed precise image-guided tumour-removal surgery.
Detection of flaws in hazelnuts using VIS/NIR spectroscopy
The feasibility of VIS/NIR spectroscopy for detection of flaws in hazelnut kernels was demonstrated. Feature datasets comprising raw absorbance values, raw absorbance Ratios (Abs['1] : Abs['2]) and Differences (Abs['1] – Abs['2]) for all possible pairs of wavelengths from 306.5 nm to 1710.9 nm were ...
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,
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,
A small-molecule dye for NIR-II imaging
Antaris, Alexander L.; Chen, Hao; Cheng, Kai; Sun, Yao; Hong, Guosong; Qu, Chunrong; Diao, Shuo; Deng, Zixin; Hu, Xianming; Zhang, Bo; Zhang, Xiaodong; Yaghi, Omar K.; Alamparambil, Zita R.; Hong, Xuechuan; Cheng, Zhen; Dai, Hongjie
2016-02-01
Fluorescent imaging of biological systems in the second near-infrared window (NIR-II) can probe tissue at centimetre depths and achieve micrometre-scale resolution at depths of millimetres. Unfortunately, all current NIR-II fluorophores are excreted slowly and are largely retained within the reticuloendothelial system, making clinical translation nearly impossible. Here, we report a rapidly excreted NIR-II fluorophore (~90% excreted through the kidneys within 24 h) based on a synthetic 970-Da organic molecule (CH1055). The fluorophore outperformed indocyanine green (ICG)--a clinically approved NIR-I dye--in resolving mouse lymphatic vasculature and sentinel lymphatic mapping near a tumour. High levels of uptake of PEGylated-CH1055 dye were observed in brain tumours in mice, suggesting that the dye was detected at a depth of ~4 mm. The CH1055 dye also allowed targeted molecular imaging of tumours in vivo when conjugated with anti-EGFR Affibody. Moreover, a superior tumour-to-background signal ratio allowed precise image-guided tumour-removal surgery.
Extendable nickel complex tapes that reach NIR absorptions.
Audi, Hassib; Chen, Zhongrui; Charaf-Eddin, Azzam; D'Aléo, Anthony; Canard, Gabriel; Jacquemin, Denis; Siri, Olivier
2014-12-14
Stepwise synthesis of linear nickel complex oligomer tapes with no need for solid-phase support has been achieved. The control of the length in flat arrays allows a fine-tuning of the absorption properties from the UV to the NIR region.
Concurrent EEG And NIRS Tomographic Imaging Based on Wearable Electro-Optodes
2014-04-13
simultaneous electroencephalogram ( EEG ) and functional NIR spectroscopic (fNIRS) acquisition for biological or cognitive neuroscience studies in operational...environments. The system features novel EEG /NIRS electrodes, known as electro-opodes, and miniaturized supporting hardware/software. In the past few...years, our team, composed of faculty, postdoctoral fellows and graduate students, has designed and developed dry EEG and fNIR sensors that allow non
Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Kim, Moon S; Chao, Kuanglin; Qin, Jianwei; Fu, Xiaping; Baek, Insuck; Cho, Byoung-Kwan
2016-05-01
Illegal use of nitrogen-rich melamine (C3H6N6) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography-mass spectrometry (GC-MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990-1700nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders.
General regression and representation model for classification.
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.
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
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
fNIRS exhibits weak tuning to hand movement direction.
Waldert, Stephan; Tüshaus, Laura; Kaller, Christoph P; Aertsen, Ad; Mehring, Carsten
2012-01-01
Functional near-infrared spectroscopy (fNIRS) has become an established tool to investigate brain function and is, due to its portability and resistance to electromagnetic noise, an interesting modality for brain-machine interfaces (BMIs). BMIs have been successfully realized using the decoding of movement kinematics from intra-cortical recordings in monkey and human. Recently, it has been shown that hemodynamic brain responses as measured by fMRI are modulated by the direction of hand movements. However, quantitative data on the decoding of movement direction from hemodynamic responses is still lacking and it remains unclear whether this can be achieved with fNIRS, which records signals at a lower spatial resolution but with the advantage of being portable. Here, we recorded brain activity with fNIRS above different cortical areas while subjects performed hand movements in two different directions. We found that hemodynamic signals in contralateral sensorimotor areas vary with the direction of movements, though only weakly. Using these signals, movement direction could be inferred on a single-trial basis with an accuracy of ∼65% on average across subjects. The temporal evolution of decoding accuracy resembled that of typical hemodynamic responses observed in motor experiments. Simultaneous recordings with a head tracking system showed that head movements, at least up to some extent, do not influence the decoding of fNIRS signals. Due to the low accuracy, fNIRS is not a viable alternative for BMIs utilizing decoding of movement direction. However, due to its relative resistance to head movements, it is promising for studies investigating brain activity during motor experiments.
[Proximate analysis of straw by near infrared spectroscopy (NIRS)].
Huang, Cai-jin; Han, Lu-jia; Liu, Xian; Yang, Zeng-ling
2009-04-01
Proximate analysis is one of the routine analysis procedures in utilization of straw for biomass energy use. The present paper studied the applicability of rapid proximate analysis of straw by near infrared spectroscopy (NIRS) technology, in which the authors constructed the first NIRS models to predict volatile matter and fixed carbon contents of straw. NIRS models were developed using Foss 6500 spectrometer with spectra in the range of 1,108-2,492 nm to predict the contents of moisture, ash, volatile matter and fixed carbon in the directly cut straw samples; to predict ash, volatile matter and fixed carbon in the dried milled straw samples. For the models based on directly cut straw samples, the determination coefficient of independent validation (R2v) and standard error of prediction (SEP) were 0.92% and 0.76% for moisture, 0.94% and 0.84% for ash, 0.88% and 0.82% for volatile matter, and 0.75% and 0.65% for fixed carbon, respectively. For the models based on dried milled straw samples, the determination coefficient of independent validation (R2v) and standard error of prediction (SEP) were 0.98% and 0.54% for ash, 0.95% and 0.57% for volatile matter, and 0.78% and 0.61% for fixed carbon, respectively. It was concluded that NIRS models can predict accurately as an alternative analysis method, therefore rapid and simultaneous analysis of multicomponents can be achieved by NIRS technology, decreasing the cost of proximate analysis for straw.
Inno, L; Bono, G; Caputo, F; Buonanno, R; Genovali, K; Laney, C D; Marconi, M; Piersimoni, A M; Primas, F; Romaniello, M
2012-01-01
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<\\log P_{\\rm FU} \\le1.65 $) 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. B...
Rutsch, Wolfgang; Kiemeneij, Ferdinand; Colombo, Antonio; Macaya, Carlos; Guermonprez, Jean-Leon; Grip, Lars; Hamburger, Jaap; Umans, Victor; Gotsman, Mervyn; Almagor, Yaron; Morice, Marie-Claude; Garcia, Eulogio; Chevalier, Bernard; Erbel, Raimund; Cobaugh, Michael; Morel, Marie-Angèle; Serruys, Patrick W
2000-09-01
BACKGROUND: Although safety and efficacy of the NIR trade mark stent have been reported, the long-term angiographic and clinical outcomes have yet to be investigated. The FINESS-II study (First International NIR Endovascular Stent Study) was designed to assess the procedural safety of single 9 and 16 mm NIR stent implantation, the six-month restenosis rate and finally the six- and 12-month clinical outcome of patients treated with this novel coronary stent. METHODS: Patients with angina and a single de novo lesion in a native coronary artery of >3 and 50% diameter stenosis criterion at six month follow-up was 19% (26/136). At 12 months, the event-free survival rate was 83% (two deaths, one Q-wave and three non-Q-wave myocardial infarctions, four bypass surgery and 17 target lesion revascularizations), while 87% of the patients were free of angina pectoris. CONCLUSION: the outcome of the FINESS-II trial is comparable to those observed in previous stent trials (Benestent II), indicating that the coronary NIR stent is safe and effective as a primary device for the treatment of native coronary artery lesions in patients with (un)stable angina pectoris.
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.
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.
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.
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.
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.
Sparse Regression as a Sparse Eigenvalue Problem
Moghaddam, Baback; Gruber, Amit; Weiss, Yair; Avidan, Shai
2008-01-01
We extend the l0-norm "subspectral" algorithms for sparse-LDA [5] and sparse-PCA [6] to general quadratic costs such as MSE in linear (kernel) regression. The resulting "Sparse Least Squares" (SLS) problem is also NP-hard, by way of its equivalence to a rank-1 sparse eigenvalue problem (e.g., binary sparse-LDA [7]). Specifically, for a general quadratic cost we use a highly-efficient technique for direct eigenvalue computation using partitioned matrix inverses which leads to dramatic x103 speed-ups over standard eigenvalue decomposition. This increased efficiency mitigates the O(n4) scaling behaviour that up to now has limited the previous algorithms' utility for high-dimensional learning problems. Moreover, the new computation prioritizes the role of the less-myopic backward elimination stage which becomes more efficient than forward selection. Similarly, branch-and-bound search for Exact Sparse Least Squares (ESLS) also benefits from partitioned matrix inverse techniques. Our Greedy Sparse Least Squares (GSLS) generalizes Natarajan's algorithm [9] also known as Order-Recursive Matching Pursuit (ORMP). Specifically, the forward half of GSLS is exactly equivalent to ORMP but more efficient. By including the backward pass, which only doubles the computation, we can achieve lower MSE than ORMP. Experimental comparisons to the state-of-the-art LARS algorithm [3] show forward-GSLS is faster, more accurate and more flexible in terms of choice of regularization
NIR is degraded by the anaphase-promoting complex proteasome pathway
Jeong Ho Myong
2014-01-01
Full Text Available Novel INHAT Repressor (NIR is a histone acetylation inhibitor that can directly bind histone complexes and the tumor suppressors p53 and p63. Because NIR is mainly localized in the nucleolus and disappears from the nucleolus upon RNase treatment, it is thought to bind RNA or ribonucleoproteins. When NIR moves to the cytoplasm, it is immediately degraded; this degradation was blocked by MG132, a proteasome inhibitor. Furthermore, the central domain of NIR specifically bound APC-CCdh1. These data show that the stability of NIR is governed by the ubiquitin/proteasome pathway.
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......-AES and NIR transmittance spectroscopy exhibit comparable precision and accuracy. The NIR method provides several advantages: no complicated sample preparation; easy to operate; fast and non-destructive. In conclusion, NIR transmittance spectroscopy can be an alternative analytical method for determining...
Exploring compact reinforcement-learning representations with linear regression
Walsh, Thomas J; Diuk, Carlos; Littman, Michael L
2012-01-01
This paper presents a new algorithm for online linear regression whose efficiency guarantees satisfy the requirements of the KWIK (Knows What It Knows) framework. The algorithm improves on the complexity bounds of the current state-of-the-art procedure in this setting. We explore several applications of this algorithm for learning compact reinforcement-learning representations. We show that KWIK linear regression can be used to learn the reward function of a factored MDP and the probabilities of action outcomes in Stochastic STRIPS and Object Oriented MDPs, none of which have been proven to be efficiently learnable in the RL setting before. We also combine KWIK linear regression with other KWIK learners to learn larger portions of these models, including experiments on learning factored MDP transition and reward functions together.
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.
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.
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
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
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.
Large unbalanced credit scoring using Lasso-logistic regression ensemble.
Hong Wang
Full Text Available Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.
Large unbalanced credit scoring using Lasso-logistic regression ensemble.
Wang, Hong; Xu, Qingsong; Zhou, Lifeng
2015-01-01
Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.
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
Harnly, James M.; Peter de B. Harrington; Botros, Lucy L.; Jablonski, Joseph; Chang, Claire; Bergana, Marti Mamula; Wehling, Paul; Downey, Gerard; Potts, Alan R.; Moore, Jeffrey C.
2014-01-01
Forty-one samples of skim milk powder (SMP) and nonfat dry milk (NFDM) from 8 suppliers, 13 production sites, and 3 processing temperatures were analyzed by NIR diffuse reflectance spectrometry over a period of 3 days. NIR reflectance spectra (1700–2500 nm) were converted to pseudoabsorbance and examined using (a) analysis of variance-principal component analysis (ANOVA-PCA), (b) pooled-ANOVA based on data submatrices, and (c) partial least-squares regression (PLSR) coupled with pooled-ANOVA....
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.
Liu, Fei; He, Yong
2008-03-01
Three different chemometric methods were performed for the determination of sugar content of cola soft drinks using visible and near infrared spectroscopy (Vis/NIRS). Four varieties of colas were prepared and 180 samples (45 samples for each variety) were selected for the calibration set, while 60 samples (15 samples for each variety) for the validation set. The smoothing way of Savitzky-Golay, standard normal variate (SNV) and Savitzky-Golay first derivative transformation were applied for the pre-processing of spectral data. The first eleven principal components (PCs) extracted by partial least squares (PLS) analysis were employed as the inputs of BP neural network (BPNN) and least squares-support vector machine (LS-SVM) model. Then the BPNN model with the optimal structural parameters and LS-SVM model with radial basis function (RBF) kernel were applied to build the regression model with a comparison of PLS regression. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for prediction were 0.971, 1.259 and -0.335 for PLS, 0.986, 0.763, and -0.042 for BPNN, while 0.978, 0.995 and -0.227 for LS-SVM, respectively. All the three methods supplied a high and satisfying precision. The results indicated that Vis/NIR spectroscopy combined with chemometric methods could be utilized as a high precision way for the determination of sugar content of cola soft drinks.
Regression Model Optimization for the Analysis of Experimental Data
Ulbrich, N.
2009-01-01
A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.
Human brain activity with functional NIR optical imager
Luo, Qingming
2001-08-01
In this paper we reviewed the applications of functional near infrared optical imager in human brain activity. Optical imaging results of brain activity, including memory for new association, emotional thinking, mental arithmetic, pattern recognition ' where's Waldo?, occipital cortex in visual stimulation, and motor cortex in finger tapping, are demonstrated. It is shown that the NIR optical method opens up new fields of study of the human population, in adults under conditions of simulated or real stress that may have important effects upon functional performance. It makes practical and affordable for large populations the complex technology of measuring brain function. It is portable and low cost. In cognitive tasks subjects could report orally. The temporal resolution could be millisecond or less in theory. NIR method will have good prospects in exploring human brain secret.
NIR Analysis of Powder Mixing Quality in a Ribbon Blender
Goodridge, Chris; Duong, Nhat-Hang; Muzzio, Fernando
2001-11-01
We present experimental results on powder mixing performed in a common industrial mixer, the batch ribbon blender. Our experiments explore the effectiveness of this device on mixture quality as a function of fill level, loading pattern, ribbon speed, mixing time, and ribbon angle. We study two powder formulations consisting of common compounds used in food and pharmaceutical processing. Mixture quality is evaluated by core sampling throughout the blender and determining the composition of small samples using NIR spectroscopy. We use the spectra from NIR to calculate the intensity and scale of segregation for the three-dimensional mixing region. The mixing rates in the axial and radial directions are obtained from plots of composition variance vs. mixing time. We examine the effects of ribbon speed and fill level as the main parameters affecting mixing rate. Dead regions that remain isolated from the remainder of the flow are identified.
NIR FRET Fluorophores for Use as an Implantable Glucose Biosensor
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.
A NIR Flare of the Quasar PKS0446+112
Carrasco, L.; Porras, A.; Escobedo, G.; Recillas, E.; Chabushyan, V.; Carraminana, A.; Mayya, D.
2014-01-01
We report on the NIR flare of the high redshift quasar PKS0446+112 (z=1.207), also known as CGRaBSJ0449+1121, associated with the gamma-ray source 1FGL0448.6+1118. The source has shown Gamma-ray flares in the past. Our NIR photometry for this source shows that on Jan 4th,2014 (JD2456661.773275), the object brightness corresponded to J = 15.43+/- 0.04, H = 14.210 +/- 0.04 and Ks = 13.455 +/- 0.06. These values are about 1 magnitud brighter than our previous photometry, obtained on JD2456609, for this field.
Polynomial Regression on Riemannian Manifolds
Hinkle, Jacob; Fletcher, P Thomas; Joshi, Sarang
2012-01-01
In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.
Classification and Regression Trees(CART) Theory and Applications
Timofeev, Roman
2004-01-01
This master thesis is devoted to Classification and Regression Trees (CART). CART is classification method which uses historical data to construct decision trees. Depending on available information about the dataset, classification tree or regression tree can be constructed. Constructed tree can be then used for classification of new observations. The first part of the thesis describes fundamental principles of tree construction, different splitting algorithms and pruning procedures. Seco...
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.
Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models
Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung
2015-01-01
Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…
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
Business applications of multiple regression
Richardson, Ronny
2015-01-01
This second edition of Business Applications of Multiple Regression describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in deta
[EMD Time-Frequency Analysis of Raman Spectrum and NIR].
Zhao, Xiao-yu; Fang, Yi-ming; Tan, Feng; Tong, Liang; Zhai, Zhe
2016-02-01
This paper analyzes the Raman spectrum and Near Infrared Spectrum (NIR) with time-frequency method. The empirical mode decomposition spectrum becomes intrinsic mode functions, which the proportion calculation reveals the Raman spectral energy is uniform distributed in each component, while the NIR's low order intrinsic mode functions only undertakes fewer primary spectroscopic effective information. Both the real spectrum and numerical experiments show that the empirical mode decomposition (EMD) regard Raman spectrum as the amplitude-modulated signal, which possessed with high frequency adsorption property; and EMD regards NIR as the frequency-modulated signal, which could be preferably realized high frequency narrow-band demodulation during first-order intrinsic mode functions. The first-order intrinsic mode functions Hilbert transform reveals that during the period of empirical mode decomposes Raman spectrum, modal aliasing happened. Through further analysis of corn leaf's NIR in time-frequency domain, after EMD, the first and second orders components of low energy are cut off, and reconstruct spectral signal by using the remaining intrinsic mode functions, the root-mean-square error is 1.001 1, and the correlation coefficient is 0.981 3, both of these two indexes indicated higher accuracy in re-construction; the decomposition trend term indicates the absorbency is ascending along with the decreasing to wave length in the near-infrared light wave band; and the Hilbert transform of characteristic modal component displays, 657 cm⁻¹ is the specific frequency by the corn leaf stress spectrum, which could be regarded as characteristic frequency for identification.
Temperature dependent NIR emitting lanthanide-PMO/silica hybrid materials.
Kaczmarek, Anna M; Esquivel, Dolores; Ouwehand, Judith; Van Der Voort, Pascal; Romero-Salguero, Francisco J; Van Deun, Rik
2017-06-28
Two materials - a mesoporous silica (MS) and a periodic mesoporous organosilica (PMO) functionalized with dipyridyl-pyridazine (dppz) units were grafted with near-infrared (NIR) emitting lanthanide (Nd(3+), Er(3+), Yb(3+)) complexes in an attempt to obtain hybrid NIR emitting materials. The parent materials: dppz-vSilica and dppz-ePMO were prepared by a hetero Diels-Alder reaction between 3,6-di(2-pyridyl)-1,2,4,5-tetrazine (dptz) and the double bonds of either ethenylene-bridged PMO (ePMO) or vinyl-silica (vSilica) and subsequent oxidation. The dppz-vSilica is reported here for the first time. The prepared lanthanide-PMO/silica hybrid materials were studied in depth for their luminescence properties at room temperature and chosen Nd(3+) and Yb(3+) samples also at low temperature (as low as 10 K). We show that both the dppz-vSilica and dppz-ePMO materials can be used as "platforms" for obtaining porous materials showing NIR luminescence. To obtain NIR emission these materials can be excited either in the UV or Vis region (into the π→π* transitions of the ligands or directly into the f-f transitions of the Ln(3+) ions). More interestingly, when functionalized with Nd(3+) or Yb(3+)β-diketonate complexes these materials showed interesting luminescence properties over a wide temperature range (10-360 K). The Yb(3+) materials were investigated for their potential use as ratiometric temperature sensors.
Relationship between muscle oxygenation by NIRS and blood lactate
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.
Classification of maize kernels using NIR hyperspectral imaging
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....
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.
Initial development of an NIR strain measurement technique in brittle geo-materials
Butcher, Emily; Gibson, Andrew; Benson, Philip
2016-04-01
Visible-Near Infrared Spectroscopy (VIS-NIR) is a technique developed for the non-contact measurement of compositional characteristics of surfaces. The technique is rapid, sensitive to change in surface topology and has found applications ranging from planetary geology, soil science, pharmacy to materials testing. The technique has also been used in a limited fashion to measure strain changes in rocks and minerals (Ord and Hobbs 1986). However, there have been few quantitative studies linking such changes in material strains (and other rock physics parameters) to the resulting VIS-NIT signature. This research seeks to determine whether improvements in VIS-NIR equipment means that such a technique is a viable method to measure strains in rock via this remote (non-contact) method. We report new experiments carried out using 40 mm Brazilian Tensile discs of Carrera Marble and Darley Dale Sandstone using an Instron 600LX in the University of Portsmouth Rock Mechanics Laboratory. The tensile test was selected for this experiment as the sample shape and sensor arrangements allow access to a 'flat' surface area throughout the test, allowing surface measurements to be continuously taken whilst the discs are strained to failure. An ASD Labspec 5000 with 25 mm foreoptic was used to collect reflectance spectra in the range 350-2500 nm during each tensile test. Results from Carrera Marble experiments show that reflectance at 2050 nm negatively correlates (by polynomial regression) with axial strain between 0.05-0.5%, with r2 of 0.99. Results from Darley Dale Sandstone data show that reflectance at 1970 nm positively correlates with axial deformation between 0.05-0.5%, with r2 of 0.98. Initial analyses suggests that the VIS-NIR possesses an output that scales in a quantifiable manner with rock strain, and shows promise as a technique for strain measurement. The method has particular application for allowing our laboratory measurements to "ground truth" data taken from drone and
GSH-Activated NIR Fluorescent Prodrug for Podophyllotoxin Delivery.
Liu, Yajing; Zhu, Shaojia; Gu, Kaizhi; Guo, Zhiqian; Huang, Xiaoyu; Wang, Mingwei; Amin, Hesham M; Zhu, Weihong; Shi, Ping
2017-09-06
Theranostic prodrug therapy enables the targeted delivery of anticancer drugs with minimized adverse effects and real-time in situ monitoring of activation of the prodrugs. In this work, we report the synthesis and biological assessment of the near-infrared (NIR) prodrug DCM-S-PPT and its amphiphilic copolymer (mPEG-DSPE)-encapsulated nanoparticles. DCM-S-PPT is composed of podophyllotoxin (PPT) as the anticancer moiety and a dicyanomethylene-4H-pyran (DCM) derivative as the NIR fluorescent reporter, which are linked by a thiol-specific cleavable disulfide bond. In vitro experiments indicated that DCM-S-PPT has low cytotoxicity and that glutathione (GSH) can activate DCM-S-PPT resulting in PPT release and a concomitant significant enhancement in NIR fluorescence at 665 nm. After being intravenously injected into tumor-bearing nude mice, DCM-S-PPT exhibited excellent tumor-activated performance. Furthermore, we have demonstrated that mPEG-DSPE as a nanocarrier loaded with DCM-S-PPT (mPEG-DSPE/DCM-S-PPT) showed even greater tumor-targeting performance than DCM-S-PPT on account of the enhanced permeability and retention effect. Its tumor-targeting ability and specific drug release in tumors make DCM-S-PPT a promising prodrug that could provide a significant strategy for theranostic drug delivery systems.
White Asparagus Harvest Date Discrimination Using Nirs Technology
Jarén, C.; Arazuri, S.; García, M. J.; Arnal, P.; Arana, J. I.
2006-03-01
Asparagus is still an important resource for mid-size and small farms. It has been traditionally believed that factors such as the asparagus harvesting date have an influence on its quality. This research sought to identify the harvesting dates of different fruits by using Near Infrared Spectroscopy (NIRS) technology as quality indicators and the best zone a long of the asparagus to acquire the spectrum. All the asparagus tested came from the same manufacturer but had been canned on three different dates, giving a total of nine lots. There were one hundred asparagus per lot and the experiment data were taken from three different parts (tip, middle, and base) of each spear. Reflectance spectrum in the near infrared between 800 1700 nm was determined. Differences NIRS among the asparagus harvested on different dates were found. NIRS technology was able to classify the asparagus correctly in terms of harvest dates (71% well classified). The base of the asparagus turned out to be the best part to use in order to establish the harvest date.
Characterization of mind wandering using fNIRS.
Durantin, Gautier; Dehais, Frederic; Delorme, Arnaud
2015-01-01
Assessing whether someone is attending to a task has become important for educational and professional applications. Such attentional drifts are usually termed mind wandering (MW). The purpose of the current study is to test to what extent a recent neural imaging modality can be used to detect MW episodes. Functional near infrared spectroscopy is a non-invasive neuroimaging technique that has never been used so far to measure MW. We used the Sustained Attention to Response Task (SART) to assess when subjects attention leaves a primary task. Sixteen-channel fNIRS data were collected over frontal cortices. We observed significant activations over the medial prefrontal cortex (mPFC) during MW, a brain region associated with the default mode network (DMN). fNIRS data were used to classify MW data above chance level. In line with previous brain-imaging studies, our results confirm the ability of fNIRS to detect Default Network activations in the context of MW.
Characterization of Mind Wandering using fNIRS
Gautier eDurantin
2015-03-01
Full Text Available Assessing whether someone is attending to a task has become importantfor educational and professional applications. Such attentional drifts are usually termed mind wandering. The purpose of the current study is to test to what extent a recent neural imaging modality can be used to detect mind wandering episodes. Functional near infra-red spectroscopy is a non-invasive neuro-imaging technique that has never been studied so far to measure mind wandering. The Sustained Attention to Response Task was used to assess when subjects attention leaves a primary task. 16-channel fNIRS data were collected over frontal cortices. We observed significant activations over the medial prefrontal cortex during mind wandering, a brain region associated with the default mode network. fNIRS data were used to classify mind wandering data above chance level. In line with previous brain-imaging studies of mind wandering, our results confirm the ability of fNIRS to detect Default Network activations in the context of mind wandering.
The application of near infrared spectroscopy (NIR technique for
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.
UV-VIS-NIR spectroscopy and microscopy of heterogeneous catalysts.
Schoonheydt, Robert A
2010-12-01
This critical review article discusses the characterization of heterogeneous catalysts by UV-VIS-NIR spectroscopy and microscopy with special emphasis on transition metal ion containing catalysts. A review is given of the transitions, that can be observed in the UV-VIS-NIR region and the peculiarities of catalytic solids that have to be taken into account. This is followed by a short discussion of the techniques that have been developed over the years: diffuse reflectance spectroscopy, UV-VIS microscopy, in situ or operando spectroscopy, the combination of UV-VIS spectroscopy with other spectroscopic techniques, with chemometrics and with quantum chemistry. In the third part of this paper four successes of UV-VIS-NIR spectroscopy and microscopy are discussed; (1) coordination of transition metal ions to surface oxygens; (2) quantitative determination of the oxidation states of transition metal ions; (3) characterization of active sites and (4) study of the distribution of transition metal ions and carbocations in catalytic bodies, particles and crystals (104 references).
Near-infrared (NIR) up-conversion optogenetics
Hososhima, Shoko; Yuasa, Hideya; Ishizuka, Toru; Hoque, Mohammad Razuanul; Yamashita, Takayuki; Yamanaka, Akihiro; Sugano, Eriko; Tomita, Hiroshi; Yawo, Hiromu
2015-11-01
Non-invasive remote control technologies designed to manipulate neural functions have been long-awaited for the comprehensive and quantitative understanding of neuronal network in the brain as well as for the therapy of neurological disorders. Recently, it has become possible for the neuronal activity to be optically manipulated using biological photo-reactive molecules such as channelrhodopsin (ChR)-2. 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 (650-1450 nm) 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), composed of rare-earth elements, as luminous bodies to activate ChRs since they absorb low-energy NIR light to emit high-energy visible light (up-conversion). Here, we created a new type of optogenetic system which consists of the donor LNPs and the acceptor ChRs. The NIR laser irradiation emitted visible light from LNPs, then induced the photo-reactive responses in the near-by cells that expressed ChRs. However, there remains room for large improvements in the energy efficiency of the LNP-ChR system.
Near-infrared chemical imaging (NIR-CI) of 3D printed pharmaceuticals.
Khorasani, Milad; Edinger, Magnus; Raijada, Dhara; Bøtker, Johan; Aho, Johanna; Rantanen, Jukka
2016-12-30
Hot-melt extrusion and 3D printing are enabling manufacturing approaches for patient-centred medicinal products. Hot-melt extrusion is a flexible and continuously operating technique which is a crucial part of a typical processing cycle of printed medicines. In this work we use hot-melt extrusion for manufacturing of medicinal films containing indomethacin (IND) and polycaprolactone (PCL), extruded strands with nitrofurantoin monohydrate (NFMH) and poly (ethylene oxide) (PEO), and feedstocks for 3D printed dosage forms with nitrofurantoin anhydrate (NFAH), hydroxyapatite (HA) and poly (lactic acid) (PLA). These feedstocks were printed into a prototype solid dosage form using a desktop 3D printer. These model formulations were characterized using near-infrared chemical imaging (NIR-CI) and, more specifically, the image analytical data were analysed using multivariate curve resolution-alternating least squares (MCR-ALS). The MCR-ALS algorithm predicted the spatial distribution of IND and PCL in the films with reasonable accuracy. In the extruded strands both the chemical mapping of the components in the formulation as well as the solid form of the active compound could be visualized. Based on the image information the total nitrofurantoin and PEO contents could be estimated., The dehydration of NFMH to NFAH, a process-induced solid form change, could be visualized as well. It was observed that the level of dehydration increased with increasing processing time (recirculation during the mixing phase of molten PEO and nitrofurantoin). Similar results were achieved in the 3D printed solid dosage forms produced from the extruded feedstocks. The results presented in this work clearly demonstrate that NIR-CI in combination with MCR-ALS can be used for chemical mapping of both active compound and excipients, as well as for visualization of solid form variation in the final product. The suggested NIR-CI approach is a promising process control tool for characterization of
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.
Accurate, in vivo NIR measurement of skeletal muscle oxygenation through fat
Jin, Chunguang; Zou, Fengmei; Ellerby, Gwenn E. C.; Scott, Peter; Peshlov, Boyan; Soller, Babs R.
2010-02-01
Noninvasive near infrared (NIR) spectroscopic measurement of muscle oxygenation requires the penetration of light through overlying skin and fat layers. We have previously demonstrated a dual-light source design and orthogonalization algorithm that corrects for inference from skin absorption and fat scattering. To achieve accurate muscle oxygen saturation (SmO2) measurement, one must select the appropriate source-detector distance (SD) to completely penetrate the fat layer. Methods: Six healthy subjects were supine for 15min to normalize tissue oxygenation across the body. NIR spectra were collected from the calf, shoulder, lower and upper thigh muscles with long SD distances of 30mm, 35mm, 40mm and 45mm. Spectral preprocessing with the short SD (3mm) spectrum preceded SmO2 calculation with a Taylor series expansion method. Three-way ANOVA was used to compare SmO2 values over varying fat thickness, subjects and SD distances. Results: Overlying fat layers varied in thickness from 4.9mm to 19.6mm across all subjects. SmO2 measured at the four locations were comparable for each subject (p=0.133), regardless of fat thickness and SD distance. SmO2 (mean+/-std dev) measured at calf, shoulder, low and high thigh were 62+/-3%, 59+/-8%, 61+/-2%, 61+/-4% respectively for SD distance of 30mm. In these subjects no significant influence of SD was observed (p=0.948). Conclusions: The results indicate that for our sensor design a 30mm SD is sufficient to penetrate through a 19mm fat layer and that orthogonalization with short SD effectively removed spectral interference from fat to result in a reproducible determination of SmO2.
[Fast catalogue of alien invasive weeds by Vis/NIR spectroscopy].
Yu, Jia-Jia; Zou, Wei; He, Yong; Xu, Zheng-Hao
2009-11-01
The feasibility of visible and short-wave near-infrared spectroscopy (VIS/WNIR) techniques as means for the nondestructive and fast detection of alien invasive weeds was evaluated. Selected sensitive bands were found validated. In the present study, 3 kinds of alien invasive weeds, Veronica persica, Veronica polita, and Veronica arvensis Linn, and one kind of local weed, Lamiaceae amplexicaule Linn, were employed. The results showed that visible and NIR (Vis/NIR) technology could be introduced in classification of the alien invasive weeds or local weed with the similar outline. Thirty x 4 weeds samples were randomly selected for the calibration set, while the remaining 20 x 4 samples for the prediction set. Smoothing methods of moving average and standard normal variate (SNV) were used to pretreat spectra data. Based on principal components analysis, soft independent models of class analogy (SIMCA) were applied to make the model. Four frontal principal components of each catalogues were applied as the input of SIMCA, and with a significance level of 0.05, recognition ratio of 78.75% was obtained. The average prediction result is 90% except for Veronica polita. According to the modeling power of each spectra data in SIMCA, some possible sensitive bands, 496-521, 589-626 and 789-926 nm, were founded. By using these possible sensitive bands as the inputs of least squares support vector machine (LS-SVM), and setting the result of LS-SVM as the object function value of genetic algorithm (GA), mutational rate, crossover rate and population size were set up as 0.9, 0.5 and 50 respectively. Finally recognition ratio of 95.63% was obtained. The prediction results of 95.63% indicated that the selected wavelengths reflected the main characteristics of the four weeds, which proposed a new way to accelerate the research on cataloguing alien invasive weeds.
Kleinberg, Jon
2006-01-01
Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science.
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.
Bourdolle, Adrien; D'Aléo, Anthony; Philippot, Cécile; Baldeck, Patrice L; Guyot, Yannick; Dubois, Fabien; Ibanez, Alain; Andraud, Chantal; Brasselet, Sophie; Maury, Olivier
2016-01-04
The photophysical and nonlinear optical properties of water-soluble chromophore-functionalised tris-dipicolinate complexes [LnL3](3-) (Ln=Yb and Nd) are thoroughly studied, revealing that only the Yb(III) luminescence can be sensitized by a two-photon excitation process. The stability of the complex in water is strongly enhanced by embedding in dispersible organosilicate nanoparticles (NPs). Finally, the spectroscopic properties of [NBu4]3 [YbL3] are studied in solution and in the solid state. The high brightness of the NPs allows imaging them as single objects using a modified two-photon microscopy setup in a NIR-to-NIR configuration.
Yao, Chi; Wang, Peiyuan; Wang, Rui; Zhou, Lei; El-Toni, Ahmed Mohamed; Lu, Yiqing; Li, Xiaomin; Zhang, Fan
2016-02-02
Peptide modification of nanoparticles is a challenging task for bioapplications. Here, we show that noncovalent surface engineering based on ligand exchange of peptides for lanthanide based upconversion and downconversion near-infrared (NIR) luminescent nanoparticles can be efficiently realized by modifying the hydroxyl functional group of a side grafted serine of peptides into a phosphate group (phosphorylation). By using the phosphorylated peptide with the arginine-glycine-aspartic acid (RGD) targeting motifs as typical examples, the modification allows improving the selectivity, sensitivity, and signal-to-noise ratio for the cancer targeting and bioimaging and reducing the toxicity derived from nonspecific interactions of nanoparticles with cells. The in vivo NIR bioimaging signal could even be detected at low injection amounts down to 20 μg per animal.
Cianflone, F. [Foss Electric Italia SpA, Este, PD (Italy)
2000-10-01
The Italian subsidiary of the Danish Foss Electric distributes in Italy sophisticated equipment for humidity measurement in corn, in proteins, in milk glucides and lipids, and in many other foodstuffs. Analysis are reliable and very fast, workable also in other industrial sectors and in pharmaceutical labs. Measurement technique, non destructive, is NIR spectroscopy. [Italian] La filiale italiana di un'azienda internazionale, la danese Foss Electric, commercializza nel nostro Paese sofisticate apparecchiature per la determinazione dell'umidita' nei cereali, delle proteine, dei glucidi e dei lipidi nel latte, nelle graminacee e in molte altre derrate. Le analisi sono affidabili e molto rapide, eseguibili anche in altri comparti industriali e farmaceutici. La tecnica di misura, non distruttiva, e' quella della spettroscopia nel vicino infrarosso (NIR).
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
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…
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
Mabood, Fazal; Jabeen, Farah; Ahmed, Manzor; Hussain, Javid; Al Mashaykhi, Saaida A A; Al Rubaiey, Zainb M A; Farooq, Saim; Boqué, Ricard; Ali, Liaqat; Hussain, Zahid; Al-Harrasi, Ahmed; Khan, Abdul Latif; Naureen, Zakira; Idrees, Mohammed; Manzoor, Suryyia
2017-04-15
New NIR spectroscopy combined with multivariate analysis for detection and quantification of camel milk adulteration with goat milk was investigated. Camel milk samples were collected from Aldhahira and Sharqia regions of Sultanate of Oman and were measured using NIR spectroscopy in absorption mode in the wavelength range from 700 to 2500nm, at 2cm(-1) resolution and using a 0.2mm path length CaF2 sealed cell. The multivariate methods like PCA, PLS-DA and PLS regression were used for interpretation of NIR spectral data. PLS-DA was used to detect the discrimination between the pure and adulterated milk samples. For PLSDA model the R-square value obtained was 0.974 with 0.08 RMSE. Furthermore, PLS regression model was used to quantify the levels of adulteration from, 0%, 2%, 5%, 10%, 15% and 20%. The PLS model showed the RMSEC=1.10% with R(2)=94%. This method is simple, reproducible, having excellent sensitivity. The limit of detection was found 0.5%, while the limit of quantification was 2%. Copyright © 2016 Elsevier Ltd. All rights reserved.
Gao, Juan; Hou, Lijun; Zheng, Yanling; Liu, Min; Yin, Guoyu; Li, Xiaofei; Lin, Xianbiao; Yu, Chendi; Wang, Rong; Jiang, Xiaofen; Sun, Xiuru
2016-10-01
For the past few decades, human activities have intensively increased the reactive nitrogen enrichment in China's coastal wetlands. Although denitrification is a critical pathway of nitrogen removal, the understanding of denitrifier community dynamics driving denitrification remains limited in the coastal wetlands. In this study, the diversity, abundance, and community composition of nirS-encoding denitrifiers were analyzed to reveal their variations in China's coastal wetlands. Diverse nirS sequences were obtained and more than 98 % of them shared considerable phylogenetic similarity with sequences obtained from aquatic systems (marine/estuarine/coastal sediments and hypoxia sea water). Clone library analysis revealed that the distribution and composition of nirS-harboring denitrifiers had a significant latitudinal differentiation, but without a seasonal shift. Canonical correspondence analysis showed that the community structure of nirS-encoding denitrifiers was significantly related to temperature and ammonium concentration. The nirS gene abundance ranged from 4.3 × 10(5) to 3.7 × 10(7) copies g(-1) dry sediment, with a significant spatial heterogeneity. Among all detected environmental factors, temperature was a key factor affecting not only the nirS gene abundance but also the community structure of nirS-type denitrifiers. Overall, this study significantly enhances our understanding of the structure and dynamics of denitrifying communities in the coastal wetlands of China.
Differential AR algorithm for packet delay prediction
无
2006-01-01
Different delay prediction algorithms have been applied in multimedia communication, among which linear prediction is attractive because of its low complexity. AR (auto regressive) algorithm is a traditional one with low computation cost, while NLMS (normalize least mean square) algorithm is more precise. In this paper, referring to ARIMA (auto regression integrated with moving averages) model, a differential AR algorithm (DIAR) is proposed based on the analyses of both AR and NLMS algorithms. The prediction precision of the new algorithm is about 5-10 db higher than that of the AR algorithm without increasing the computation complexity.Compared with NLMS algorithm, its precision slightly improves by 0.1 db on average, but the algorithm complexity reduces more than 90%. Our simulation and tests also demonstrate that this method improves the performance of the average end-to-end delay and packet loss ratio significantly.
Regression Testing Cost Reduction Suite
Mohamed Alaa El-Din
2014-08-01
Full Text Available The estimated cost of software maintenance exceeds 70 percent of total software costs [1], and large portion of this maintenance expenses is devoted to regression testing. Regression testing is an expensive and frequently executed maintenance activity used to revalidate the modified software. Any reduction in the cost of regression testing would help to reduce the software maintenance cost. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant when the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. This paper focuses on proposing an effective approach for reducing the cost of regression testing process. The proposed approach is applied on real-time case study. It was found that the reduction in cost of regression testing for each regression testing cycle is ranging highly improved in the case of programs containing high number of selected statements which in turn maximize the benefits of using it in regression testing of complex software systems. The reduction in the regression test suite size will reduce the effort and time required by the testing teams to execute the regression test suite. Since regression testing is done more frequently in software maintenance phase, the overall software maintenance cost can be reduced considerably by applying the proposed approach.
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.
Endmember finding and spectral unmixing using least-angle regression
Boisvert, Alexander R.; Villeneuve, Pierre V.; Stocker, Alan D.
2010-04-01
A new endmember finder and spectral unmixing algorithm based on the LARS/Lasso method for linear regression is developed. The endmember finder is sequential; a single endmember is identified at first and further endmembers which depend on the previous ones are found. The process terminates once a pre-determined number of endmembers have been found, or when the modeling error has attained the noise floor. The unmixing algorithm is a straightforward procedure that expresses each pixel as a linear combination of endmembers in a physically meaningful way. This algorithm successfully unmixes simulated data, and shows promising results on real hyperspectral images as well.
Bayesian Inference of a Multivariate Regression Model
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.
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.
Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation
Kekatos, Vassilis
2011-01-01
Volterra and polynomial regression models play a major role in nonlinear system identification and inference tasks. Exciting applications ranging from neuroscience to genome-wide association analysis build on these models with the additional requirement of parsimony. This requirement has high interpretative value, but unfortunately cannot be met by least-squares based or kernel regression methods. To this end, compressed sampling (CS) approaches, already successful in linear regression settings, can offer a viable alternative. The viability of CS for sparse Volterra and polynomial models is the core theme of this work. A common sparse regression task is initially posed for the two models. Building on (weighted) Lasso-based schemes, an adaptive RLS-type algorithm is developed for sparse polynomial regressions. The identifiability of polynomial models is critically challenged by dimensionality. However, following the CS principle, when these models are sparse, they could be recovered by far fewer measurements. ...