Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

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Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

2015-11-03

Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

Supervised chemical pattern recognition in almond ( Prunus dulcis ) Portuguese PDO cultivars: PCA- and LDA-based triennial study.

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Barreira, João C M; Casal, Susana; Ferreira, Isabel C F R; Peres, António M; Pereira, José Alberto; Oliveira, M Beatriz P P

2012-09-26

Almonds harvested in three years in Trás-os-Montes (Portugal) were characterized to find differences among Protected Designation of Origin (PDO) Amêndoa Douro and commercial non-PDO cultivars. Nutritional parameters, fiber (neutral and acid detergent fibers, acid detergent lignin, and cellulose), fatty acids, triacylglycerols (TAG), and tocopherols were evaluated. Fat was the major component, followed by carbohydrates, protein, and moisture. Fatty acids were mostly detected as monounsaturated and polyunsaturated forms, with relevance of oleic and linoleic acids. Accordingly, 1,2,3-trioleoylglycerol and 1,2-dioleoyl-3-linoleoylglycerol were the major TAG. α-Tocopherol was the leading tocopherol. To verify statistical differences among PDO and non-PDO cultivars independent of the harvest year, data were analyzed through an analysis of variance, a principal component analysis, and a linear discriminant analysis (LDA). These differences identified classification parameters, providing an important tool for authenticity purposes. The best results were achieved with TAG analysis coupled with LDA, which proved its effectiveness to discriminate almond cultivars.

Boosting Discriminant Learners for Gait Recognition Using MPCA Features

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Haiping Lu

2009-01-01

Full Text Available This paper proposes a boosted linear discriminant analysis (LDA solution on features extracted by the multilinear principal component analysis (MPCA to enhance gait recognition performance. Three-dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial features. Then, lower-dimensional vectorial features are obtained through discriminative feature selection. These feature vectors are then fed into an LDA-style booster, where several regularized and weakened LDA learners work together to produce a strong learner through a novel feature weighting and sampling process. The LDA learner employs a simple nearest-neighbor classifier with a weighted angle distance measure for classification. The experimental results on the NIST/USF “Gait Challenge” data-sets show that the proposed solution has successfully improved the gait recognition performance and outperformed several state-of-the-art gait recognition algorithms.

Fast Solution in Sparse LDA for Binary Classification

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Moghaddam, Baback

2010-01-01

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic

Hyperspectral Imaging and SPA-LDA Quantitative Analysis for Detection of Colon Cancer Tissue

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Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.

2018-05-01

Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis-NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis-NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.

Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

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Yu, Jianbo

2016-11-01

The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

MANOVA, LDA, and FA criteria in clusters parameter estimation

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Stan Lipovetsky

2015-12-01

Full Text Available Multivariate analysis of variance (MANOVA and linear discriminant analysis (LDA apply such well-known criteria as the Wilks’ lambda, Lawley–Hotelling trace, and Pillai’s trace test for checking quality of the solutions. The current paper suggests using these criteria for building objectives for finding clusters parameters because optimizing such objectives corresponds to the best distinguishing between the clusters. Relation to Joreskog’s classification for factor analysis (FA techniques is also considered. The problem can be reduced to the multinomial parameterization, and solution can be found in a nonlinear optimization procedure which yields the estimates for the cluster centers and sizes. This approach for clustering works with data compressed into covariance matrix so can be especially useful for big data.

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

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Zafar, I.; Edirisinghe, E. A.; Acar, S.; Bez, H. E.

2007-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

Quantization of liver tissue in dual kVp computed tomography using linear discriminant analysis

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Tkaczyk, J. Eric; Langan, David; Wu, Xiaoye; Xu, Daniel; Benson, Thomas; Pack, Jed D.; Schmitz, Andrea; Hara, Amy; Palicek, William; Licato, Paul; Leverentz, Jaynne

2009-02-01

Linear discriminate analysis (LDA) is applied to dual kVp CT and used for tissue characterization. The potential to quantitatively model both malignant and benign, hypo-intense liver lesions is evaluated by analysis of portal-phase, intravenous CT scan data obtained on human patients. Masses with an a priori classification are mapped to a distribution of points in basis material space. The degree of localization of tissue types in the material basis space is related to both quantum noise and real compositional differences. The density maps are analyzed with LDA and studied with system simulations to differentiate these factors. The discriminant analysis is formulated so as to incorporate the known statistical properties of the data. Effective kVp separation and mAs relates to precision of tissue localization. Bias in the material position is related to the degree of X-ray scatter and partial-volume effect. Experimental data and simulations demonstrate that for single energy (HU) imaging or image-based decomposition pixel values of water-like tissues depend on proximity to other iodine-filled bodies. Beam-hardening errors cause a shift in image value on the scale of that difference sought between in cancerous and cystic lessons. In contrast, projection-based decomposition or its equivalent when implemented on a carefully calibrated system can provide accurate data. On such a system, LDA may provide novel quantitative capabilities for tissue characterization in dual energy CT.

Robust linear discriminant models to solve financial crisis in banking sectors

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Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Idris, Faoziah; Ali, Hazlina; Omar, Zurni

2014-12-01

Linear discriminant analysis (LDA) is a widely-used technique in patterns classification via an equation which will minimize the probability of misclassifying cases into their respective categories. However, the performance of classical estimators in LDA highly depends on the assumptions of normality and homoscedasticity. Several robust estimators in LDA such as Minimum Covariance Determinant (MCD), S-estimators and Minimum Volume Ellipsoid (MVE) are addressed by many authors to alleviate the problem of non-robustness of the classical estimates. In this paper, we investigate on the financial crisis of the Malaysian banking institutions using robust LDA and classical LDA methods. Our objective is to distinguish the "distress" and "non-distress" banks in Malaysia by using the LDA models. Hit ratio is used to validate the accuracy predictive of LDA models. The performance of LDA is evaluated by estimating the misclassification rate via apparent error rate. The results and comparisons show that the robust estimators provide a better performance than the classical estimators for LDA.

Statistics that learn: can logistic discriminant analysis improve diagnosis in brain SPECT?

International Nuclear Information System (INIS)

Behin-Ain, S.; Barnden, L.; Kwiatek, R.; Del Fante, P.; Casse, R.; Burnet, R.; Chew, G.; Kitchener, M.; Boundy, K.; Unger, S.

2002-01-01

Full text: Logistic discriminant analysis (LDA) is a statistical technique capable of discriminating individuals within a diseased group against normals. It also enables classification of various diseases within a group of patients. This technique provides a quantitative, automated and non-subjective clinical diagnostic tool. Based on a population known to have the disease and a normal control group, an algorithm was developed and trained to identify regions in the human brain responsible for the disease in question. The algorithm outputs a statistical map representing diseased or normal probability on a voxel or cluster basis from which an index is generated for each subject. The algorithm also generates a set of coefficients which is used to generate an index for the purpose of classification of new subjects. The results are comparable and complement those of Statistical Parametric Mapping (SPM) which employs a more common linear discriminant technique. The results are presented for brain SPECT studies of two diseases: chronic fatigue syndrome (CFS) and fibromyalgia (FM). A 100% specificity and 94% sensitivity is achieved for the CFS study (similar to SPM results) and for the FM study 82% specificity and 94% sensitivity is achieved with corresponding SPM results showing 90% specificity and 82% sensitivity. The results encourages application of LDA for discrimination of new single subjects as well as of diseased and normal groups. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc

Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

International Nuclear Information System (INIS)

Arjona, Cristian; Pentácolo, José; Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Rufiner, Leonardo

2011-01-01

The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.

Discriminant forest classification method and system

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Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

2012-11-06

A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

Evaluation of sensory panels of consumers of specialty coffee beverages using the boosting method in discriminant analysis

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Gilberto Rodrigues Liska

2015-12-01

Full Text Available Automatic classification methods have been widely used in numerous situations and the boosting method has become known for use of a classification algorithm, which considers a set of training data and, from that set, constructs a classifier with reweighted versions of the training set. Given this characteristic, the aim of this study is to assess a sensory experiment related to acceptance tests with specialty coffees, with reference to both trained and untrained consumer groups. For the consumer group, four sensory characteristics were evaluated, such as aroma, body, sweetness, and final score, attributed to four types of specialty coffees. In order to obtain a classification rule that discriminates trained and untrained tasters, we used the conventional Fisher’s Linear Discriminant Analysis (LDA and discriminant analysis via boosting algorithm (AdaBoost. The criteria used in the comparison of the two approaches were sensitivity, specificity, false positive rate, false negative rate, and accuracy of classification methods. Additionally, to evaluate the performance of the classifiers, the success rates and error rates were obtained by Monte Carlo simulation, considering 100 replicas of a random partition of 70% for the training set, and the remaining for the test set. It was concluded that the boosting method applied to discriminant analysis yielded a higher sensitivity rate in regard to the trained panel, at a value of 80.63% and, hence, reduction in the rate of false negatives, at 19.37%. Thus, the boosting method may be used as a means of improving the LDA classifier for discrimination of trained tasters.

Discrimination of lymphoma using laser-induced breakdown spectroscopy conducted on whole blood samples

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Chen, Xue; Li, Xiaohui; Yang, Sibo; Yu, Xin; Liu, Aichun

2018-01-01

Lymphoma is a significant cancer that affects the human lymphatic and hematopoietic systems. In this work, discrimination of lymphoma using laser-induced breakdown spectroscopy (LIBS) conducted on whole blood samples is presented. The whole blood samples collected from lymphoma patients and healthy controls are deposited onto standard quantitative filter papers and ablated with a 1064 nm Q-switched Nd:YAG laser. 16 atomic and ionic emission lines of calcium (Ca), iron (Fe), magnesium (Mg), potassium (K) and sodium (Na) are selected to discriminate the cancer disease. Chemometric methods, including principal component analysis (PCA), linear discriminant analysis (LDA) classification, and k nearest neighbor (kNN) classification are used to build the discrimination models. Both LDA and kNN models have achieved very good discrimination performances for lymphoma, with an accuracy of over 99.7%, a sensitivity of over 0.996, and a specificity of over 0.997. These results demonstrate that the whole-blood-based LIBS technique in combination with chemometric methods can serve as a fast, less invasive, and accurate method for detection and discrimination of human malignancies. PMID:29541503

Fast Depiction Invariant Visual Similarity for Content Based Image Retrieval Based on Data-driven Visual Similarity using Linear Discriminant Analysis

Science.gov (United States)

Wihardi, Y.; Setiawan, W.; Nugraha, E.

2018-01-01

On this research we try to build CBIRS based on Learning Distance/Similarity Function using Linear Discriminant Analysis (LDA) and Histogram of Oriented Gradient (HoG) feature. Our method is invariant to depiction of image, such as similarity of image to image, sketch to image, and painting to image. LDA can decrease execution time compared to state of the art method, but it still needs an improvement in term of accuracy. Inaccuracy in our experiment happen because we did not perform sliding windows search and because of low number of negative samples as natural-world images.

Pulse shape discrimination and classification methods for continuous depth of interaction encoding PET detectors

International Nuclear Information System (INIS)

Roncali, Emilie; Phipps, Jennifer E; Marcu, Laura; Cherry, Simon R

2012-01-01

In previous work we demonstrated the potential of positron emission tomography (PET) detectors with depth-of-interaction (DOI) encoding capability based on phosphor-coated crystals. A DOI resolution of 8 mm full-width at half-maximum was obtained for 20 mm long scintillator crystals using a delayed charge integration linear regression method (DCI-LR). Phosphor-coated crystals modify the pulse shape to allow continuous DOI information determination, but the relationship between pulse shape and DOI is complex. We are therefore interested in developing a sensitive and robust method to estimate the DOI. Here, linear discriminant analysis (LDA) was implemented to classify the events based on information extracted from the pulse shape. Pulses were acquired with 2×2×20 mm 3 phosphor-coated crystals at five irradiation depths and characterized by their DCI values or Laguerre coefficients. These coefficients were obtained by expanding the pulses on a Laguerre basis set and constituted a unique signature for each pulse. The DOI of individual events was predicted using LDA based on Laguerre coefficients (Laguerre-LDA) or DCI values (DCI-LDA) as discriminant features. Predicted DOIs were compared to true irradiation depths. Laguerre-LDA showed higher sensitivity and accuracy than DCI-LDA and DCI-LR and was also more robust to predict the DOI of pulses with higher statistical noise due to low light levels (interaction depths further from the photodetector face). This indicates that Laguerre-LDA may be more suitable to DOI estimation in smaller crystals where lower collected light levels are expected. This novel approach is promising for calculating DOI using pulse shape discrimination in single-ended readout depth-encoding PET detectors. (paper)

Discrimination of rectal cancer through human serum using surface-enhanced Raman spectroscopy

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Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Zhang, Su; Jin, Lili

2015-05-01

In this paper, surface-enhanced Raman spectroscopy (SERS) was used to detect the changes in blood serum components that accompany rectal cancer. The differences in serum SERS data between rectal cancer patients and healthy controls were examined. Postoperative rectal cancer patients also participated in the comparison to monitor the effects of cancer treatments. The results show that there are significant variations at certain wavenumbers which indicates alteration of corresponding biological substances. Principal component analysis (PCA) and parameters of intensity ratios were used on the original SERS spectra for the extraction of featured variables. These featured variables then underwent linear discriminant analysis (LDA) and classification and regression tree (CART) for the discrimination analysis. Accuracies of 93.5 and 92.4 % were obtained for PCA-LDA and parameter-CART, respectively.

LDA measurements and turbulence spectral analysis in an agitated vessel

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Chára Zdeněk

2013-04-01

Full Text Available During the last years considerable improvement of the derivation of turbulence power spectrum from Laser Doppler Anemometry (LDA has been achieved. The irregularly sampled LDA data is proposed to approximate by several methods e.g. Lomb-Scargle method, which estimates amplitude and phase of spectral lines from missing data, methods based on the reconstruction of the auto-correlation function (referred to as correlation slotting technique, methods based on the reconstruction of the time series using interpolation between the uneven sampling and subsequent resampling etc. These different methods were used on the LDA data measured in an agitated vessel and the results of the power spectrum calculations were compared. The measurements were performed in the mixing vessel with flat bottom. The vessel was equipped with four baffles and agitated with a six-blade pitched blade impeller. Three values of the impeller speed (Reynolds number were tested. Long time series of the axial velocity component were measured in selected points. In each point the time series were analyzed and evaluated in a form of power spectrum.

LDA measurements and turbulence spectral analysis in an agitated vessel

Science.gov (United States)

Kysela, Bohuš; Konfršt, Jiří; Chára, Zdeněk

2013-04-01

During the last years considerable improvement of the derivation of turbulence power spectrum from Laser Doppler Anemometry (LDA) has been achieved. The irregularly sampled LDA data is proposed to approximate by several methods e.g. Lomb-Scargle method, which estimates amplitude and phase of spectral lines from missing data, methods based on the reconstruction of the auto-correlation function (referred to as correlation slotting technique), methods based on the reconstruction of the time series using interpolation between the uneven sampling and subsequent resampling etc. These different methods were used on the LDA data measured in an agitated vessel and the results of the power spectrum calculations were compared. The measurements were performed in the mixing vessel with flat bottom. The vessel was equipped with four baffles and agitated with a six-blade pitched blade impeller. Three values of the impeller speed (Reynolds number) were tested. Long time series of the axial velocity component were measured in selected points. In each point the time series were analyzed and evaluated in a form of power spectrum.

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.

Science.gov (United States)

Wang, Shunfang; Liu, Shuhui

2015-12-19

An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

Principle component analysis and linear discriminant analysis of multi-spectral autofluorescence imaging data for differentiating basal cell carcinoma and healthy skin

Science.gov (United States)

Chernomyrdin, Nikita V.; Zaytsev, Kirill I.; Lesnichaya, Anastasiya D.; Kudrin, Konstantin G.; Cherkasova, Olga P.; Kurlov, Vladimir N.; Shikunova, Irina A.; Perchik, Alexei V.; Yurchenko, Stanislav O.; Reshetov, Igor V.

2016-09-01

In present paper, an ability to differentiate basal cell carcinoma (BCC) and healthy skin by combining multi-spectral autofluorescence imaging, principle component analysis (PCA), and linear discriminant analysis (LDA) has been demonstrated. For this purpose, the experimental setup, which includes excitation and detection branches, has been assembled. The excitation branch utilizes a mercury arc lamp equipped with a 365-nm narrow-linewidth excitation filter, a beam homogenizer, and a mechanical chopper. The detection branch employs a set of bandpass filters with the central wavelength of spectral transparency of λ = 400, 450, 500, and 550 nm, and a digital camera. The setup has been used to study three samples of freshly excised BCC. PCA and LDA have been implemented to analyze the data of multi-spectral fluorescence imaging. Observed results of this pilot study highlight the advantages of proposed imaging technique for skin cancer diagnosis.

Weighted piecewise LDA for solving the small sample size problem in face verification.

Science.gov (United States)

Kyperountas, Marios; Tefas, Anastasios; Pitas, Ioannis

2007-03-01

A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of weighted piecewise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than the one produced by the traditional linear discriminant analysis (LDA) methodology. The expected classification ability of this method is investigated throughout a series of simulations. The second phase defines proper combinations for person-specific similarity scores and describes an outlier removal process that further enhances the classification ability. The proposed technique has been tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the face-verification performance.

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

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Lijun Yao

2018-03-01

Full Text Available The moving-window bis-correlation coefficients (MW-BiCC was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and β-thalassemia with visible and near-infrared (Vis–NIR spectroscopy. The well-performed moving-window principal component analysis linear discriminant analysis (MW-PCA–LDA was also conducted for comparison. A total of 306 transgenic (positive and 150 nontransgenic (negative leave samples of sugarcane were collected and divided to calibration, prediction, and validation. The diffuse reflection spectra were corrected using Savitzky–Golay (SG smoothing with first-order derivative (d=1, third-degree polynomial (p=3 and 25 smoothing points (m=25. The selected waveband was 736–1054nm with MW-BiCC, and the positive and negative validation recognition rates (V_REC+, V_REC− were 100%, 98.0%, which achieved the same effect as MW-PCA–LDA. Another example, the 93 β-thalassemia (positive and 148 nonthalassemia (negative of human hemolytic samples were collected. The transmission spectra were corrected using SG smoothing with d=1, p=3 and m=53. Using MW-BiCC, many best wavebands were selected (e.g., 1116–1146, 1794–1848 and 2284–2342nm. The V_REC+ and V_REC− were both 100%, which achieved the same effect as MW-PCA–LDA. Importantly, the BiCC only required calculating correlation coefficients between the spectrum of prediction sample and the average spectra of two types of calibration samples. Thus, BiCC was very simple in algorithm, and expected to obtain more applications. The results first confirmed the feasibility of distinguishing β-thalassemia and normal control samples by NIR spectroscopy, and provided a promising simple tool for large population thalassemia screening.

Liquid contrabands classification based on energy dispersive X-ray diffraction and hybrid discriminant analysis

International Nuclear Information System (INIS)

YangDai, Tianyi; Zhang, Li

2016-01-01

Energy dispersive X-ray diffraction (EDXRD) combined with hybrid discriminant analysis (HDA) has been utilized for classifying the liquid materials for the first time. The XRD spectra of 37 kinds of liquid contrabands and daily supplies were obtained using an EDXRD test bed facility. The unique spectra of different samples reveal XRD's capability to distinguish liquid contrabands from daily supplies. In order to create a system to detect liquid contrabands, the diffraction spectra were subjected to HDA which is the combination of principal components analysis (PCA) and linear discriminant analysis (LDA). Experiments based on the leave-one-out method demonstrate that HDA is a practical method with higher classification accuracy and lower noise sensitivity than the other methods in this application. The study shows the great capability and potential of the combination of XRD and HDA for liquid contrabands classification.

Liquid contrabands classification based on energy dispersive X-ray diffraction and hybrid discriminant analysis

Energy Technology Data Exchange (ETDEWEB)

YangDai, Tianyi [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education (China); Zhang, Li, E-mail: zhangli@nuctech.com [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education (China)

2016-02-01

Energy dispersive X-ray diffraction (EDXRD) combined with hybrid discriminant analysis (HDA) has been utilized for classifying the liquid materials for the first time. The XRD spectra of 37 kinds of liquid contrabands and daily supplies were obtained using an EDXRD test bed facility. The unique spectra of different samples reveal XRD's capability to distinguish liquid contrabands from daily supplies. In order to create a system to detect liquid contrabands, the diffraction spectra were subjected to HDA which is the combination of principal components analysis (PCA) and linear discriminant analysis (LDA). Experiments based on the leave-one-out method demonstrate that HDA is a practical method with higher classification accuracy and lower noise sensitivity than the other methods in this application. The study shows the great capability and potential of the combination of XRD and HDA for liquid contrabands classification.

Liquid contrabands classification based on energy dispersive X-ray diffraction and hybrid discriminant analysis

Science.gov (United States)

YangDai, Tianyi; Zhang, Li

2016-02-01

Energy dispersive X-ray diffraction (EDXRD) combined with hybrid discriminant analysis (HDA) has been utilized for classifying the liquid materials for the first time. The XRD spectra of 37 kinds of liquid contrabands and daily supplies were obtained using an EDXRD test bed facility. The unique spectra of different samples reveal XRD's capability to distinguish liquid contrabands from daily supplies. In order to create a system to detect liquid contrabands, the diffraction spectra were subjected to HDA which is the combination of principal components analysis (PCA) and linear discriminant analysis (LDA). Experiments based on the leave-one-out method demonstrate that HDA is a practical method with higher classification accuracy and lower noise sensitivity than the other methods in this application. The study shows the great capability and potential of the combination of XRD and HDA for liquid contrabands classification.

Application of linear discriminant analysis and Attenuated Total Reflectance Fourier Transform Infrared microspectroscopy for diagnosis of colon cancer.

Science.gov (United States)

Khanmohammadi, Mohammadreza; Bagheri Garmarudi, Amir; Samani, Simin; Ghasemi, Keyvan; Ashuri, Ahmad

2011-06-01

Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) microspectroscopy was applied for detection of colon cancer according to the spectral features of colon tissues. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. A total of 78 colon tissues were used in spectroscopy studies. Major spectral differences were observed in 1,740-900 cm(-1) spectral region. Several chemometric methods such as analysis of variance (ANOVA), cluster analysis (CA) and linear discriminate analysis (LDA) were applied for classification of IR spectra. Utilizing the chemometric techniques, clear and reproducible differences were observed between the spectra of normal and cancer cases, suggesting that infrared microspectroscopy in conjunction with spectral data processing would be useful for diagnostic classification. Using LDA technique, the spectra were classified into cancer and normal tissue classes with an accuracy of 95.8%. The sensitivity and specificity was 100 and 93.1%, respectively.

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

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Shunfang Wang

2015-12-01

Full Text Available An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC, pseudo-amino acid composition (PseAAC and position specific scoring matrix (PSSM, are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

Why Does Rebalancing Class-Unbalanced Data Improve AUC for Linear Discriminant Analysis?

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Xue, Jing-Hao; Hall, Peter

2015-05-01

Many established classifiers fail to identify the minority class when it is much smaller than the majority class. To tackle this problem, researchers often first rebalance the class sizes in the training dataset, through oversampling the minority class or undersampling the majority class, and then use the rebalanced data to train the classifiers. This leads to interesting empirical patterns. In particular, using the rebalanced training data can often improve the area under the receiver operating characteristic curve (AUC) for the original, unbalanced test data. The AUC is a widely-used quantitative measure of classification performance, but the property that it increases with rebalancing has, as yet, no theoretical explanation. In this note, using Gaussian-based linear discriminant analysis (LDA) as the classifier, we demonstrate that, at least for LDA, there is an intrinsic, positive relationship between the rebalancing of class sizes and the improvement of AUC. We show that the largest improvement of AUC is achieved, asymptotically, when the two classes are fully rebalanced to be of equal sizes.

Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

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Khattab M.Ali Alheeti

2017-08-01

Full Text Available Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoc networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DoS and black hole attacks on vehicular ad hoc networks (VANETs. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA and Quadratic Discriminant Analysis (QDA which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.

Classification of Surface and Deep Soil Samples Using Linear Discriminant Analysis

International Nuclear Information System (INIS)

Wasim, M.; Ali, M.; Daud, M.

2015-01-01

A statistical analysis was made of the activity concentrations measured in surface and deep soil samples for natural and anthropogenic gamma-emitting radionuclides. Soil samples were obtained from 48 different locations in Gilgit, Pakistan covering about 50 km/sup 2/ areas at an average altitude of 1550 m above sea level. From each location two samples were collected: one from the top soil (2-6 cm) and another from a depth of 6-10 cm. Four radionuclides including /sup 226/Ra, /sup 232/Th, /sup 40/K and /sup 137/Cs were quantified. The data was analyzed using t-test to find out activity concentration difference between the surface and depth samples. At the surface, the median activity concentrations were 23.7, 29.1, 4.6 and 115 Bq kg/sup -1/ for 226Ra, 232Th, 137Cs and 40K respectively. For the same radionuclides, the activity concentrations were respectively 25.5, 26.2, 2.9 and 191 Bq kg/sup -1/ for the depth samples. Principal component analysis (PCA) was applied to explore patterns within the data. A positive significant correlation was observed between the radionuclides /sup 226/Ra and /sup 232/Th. The data from PCA was further utilized in linear discriminant analysis (LDA) for the classification of surface and depth samples. LDA classified surface and depth samples with good predictability. (author)

Discrimination of wine from grape cultivated in Japan, imported wine, and others by multi-elemental analysis.

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Shimizu, Hideaki; Akamatsu, Fumikazu; Kamada, Aya; Koyama, Kazuya; Okuda, Masaki; Fukuda, Hisashi; Iwashita, Kazuhiro; Goto-Yamamoto, Nami

2018-04-01

Differences in mineral concentrations were examined among three types of wine in the Japanese market place: Japan wine, imported wine, and domestically produced wine mainly from foreign ingredients (DWF), where Japan wine has been recently defined by the National Tax Agency as domestically produced wine from grapes cultivated in Japan. The main objective of this study was to examine the possibility of controlling the authenticity of Japan wine. The concentrations of 18 minerals (Li, B, Na, Mg, Si, P, S, K, Ca, Mn, Co, Ni, Ga, Rb, Sr, Mo, Ba, and Pb) in 214 wine samples were determined by inductively coupled-plasma mass spectrometry (ICP-MS) and ICP-atomic emission spectrometry (ICP-AES). In general, Japan wine had a higher concentration of potassium and lower concentrations of eight elements (Li, B, Na, Si, S, Co, Sr, and Pb) as compared with the other two groups of wine. Linear discriminant analysis (LDA) models based on concentrations of the 18 minerals facilitated the identification of three wine groups: Japan wine, imported wine, and DWF with a 91.1% classification score and 87.9% prediction score. In addition, an LDA model for discrimination of wine from four domestic geographic origins (Yamanashi, Nagano, Hokkaido, and Yamagata Prefectures) using 18 elements gave a classification score of 93.1% and a prediction score of 76.4%. In summary, we have shown that an LDA model based on mineral concentrations is useful for distinguishing Japan wine from other wine groups, and can contribute to classification of the four main domestic wine-producing regions of Japan. Copyright © 2017 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

On the use of LDA performance as a metric of feature extraction methods for a P300 BCI classification task

International Nuclear Information System (INIS)

Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Bañuelos, Verónica Medina; Rufiner, Leonardo

2011-01-01

Brain computer interfaces (BCIs) translate brain activity into computer commands. To enhance the performance of a BCI, it is necessary to improve the feature extraction techniques being applied to decode the users' intentions. Objective comparison methods are needed to analyze different feature extraction techniques. One possibility is to use the classifier performance as a comparative measure. In this work the effect of several variables that affect the behaviour of linear discriminant analysis (LDA) has been studied when used to distinguish between electroencephalographic signals with and without the presence of event related potentials (ERPs). The error rate (ER) and the area under the receiver operating characteristic curve (AUC) were used as performance estimators of LDA. The results show that the number of characteristics, the degree of balance of the training patterns set and the number of averaged trials affect the classifier's performance and therefore, must be considered in the design of the integrated system.

A study on native defects and magnetic properties in undoped rutile TiO2 using LDA and LDA+UO p+UTi d methods

Science.gov (United States)

Shi, Li-Bin; Wang, Yong Ping

2016-05-01

The native defects and magnetic properties in undoped rutile TiO2 are studied using local density approximation (LDA) and LDA adding Hubbard parameters (U) schemes. The band gap is adjusted to experimental value of 3.0 eV by combination of UTi d=4.2 eV and UO p=4.8 eV. This LDA+U methodology overcomes the band-gap problem and renders the approach more predictive. The formation energies of oxygen vacancy (VO), oxygen interstitial (Oi), titanium vacancy (VTi), titanium interstitial (Tii), oxygen anti-sites (OTi), and titanium anti-sites (TiO) are investigated by the LDA and LDA+U methods. In addition, some ground state configurations can be obtained by optimization of total spin. It is found that native defects can induce spin polarization and produce magnetic moment.

Tile-Level Annotation of Satellite Images Using Multi-Level Max-Margin Discriminative Random Field

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Hong Sun

2013-05-01

Full Text Available This paper proposes a multi-level max-margin discriminative analysis (M3DA framework, which takes both coarse and fine semantics into consideration, for the annotation of high-resolution satellite images. In order to generate more discriminative topic-level features, the M3DA uses the maximum entropy discrimination latent Dirichlet Allocation (MedLDA model. Moreover, for improving the spatial coherence of visual words neglected by M3DA, conditional random field (CRF is employed to optimize the soft label field composed of multiple label posteriors. The framework of M3DA enables one to combine word-level features (generated by support vector machines and topic-level features (generated by MedLDA via the bag-of-words representation. The experimental results on high-resolution satellite images have demonstrated that, using the proposed method can not only obtain suitable semantic interpretation, but also improve the annotation performance by taking into account the multi-level semantics and the contextual information.

Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

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Santoso, Noviyanti; Wibowo, Wahyu

2018-03-01

A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

Application of PCA-LDA method to determine the geographical origin of tea based on determination of stable isotopes and multi-elements

International Nuclear Information System (INIS)

Yuan Yuwei; Zhang Yongzhi; Yang Guiling; Zhang Zhiheng; Fu Haiyan; Han Wenyan; Li Shufang

2013-01-01

The ratio of stable isotope and concentration of multi-element in tea was determinated with isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition techniques with principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify the geographical origins of tea from Fujian, Shandong and Zhejiang province, and Yuyao, Jinhua and Xihu region of Zhejiang. The results showed the values of δ"1"5N, δ"1"3C, δD, δ"1"8O and the ratios of "2"0"6Pb/"2"0"7Pb, "2"0"8Pb/"2"0"6Pb and "8"7Sr/"8"6Sr in tea samples were different from different origins. There was also large variable for the concentrations of 27 mineral elements, such as Li, Be, Na and so on, with a specific character of origin. The method of PCA could be used to classify the geographical origin of tea from different origins but with a cross in the scatter plot. However, PCA combining with LDA could gave correct assignation percentages of 99% for the tea samples among Fujian, Shandong and Zhejiang provinces, and 87% for the tea samples among Yuyao, Jinhua and Xihu region of Zhejiang. These results revealed that it was possible and feasible to classify the geographical origin of tea by the method of PCA-LDA based on the determination of isotopes and multi-elements. (authors)

The classification of lung cancers and their degree of malignancy by FTIR, PCA-LDA analysis, and a physics-based computational model.

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Kaznowska, E; Depciuch, J; Łach, K; Kołodziej, M; Koziorowska, A; Vongsvivut, J; Zawlik, I; Cholewa, M; Cebulski, J

2018-08-15

Lung cancer has the highest mortality rate of all malignant tumours. The current effects of cancer treatment, as well as its diagnostics, are unsatisfactory. Therefore it is very important to introduce modern diagnostic tools, which will allow for rapid classification of lung cancers and their degree of malignancy. For this purpose, the authors propose the use of Fourier Transform InfraRed (FTIR) spectroscopy combined with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) and a physics-based computational model. The results obtained for lung cancer tissues, adenocarcinoma and squamous cell carcinoma FTIR spectra, show a shift in wavenumbers compared to control tissue FTIR spectra. Furthermore, in the FTIR spectra of adenocarcinoma there are no peaks corresponding to glutamate or phospholipid functional groups. Moreover, in the case of G2 and G3 malignancy of adenocarcinoma lung cancer, the absence of an OH groups peak was noticed. Thus, it seems that FTIR spectroscopy is a valuable tool to classify lung cancer and to determine the degree of its malignancy. Copyright © 2018 Elsevier B.V. All rights reserved.

Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

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Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

Applying linear discriminant analysis to predict groundwater redox conditions conducive to denitrification

Science.gov (United States)

Wilson, S. R.; Close, M. E.; Abraham, P.

2018-01-01

Diffuse nitrate losses from agricultural land pollute groundwater resources worldwide, but can be attenuated under reducing subsurface conditions. In New Zealand, the ability to predict where groundwater denitrification occurs is important for understanding the linkage between land use and discharges of nitrate-bearing groundwater to streams. This study assesses the application of linear discriminant analysis (LDA) for predicting groundwater redox status for Southland, a major dairy farming region in New Zealand. Data cases were developed by assigning a redox status to samples derived from a regional groundwater quality database. Pre-existing regional-scale geospatial databases were used as training variables for the discriminant functions. The predictive accuracy of the discriminant functions was slightly improved by optimising the thresholds between sample depth classes. The models predict 23% of the region as being reducing at shallow depths (water table, and low-permeability clastic sediments. The coastal plains are an area of widespread groundwater discharge, and the soil and hydrology characteristics require the land to be artificially drained to render the land suitable for farming. For the improvement of water quality in coastal areas, it is therefore important that land and water management efforts focus on understanding hydrological bypassing that may occur via artificial drainage systems.

Comparative study on fast classification of brick samples by combination of principal component analysis and linear discriminant analysis using stand-off and table-top laser-induced breakdown spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Vítková, Gabriela [Department of Chemistry, Faculty of Science, Masaryk University, Kamenice 5/A14, 625 00 Brno (Czech Republic); Prokeš, Lubomír [Department of Chemistry, Faculty of Science, Masaryk University, Kamenice 5/A14, 625 00 Brno (Czech Republic); Department of Physical Electronics, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno (Czech Republic); CEPLANT, R and D Center for Low-Cost Plasma and Nanotechnology Surface Modifications, Masaryk University, Kotlářská 2, 611 37 Brno (Czech Republic); Novotný, Karel [Department of Chemistry, Faculty of Science, Masaryk University, Kamenice 5/A14, 625 00 Brno (Czech Republic); CEITEC — Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno (Czech Republic); Pořízka, Pavel [Institute of Physical Engineering, Faculty of Mechanical Engineering, Brno University of Technology, Technická 2896/2, 616 69 Brno (Czech Republic); Novotný, Jan [CEITEC — Central European Institute of Technology, Brno University of Technology, Technická 3058/10, 616 00 Brno (Czech Republic); Všianský, Dalibor [Department of Geological Sciences, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno (Czech Republic); Čelko, Ladislav [CEITEC — Central European Institute of Technology, Brno University of Technology, Technická 3058/10, 616 00 Brno (Czech Republic); and others

2014-11-01

Focusing on historical aspect, during archeological excavation or restoration works of buildings or different structures built from bricks it is important to determine, preferably in-situ and in real-time, the locality of bricks origin. Fast classification of bricks on the base of Laser-Induced Breakdown Spectroscopy (LIBS) spectra is possible using multivariate statistical methods. Combination of principal component analysis (PCA) and linear discriminant analysis (LDA) was applied in this case. LIBS was used to classify altogether the 29 brick samples from 7 different localities. Realizing comparative study using two different LIBS setups — stand-off and table-top it is shown that stand-off LIBS has a big potential for archeological in-field measurements. - Highlights: • Comparison of two potentially field-deployable LIBS setups is introduced. • The aim is classification of LIBS spectra of brick samples into several localities. • LDA filled with PCA scores is proposed as an effective classification method. • An influence of brick firing temperature on LIBS spectra is discovered and examined.

On the Use of Shortwave Infrared for Tree Species Discrimination in Tropical Semideciduous Forest

Science.gov (United States)

Ferreira, M. P.; Zortea, M.; Zanotta, D. C.; Féret, J. B.; Shimabukuro, Y. E.; Souza Filho, C. R.

2015-08-01

Tree species mapping in tropical forests provides valuable insights for forest managers. Keystone species can be located for collection of seeds for forest restoration, reducing fieldwork costs. However, mapping of tree species in tropical forests using remote sensing data is a challenge due to high floristic and spectral diversity. Little is known about the use of different spectral regions as most of studies performed so far used visible/near-infrared (390-1000 nm) features. In this paper we show the contribution of shortwave infrared (SWIR, 1045-2395 nm) for tree species discrimination in a tropical semideciduous forest. Using high-resolution hyperspectral data we also simulated WorldView-3 (WV-3) multispectral bands for classification purposes. Three machine learning methods were tested to discriminate species at the pixel-level: Linear Discriminant Analysis (LDA), Support Vector Machines with Linear (L-SVM) and Radial Basis Function (RBF-SVM) kernels, and Random Forest (RF). Experiments were performed using all and selected features from the VNIR individually and combined with SWIR. Feature selection was applied to evaluate the effects of dimensionality reduction and identify potential wavelengths that may optimize species discrimination. Using VNIR hyperspectral bands, RBF-SVM achieved the highest average accuracy (77.4%). Inclusion of the SWIR increased accuracy to 85% with LDA. The same pattern was also observed when WV-3 simulated channels were used to classify the species. The VNIR bands provided and accuracy of 64.2% for LDA, which was increased to 79.8 % using the new SWIR bands that are operationally available in this platform. Results show that incorporating SWIR bands increased significantly average accuracy for both the hyperspectral data and WorldView-3 simulated bands.

A novel method for qualitative analysis of edible oil oxidation using an electronic nose.

Science.gov (United States)

Xu, Lirong; Yu, Xiuzhu; Liu, Lei; Zhang, Rui

2016-07-01

An electronic nose (E-nose) was used for rapid assessment of the degree of oxidation in edible oils. Peroxide and acid values of edible oil samples were analyzed using data obtained by the American Oil Chemists' Society (AOCS) Official Method for reference. Qualitative discrimination between non-oxidized and oxidized oils was conducted using the E-nose technique developed in combination with cluster analysis (CA), principal component analysis (PCA), and linear discriminant analysis (LDA). The results from CA, PCA and LDA indicated that the E-nose technique could be used for differentiation of non-oxidized and oxidized oils. LDA produced slightly better results than CA and PCA. The proposed approach can be used as an alternative to AOCS Official Method as an innovative tool for rapid detection of edible oil oxidation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose

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Abbas Gorji-Chakespari

2016-05-01

Full Text Available Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP and linear discriminant analysis (LDA algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world using an electronic nose (EN system based on seven metal oxide semiconductor (MOS sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively based on the total percent of the most crucial qualitative compounds. An ad-hoc electronic nose (EN system was implemented to sense the samples and acquire signals. Forty-nine features were extracted from the EN sensor matrix (seven parameters to describe each sensor curve response. The extracted features were ordered in relevance by the intra/inter variance criterion (Vr, also known as the Fisher discriminant. A leave-one-out cross validation technique was implemented for estimating the classification accuracy reached by both algorithms. Success rates were calculated using 10, 20, 30, and the entire selected features from the response of the sensor array. The results revealed a maximum classification accuracy of 99% when applying the Fuzzy ARTMAP algorithm and 82% for LDA, using the first 10 features in both cases. Further classification results explained that sub-optimal performance is likely to occur when all the response features are applied. It was found that an electronic nose system employing a Fuzzy ARTMAP classifier could become an accurate, easy, and inexpensive alternative tool for qualitative control in the production of Rosa damascene essential oil.

Tracking topic birth and death in LDA.

Energy Technology Data Exchange (ETDEWEB)

Wilson, Andrew T.; Robinson, David Gerald

2011-09-01

Most topic modeling algorithms that address the evolution of documents over time use the same number of topics at all times. This obscures the common occurrence in the data where new subjects arise and old ones diminish or disappear entirely. We propose an algorithm to model the birth and death of topics within an LDA-like framework. The user selects an initial number of topics, after which new topics are created and retired without further supervision. Our approach also accommodates many of the acceleration and parallelization schemes developed in recent years for standard LDA. In recent years, topic modeling algorithms such as latent semantic analysis (LSA)[17], latent Dirichlet allocation (LDA)[10] and their descendants have offered a powerful way to explore and interrogate corpora far too large for any human to grasp without assistance. Using such algorithms we are able to search for similar documents, model and track the volume of topics over time, search for correlated topics or model them with a hierarchy. Most of these algorithms are intended for use with static corpora where the number of documents and the size of the vocabulary are known in advance. Moreover, almost all current topic modeling algorithms fix the number of topics as one of the input parameters and keep it fixed across the entire corpus. While this is appropriate for static corpora, it becomes a serious handicap when analyzing time-varying data sets where topics come and go as a matter of course. This is doubly true for online algorithms that may not have the option of revising earlier results in light of new data. To be sure, these algorithms will account for changing data one way or another, but without the ability to adapt to structural changes such as entirely new topics they may do so in counterintuitive ways.

Spectral Discrimination of Vegetation Classes in Ice-Free Areas of Antarctica

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María Calviño-Cancela

2016-10-01

Full Text Available Detailed monitoring of vegetation changes in ice-free areas of Antarctica is crucial to determine the effects of climate warming and increasing human presence in this vulnerable ecosystem. Remote sensing techniques are especially suitable in this distant and rough environment, with high spectral and spatial resolutions needed owing to the patchiness and similarity between vegetation elements. We analyze the reflectance spectra of the most representative vegetation elements in ice-free areas of Antarctica to assess the potential for discrimination. This research is aimed as a basis for future aircraft/satellite research for long-term vegetation monitoring. The study was conducted in the Barton Peninsula, King George Island. The reflectance of ground patches of different types of vegetation or bare ground (c. 0.25 m 2 , n = 30 patches per class was recorded with a spectrophotometer measuring between 340 nm to 1025 nm at a resolution of 0.38 n m . We used Linear Discriminant Analysis (LDA to classify the cover classes according to reflectance spectra, after reduction of the number of bands using Principal Component Analysis (PCA. The first five principal components explained an accumulated 99.4% of the total variance and were added to the discriminant function. The LDA classification resulted in c. 92% of cases correctly classified (a hit ratio 11.9 times greater than chance. The most important region for discrimination was the visible and near ultraviolet (UV, with the relative importance of spectral bands steeply decreasing in the Near Infra-Red (NIR region. Our study shows the feasibility of discriminating among representative taxa of Antarctic vegetation using their spectral patterns in the near UV, visible and NIR. The results are encouraging for hyperspectral vegetation mapping in Antarctica, which could greatly facilitate monitoring vegetation changes in response to a changing environment, reducing the costs and environmental impacts of

Linear Discriminant Analysis achieves high classification accuracy for the BOLD fMRI response to naturalistic movie stimuli.

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Hendrik eMandelkow

2016-03-01

Full Text Available Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI. However, conventional fMRI analysis based on statistical parametric mapping (SPM and the general linear model (GLM is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA, have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbour (NN, Gaussian Naïve Bayes (GNB, and (regularised Linear Discriminant Analysis (LDA in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie.Results show that LDA regularised by principal component analysis (PCA achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2s apart during a 300s movie (chance level 0.7% = 2s/300s. The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these

Application of FT-IR spectroscopy on breast cancer serum analysis

Science.gov (United States)

Elmi, Fatemeh; Movaghar, Afshin Fayyaz; Elmi, Maryam Mitra; Alinezhad, Heshmatollah; Nikbakhsh, Novin

2017-12-01

Breast cancer is regarded as the most malignant tumor among women throughout the world. Therefore, early detection and proper diagnostic methods have been known to help save women's lives. Fourier Transform Infrared (FT-IR) spectroscopy, coupled with PCA-LDA analysis, is a new technique to investigate the characteristics of serum in breast cancer. In this study, 43 breast cancer and 43 healthy serum samples were collected, and the FT-IR spectra were recorded for each one. Then, PCA analysis and linear discriminant analysis (LDA) were used to analyze the spectral data. The results showed that there were differences between the spectra of the two groups. Discriminating wavenumbers were associated with several spectral differences over the 950-1200 cm- 1(sugar), 1190-1350 cm- 1 (collagen), 1475-1710 cm- 1 (protein), 1710-1760 cm- 1 (ester), 2800-3000 cm- 1 (stretching motions of -CH2 & -CH3), and 3090-3700 cm- 1 (NH stretching) regions. PCA-LDA performance on serum IR could recognize changes between the control and the breast cancer cases. The diagnostic accuracy, sensitivity, and specificity of PCA-LDA analysis for 3000-3600 cm- 1 (NH stretching) were found to be 83%, 84%, 74% for the control and 80%, 76%, 72% for the breast cancer cases, respectively. The results showed that the major spectral differences between the two groups were related to the differences in protein conformation in serum samples. It can be concluded that FT-IR spectroscopy, together with multivariate data analysis, is able to discriminate between breast cancer and healthy serum samples.

Characteristic fingerprint based on gingerol derivative analysis for discrimination of ginger (Zingiber officinale) according to geographical origin using HPLC-DAD combined with chemometrics.

Science.gov (United States)

Yudthavorasit, Soparat; Wongravee, Kanet; Leepipatpiboon, Natchanun

2014-09-01

Chromatographic fingerprints of gingers from five different ginger-producing countries (China, India, Malaysia, Thailand and Vietnam) were newly established to discriminate the origin of ginger. The pungent bioactive principles of ginger, gingerols and six other gingerol-related compounds were determined and identified. Their variations in HPLC profiles create the characteristic pattern of each origin by employing similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and linear discriminant analysis (LDA). As results, the ginger profiles tended to be grouped and separated on the basis of the geographical closeness of the countries of origin. An effective mathematical model with high predictive ability was obtained and chemical markers for each origin were also identified as the characteristic active compounds to differentiate the ginger origin. The proposed method is useful for quality control of ginger in case of origin labelling and to assess food authenticity issues. Copyright © 2014 Elsevier Ltd. All rights reserved.

An example of multidimensional analysis: Discriminant analysis

International Nuclear Information System (INIS)

Lutz, P.

1990-01-01

Among the approaches on the data multi-dimensional analysis, lectures on the discriminant analysis including theoretical and practical aspects are presented. The discrimination problem, the analysis steps and the discrimination categories are stressed. Examples on the descriptive historical analysis, the discrimination for decision making, the demonstration and separation of the top quark are given. In the linear discriminant analysis the following subjects are discussed: Huyghens theorem, projection, discriminant variable, geometrical interpretation, case for g=2, classification method, separation of the top events. Criteria allowing the obtention of relevant results are included [fr

Identifikasi Huruf Kapital Tulisan Tangan Menggunakan Linear Discriminant Analysis dan Euclidean Distance

Directory of Open Access Journals (Sweden)

Septa Cahyani

2018-04-01

Full Text Available The human ability to recognize a variety of objects, however complex the object, is the special ability that humans possess. Any normal human will have no difficulty in recognizing handwriting objects between an author and another author. With the rapid development of digital technology, the human ability to recognize handwriting objects has been applied in a program known as Computer Vision. This study aims to create identification system different types of handwriting capital letters that have different sizes, thickness, shape, and tilt (distinctive features in handwriting using Linear Discriminant Analysis (LDA and Euclidean Distance methods. LDA is used to obtain characteristic characteristics of the image and provide the distance between the classes becomes larger, while the distance between training data in one class becomes smaller, so that the introduction time of digital image of handwritten capital letter using Euclidean Distance becomes faster computation time (by searching closest distance between training data and data testing. The results of testing the sample data showed that the image resolution of 50x50 pixels is the exact image resolution used for data as much as 1560 handwritten capital letter data compared to image resolution 25x25 pixels and 40x40 pixels. While the test data and training data testing using the method of 10-fold cross validation where 1404 for training data and 156 for data testing showed identification of digital image handwriting capital letter has an average effectiveness of the accuracy rate of 75.39% with the average time computing of 0.4199 seconds.

Dynamical spin susceptibility in the TD-LDA and QSGW approximations

Energy Technology Data Exchange (ETDEWEB)

Schilfgaarde, Mark Van [Arizona State Univ., Mesa, AZ (United States); Kotani, Takao [Arizona State Univ., Mesa, AZ (United States)

2012-10-15

Abstract. This project was aimed at building the transverse dynamical spin susceptibility with the TD-LDA and the recently-developed Quasparticle Self-Consisent Approximations, which determines an optimum quasiparticle picture in a self-consistent manner within the GW approximation. Our main results were published into two papers, (J. Phys. Cond. Matt. 20, 95214 (2008), and Phys. Rev. B83, 060404(R) (2011). In the first paper we present spin wave dispersions for MnO, NiO, and -MnAs based on quasiparticle self-consistent GW approximation (QSGW). For MnO and NiO, QSGW results are in rather good agreement with experiments, in contrast to the LDA and LDA+U descriptions. For -MnAs, we find a collinear ferromagnetic ground state in QSGW, while this phase is unstable in the LDA. In the second, we apply TD-LDA to the CaFeAs₂ the first attempt the first ab initio calculation of dynamical susceptibililty in a system with complex electronic structure Magnetic excitations in the striped phase of CaFe₂As₂ are studied as a function of local moment amplitude. We find a new kind of excitation: sharp resonances of Stoner-like (itinerant) excitations at energies comparable to the ´eel temperature, originating largely from a narrow band of Fe d states near the Fermi level, and coexisting with more conventional (localized) spin waves. Both kinds of excitations can show multiple branches, highlighting the inadequacy of a description based on a localized spin model.

First-principles modeling of localized d states with the GW@LDA+U approach

Science.gov (United States)

Jiang, Hong; Gomez-Abal, Ricardo I.; Rinke, Patrick; Scheffler, Matthias

2010-07-01

First-principles modeling of systems with localized d states is currently a great challenge in condensed-matter physics. Density-functional theory in the standard local-density approximation (LDA) proves to be problematic. This can be partly overcome by including local Hubbard U corrections (LDA+U) but itinerant states are still treated on the LDA level. Many-body perturbation theory in the GW approach offers both a quasiparticle perspective (appropriate for itinerant states) and an exact treatment of exchange (appropriate for localized states), and is therefore promising for these systems. LDA+U has previously been viewed as an approximate GW scheme. We present here a derivation that is simpler and more general, starting from the static Coulomb-hole and screened exchange approximation to the GW self-energy. Following our previous work for f -electron systems [H. Jiang, R. I. Gomez-Abal, P. Rinke, and M. Scheffler, Phys. Rev. Lett. 102, 126403 (2009)10.1103/PhysRevLett.102.126403] we conduct a systematic investigation of the GW method based on LDA+U(GW@LDA+U) , as implemented in our recently developed all-electron GW code FHI-gap (Green’s function with augmented plane waves) for a series of prototypical d -electron systems: (1) ScN with empty d states, (2) ZnS with semicore d states, and (3) late transition-metal oxides (MnO, FeO, CoO, and NiO) with partially occupied d states. We show that for ZnS and ScN, the GW band gaps only weakly depend on U but for the other transition-metal oxides the dependence on U is as strong as in LDA+U . These different trends can be understood in terms of changes in the hybridization and screening. Our work demonstrates that GW@LDA+U with “physical” values of U provides a balanced and accurate description of both localized and itinerant states.

Bearing Performance Degradation Assessment Using Linear Discriminant Analysis and Coupled HMM

International Nuclear Information System (INIS)

Liu, T; Chen, J; Zhou, X N; Xiao, W B

2012-01-01

Bearing is one of the most important units in rotary machinery, its performance may vary significantly under different working stages. Thus it is critical to choose the most effective features for bearing performance degradation prediction. Linear Discriminant Analysis (LDA) is a useful method in finding few feature's dimensions that best discriminate a set of features extracted from original vibration signals. Another challenge in bearing performance degradation is how to build a model to recognize the different conditions with the data coming from different monitoring channels. In this paper, coupled hidden Markov models (CHMM) is presented to model interacting processes which can overcome the defections of the HMM. Because the input data in CHMM are collected by several sensors, and the interacting information can be fused by coupled modalities, it is more effective than HMM which used only one state chain. The model can be used in estimating the bearing performance degradation states according to several observation data. When becoming degradation pattern recognition, the new observation features should be input into the pre-trained CHMM and calculate the performance index (PI) of the outputs, the changing of PI could be used to describe the different degradation level of the bearings. The results show that PI will decline with the increase of the bearing degradation. Assessment results of the whole life time experimental bearing signals validate the feasibility and effectiveness of this method.

Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis

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Gifty E. Acquah

2016-08-01

Full Text Available As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS and Fourier transform infrared spectroscopy (FTIRS together with linear discriminant analysis (LDA. Forest logging residue harvested from several Pinus taeda (loblolly pine plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage. Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability

LDA measurements in the Francis-99 draft tube cone

International Nuclear Information System (INIS)

Sundstrom, L R J; Amiri, K; Cervantes, M J; Bergan, C; Dahlhaug, O G

2014-01-01

Velocity measurements were performed in the draft tube cone of a 1:5.1 scaled model of the Tokke hydropower plant, Norway; also known as the Francis-99 model. Results from the laser Doppler anemometry measurements undertaken at three operating points will be used as validation data for an upcoming workshop on the state of the art of Francis turbine numerical simulation. With the turbine operating at the best efficiency point, a sensitivity analysis of the flow parameters head, flow rate and runner rotational speed shows that the effects on the dimensionless velocity profiles are small as long as n ED and Q ED are held constant. The results indicate a well-functioning turbine at the best efficiency point and high load. At the part load operating point, a vortex breakdown occurs which distorts the velocity profiles and significantly lowers the turbine's hydraulic efficiency. Frequency spectrums of each LDA signal at part load reveals a peak which is asynchronous to that of the runner angular speed. The peaks might be related to the precession of a rotating vortex rope but the characteristics of the LDA signals are different compared to previous studies involving rotating vortex ropes

Latent Dirichlet Allocation (LDA) for Sentiment Analysis Toward Tourism Review in Indonesia

Science.gov (United States)

Putri, IR; Kusumaningrum, R.

2017-01-01

The tourism industry is one of foreign exchange sector, which has considerable potential development in Indonesia. Compared to other Southeast Asia countries such as Malaysia with 18 million tourists and Singapore 20 million tourists, Indonesia which is the largest Southeast Asia’s country have failed to attract higher tourist numbers compared to its regional peers. Indonesia only managed to attract 8,8 million foreign tourists in 2013, with the value of foreign tourists each year which is likely to decrease. Apart from the infrastructure problems, marketing and managing also form of obstacles for tourism growth. An evaluation and self-analysis should be done by the stakeholder to respond toward this problem and capture opportunities that related to tourism satisfaction from tourists review. Recently, one of technology to answer this problem only relying on the subjective of statistical data which collected by voting or grading from user randomly. So the result is still not to be accountable. Thus, we proposed sentiment analysis with probabilistic topic model using Latent Dirichlet Allocation (LDA) method to be applied for reading general tendency from tourist review into certain topics that can be classified toward positive and negative sentiment.

Local connected fractal dimension analysis in gill of fish experimentally exposed to toxicants

Energy Technology Data Exchange (ETDEWEB)

Manera, Maurizio, E-mail: mmanera@unite.it [Faculty of Biosciences, Food and Environmental Technologies, University of Teramo, Piano d’Accio, I-64100 Teramo (Italy); Giari, Luisa [Department of Life Sciences and Biotechnology, University of Ferrara, St. Borsari 46, I-44121 Ferrara (Italy); De Pasquale, Joseph A. [Morphogenyx Inc., PO Box 717, East Northport, NY 11731 (United States); Sayyaf Dezfuli, Bahram [Department of Life Sciences and Biotechnology, University of Ferrara, St. Borsari 46, I-44121 Ferrara (Italy)

2016-06-15

Highlights: • An objective, operator unbiased method was developed to evaluate gill pathology. • The method relies on the measure of local connected fractal dimension frequency. • Exposure classes were adequately discriminated by linear discriminant analysis. - Abstract: An operator-neutral method was implemented to objectively assess European seabass, Dicentrarchus labrax (Linnaeus, 1758) gill pathology after experimental exposure to cadmium (Cd) and terbuthylazine (TBA) for 24 and 48 h. An algorithm-derived local connected fractal dimension (LCFD) frequency measure was used in this comparative analysis. Canonical variates (CVA) and linear discriminant analysis (LDA) were used to evaluate the discrimination power of the method among exposure classes (unexposed, Cd exposed, TBA exposed). Misclassification, sensitivity and specificity, both with original and cross-validated cases, were determined. LCFDs frequencies enhanced the differences among classes which were visually selected after their means, respective variances and the differences between Cd and TBA exposed means, with respect to unexposed mean, were analyzed by scatter plots. Selected frequencies were then scanned by means of LDA, stepwise analysis, and Mahalanobis distance to detect the most discriminative frequencies out of ten originally selected. Discrimination resulted in 91.7% of cross-validated cases correctly classified (22 out of 24 total cases), with sensitivity and specificity, respectively, of 95.5% (1 false negative with respect to 21 really positive cases) and 75% (1 false positive with respect to 3 really negative cases). CVA with convex hull polygons ensured prompt, visually intuitive discrimination among exposure classes and graphically supported the false positive case. The combined use of semithin sections, which enhanced the visual evaluation of the overall lamellar structure; of LCFD analysis, which objectively detected local variation in complexity, without the possible bias

Local connected fractal dimension analysis in gill of fish experimentally exposed to toxicants

International Nuclear Information System (INIS)

Manera, Maurizio; Giari, Luisa; De Pasquale, Joseph A.; Sayyaf Dezfuli, Bahram

2016-01-01

Highlights: • An objective, operator unbiased method was developed to evaluate gill pathology. • The method relies on the measure of local connected fractal dimension frequency. • Exposure classes were adequately discriminated by linear discriminant analysis. - Abstract: An operator-neutral method was implemented to objectively assess European seabass, Dicentrarchus labrax (Linnaeus, 1758) gill pathology after experimental exposure to cadmium (Cd) and terbuthylazine (TBA) for 24 and 48 h. An algorithm-derived local connected fractal dimension (LCFD) frequency measure was used in this comparative analysis. Canonical variates (CVA) and linear discriminant analysis (LDA) were used to evaluate the discrimination power of the method among exposure classes (unexposed, Cd exposed, TBA exposed). Misclassification, sensitivity and specificity, both with original and cross-validated cases, were determined. LCFDs frequencies enhanced the differences among classes which were visually selected after their means, respective variances and the differences between Cd and TBA exposed means, with respect to unexposed mean, were analyzed by scatter plots. Selected frequencies were then scanned by means of LDA, stepwise analysis, and Mahalanobis distance to detect the most discriminative frequencies out of ten originally selected. Discrimination resulted in 91.7% of cross-validated cases correctly classified (22 out of 24 total cases), with sensitivity and specificity, respectively, of 95.5% (1 false negative with respect to 21 really positive cases) and 75% (1 false positive with respect to 3 really negative cases). CVA with convex hull polygons ensured prompt, visually intuitive discrimination among exposure classes and graphically supported the false positive case. The combined use of semithin sections, which enhanced the visual evaluation of the overall lamellar structure; of LCFD analysis, which objectively detected local variation in complexity, without the possible bias

On the peculiarities of LDA method in two-phase flows with high concentrations of particles

Science.gov (United States)

Poplavski, S. V.; Boiko, V. M.; Nesterov, A. U.

2016-10-01

Popular applications of laser Doppler anemometry (LDA) in gas dynamics are reviewed. It is shown that the most popular method cannot be used in supersonic flows and two-phase flows with high concentrations of particles. A new approach to implementation of the known LDA method based on direct spectral analysis, which offers better prospects for such problems, is presented. It is demonstrated that the method is suitable for gas-liquid jets. Owing to the progress in laser engineering, digital recording of spectra, and computer processing of data, the method is implemented at a higher technical level and provides new prospects of diagnostics of high-velocity dense two-phase flows.

Ms2lda.org: web-based topic modelling for substructure discovery in mass spectrometry.

Science.gov (United States)

Wandy, Joe; Zhu, Yunfeng; van der Hooft, Justin J J; Daly, Rónán; Barrett, Michael P; Rogers, Simon

2017-09-14

We recently published MS2LDA, a method for the decomposition of sets of molecular fragment data derived from large metabolomics experiments. To make the method more widely available to the community, here we present ms2lda.org, a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualisations. Ms2lda.org takes tandem mass spectrometry data in many standard formats and allows the user to infer the sets of fragment and neutral loss features that co-occur together (Mass2Motifs). As an alternative workflow, the user can also decompose a dataset onto predefined Mass2Motifs. This is accomplished through the web interface or programmatically from our web service. The website can be found at http://ms2lda.org , while the source code is available at https://github.com/sdrogers/ms2ldaviz under the MIT license. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

Directory of Open Access Journals (Sweden)

Joachim Goschnick

2004-05-01

Full Text Available Abstract: The paper presents results concerning the effect of spatial inhomogeneous operating temperature on the gas discrimination power of a gas-sensor microarray, with the latter based on a thin SnO2 film employed in the KAMINA electronic nose. Three different temperature distributions over the substrate are discussed: a nearly homogeneous one and two temperature gradients, equal to approx. 3.3 oC/mm and 6.7 oC/mm, applied across the sensor elements (segments of the array. The gas discrimination power of the microarray is judged by using the Mahalanobis distance in the LDA (Linear Discrimination Analysis coordinate system between the data clusters obtained by the response of the microarray to four target vapors: ethanol, acetone, propanol and ammonia. It is shown that the application of a temperature gradient increases the gas discrimination power of the microarray by up to 35 %.

Local self-energies for V and Pd emergent from a nonlocal LDA+FLEX implementation

Science.gov (United States)

Savrasov, Sergey Y.; Resta, Giacomo; Wan, Xiangang

2018-04-01

In the spirit of recently developed LDA+U and LDA+DMFT methods, we implement a combination of density functional theory in its local density approximation (LDA) with a k - and ω -dependent self-energy found from diagrammatic fluctuational exchange (FLEX) approximation. The active Hilbert space here is described by the correlated subset of electrons which allows one to tremendously reduce the sizes of the matrices needed to represent charge and spin susceptibilities. The method is perturbative in nature but accounts for both bubble and ladder diagrams and accumulates the physics of momentum-resolved spin fluctuations missing in such popular approach as GW. As an application, we study correlation effects on band structures in V and Pd. The d -electron self-energies emergent from this calculation are found to be remarkably k independent. However, when we compare our calculated electronic mass enhancements against LDA+DMFT, we find that for the longstanding problem of spin fluctuations in Pd, LDA+FLEX delivers a better agreement with experiment, although this conclusion depends on a particular value of the Hubbard U used in the simulation. We also discuss outcomes of a recently proposed combination of k -dependent FLEX with dynamical mean-field theory (DMFT).

Using otolith shape for intraspecific discrimination: the case of gurnards (Scorpaeniformes, Triglidae

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Stefano Montanini

2015-11-01

Full Text Available The sagittal otoliths are sound transducers and play an important role in fish hearing. Triglidae (Teleostei, Scorpaeniformes are known for sound producing ability in agonistic contexts related to territorial defence, reproduction and competitive feeding (Amorim et al., 2004. Chelidonichthys cuculus and C. lucerna show a significant body size-depth relationship and specie-specific feeding strategies with growth. Both juveniles and adults of C. cuculus prey necto-benthic invertebrates while C. lucerna specimens change diet from crustaceans to teleost during growth (Stagioni et al., 2012; Vallisneri et al., 2014; Montanini et al., 2015. The goal of this study was to analyze intraspecific shape variations in sagitta of model species of gurnards. 217 specimens were collected during bottom trawl surveys in Adriatic sea (northeastern Mediterranean. Each left sagitta was removed, cleaned in ultrasounds bath and kept dry. The otolith digital images were processed to calculate five shape indices (aspect ratio, roundness, rectangularity, ellipticity and circularity. Indices were normalised to avoid allometric effects according to Lleonart et al. (2000, than processed by linear discriminant analysis (LDA. The SHAPE program was used to extract the outline and to assess the variability of shapes (EFA method and estimated it through the study of principal component analysis (PCA. Considering the first two discriminant functions, LDA plot showed a clearly separation between juvenile and adults for both species. About EFA, the first 4 principal component discriminated over 80% of variance and significant differences were found at critical size between juveniles and adults for all the components analysed. The allometric trends corresponded to a relative elongation of the sulcus acusticus and an increase of excisura ostii. The combined use of the two external outlines methods should be highly informative for intraspecific discrimination and might be related to

Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features.

Science.gov (United States)

McDonald, Linda S; Panozzo, Joseph F; Salisbury, Phillip A; Ford, Rebecca

2016-01-01

Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective.

Loss Distribution Approach (LDA: metodología actuarial aplicada al riesgo operacional Loss Distribution Approach (LDA: actuarial methodology apply to operational risk

Directory of Open Access Journals (Sweden)

Luis Ceferino Franco Arbeláez

2008-07-01

Full Text Available Este artículo es resultado de un proyecto de investigación sobre la gestión integral del riesgo operacional promovido por la Vicerrectoria de Investigaciones de la Universidad de Medellín, y cofinanciado por una firma comisionista. Se presenta una aplicación del modelo LDA, el cual se basa en la recopilación de los datos de pérdidas históricas (frecuencia y severidad, que se registran internamente en las organizaciones. Dichos datos pueden ser complementados con datos externos. Estas pérdidas son clasificadas en una matriz que relaciona las líneas de negocio de la organización y los eventos operacionales de pérdida, a partir de la cual se calcula la carga de capital. La aplicación se desarrolló para una entidad financiera. El artículo está organizado de la siguiente forma: la primera sección es introductoria al tema. En la segunda parte se presenta formalmente el modelo LDA; luego se realiza una aplicación, y en la cuarta sección se presentan algunas conclusiones.This paper is the result of a research project on integrated management of operational risk, promoted by Universidad de Medellin Research Vice-Principal's Office and co-financed by a financial company. It presents an application of the LDA model, which is based on data collection of historical losses (frequency and severity, which are recorded internally in organizations. Such data can be supplemented with external data. These losses are classified in a matrix that relates business lines of the organization and operational loss events, from which capital charge is estimated. The application was developed for a financial institution. The paper is organized as follows: The first section is introductory to the subject. The second part formally presents a LDA model; then an application is made, and in the fourth part some conclusions are presented.

Discriminant analysis of plasma fusion data

International Nuclear Information System (INIS)

Kardaun, O.J.W.F.; Kardaun, J.W.P.F.; Itoh, S.; Itoh, K.

1992-06-01

Several discriminant analysis methods has been applied and compared to predict the type of ELM's in H-mode discharges: (a) quadratic discriminant analysis (linear discriminant analysis being a special case), (b) discrimination by non-parametric (kernel-) density estimates, and (c) discrimination by a product multinomial model on a discretised scale. Practical evaluation was performed using SAS in the first two cases, and INDEP, a standard FORTRAN program, initially developed for medical applications, in the last case. We give here a flavour of the approach and its results. In summary, discriminant analysis can be used as a useful descriptive method of specifying regions where particular types of plasma discharges can be produced. Parametric methods have the advantage of a rather compact mathematical formulation . Pertinent graphical representations are useful to make the theory and the results more palatable to the experimental physicists. (J.P.N.)

MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

Science.gov (United States)

Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

2013-01-01

Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

STARTING BLOCK PERFORMANCE IN SPRINTERS: A STATISTICAL METHOD FOR IDENTIFYING DISCRIMINATIVE PARAMETERS OF THE PERFORMANCE AND AN ANALYSIS OF THE EFFECT OF PROVIDING FEEDBACK OVER A 6-WEEK PERIOD

Directory of Open Access Journals (Sweden)

Sylvie Fortier

2005-06-01

Full Text Available The purpose of this study was twofold: (a to examine if kinetic and kinematic parameters of the sprint start could differentiate elite from sub-elite sprinters and, (b to investigate whether providing feedback (FB about selected parameters could improve starting block performance of intermediate sprinters over a 6-week training period. Twelve male sprinters, assigned to an elite or a sub-elite group, participated in Experiment 1. Eight intermediate sprinters participated in Experiment 2. All athletes were required to perform three sprint starts at maximum intensity followed by a 10-m run. To detect differences between elite and sub-elite groups, comparisons were made using t-tests for independent samples. Parameters reaching a significant group difference were retained for the linear discriminant analysis (LDA. The LDA yielded four discriminative kinetic parameters. Feedback about these selected parameters was given to sprinters in Experiment 2. For this experiment, data acquisition was divided into three periods. The first six sessions were without specific FB, whereas the following six sessions were enriched by kinetic FB. Finally, athletes underwent a retention session (without FB 4 weeks after the twelfth session. Even though differences were found in the time to front peak force, the time to rear peak force, and the front peak force in the retention session, the results of the present study showed that providing FB about selected kinetic parameters differentiating elite from sub-elite sprinters did not improve the starting block performance of intermediate sprinters

Double counting in LDA + DMFT-The example of NiO

Energy Technology Data Exchange (ETDEWEB)

Karolak, M., E-mail: mkarolak@physnet.uni-hamburg.d [I. Institut fuer Theoretische Physik, Universitaet Hamburg, Jungiusstrasse 9, D-20355 Hamburg (Germany); Ulm, G.; Wehling, T. [I. Institut fuer Theoretische Physik, Universitaet Hamburg, Jungiusstrasse 9, D-20355 Hamburg (Germany); Mazurenko, V. [Theoretical Physics and Applied Mathematic Department, Urals State Technical University, 620002, Mira street 19, Yekaterinburg (Russian Federation); Poteryaev, A. [Institute of Metal Physics, Russian Academy of Sciences, 620041 Yekaterinburg GSP-170 (Russian Federation); Lichtenstein, A. [I. Institut fuer Theoretische Physik, Universitaet Hamburg, Jungiusstrasse 9, D-20355 Hamburg (Germany)

2010-07-15

An intrinsic issue of the LDA + DMFT approach is the so called double counting of interaction terms. How to choose the double-counting potential in a manner that is both physically sound and consistent is unknown. We have conducted an extensive study of the charge-transfer system NiO in the LDA + DMFT framework using quantum Monte Carlo and exact diagonalization as impurity solvers. By explicitly treating the double-counting correction as an adjustable parameter we systematically investigated the effects of different choices for the double counting on the spectral function. Different methods for fixing the double counting can drive the result from Mott insulating to almost metallic. We propose a reasonable scheme for the determination of double-counting corrections for insulating systems.

Provenance Establishment of Stingless Bee Honey Using Multi-element Analysis in Combination with Chemometrics Techniques.

Science.gov (United States)

Shadan, Aidil Fahmi; Mahat, Naji A; Wan Ibrahim, Wan Aini; Ariffin, Zaiton; Ismail, Dzulkiflee

2018-01-01

As consumption of stingless bee honey has been gaining popularity in many countries including Malaysia, ability to identify accurately its geographical origin proves pertinent for investigating fraudulent activities for consumer protection. Because a chemical signature can be location-specific, multi-element distribution patterns may prove useful for provenancing such product. Using the inductively coupled-plasma optical emission spectrometer as well as principal component analysis (PCA) and linear discriminant analysis (LDA), the distributions of multi-elements in stingless bee honey collected at four different geographical locations (North, West, East, and South) in Johor, Malaysia, were investigated. While cross-validation using PCA demonstrated 87.0% correct classification rate, the same was improved (96.2%) with the use of LDA, indicating that discrimination was possible for the different geographical regions. Therefore, utilization of multi-element analysis coupled with chemometrics techniques for assigning the provenance of stingless bee honeys for forensic applications is supported. © 2017 American Academy of Forensic Sciences.

Analysis of Fringe Field Formed Inside LDA Measurement Volume Using Compact Two Hololens Imaging Systems

Science.gov (United States)

Ghosh, Abhijit; Nirala, A. K.; Yadav, H. L.

2018-03-01

We have designed and fabricated four LDA optical setups consisting of aberration compensated four different compact two hololens imaging systems. We have experimentally investigated and realized a hololens recording geometry which is interferogram of converging spherical wavefront with mutually coherent planar wavefront. Proposed real time monitoring and actual fringe field analysis techniques allow complete characterizations of fringes formed at measurement volume and permit to evaluate beam quality, alignment and fringe uniformity with greater precision. After experimentally analyzing the fringes formed at measurement volume by all four imaging systems, it is found that fringes obtained using compact two hololens imaging systems get improved both qualitatively and quantitatively compared to that obtained using conventional imaging system. Results indicate qualitative improvement of non-uniformity in fringe thickness and micro intensity variations perpendicular to the fringes, and quantitative improvement of 39.25% in overall average normalized standard deviations of fringe width formed by compact two hololens imaging systems compare to that of conventional imaging system.

High-Pressure Behaviour of β-HMX Crystal Studied by DFT-LDA

International Nuclear Information System (INIS)

Dan, Lian; Lai-Yu, Lu; Dong-Qing, Wei; Qing-Ming, Zhang; Zi-Zheng, Gong; Yong-Xin, Guo

2008-01-01

Density functional theory (DFT) with local density approximation (LDA) is employed to study the structural and electronic properties of the high explosive octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) under high pressure compression up to 40 GPa. Pressure dependences of the cell volume, lattice constants, and molecular geometry of solid β-HMX are presented and discussed. It is found that N-N and N-C bonds are subject to significant change. This may implies that these bonds may be related to the sensitivity. The band gap is calculated and plotted as a function of pressure. Compared the experimental results with other theoretical works we find that LDA gives good results

LDA+U and tight-binding electronic structure of InN nanowires

Science.gov (United States)

Molina-Sánchez, A.; García-Cristóbal, A.; Cantarero, A.; Terentjevs, A.; Cicero, G.

2010-10-01

In this paper we employ a combined ab initio and tight-binding approach to obtain the electronic and optical properties of hydrogenated Indium nitride (InN) nanowires. We first discuss InN band structure for the wurtzite structure calculated at the LDA+U level and use this information to extract the parameters needed for an empirical tight-binging implementation. These parameters are then employed to calculate the electronic and optical properties of InN nanowires in a diameter range that would not be affordable by ab initio techniques. The reliability of the large nanowires results is assessed by explicitly comparing the electronic structure of a small diameter wire studied both at LDA+U and tight-binding level.

Recognition of beer brand based on multivariate analysis of volatile fingerprint.

Science.gov (United States)

Cajka, Tomas; Riddellova, Katerina; Tomaniova, Monika; Hajslova, Jana

2010-06-18

Automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography-time-of-flight mass spectrometry (GC-TOFMS), was developed and employed for obtaining of fingerprints (GC profiles) of beer volatiles. In total, 265 speciality beer samples were collected over a 1-year period with the aim to distinguish, based on analytical (profiling) data, (i) the beers labelled as Rochefort 8; (ii) a group consisting of Rochefort 6, 8, 10 beers; and (iii) Trappist beers. For the chemometric evaluation of the data, partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction ability was obtained for the model that distinguished a group of Rochefort 6, 8, 10 beers from the rest of beers. In this case, all chemometric tools employed provided 100% correct classification. Slightly worse prediction abilities were achieved for the models "Trappist vs. non-Trappist beers" with the values of 93.9% (PLS-DA), 91.9% (LDA) and 97.0% (ANN-MLP) and "Rochefort 8 vs. the rest" with the values of 87.9% (PLS-DA) and 84.8% (LDA) and 93.9% (ANN-MLP). In addition to chromatographic profiling, also the potential of direct coupling of SPME (extraction/pre-concentration device) with high-resolution TOFMS employing a direct analysis in real time (DART) ion source has been demonstrated as a challenging profiling approach. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Combining features from ERP components in single-trial EEG for discriminating four-category visual objects

Science.gov (United States)

Wang, Changming; Xiong, Shi; Hu, Xiaoping; Yao, Li; Zhang, Jiacai

2012-10-01

Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

Object-Oriented Semisupervised Classification of VHR Images by Combining MedLDA and a Bilateral Filter

Directory of Open Access Journals (Sweden)

Shi He

2015-01-01

Full Text Available A Bayesian hierarchical model is presented to classify very high resolution (VHR images in a semisupervised manner, in which both a maximum entropy discrimination latent Dirichlet allocation (MedLDA and a bilateral filter are combined into a novel application framework. The primary contribution of this paper is to nullify the disadvantages of traditional probabilistic topic models on pixel-level supervised information and to achieve the effective classification of VHR remote sensing images. This framework consists of the following two iterative steps. In the training stage, the model utilizes the central labeled pixel and its neighborhood, as a squared labeled image object, to train the classifiers. In the classification stage, each central unlabeled pixel with its neighborhood, as an unlabeled object, is classified as a user-provided geoobject class label with the maximum posterior probability. Gibbs sampling is adopted for model inference. The experimental results demonstrate that the proposed method outperforms two classical SVM-based supervised classification methods and probabilistic-topic-models-based classification methods.

LDA measurements under plasma conditions

International Nuclear Information System (INIS)

Lesinski, J.; Mizera-Lesinska, B.; Fanton, J.C.; Boulos, M.I.

1979-01-01

A study was made of the application of Laser Doppler Anemometry (LDA) for the measurement of the fluid and particle velocities under plasma conditions. The flow configuration, is that of a dc plasma jet called the principal jet, in which an alumina powder of a mean particle diameter of 115 μm and a standard deviation of 11.3 μm was injected using a secondary jet. The plasma jet immerged from a 7.1 mm ID nozzle while that of the secondary jet was 2 nm in diameter. The secondary jet was introduced at the nozzle level of the plasma jet directed 90 0 to its axis. Details of the nozzle and the gas flow system are shown in Figure 2

Discrimination of side-window glass of Korean autos by laser ablation inductively coupled plasma mass spectrometry.

Science.gov (United States)

Lee, Sin-Woo; Ryu, Jong-Sik; Min, Ji-Sook; Choi, Man-Yong; Lee, Kwang-Sik; Shin, Woo-Jin

2016-07-15

Fragments of glass from cars are often found at crime scenes and can be crucial evidence for solving the crime. The glass fragments are important as trace evidence at crime scenes related to car accidents and burgled homes. By identifying the origin of glass fragments, it is possible to infer the identity of a suspect. Our results represent a promising approach to a thorough forensic investigation of car glass. Thirty-five samples from the side windows of cars produced and used in South Korea were collected from the official agencies of five car manufacturers and from two glassmakers. In addition, 120 samples from side mirrors were collected from the same suppliers as well as from small businesses. Their chemical compositions (including Pb isotopes) were analyzed using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and linear discriminant analysis (LDA) was performed. The percentages of major elements (Si, Ca, and Fe) in side-window glass varied within narrow ranges (30.0 ± 2.36%, 5.93 ± 0.52%, and 0.33 ± 0.05%, respectively), while the differences among Pb isotope ratios were not significant. In contrast, light rare earth elements (LREEs) were different from each glassmaker. From the LDA, the types of side-window glass were successfully discriminated according to car manufacturer, glassmaker, and even glass thickness. However, glass from side mirrors cannot be used for good forensic identifiers. Discrimination techniques for side-window glass, although not for side mirrors, using chemical compositions combined with multivariate statistical analyses provide evidence for forensic investigations. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Methodological considerations in discriminating olive-orchard management type using olive-canopy arthropod fauna at the level of order

Energy Technology Data Exchange (ETDEWEB)

Jerez-Valle, C.; García-López, P.A.; Campos, M.; Pascual, F.

2015-07-01

The cultivation of the olive tree (Olea europaea L.) has great importance in the entire Mediterranean basin, so that the implementation of organic practices in their management directly affects the sustainability of the agricultural system. Bioindication with arthropods can help to detect the different agricultural practices. In this work, we analyse the most appropriate methodology for discriminating between management using arthropods at the taxonomic level of order, with the novelty of taking into account the weather conditions to select the sampling dates. Between 12 and 15 sampling stations (depending on the year) were selected from olive orchards belonging to organic, conventional non-tillage, and strict conventional management, being sampled by beating the canopy fortnightly in the spring-summer period of 2007, 2008 and 2009. Organic management was more abundant and richer than the rest for the three years. Most groups with significant differences in terms of relative abundance were more abundant in organic orchard, except Neuroptera. Finally, different discriminant methods were evaluated (Linear Discriminant Analysis, Multiple Discriminant Analysis, and Support Vector Machine) with several different data sets. The discriminant analysis with interannual variability reached 97.9% accuracy in differentiating between organic and non-organic management using the LDA method, considering the taxa with significant differences from the abundance, excluding pests, and using samples with more uniform and stable weather patterns (late summer. (Author)

Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast

Directory of Open Access Journals (Sweden)

Masatoshi Yamada

2016-01-01

Full Text Available Background: Intraductal proliferative lesions (IDPLs of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH and low-grade ductal carcinoma in situ (LG-DCIS. In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast. Methods: We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH, ADH, LG-DCIS, intermediate-grade (IM-DCIS, and high-grade (HG-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA and a support vector machine. Results: High diagnostic accuracy (81.8–99.3% was achieved between pathologists' diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance. Conclusions: In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer.

Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast

Science.gov (United States)

Yamada, Masatoshi; Saito, Akira; Yamamoto, Yoichiro; Cosatto, Eric; Kurata, Atsushi; Nagao, Toshitaka; Tateishi, Ayako; Kuroda, Masahiko

2016-01-01

Background: Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH) and low-grade ductal carcinoma in situ (LG-DCIS). In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast. Methods: We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH), ADH, LG-DCIS, intermediate-grade (IM)-DCIS, and high-grade (HG)-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA) and a support vector machine. Results: High diagnostic accuracy (81.8–99.3%) was achieved between pathologists’ diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance. Conclusions: In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer. PMID:26955499

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

Science.gov (United States)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care

Derivation of LDA log likelihood ratio one-to-one classifier

NARCIS (Netherlands)

Spreeuwers, Lieuwe Jan

2014-01-01

The common expression for the Likelihood Ratio classifier using LDA assumes that the reference class mean is available. In biometrics, this is often not the case and only a single sample of the reference class is available. In this paper expressions are derived for biometric comparison between

Detection of irradiated beef by nuclear magnetic resonance lipid profiling combined with chemometric techniques.

Science.gov (United States)

Zanardi, Emanuela; Caligiani, Augusta; Padovani, Enrico; Mariani, Mario; Ghidini, Sergio; Palla, Gerardo; Ianieri, Adriana

2013-02-01

The combination of (1)H NMR lipid profiling with multivariate analysis was applied to differentiate irradiated and non-irradiated beef. Two pattern recognition chemometric procedures, stepwise linear discriminant analysis (sLDA) and artificial neural networks (ANNs), provided a successful discrimination between the groups investigated. sLDA allowed the classification of 100% of the samples into irradiated or non-irradiated beef groups; the same result was obtained by ANNs using the 1 kGy irradiation dose as discriminant value suggested by the network. Furthermore, sLDA allowed the classification of 81.9% of the beef samples according to the irradiation dose (0, 2.5, 4.5 and 8 kGy). (1)H NMR lipid profiling, coupled with multivariate analysis may be considered a suitable and promising screening tool for the rapid detection of irradiated meat in official control of food. Copyright © 2012 Elsevier Ltd. All rights reserved.

Use of linear discriminant analysis to characterise three dairy cattle breeds on the basis of several milk characteristics

Directory of Open Access Journals (Sweden)

Roberto Leotta

2010-01-01

Full Text Available To characterise individuals of differents breeds on the basis of milk composition and to identify the best set of variablesa linear discriminant analysis (LDA, on 14 milk production traits, was performed on milk samples from 199 cows of differentbreeds (respectively, 127 subjects were Italian Friesians (IF, 62 were German Friesians (GF, and 10 were Jerseys(J and all came from the same breeding farm in Tuscany. The variables were: test day milk yield (kg milk, % Fat, %Protein,% Lactose, % solid non fat (SNF, % total solid (TS, pH and titratable acidity (TA; five rheological variables: r,k20, a30, a45, and somatic cell counts /ml (SCC; and one hygiene-related variable: total bacterial count (TBC. The analysisperformed on the 14 variables, with regard to the three breeds, allowed us to identify 10 of these as variables usefulfor discrimination (leaving out kg milk, pH, a45, and TBC. The most important variables were the percentage of Fat andTS for the first canonical variate and SNF, Lactose and Protein for the second. Fat and TS play an important role sincethey present significant values (even if opposite sign in the two variates. The resulting classification of subjects was satisfactory:79% of the Italian Friesians, 73% of German Friesians and 100% of the Jersey cows were classified correctly.

Characterization of Chinese rice wine taste attributes using liquid chromatographic analysis, sensory evaluation, and an electronic tongue.

Science.gov (United States)

Yu, HaiYan; Zhao, Jie; Li, Fenghua; Tian, Huaixiang; Ma, Xia

2015-08-01

To evaluate the taste characteristics of Chinese rice wine, wine samples sourced from different vintage years were analyzed using liquid chromatographic analysis, sensory evaluation, and an electronic tongue. Six organic acids and seventeen amino acids were measured using high performance liquid chromatography (HPLC). Five monosaccharides were measured using anion-exchange chromatography. The global taste attributes were analyzed using an electronic tongue (E-tongue). The correlations between the 28 taste-active compounds and the sensory attributes, and the correlations between the E-tongue response and the sensory attributes were established via partial least square discriminant analysis (PLSDA). E-tongue response data combined with linear discriminant analysis (LDA) were used to discriminate the Chinese rice wine samples sourced from different vintage years. Sensory evaluation indicated significant differences in the Chinese rice wine samples sourced from 2003, 2005, 2008, and 2010 vintage years in the sensory attributes of harmony and mellow. The PLSDA model for the taste-active compounds and the sensory attributes showed that proline, fucose, arabinose, lactic acid, glutamic acid, arginine, isoleucine, valine, threonine, and lysine had an influence on the taste characteristic of Chinese rice wine. The Chinese rice wine samples were all correctly classified using the E-tongue and LDA. The electronic tongue was an effective tool for rapid discrimination of Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

Improving the analysis of near-spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation.

Science.gov (United States)

Gemignani, Jessica; Middell, Eike; Barbour, Randall L; Graber, Harry L; Blankertz, Benjamin

2018-04-04

The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as 'active' or 'not active' with a multivariate classifier based on linear discriminant analysis (LDA). This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis. The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results. The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions

Estimation of burst-mode LDA power spectra

DEFF Research Database (Denmark)

Velte, Clara Marika; George, William K.; Buchhave, Preben

2014-01-01

. The signal can be interpreted correctly by applying residence time weighting to all statistics and using the residence time-weighted discrete Fourier transform to compute the Fourier transform. A new spectral algorithm using the latter is applied to two experiments: a cylinder wake and an axisymmetric......The estimation of power spectra from LDA data provides signal processing challenges for fluid dynamicists for several reasons: acquisition is dictated by randomly arriving particles, the registered particle velocities tend to be biased toward higher values, and the signal is highly intermittent...

Early detection of rice blast ( Pyricularia ) at seedling stage in ...

African Journals Online (AJOL)

Principal component analysis (PCA) was performed on the images and the distribution of PCA scores within individual leaf were measured to develop linear discriminant analysis (LDA) models for predicting the infected leaves from healthy leaves. An LDA model classified all the leaves into infected and healthy categories, ...

Linear Discriminant Analysis for the in Silico Discovery of Mechanism-Based Reversible Covalent Inhibitors of a Serine Protease: Application of Hydration Thermodynamics Analysis and Semi-empirical Molecular Orbital Calculation.

Science.gov (United States)

Masuda, Yosuke; Yoshida, Tomoki; Yamaotsu, Noriyuki; Hirono, Shuichi

2018-01-01

We recently reported that the Gibbs free energy of hydrolytic water molecules (ΔG wat ) in acyl-trypsin intermediates calculated by hydration thermodynamics analysis could be a useful metric for estimating the catalytic rate constants (k cat ) of mechanism-based reversible covalent inhibitors. For thorough evaluation, the proposed method was tested with an increased number of covalent ligands that have no corresponding crystal structures. After modeling acyl-trypsin intermediate structures using flexible molecular superposition, ΔG wat values were calculated according to the proposed method. The orbital energies of antibonding π* molecular orbitals (MOs) of carbonyl C=O in covalently modified catalytic serine (E orb ) were also calculated by semi-empirical MO calculations. Then, linear discriminant analysis (LDA) was performed to build a model that can discriminate covalent inhibitor candidates from substrate-like ligands using ΔG wat and E orb . The model was built using a training set (10 compounds) and then validated by a test set (4 compounds). As a result, the training set and test set ligands were perfectly discriminated by the model. Hydrolysis was slower when (1) the hydrolytic water molecule has lower ΔG wat ; (2) the covalent ligand presents higher E orb (higher reaction barrier). Results also showed that the entropic term of hydrolytic water molecule (-TΔS wat ) could be used for estimating k cat and for covalent inhibitor optimization; when the rotational freedom of the hydrolytic water molecule is limited, the chance for favorable interaction with the electrophilic acyl group would also be limited. The method proposed in this study would be useful for screening and optimizing the mechanism-based reversible covalent inhibitors.

Application of Discriminant Analysis on Romanian Insurance Market

OpenAIRE

Constantin Anghelache; Dan Armeanu

2008-01-01

Discriminant analysis is a supervised learning technique that can be used in order to determine which variables are the best predictors of the classification of objects belonging to a population into predetermined classes. At the same time, discriminant analysis provides a powerful tool that enables researchers to make predictions regarding the classification of new objects into predefined classes. The main goal of discriminant analysis is to determine which of the N descrip...

NIR spectroscopy as a tool for discriminating between lichens exposed to air pollution.

Science.gov (United States)

Casale, Monica; Bagnasco, Lucia; Giordani, Paolo; Mariotti, Mauro Giorgio; Malaspina, Paola

2015-09-01

Lichens are used as biomonitors of air pollution because they are extremely sensitive to the presence of substances that alter atmospheric composition. Fifty-one thalli of two different varieties of Pseudevernia furfuracea (var. furfuracea and var. ceratea) were collected far from local sources of air pollution. Twenty-six of these thalli were then exposed to the air for one month in the industrial port of Genoa, which has high levels of environmental pollution. The possibility of using Near-infrared spectroscopy (NIRS) for generating a 'fingerprint' of lichens was investigated. Chemometric methods were successfully applied to discriminate between samples from polluted and non-polluted areas. In particular, Principal Component Analysis (PCA) was applied as a multivariate display method on the NIR spectra to visualise the data structure. This showed that the difference between samples of different varieties was not significant in comparison to the difference between samples exposed to different levels of environmental pollution. Then Linear Discriminant Analysis (LDA) was carried out to discriminate between lichens based on their exposure to pollutants. The distinction between control samples (not exposed) and samples exposed to the air in the industrial port of Genoa was evaluated. On average, 95.2% of samples were correctly classified, 93.0% of total internal prediction (5 cross-validation groups) and 100.0% of external prediction (on the test set) was achieved. Copyright © 2015 Elsevier Ltd. All rights reserved.

Pharmacokinetic-Pharmacodynamic (PKPD) Analysis with Drug Discrimination.

Science.gov (United States)

Negus, S Stevens; Banks, Matthew L

2016-08-30

Discriminative stimulus and other drug effects are determined by the concentration of drug at its target receptor and by the pharmacodynamic consequences of drug-receptor interaction. For in vivo procedures such as drug discrimination, drug concentration at receptors in a given anatomical location (e.g., the brain) is determined both by the dose of drug administered and by pharmacokinetic processes of absorption, distribution, metabolism, and excretion that deliver drug to and from that anatomical location. Drug discrimination data are often analyzed by strategies of dose-effect analysis to determine parameters such as potency and efficacy. Pharmacokinetic-Pharmacodynamic (PKPD) analysis is an alternative to conventional dose-effect analysis, and it relates drug effects to a measure of drug concentration in a body compartment (e.g., venous blood) rather than to drug dose. PKPD analysis can yield insights on pharmacokinetic and pharmacodynamic determinants of drug action. PKPD analysis can also facilitate translational research by identifying species differences in pharmacokinetics and providing a basis for integrating these differences into interpretation of drug effects. Examples are discussed here to illustrate the application of PKPD analysis to the evaluation of drug effects in rhesus monkeys trained to discriminate cocaine from saline.

USING DISCRIMINANT ANALYSIS IN RELATIONSHIP MARKETING

OpenAIRE

Iacob Catoiu; Mihai Èšichindelean; Simona Vinerean

2013-01-01

The purpose of the present paper is to describe and apply discriminant analysis withina relationship marketing context. The paper is structured into two parts; the first part contains aliterature review regarding the value chain concept and the dimensions it is built on, while thesecond part includes the results of applying discriminant analysis on several value chaindimensions. The authors have considered the client-company relationships of the gas-station marketas proper for studying the di...

e-IQ and IQ knowledge mining for generalized LDA

Science.gov (United States)

Jenkins, Jeffrey; van Bergem, Rutger; Sweet, Charles; Vietsch, Eveline; Szu, Harold

2015-05-01

How can the human brain uncover patterns, associations and features in real-time, real-world data? There must be a general strategy used to transform raw signals into useful features, but representing this generalization in the context of our information extraction tool set is lacking. In contrast to Big Data (BD), Large Data Analysis (LDA) has become a reachable multi-disciplinary goal in recent years due in part to high performance computers and algorithm development, as well as the availability of large data sets. However, the experience of Machine Learning (ML) and information communities has not been generalized into an intuitive framework that is useful to researchers across disciplines. The data exploration phase of data mining is a prime example of this unspoken, ad-hoc nature of ML - the Computer Scientist works with a Subject Matter Expert (SME) to understand the data, and then build tools (i.e. classifiers, etc.) which can benefit the SME and the rest of the researchers in that field. We ask, why is there not a tool to represent information in a meaningful way to the researcher asking the question? Meaning is subjective and contextual across disciplines, so to ensure robustness, we draw examples from several disciplines and propose a generalized LDA framework for independent data understanding of heterogeneous sources which contribute to Knowledge Discovery in Databases (KDD). Then, we explore the concept of adaptive Information resolution through a 6W unsupervised learning methodology feedback system. In this paper, we will describe the general process of man-machine interaction in terms of an asymmetric directed graph theory (digging for embedded knowledge), and model the inverse machine-man feedback (digging for tacit knowledge) as an ANN unsupervised learning methodology. Finally, we propose a collective learning framework which utilizes a 6W semantic topology to organize heterogeneous knowledge and diffuse information to entities within a society in

An investigation of particle behavior in gas-solid horizontal pipe flow by an extended LDA technique

Energy Technology Data Exchange (ETDEWEB)

Yong Lu; Donald H. Glass; William J. Easson [University of Edinburgh, Edinburgh (United Kingdom). Institute for Materials and Processes

2009-12-15

An extended Laser Doppler Anemometry (LDA) technique has been developed to measure the distributions of particle velocities and particle number rates over a whole pipe cross-section in a dilute pneumatic conveying system. The first extension concentrates on the transform matrix for predicting the laser beams' cross point in a pipe according to the shift coordinate of the 3D computer-controlled traverse system on which the probes of the LDA system were mounted. The second focuses on the proper LDA sample rate for the measurement of gas-solid pipe flow with polydisperse particles. A suitable LDA sample rate should ensure that enough data is recorded in the measurement interval to precisely calculate the particle mean velocity or other statistical values at every sample point. The present study explores the methodology as well as the fundamentals of measurements, using a laser facility, of the cross-sectional distributions of solid phase. In the horizontal gas-solid pipe flow (glass beads less than 110 {mu}m), the experimental data show that the cross-sectional flow patterns of the solid phase can be classified by annulus-like flow describing the axial particle velocity contours and stratified flow characterising particle number rate distribution over a cross-section. Thus, the cross-sectional flow pattern of the solid phase in a horizontal pipe may be annular or stratified dependent on whether the axial particle velocity or particle number rate is the phenomenon studied. 13 refs., 16 figs., 1 tab.

Authentication of animal fats using direct analysis in real time (DART) ionization-mass spectrometry and chemometric tools.

Science.gov (United States)

Vaclavik, Lukas; Hrbek, Vojtech; Cajka, Tomas; Rohlik, Bo-Anne; Pipek, Petr; Hajslova, Jana

2011-06-08

A combination of direct analysis in real time (DART) ionization coupled to time-of-flight mass spectrometry (TOFMS) and chemometrics was used for animal fat (lard and beef tallow) authentication. This novel instrumentation was employed for rapid profiling of triacylglycerols (TAGs) and polar compounds present in fat samples and their mixtures. Additionally, fat isolated from pork, beef, and pork/beef admixtures was analyzed. Mass spectral records were processed by principal component analysis (PCA) and stepwise linear discriminant analysis (LDA). DART-TOFMS profiles of TAGs were found to be more suitable for the purpose of discrimination among the examined fat types as compared to profiles of polar compounds. The LDA model developed using TAG data enabled not only reliable classification of samples representing neat fats but also detection of admixed lard and tallow at adulteration levels of 5 and 10% (w/w), respectively. The presented approach was also successfully applied to minced meat prepared from pork and beef with comparable fat content. Using the DART-TOFMS TAG profiles of fat isolated from meat mixtures, detection of 10% pork added to beef and vice versa was possible.

Spectral discrimination of macrophyte species during different seasons in a tropical wetland using in-situ hyperspectral remote sensing

Science.gov (United States)

Saluja, Ridhi; Garg, J. K.

2017-10-01

Wetlands, one of the most productive ecosystems on Earth, perform myriad ecological functions and provide a host of ecological services. Despite their ecological and economic values, wetlands have experienced significant degradation during the last century and the trend continues. Hyperspectral sensors provide opportunities to map and monitor macrophyte species within wetlands for their management and conservation. In this study, an attempt has been made to evaluate the potential of narrowband spectroradiometer data in discriminating wetland macrophytes during different seasons. main objectives of the research were (1) to determine whether macrophyte species could be discriminated based on in-situ hyperspectral reflectance collected over different seasons and at each measured waveband (400-950nm), (2) to compare the effectiveness of spectral reflectance and spectral indices in discriminating macrophyte species, and (3) to identify spectral wavelengths that are most sensitive in discriminating macrophyte species. Spectral characteristics of dominant wetland macrophyte species were collected seasonally using SVC GER 1500 portable spectroradiometer over the 400 to 1050nm spectral range at 1.5nm interval, at the Bhindawas wetland in the state of Haryana, India. Hyperspectral observations were pre-processed and subjected to statistical analysis, which involved a two-step approach including feature selection (ANOVA and KW test) and feature extraction (LDA and PCA). Statistical analysis revealed that the most influential wavelengths for discrimination were distributed along the spectral profile from visible to the near-infrared regions. The results suggest that hyperspectral data can be used discriminate wetland macrophyte species working as an effective tool for advanced mapping and monitoring of wetlands.

First-principle study on bonding mechanism of ZnO by LDA+U method

International Nuclear Information System (INIS)

Zhou, G.C.; Sun, L.Z.; Zhong, X.L.; Chen Xiaoshuang; Wei Lu; Wang, J.B.

2007-01-01

The electronic structure and the bonding mechanism of ZnO have been studied by using the Full-Potential Linear Augmented Plane Wave (FP-LAPW) method within the density-functional theory (DFT) based on LDA+U exchange correlation potential. The valence and the bonding charge density are calculated and compared with those derived from LDA and GGA to describe the bonding mechanism. The charge transfer along with the bonding process is analyzed by using the theory of Atoms in Molecules (AIM). The bonding, the topological characteristics and the p-d coupling effects on the bonding mechanism of ZnO are shown quantitatively with the critical points (CPs) along the bonding trajectory and the charge in the atomic basins. Meanwhile, the bonding characteristics for wurtzite, zinc blende and rocksalt phase of ZnO are discussed systematically in the present paper

Credit scoring analysis using kernel discriminant

Science.gov (United States)

Widiharih, T.; Mukid, M. A.; Mustafid

2018-05-01

Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.

Application of Discriminant Analysis on Romanian Insurance Market

Directory of Open Access Journals (Sweden)

Constantin Anghelache

2008-11-01

Full Text Available Discriminant analysis is a supervised learning technique that can be used in order to determine which variables are the best predictors of the classification of objects belonging to a population into predetermined classes. At the same time, discriminant analysis provides a powerful tool that enables researchers to make predictions regarding the classification of new objects into predefined classes. The main goal of discriminant analysis is to determine which of the N descriptive variables have the most discriminatory power, that is, which of them are the most relevant for the classification of objects into classes. In order to classify objects, we need a mathematical model that provides the rules for optimal allocation. This is the classifier. In this paper we will discuss three of the most important models of classification: the Bayesian criterion, the Mahalanobis criterion and the Fisher criterion. In this paper, we will use discriminant analysis to classify the insurance companies that operated on the Romanian market in 2006. We have selected a number of eigth (8 relevant variables: gross written premium (GR_WRI_PRE, net mathematical reserves (NET_M_PES, gross claims paid (GR_CL_PAID, net premium reserves (NET_PRE_RES, net claim reserves (NET_CL_RES, net income (NE—_INCOME, share capital (SHARE_CAP and gross written premium ceded in Reinsurance (GR_WRI_PRE_CED. Before proceeding to discriminant analysis, we performed cluster analysis on the initial data in order to identify classes (clusters that emerge from the data.

Discrete Discriminant analysis based on tree-structured graphical models

DEFF Research Database (Denmark)

Perez de la Cruz, Gonzalo; Eslava, Guillermina

The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant a...... analysis based on tree{structured graphical models is a simple nonlinear method competitive with, and sometimes superior to, other well{known linear methods like those assuming mutual independence between variables and linear logistic regression.......The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant...

Experimental study of the polyamorphism of water. I. The isobaric transitions from amorphous ices to LDA at 4 MPa

Science.gov (United States)

Handle, Philip H.; Loerting, Thomas

2018-03-01

The existence of more than one solid amorphous state of water is an extraordinary feature. Since polyamorphism might be connected to the liquid-liquid critical point hypothesis, it is particularly important to study the relations amongst the different amorphous ices. Here we study the polyamorphic transformations of several high pressure amorphous ices to low-density amorphous ice (LDA) at 4 MPa by isobaric heating utilising in situ volumetry and ex situ X-ray diffraction. We find that very-high density amorphous ice (VHDA) and unannealed high density amorphous ice (HDA) show significant relaxation before transforming to LDA, whereby VHDA is seen to relax toward HDA. By contrast, expanded HDA shows almost no relaxation prior to the transformation. The transition to LDA itself obeys criteria for a first-order-like transition in all cases. In the case of VHDA, even macroscopic phase separation is observed. These findings suggest that HDA and LDA are two clearly distinct polyamorphs. We further present evidence that HDA reaches the metastable equilibrium at 140 K and 0.1 GPa but only comes close to that at 140 K and 0.2 GPa. The most important is the path independence of the amorphous phase reached at 140 K and 0.1 GPa.

Thyroid nodule classification using ultrasound elastography via linear discriminant analysis.

Science.gov (United States)

Luo, Si; Kim, Eung-Hun; Dighe, Manjiri; Kim, Yongmin

2011-05-01

The non-surgical diagnosis of thyroid nodules is currently made via a fine needle aspiration (FNA) biopsy. It is estimated that somewhere between 250,000 and 300,000 thyroid FNA biopsies are performed in the United States annually. However, a large percentage (approximately 70%) of these biopsies turn out to be benign. Since the aggressive FNA management of thyroid nodules is costly, quantitative risk assessment and stratification of a nodule's malignancy is of value in triage and more appropriate healthcare resources utilization. In this paper, we introduce a new method for classifying the thyroid nodules based on the ultrasound (US) elastography features. Unlike approaches to assess the stiffness of a thyroid nodule by visually inspecting the pseudo-color pattern in the strain image, we use a classification algorithm to stratify the nodule by using the power spectrum of strain rate waveform extracted from the US elastography image sequence. Pulsation from the carotid artery was used to compress the thyroid nodules. Ultrasound data previously acquired from 98 thyroid nodules were used in this retrospective study to evaluate our classification algorithm. A classifier was developed based on the linear discriminant analysis (LDA) and used to differentiate the thyroid nodules into two types: (I) no FNA (observation-only) and (II) FNA. Using our method, 62 nodules were classified as type I, all of which were benign, while 36 nodules were classified as Type-II, 16 malignant and 20 benign, resulting in a sensitivity of 100% and specificity of 75.6% in detecting malignant thyroid nodules. This indicates that our triage method based on US elastography has the potential to substantially reduce the number of FNA biopsies (63.3%) by detecting benign nodules and managing them via follow-up observations rather than an FNA biopsy. Published by Elsevier B.V.

Contributions to sensitivity analysis and generalized discriminant analysis

International Nuclear Information System (INIS)

Jacques, J.

2005-12-01

Two topics are studied in this thesis: sensitivity analysis and generalized discriminant analysis. Global sensitivity analysis of a mathematical model studies how the output variables of this last react to variations of its inputs. The methods based on the study of the variance quantify the part of variance of the response of the model due to each input variable and each subset of input variables. The first subject of this thesis is the impact of a model uncertainty on results of a sensitivity analysis. Two particular forms of uncertainty are studied: that due to a change of the model of reference, and that due to the use of a simplified model with the place of the model of reference. A second problem was studied during this thesis, that of models with correlated inputs. Indeed, classical sensitivity indices not having significance (from an interpretation point of view) in the presence of correlation of the inputs, we propose a multidimensional approach consisting in expressing the sensitivity of the output of the model to groups of correlated variables. Applications in the field of nuclear engineering illustrate this work. Generalized discriminant analysis consists in classifying the individuals of a test sample in groups, by using information contained in a training sample, when these two samples do not come from the same population. This work extends existing methods in a Gaussian context to the case of binary data. An application in public health illustrates the utility of generalized discrimination models thus defined. (author)

Rapid thyroid dysfunction screening based on serum surface-enhanced Raman scattering and multivariate statistical analysis

Science.gov (United States)

Tian, Dayong; Lü, Guodong; Zhai, Zhengang; Du, Guoli; Mo, Jiaqing; Lü, Xiaoyi

2018-01-01

In this paper, serum surface-enhanced Raman scattering and multivariate statistical analysis are used to investigate a rapid screening technique for thyroid function diseases. At present, the detection of thyroid function has become increasingly important, and it is urgently necessary to develop a rapid and portable method for the detection of thyroid function. Our experimental results show that, by using the Silmeco-based enhanced Raman signal, the signal strength greatly increases and the characteristic peak appears obviously. It is also observed that the Raman spectra of normal and anomalous thyroid function human serum are significantly different. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose thyroid dysfunction, and the diagnostic accuracy was 87.4%. The use of serum surface-enhanced Raman scattering technology combined with PCA-LDA shows good diagnostic performance for the rapid detection of thyroid function. By means of Raman technology, it is expected that a portable device for the rapid detection of thyroid function will be developed.

Classification of adulterated honeys by multivariate analysis.

Science.gov (United States)

Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

2017-06-01

In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Flow field measurements using LDA and numerical computation for rod bundle of reactor fuel assembly

International Nuclear Information System (INIS)

Hu Jun; Zou Zunyu

1995-02-01

Local mean velocity and turbulence intensity measurements were obtained with DANTEC 55 X two-dimensional Laser Dopper Anemometry (LDA) for rod bundle of reactor fuel assembly test model which was a 4 x 4 rod bundle. The data were obtained from different experimental cross-sections both upstream and downstream of the model support plate. Measurements performed at test Reynolds numbers of 1.8 x 10 4 ∼3.6 x 10 4 . The results described the local and gross effects of the support plate on upstream and downstream flow distributions. A numerical computation was also given, the experimental results are in good agreement with the numerical one and the others in references. Finally, a few suggestions were proposed for how to use the LDA system well. (11 figs.)

Analysis of select Dalbergia and trade timber using direct analysis in real time and time-of-flight mass spectrometry for CITES enforcement.

Science.gov (United States)

Lancaster, Cady; Espinoza, Edgard

2012-05-15

International trade of several Dalbergia wood species is regulated by The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). In order to supplement morphological identification of these species, a rapid chemical method of analysis was developed. Using Direct Analysis in Real Time (DART) ionization coupled with Time-of-Flight (TOF) Mass Spectrometry (MS), selected Dalbergia and common trade species were analyzed. Each of the 13 wood species was classified using principal component analysis and linear discriminant analysis (LDA). These statistical data clusters served as reliable anchors for species identification of unknowns. Analysis of 20 or more samples from the 13 species studied in this research indicates that the DART-TOFMS results are reproducible. Statistical analysis of the most abundant ions gave good classifications that were useful for identifying unknown wood samples. DART-TOFMS and LDA analysis of 13 species of selected timber samples and the statistical classification allowed for the correct assignment of unknown wood samples. This method is rapid and can be useful when anatomical identification is difficult but needed in order to support CITES enforcement. Published 2012. This article is a US Government work and is in the public domain in the USA.

A LDA-based approach to promoting ranking diversity for genomics information retrieval.

Science.gov (United States)

Chen, Yan; Yin, Xiaoshi; Li, Zhoujun; Hu, Xiaohua; Huang, Jimmy Xiangji

2012-06-11

In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc. Hence, it is important of a biomedical IR system to be able to provide relevant and diverse answers to fulfill biologists' information needs. However traditional IR model only concerns with the relevance between retrieved documents and user query, but does not take redundancy between retrieved documents into account. This will lead to high redundancy and low diversity in the retrieval ranked lists. In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. We perform our approach on TREC 2007 Genomics collection and two distinctive IR baseline runs, which can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track. The proposed method is the first study of adopting topic model to genomics information retrieval, and demonstrates its effectiveness in promoting ranking diversity as well as in improving relevance of ranked lists of genomics search

Using Fourier transform infrared spectroscopy to evaluate biological effects induced by photodynamic therapy.

Science.gov (United States)

Lima, Cassio A; Goulart, Viviane P; Correa, Luciana; Zezell, Denise M

2016-07-01

Vibrational spectroscopic methods associated with multivariate statistical techniques have been succeeded in discriminating skin lesions from normal tissues. However, there is no study exploring the potential of these techniques to assess the alterations promoted by photodynamic effect in tissue. The present study aims to demonstrate the ability of Fourier Transform Infrared (FTIR) spectroscopy on Attenuated total reflection (ATR) sampling mode associated with principal component-linear discriminant analysis (PC-LDA) to evaluate the biochemical changes caused by photodynamic therapy (PDT) in skin neoplastic tissue. Cutaneous neoplastic lesions, precursors of squamous cell carcinoma (SCC), were chemically induced in Swiss mice and submitted to a single session of 5-aminolevulinic acid (ALA)-mediated PDT. Tissue sections with 5 μm thickness were obtained from formalin-fixed paraffin-embedded (FFPE) and processed prior to the histopathological analysis and spectroscopic measurements. Spectra were collected in mid-infrared region using a FTIR spectrometer on ATR sampling mode. Principal Component-Linear Discriminant Analysis (PC-LDA) was applied on preprocessed second derivatives spectra. Biochemical changes were assessed using PCA-loadings and accuracy of classification was obtained from PC-LDA . Sub-bands of Amide I (1,624 and 1,650 cm(-1) ) and Amide II (1,517 cm(-1) ) indicated a protein overexpression in non-treated and post-PDT neoplastic tissue compared with healthy skin, as well as a decrease in collagen fibers (1,204, 1,236, 1,282, and 1,338 cm(-1) ) and glycogen (1,028, 1,082, and 1,151 cm(-1) ) content. Photosensitized neoplastic tissue revealed shifted peak position and decreased β-sheet secondary structure of proteins (1,624 cm(-1) ) amount in comparison to non-treated neoplastic lesions. PC-LDA score plots discriminated non-treated neoplastic skin spectra from post-PDT cutaneous lesions with accuracy of 92.8%, whereas non-treated neoplastic

Pseudogap in normal underdoped phase of Bi2212: LDA + DMFT + Σk

International Nuclear Information System (INIS)

Nekrasov, I.A.; Kuchinskii, E.Z.; Pchelkina, Z.V.; Sadovskii, M.V.

2007-01-01

Pseudogap phenomena are observed for normal underdoped phase of different high-T c cuprates. Among others Bi 2 Sr 2 CaCu 2 O 8-δ (Bi2212) compound is one of the most studied experimentally [A. Damascelli, Z. Hussain, Z.-X. Shen, Rev. Mod. Phys. 75 (2003) 473; J.C. Campuzano, M.R. Norman, M. Randeria, in: K.H. Bennemann, J.B. Ketterson (Eds.), Physics of Superconductors, vol. 2, Springer, Berlin, 2004, p. 167; J. Fink et al., (cond-mat/0512307); X.J. Zhou et al., (cond-mat/0604284)]. To describe pseudogap regime in Bi2212, we employ novel generalized DMFT + Σ k approach [E.Z. Kuchinskii, I.A. Nekrasov, M.V. Sadovskii, JETP Lett. 82 (2005) 198; M.V. Sadovskii et al., Phys. Rev. B 72 (2005) 155105, and these proceedings, (doi:10.1016/j.physc.2007.03.367)]. This approach gives possibility to preserve conventional dynamical mean-field theory (DMFT) equations [A. Georges et al., Rev. Mod. Phys. 68 (1996) 13] and include an additional (momentum dependent) self-energy Σ k . In the present case, Σ k describes non-local dynamical correlations induced by short-ranged collective Heisenberg-like antiferromagnetic spin fluctuations [M.V. Sadovskii, Physics-Uspekhi 44 (2001) 515, (cond-mat/0408489)]. The effective single impurity problem in the DMFT + Σ k is solved by numerical renormalization group (NRG) [R. Bulla, A.C. Hewson, Th. Pruschke, J. Phys. Cond. Mat. 10 (1998) 8365; R. Bulla, Phys. Rev. Lett. 83 (1999) 136]. To take into account material specific properties of two neighboring CuO 2 layers of Bi2212 we employ local density approximation (LDA) to calculate necessary model parameters, e.g. the values of intra- and interlayer hopping integrals between Cu-sites. Onsite Coulomb interaction U for x 2 -y 2 orbital was calculated in constrained LDA method [O. Gunnarsson et al., Phys. Rev. B 39 (1989) 1708]. The value of pseudogap potential Δ was obtained within DMFT(NRG) [E.Z. Kuchinskii, I.A. Nekrasov, M.V. Sadovskii, JETP Lett. 82 (2005) 198; M.V. Sadovskii et al

Discriminant Analysis of Student Loan Applications

Science.gov (United States)

Dyl, Edward A.; McGann, Anthony F.

1977-01-01

The use of discriminant analysis in identifying potentially "good" versus potentially "bad" student loans is explained. The technique is applied to a sample of 200 student loan applications at the University of Wyoming. (LBH)

DFT calculations of electronic and optical properties of SrS with LDA, GGA and mGGA functionals

Energy Technology Data Exchange (ETDEWEB)

Sharma, Shatendra, E-mail: shatendra@gmai.com [University Science Instrumentation Centre, Jawaharlal Nehru University, New Delhi-110067 (India); Sharma, Jyotsna [School of Basic & Applied Sciences, K. R. Mangalam University, Sohna Road, Gurgaon-122103 (India); Sharma, Yogita [Department of Applied Sciences, KIIT, Sohna Road, Gurgaon-122103 (India)

2016-05-06

The theoretical investigations of electronic and optical properties of SrS are made using the first principle DFT calculations. The calculations are performed for the local-density approximation (LDA), generalized gradient approximation (GGA) and for an alternative form of GGA i.e. metaGGA for both rock salt type (B1, Fm3m) and cesium chloride (B2, Pm3m) structures. The band structure, density of states and optical spectra are calculated under various available functional. The calculations with LDA and GGA functional underestimate the values of band gaps with all functional, however the values with mGGA show reasonably good agreement with experimental and those calculated by using other methods.

PRICE DISCRIMINATION AND MARKET POWER: A THEORETICAL ANALYSIS

Directory of Open Access Journals (Sweden)

Olga Smirnova

2015-07-01

Full Text Available This paper analyzes the contemporary theoretical and empirical research in the field of impact assessment of market power and conclusions about the possibilities of the company to implement price discrimination in different market structures. The results of the analysis allow to evaluate current approaches to antitrust regulation of price discrimination.

Application of texture analysis method for classification of benign and malignant thyroid nodules in ultrasound images.

Science.gov (United States)

Abbasian Ardakani, Ali; Gharbali, Akbar; Mohammadi, Afshin

2015-01-01

The aim of this study was to evaluate computer aided diagnosis (CAD) system with texture analysis (TA) to improve radiologists' accuracy in identification of thyroid nodules as malignant or benign. A total of 70 cases (26 benign and 44 malignant) were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of interests (ROIs) in three normalization schemes (default, 3s and 1%-99%). Then features by the lowest probability of classification error and average correlation coefficients (POE+ACC), and Fisher coefficient (Fisher) eliminated to 10 best and most effective features. These features were analyzed under standard and nonstandard states. For TA of the thyroid nodules, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA) were applied. First Nearest-Neighbour (1-NN) classifier was performed for the features resulting from PCA and LDA. NDA features were classified by artificial neural network (A-NN). Receiver operating characteristic (ROC) curve analysis was used for examining the performance of TA methods. The best results were driven in 1-99% normalization with features extracted by POE+ACC algorithm and analyzed by NDA with the area under the ROC curve ( Az) of 0.9722 which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Our results indicate that TA is a reliable method, can provide useful information help radiologist in detection and classification of benign and malignant thyroid nodules.

Discrimination between smiling faces: Human observers vs. automated face analysis.

Science.gov (United States)

Del Líbano, Mario; Calvo, Manuel G; Fernández-Martín, Andrés; Recio, Guillermo

2018-05-11

This study investigated (a) how prototypical happy faces (with happy eyes and a smile) can be discriminated from blended expressions with a smile but non-happy eyes, depending on type and intensity of the eye expression; and (b) how smile discrimination differs for human perceivers versus automated face analysis, depending on affective valence and morphological facial features. Human observers categorized faces as happy or non-happy, or rated their valence. Automated analysis (FACET software) computed seven expressions (including joy/happiness) and 20 facial action units (AUs). Physical properties (low-level image statistics and visual saliency) of the face stimuli were controlled. Results revealed, first, that some blended expressions (especially, with angry eyes) had lower discrimination thresholds (i.e., they were identified as "non-happy" at lower non-happy eye intensities) than others (especially, with neutral eyes). Second, discrimination sensitivity was better for human perceivers than for automated FACET analysis. As an additional finding, affective valence predicted human discrimination performance, whereas morphological AUs predicted FACET discrimination. FACET can be a valid tool for categorizing prototypical expressions, but is currently more limited than human observers for discrimination of blended expressions. Configural processing facilitates detection of in/congruence(s) across regions, and thus detection of non-genuine smiling faces (due to non-happy eyes). Copyright © 2018 Elsevier B.V. All rights reserved.

Characterization of ionizing radiation effects on bone using Fourier Transform Infrared Spectroscopy and multivariate analysis of spectra

Energy Technology Data Exchange (ETDEWEB)

Castro, Pedro Arthur Augusto de; Dias, Derly Augusto; Zezell, Denise Maria, E-mail: zezell@usp.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2017-11-01

Ionizing radiation has been used as an important treatment and diagnostic method for several diseases. Optical techniques provides an efficient clinical diagnostic to support an accurate evaluation of the interaction of radiation with molecules. Fourier-transform infrared spectroscopy coupled with attenuated total reflectance (ATR-FTIR) is a label-free and nondestructive optical technique that can recognize functional groups in biological samples. In this work, 30 fragments of bone were collected from bovine femur diaphysis. Samples were cut and polished until 1 cm x 1 cm x 1 mm, which were then stored properly in the refrigerated environment. Samples irradiation was performed with a Cobalt-60 Gammacell Irradiator source at doses of 0.1 kGy, 1 kGy, whereas the fragments exposed to dose of 15 kGy was irradiated in a multipurpose irradiator of Cobalt-60. Spectral data was submitted to principal component analysis followed by linear discriminant analysis. Multivariate analysis was performed with Principal component analysis(PCA) followed by Linear Discriminant Analysis(LDA) using MATLAB R2015a software (The Mathworks Inc., Natick, MA, USA). We demonstrated the feasibility of using ATR-FTIR spectroscopy associated with PCA-LDA multivariate technique to evaluate the molecular changes in bone matrix caused by different doses: 0.1 kGy, 1 kGy and 15 kGy. These alterations between the groups are mainly reported in phosphate region. Our results open up new possibilities for protein monitoring relating to dose responses. (author)

Characterization of ionizing radiation effects on bone using Fourier Transform Infrared Spectroscopy and multivariate analysis of spectra

International Nuclear Information System (INIS)

Castro, Pedro Arthur Augusto de; Dias, Derly Augusto; Zezell, Denise Maria

2017-01-01

Ionizing radiation has been used as an important treatment and diagnostic method for several diseases. Optical techniques provides an efficient clinical diagnostic to support an accurate evaluation of the interaction of radiation with molecules. Fourier-transform infrared spectroscopy coupled with attenuated total reflectance (ATR-FTIR) is a label-free and nondestructive optical technique that can recognize functional groups in biological samples. In this work, 30 fragments of bone were collected from bovine femur diaphysis. Samples were cut and polished until 1 cm x 1 cm x 1 mm, which were then stored properly in the refrigerated environment. Samples irradiation was performed with a Cobalt-60 Gammacell Irradiator source at doses of 0.1 kGy, 1 kGy, whereas the fragments exposed to dose of 15 kGy was irradiated in a multipurpose irradiator of Cobalt-60. Spectral data was submitted to principal component analysis followed by linear discriminant analysis. Multivariate analysis was performed with Principal component analysis(PCA) followed by Linear Discriminant Analysis(LDA) using MATLAB R2015a software (The Mathworks Inc., Natick, MA, USA). We demonstrated the feasibility of using ATR-FTIR spectroscopy associated with PCA-LDA multivariate technique to evaluate the molecular changes in bone matrix caused by different doses: 0.1 kGy, 1 kGy and 15 kGy. These alterations between the groups are mainly reported in phosphate region. Our results open up new possibilities for protein monitoring relating to dose responses. (author)

The use of the discriminant analysis method for e π μ separation in BES

International Nuclear Information System (INIS)

Jiang Zhijin; Wang Taijie; Xie Yigang; Huang Tao

1994-01-01

We use the discriminant analysis method in multivariate statistical theory to handle the e π μ separation in BES, describing the principle of the discriminant analysis method, deriving the unstandardized discriminant functions (responsible for particle separation), giving the discriminant efficiency for e π μ and comparing the results from the discriminant analysis method with those obtained in a conventional way. ((orig.))

[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

Science.gov (United States)

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

Regularized Discriminant Analysis: A Large Dimensional Study

KAUST Repository

Yang, Xiaoke

2018-04-28

In this thesis, we focus on studying the performance of general regularized discriminant analysis (RDA) classifiers. The data used for analysis is assumed to follow Gaussian mixture model with different means and covariances. RDA offers a rich class of regularization options, covering as special cases the regularized linear discriminant analysis (RLDA) and the regularized quadratic discriminant analysis (RQDA) classi ers. We analyze RDA under the double asymptotic regime where the data dimension and the training size both increase in a proportional way. This double asymptotic regime allows for application of fundamental results from random matrix theory. Under the double asymptotic regime and some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that only depends on the data statistical parameters and dimensions. This result not only implicates some mathematical relations between the misclassification error and the class statistics, but also can be leveraged to select the optimal parameters that minimize the classification error, thus yielding the optimal classifier. Validation results on the synthetic data show a good accuracy of our theoretical findings. We also construct a general consistent estimator to approximate the true classification error in consideration of the unknown previous statistics. We benchmark the performance of our proposed consistent estimator against classical estimator on synthetic data. The observations demonstrate that the general estimator outperforms others in terms of mean squared error (MSE).

ASTM clustering for improving coal analysis by near-infrared spectroscopy.

Science.gov (United States)

Andrés, J M; Bona, M T

2006-11-15

Multivariate analysis techniques have been applied to near-infrared (NIR) spectra coals to investigate the relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding predictor variables. In this work, a whole set of coal samples was grouped into six more homogeneous clusters following the ASTM reference method for classification prior to the application of calibration methods to each coal set. The results obtained showed a considerable improvement of the error determination compared with the calibration for the whole sample set. For some groups, the established calibrations approached the quality required by the ASTM/ISO norms for laboratory analysis. To predict property values for a new coal sample it is necessary the assignation of that sample to its respective group. Thus, the discrimination and classification ability of coal samples by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) in the NIR range was also studied by applying Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) techniques. Modelling of the groups by SIMCA led to overlapping models that cannot discriminate for unique classification. On the other hand, the application of Linear Discriminant Analysis improved the classification of the samples but not enough to be satisfactory for every group considered.

Differentiation of Organically and Conventionally Grown Tomatoes by Chemometric Analysis of Combined Data from Proton Nuclear Magnetic Resonance and Mid-infrared Spectroscopy and Stable Isotope Analysis.

Science.gov (United States)

Hohmann, Monika; Monakhova, Yulia; Erich, Sarah; Christoph, Norbert; Wachter, Helmut; Holzgrabe, Ulrike

2015-11-04

Because the basic suitability of proton nuclear magnetic resonance spectroscopy ((1)H NMR) to differentiate organic versus conventional tomatoes was recently proven, the approach to optimize (1)H NMR classification models (comprising overall 205 authentic tomato samples) by including additional data of isotope ratio mass spectrometry (IRMS, δ(13)C, δ(15)N, and δ(18)O) and mid-infrared (MIR) spectroscopy was assessed. Both individual and combined analytical methods ((1)H NMR + MIR, (1)H NMR + IRMS, MIR + IRMS, and (1)H NMR + MIR + IRMS) were examined using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and common components and specific weight analysis (ComDim). With regard to classification abilities, fused data of (1)H NMR + MIR + IRMS yielded better validation results (ranging between 95.0 and 100.0%) than individual methods ((1)H NMR, 91.3-100%; MIR, 75.6-91.7%), suggesting that the combined examination of analytical profiles enhances authentication of organically produced tomatoes.

Determination of Ignitable Liquids in Fire Debris: Direct Analysis by Electronic Nose

Directory of Open Access Journals (Sweden)

Marta Ferreiro-González

2016-05-01

Full Text Available Arsonists usually use an accelerant in order to start or accelerate a fire. The most widely used analytical method to determine the presence of such accelerants consists of a pre-concentration step of the ignitable liquid residues followed by chromatographic analysis. A rapid analytical method based on headspace-mass spectrometry electronic nose (E-Nose has been developed for the analysis of Ignitable Liquid Residues (ILRs. The working conditions for the E-Nose analytical procedure were optimized by studying different fire debris samples. The optimized experimental variables were related to headspace generation, specifically, incubation temperature and incubation time. The optimal conditions were 115 °C and 10 min for these two parameters. Chemometric tools such as hierarchical cluster analysis (HCA and linear discriminant analysis (LDA were applied to the MS data (45–200 m/z to establish the most suitable spectroscopic signals for the discrimination of several ignitable liquids. The optimized method was applied to a set of fire debris samples. In order to simulate post-burn samples several ignitable liquids (gasoline, diesel, citronella, kerosene, paraffin were used to ignite different substrates (wood, cotton, cork, paper and paperboard. A full discrimination was obtained on using discriminant analysis. This method reported here can be considered as a green technique for fire debris analyses.

Determination of Ignitable Liquids in Fire Debris: Direct Analysis by Electronic Nose

Science.gov (United States)

Ferreiro-González, Marta; Barbero, Gerardo F.; Palma, Miguel; Ayuso, Jesús; Álvarez, José A.; Barroso, Carmelo G.

2016-01-01

Arsonists usually use an accelerant in order to start or accelerate a fire. The most widely used analytical method to determine the presence of such accelerants consists of a pre-concentration step of the ignitable liquid residues followed by chromatographic analysis. A rapid analytical method based on headspace-mass spectrometry electronic nose (E-Nose) has been developed for the analysis of Ignitable Liquid Residues (ILRs). The working conditions for the E-Nose analytical procedure were optimized by studying different fire debris samples. The optimized experimental variables were related to headspace generation, specifically, incubation temperature and incubation time. The optimal conditions were 115 °C and 10 min for these two parameters. Chemometric tools such as hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) were applied to the MS data (45–200 m/z) to establish the most suitable spectroscopic signals for the discrimination of several ignitable liquids. The optimized method was applied to a set of fire debris samples. In order to simulate post-burn samples several ignitable liquids (gasoline, diesel, citronella, kerosene, paraffin) were used to ignite different substrates (wood, cotton, cork, paper and paperboard). A full discrimination was obtained on using discriminant analysis. This method reported here can be considered as a green technique for fire debris analyses. PMID:27187407

LDA-PIV Diagnostics and 3D Simulation of Oscillating Swirl Flow in a Closed Cylindrical Container

DEFF Research Database (Denmark)

Naumov, Igor; Okulov, V. L.; Meyer, Knud Erik

2003-01-01

Results on unsteady vortex breakdown are obained simultaneously using two diagnostics methods: a) determination of velocity fields by particle tracks (Particle Image Velocimeter - PIV), b) determination of velocity fields by Laser Doppler Anemometry (LDA), are presented.The experiments data are i...

Cancers of unknown primary origin (CUP) are characterized by chromosomal instability (CIN) compared to metastasis of know origin

DEFF Research Database (Denmark)

Vikeså, Jonas; Møller, Anne Kirstine H; Kaczkowski, Bogumil

2015-01-01

BACKGROUND: Cancers of unknown primary (CUPs) constitute ~5% of all cancers. The tumors have an aggressive biological and clinical behavior. The aim of the present study has been to uncover whether CUPs exhibit distinct molecular features compared to metastases of known origin. METHODS: Employing......RNA signatures of chromosome instability (CIN), indicating that CUPs are chromosome unstable compared to metastases of known origin. CONCLUSIONS: CIN may account for the uncommon clinical presentation, chemoresistance and poor outcome in patients with CUP and warrant selective diagnostic strategies and treatment....... genome wide transcriptome analysis, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), we defined the putative origins of a large series of CUP and how closely related a particular CUP was to corresponding metastases of known origin. LDA predictions were subsequently used...

The geographical origin and chemical composition in phellinus mushrooms measured by instrumental neutron activation analysis

International Nuclear Information System (INIS)

Lim, J.M.; Sun, G.M.; Moon, J.H.; Chung, Y.S.; Lee, J.H.; Kim, K.H.

2012-01-01

In order to expand the utilization of phellinus mushrooms as a dietary supplement, we attempted to evaluate the chemical composition by measuring its inorganic elemental content with the aid of instrumental neutron activation analysis (INAA). Twenty seven phellinus mushrooms samples were collected from Korea, Cambodia, and Vietnam. A total of 28 elements were analyzed in the phellinus mushroom samples using the INAA. The concentrations of Ca, K, and Mg are much higher than those of other elements in phellinus mushroom samples. The sum of determined elemental concentration in Cambodia samples was about 2-6 times higher than those in Korea and Vietnam samples, respectively. Based on our measurement data, we attempted to discriminate the geographical origin using principal components analysis (PCA) and linear discriminant analysis (LDA). The geographical origins of all samples were clearly classified with correct classification rate of 100%. (author)

Determination of the stagnation point in pulverized coal swirl flames by detailed analysis of laser velocity measurements; Staupunktbestimmung in Kohlenstaub-Drallflammen mittels detaillierter Analyse von LDA-Daten

Energy Technology Data Exchange (ETDEWEB)

Ohliger, A.; Stadler, H.; Foerster, M.; Kneer, R. [RWTH Aachen University (Germany). Lehrstuhl fuer Waerme- und Stoffuebertragung

2009-07-01

When Laser Doppler Anemometry (LDA) is used for experimental investigation of flow fields in pulverised coal flames, the measured coal particle velocities are usually averaged in order to determine the gas velocity. This paper shows that this approach can lead to a misinterpretation of the data. In the burner vicinity of the investigated flame, where high accelerations in the gas phase occur, a discrepancy appears between the measured velocity distribution and the expected normal distribution. Thus, a detailed analysis of the measured particle data is conducted and compared to conventional averaging. The difference can be attributed to large particles from the inner recirculation zone of the flame, which do not follow the gas flow properly. (orig.)

Discriminant analysis of functional optical topography for schizophrenia diagnosis

Science.gov (United States)

Chuang, Ching-Cheng; Nakagome, Kazuyuki; Pu, Shenghong; Lan, Tsuo-Hung; Lee, Chia-Yen; Sun, Chia-Wei

2014-01-01

Abnormal prefrontal function plays a central role in the cognition deficits of schizophrenic patients; however, the character of the relationship between discriminant analysis and prefrontal activation remains undetermined. Recently, evidence of low prefrontal cortex (PFC) activation in individuals with schizophrenia has also been found during verbal fluency tests (VFT) and other cognitive tests with several neuroimaging methods. The purpose of this study is to assess the hemodynamic changes of the PFC and discriminant analysis between schizophrenia patients and healthy controls during VFT task by utilizing functional optical topography. A total of 99 subjects including 53 schizophrenic patients and 46 age- and gender-matched healthy controls were studied. The results showed that the healthy group had larger activation in the right and left PFC than in the middle PFC. Besides, the schizophrenic group showed weaker task performance and lower activation in the whole PFC than the healthy group. The result of the discriminant analysis showed a significant difference with P value <0.001 in six channels (CH 23, 29, 31, 40, 42, 52) between the schizophrenic and healthy groups. Finally, 68.69% and 71.72% of subjects are correctly classified as being schizophrenic or healthy with all 52 channels and six significantly different channels, respectively. Our findings suggest that the left PFC can be a feature region for discriminant analysis of schizophrenic diagnosis.

A Biomimetic Sensor for the Classification of Honeys of Different Floral Origin and the Detection of Adulteration

Directory of Open Access Journals (Sweden)

Maz Jamilah Masnan

2011-08-01

Full Text Available The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose and electronic tongue (e-tongue measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA and Linear Discriminant Analysis (LDA were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification

A Large Dimensional Analysis of Regularized Discriminant Analysis Classifiers

KAUST Repository

Elkhalil, Khalil

2017-11-01

This article carries out a large dimensional analysis of standard regularized discriminant analysis classifiers designed on the assumption that data arise from a Gaussian mixture model with different means and covariances. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under mild assumptions, we show that the asymptotic classification error approaches a deterministic quantity that depends only on the means and covariances associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized discriminant analsysis, in practical large but finite dimensions, and can be used to determine and pre-estimate the optimal regularization parameter that minimizes the misclassification error probability. Despite being theoretically valid only for Gaussian data, our findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from the popular USPS data base, thereby making an interesting connection between theory and practice.

Fluid Dynamic Models for Bhattacharyya-Based Discriminant Analysis.

Science.gov (United States)

Noh, Yung-Kyun; Hamm, Jihun; Park, Frank Chongwoo; Zhang, Byoung-Tak; Lee, Daniel D

2018-01-01

Classical discriminant analysis attempts to discover a low-dimensional subspace where class label information is maximally preserved under projection. Canonical methods for estimating the subspace optimize an information-theoretic criterion that measures the separation between the class-conditional distributions. Unfortunately, direct optimization of the information-theoretic criteria is generally non-convex and intractable in high-dimensional spaces. In this work, we propose a novel, tractable algorithm for discriminant analysis that considers the class-conditional densities as interacting fluids in the high-dimensional embedding space. We use the Bhattacharyya criterion as a potential function that generates forces between the interacting fluids, and derive a computationally tractable method for finding the low-dimensional subspace that optimally constrains the resulting fluid flow. We show that this model properly reduces to the optimal solution for homoscedastic data as well as for heteroscedastic Gaussian distributions with equal means. We also extend this model to discover optimal filters for discriminating Gaussian processes and provide experimental results and comparisons on a number of datasets.

Comparison of cranial sex determination by discriminant analysis and logistic regression.

Science.gov (United States)

Amores-Ampuero, Anabel; Alemán, Inmaculada

2016-04-05

Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).

Classification of Malaysia aromatic rice using multivariate statistical analysis

Energy Technology Data Exchange (ETDEWEB)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A. [School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600 Arau, Perlis (Malaysia); Omar, O. [Malaysian Agriculture Research and Development Institute (MARDI), Persiaran MARDI-UPM, 43400 Serdang, Selangor (Malaysia)

2015-05-15

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

Classification of Malaysia aromatic rice using multivariate statistical analysis

Science.gov (United States)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

2015-05-01

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

Classification of Malaysia aromatic rice using multivariate statistical analysis

International Nuclear Information System (INIS)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

2015-01-01

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties

Application of discriminant analysis and generalized distance measures to uranium exploration

International Nuclear Information System (INIS)

Beauchamp, J.J.; Begovich, C.L.; Kane, V.E.; Wolf, D.A.

1979-10-01

The National Uranium Resource Evaluation (NURE) Project has as its goal estimation of the nation's uranium resources. It is possible to use discriminant analysis methods on hydrogeochemical data collected in the NURE Program to aid in formulating geochemical models which can be used to identify the anomalous regions necessary for resource estimation. Discriminant analysis methods have been applied to data from the Plainview, Texas Quadrangle which has approximately 850 groundwater samples with more than 40 quantitative measurements per sample. Discriminant analysis topics involving estimation of misclassification probabilities, variable selection, and robust discrimination are applied. A method using generalized distance measures is given which enables assigning samples to a background population or a mineralized population whose parameters were estimated from separate studies. Each topic is related to its relevance in identifying areas of possible interest to uranium exploration

First-principles study of SnS electronic properties using LDA, PBE and HSE06 functionals

Science.gov (United States)

Ibragimova, R.; Ganchenkova, M.; Karazhanov, S.; Marstein, E. S.

2018-03-01

Recently, tin sulphide (SnS) has emerged as a promising alternative to conventional CIGS and CZTC for use in solar cells, possessing such properties as non-toxicity, low cost and production stability. SnS has a high theoretically predicted efficiency above 20%, but the experimentally achieved efficiency so far is as low as 4.36%. The reason for the low achieved efficiency is unclear. One of the powerful tools to get deeper insights about the nature of the problem is first-principles calculation approaches. That is why SnS has become an attractive subject for first-principles calculations recently. Previously calculated data, however, show a widespread of such fundamental value as the bandgap varying from 0.26 to 1.26 eV. In order to understand a reason for that, in this work, we concentrate on a systematic study of calculation parameters effects on the resulting electronic structure, with the particular attention paid to the influence of the exchange-correlation functional chosen for calculations. Several exchange-correlation functionals (LDA, PBE and HSE06) were considered. The systematic analysis has shown that the bandgap variation can result from a tensile/compressive hydrostatic pressure introduced by non-equilibrium lattice parameters used for the calculations. The study of the applicability of three functionals has shown that HSE06 gives the best match to both experimentally obtained bandgap and the XPS valence band spectra. LDA underestimates the bandgap but qualitatively reproduces experimentally measured valence DOS similar to that of HSE06 in contrast to PBE. PBE underestimates the bandgap and does not match to the measured XPS spectra.

FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications.

Science.gov (United States)

Backenroth, Daniel; He, Zihuai; Kiryluk, Krzysztof; Boeva, Valentina; Pethukova, Lynn; Khurana, Ekta; Christiano, Angela; Buxbaum, Joseph D; Ionita-Laza, Iuliana

2018-05-03

We describe a method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell-type- and/or tissue-specific way (FUN-LDA). Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome in 127 different tissues and cell types. We demonstrate the usefulness of our predictions by using several validation experiments. Using eQTL data from several sources, including the GTEx project, Geuvadis project, and TwinsUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap. We further show how these integrated functional scores can be used for (1) deriving the most likely cell or tissue type causally implicated for a complex trait by using summary statistics from genome-wide association studies and (2) estimating a tissue-based correlation matrix of various complex traits. We found large enrichment of heritability in functional components of relevant tissues for various complex traits, and FUN-LDA yielded higher enrichment estimates than existing methods. Finally, using experimentally validated functional variants from the literature and variants possibly implicated in disease by previous studies, we rigorously compare FUN-LDA with state-of-the-art functional annotation methods and show that FUN-LDA has better prediction accuracy and higher resolution than these methods. In particular, our results suggest that tissue- and cell-type-specific functional prediction methods tend to have substantially better prediction accuracy than organism-level prediction methods. Scores for each position in the human genome and for each ENCODE and Roadmap tissue are available online (see Web Resources). Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Investigation of the distal margins of the LVF/LDA environments in the northern midlatitudes of Mars.

Science.gov (United States)

Morgan, G. A.; Head, J. W., III

2008-09-01

Introduction The northern dichotomy boundary on Mars consists of an abrupt escarpment between the cratered uplands and the northern plains. The boundary is characterized by fretted valleys which divide the uplands into a series of plateaus and mesas which become progressively smaller to the north and eventually merge with the northern plains [1]. Lineated Valley Fill (LVF) and Lobate Debris Aprons (LDA) deposits are a prominent feature of the dichotomy boundary [2] and have been attributed by some [e.g., 3] to be the remains of debris-covered glaciers that were active during previous climatic regimes when snow was deposited along the dichotomy boundary [4-5]. Recent in depth analysis of LVF/LDA has been made possible by new comprehensive high-resolution data from Mars Odyssey, Mars Express and Mars Reconnaissance Orbiter. and has revealed evidence for multiple LVF/LDA emplacement events [6-8], as well as evidence for plateau glaciation [4] and evidence that glacial conditions extended much further from the margins of the dichotomy boundary than previously thought [4]. However, remaining unexamined, however, are the regions adjacent to the current positions of the LVF/LDA deposits where one can explore the relationship between the distal landforms present there and the main bodies of LVF/LDA. We have therefore investigated an area of the northern plains directly north of a region of the dichotomy boundary where detailed studies have reported largescale integrated LVF systems >10,000 km3 in extent, that are analogous to terrestrial debris covered glacial systems [3,6-8]. Through the utilization of the most recent data sets (CTX and HiRISE) we have categorized the distinct landform assemblages associated with this area and documented the degraded nature of lobate debris aprons and related features present there. Degraded Lobate Debris Aprons All of the LDAs in the study region have troughs cut into their surface which are aligned in the same direction as the flanks of

Rapid Discrimination for Traditional Complex Herbal Medicines from Different Parts, Collection Time, and Origins Using High-Performance Liquid Chromatography and Near-Infrared Spectral Fingerprints with Aid of Pattern Recognition Methods

Directory of Open Access Journals (Sweden)

Haiyan Fu

2015-01-01

Full Text Available As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC and near-infrared (NIR, with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information of Hibiscus mutabilis L. and Berberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA, linear discriminant analysis (LDA, and particularly partial least squares discriminant analysis (PLSDA with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines.

Understanding flood-induced water chemistry variability extracting temporal patterns with the LDA method

Science.gov (United States)

Aubert, A. H.; Tavenard, R.; Emonet, R.; De Lavenne, A.; Malinowski, S.; Guyet, T.; Quiniou, R.; Odobez, J.; Merot, P.; Gascuel-odoux, C.

2013-12-01

Studying floods has been a major issue in hydrological research for years, both in quantitative and qualitative hydrology. Stream chemistry is a mix of solutes, often used as tracers, as they originate from various sources in the catchment and reach the stream by various flow pathways. Previous studies (for instance (1)) hypothesized that stream chemistry reaction to a rainfall event is not unique but varies seasonally, and according to the yearly meteorological conditions. Identifying a typology of flood temporal chemical patterns is a way to better understand catchment processes at the flood and seasonal time scale. We applied a probabilistic model (Latent Dirichlet Allocation or LDA (2)) mining recurrent sequential patterns from a dataset of floods. A set of 472 floods was automatically extracted from a daily 12-year long record of nitrate, dissolved organic carbon, sulfate and chloride concentrations. Rainfall, discharge, water table depth and temperature are also considered. Data comes from a long-term hydrological observatory (AgrHys, western France) located at Kervidy-Naizin. From each flood, a document has been generated that is made of a set of "hydrological words". Each hydrological word corresponds to a measurement: it is a triplet made of the considered variable, the time at which the measurement is made (relative to the beginning of the flood), and its magnitude (that can be low, medium or high). The documents and the number of pattern to be mined are used as input data to the LDA algorithm. LDA relies on spotting co-occurrences (as an alternative to the more traditional study of correlation) between words that appear within the flood documents. It has two nice properties that are its ability to easily deal with missing data and its additive property that allows a document to be seen as a mixture of several flood patterns. The output of LDA is a set of patterns easily represented in graphics. These patterns correspond to typical reactions to rainfall

Multi spectral imaging analysis for meat spoilage discrimination

DEFF Research Database (Denmark)

Christiansen, Asger Nyman; Carstensen, Jens Michael; Papadopoulou, Olga

classification methods: Naive Bayes Classifier as a reference model, Canonical Discriminant Analysis (CDA) and Support Vector Classification (SVC). As the final step, generalization of the models was performed using k-fold validation (k=10). Results showed that image analysis provided good discrimination of meat......In the present study, fresh beef fillets were purchased from a local butcher shop and stored aerobically and in modified atmosphere packaging (MAP, CO2 40%/O2 30%/N2 30%) at six different temperatures (0, 4, 8, 12, 16 and 20°C). Microbiological analysis in terms of total viable counts (TVC......) was performed in parallel with videometer image snapshots and sensory analysis. Odour and colour characteristics of meat were determined by a test panel and attributed into three pre-characterized quality classes, namely Fresh; Semi Fresh and Spoiled during the days of its shelf life. So far, different...

Toward improving fine needle aspiration cytology by applying Raman microspectroscopy

Science.gov (United States)

Becker-Putsche, Melanie; Bocklitz, Thomas; Clement, Joachim; Rösch, Petra; Popp, Jürgen

2013-04-01

Medical diagnosis of biopsies performed by fine needle aspiration has to be very reliable. Therefore, pathologists/cytologists need additional biochemical information on single cancer cells for an accurate diagnosis. Accordingly, we applied three different classification models for discriminating various features of six breast cancer cell lines by analyzing Raman microspectroscopic data. The statistical evaluations are implemented by linear discriminant analysis (LDA) and support vector machines (SVM). For the first model, a total of 61,580 Raman spectra from 110 single cells are discriminated at the cell-line level with an accuracy of 99.52% using an SVM. The LDA classification based on Raman data achieved an accuracy of 94.04% by discriminating cell lines by their origin (solid tumor versus pleural effusion). In the third model, Raman cell spectra are classified by their cancer subtypes. LDA results show an accuracy of 97.45% and specificities of 97.78%, 99.11%, and 98.97% for the subtypes basal-like, HER2+/ER-, and luminal, respectively. These subtypes are confirmed by gene expression patterns, which are important prognostic features in diagnosis. This work shows the applicability of Raman spectroscopy and statistical data handling in analyzing cancer-relevant biochemical information for advanced medical diagnosis on the single-cell level.

Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis

Science.gov (United States)

Jarrell, Stephen B.; Stanley, T. D.

2004-01-01

The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.

Application of discriminant analysis and generalized distance measures to uranium exploration

International Nuclear Information System (INIS)

Beauchamp, J.J.; Begovich, C.L.; Kane, V.E.; Wolf, D.A.

1980-01-01

The National Uranium Resource Evaluation (NURE) Program has as its goal the estimation of the nation's uranium resources. It is possile to use discriminant analysis methods on hydrogeochemical data collected in the NURE Program to aid in fomulating geochemical models that can be used to identify the anomalous areas used in resource estimation. Discriminant' analysis methods have been applied to data from the Plainview, Texas Quadrangle which has approximately 850 groundwater samples with more than 40 quantitative measurements per sample. Discriminant analysis topics involving estimation of misclassification probabilities, variable selection, and robust discrimination are applied. A method using generalized distance measures is given which enables the assignment of samples to a background population or a mineralized population whose parameters were estimated from separate studies. Each topic is related to its relevance in identifying areas of possible interest to uranium exploration. However, the methodology presented here is applicable to the identification of regions associated with other types of resources. 8 figures, 3 tables

Sub-pattern based multi-manifold discriminant analysis for face recognition

Science.gov (United States)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

Application of texture analysis method for mammogram density classification

Science.gov (United States)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil.

Science.gov (United States)

Forina, M; Oliveri, P; Bagnasco, L; Simonetti, R; Casolino, M C; Nizzi Grifi, F; Casale, M

2015-11-01

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area. Copyright © 2015 Elsevier B.V. All rights reserved.

A Hybrid System Based on an Electronic Nose Coupled with an Electronic Tongue for the Characterization of Moroccan Waters

Directory of Open Access Journals (Sweden)

Z. Haddi

2014-05-01

Full Text Available A hybrid multisensor system combined with multivariate analysis was applied to the characterization of different kinds of Moroccan waters. The proposed hybrid system based on an electronic nose coupled with an electronic tongue consisted of metal oxide semiconductors and potentiometric sensors respectively. Five Taguchi Gas Sensors were implemented in the electronic nose for the discrimination between mineral, natural, sparkling, river and tap waters. Afterwards, the electronic tongue, based on series of Ion-Selective-Electrodes was applied to the analysis of the same waters. Multisensor responses obtained from the waters were processed by two chemometrics: Principal Component Analysis (PCA and Linear Discriminant Analysis (LDA. PCA results using electronic nose data depict all of the potable water samples in a separate group from the samples that were originated from river. Furthermore, PCA and LDA analysis on electronic tongue data permitted clear and rapid recognizing of the different waters due to the concentration changes of the chemical parameters from source to another.

DNA-Catalytically Active Gold Nanoparticle Conjugates-Based Colorimetric Multidimensional Sensor Array for Protein Discrimination.

Science.gov (United States)

Wei, Xiangcong; Chen, Zhengbo; Tan, Lulu; Lou, Tianhong; Zhao, Yan

2017-01-03

A series of single-strand oligonucleotides functionalized catalytically active gold nanoparticle (AuNPs) as nonspecific receptors have been designed to build a protein sensing array. We take advantage of the correlation between the catalytic activity and the exposed surface area of AuNPs, i.e., DNA-proteins interactions mask the surface area of AuNPs, leading to poor catalytic performance of AuNPs. As the number of DNA-bound proteins increases, the surfaces of AuNPs become more masked; thus, the time of 4- nitrophenol/NaBH 4 reaction for color change (yellow → colorless) of the solution increases. Taking advantage of three nonspecific SH-labeled DNA sequences (A15, C15, and T15) as array sensing elements and the color-change time (CCT) of the solution as signal readout, colorimetric response patterns can be obtained on the array and identified via linear discriminant analysis (LDA). Eleven proteins have been completely distinguished with 100% accuracy with the naked eye at the 30 nM level. Remarkably, two similar proteins (bovine serum albumin and human serum albumin), two different proteins (bovine serum albumin and concanavalin) at the same concentration, and the mixtures of the two proteins with different molar ratios have been discriminated with 100%. The practicability of this sensor array is further validated by high accuracy (100%) identification of 11 proteins in human serum samples.

Factors that Affect Poverty Areas in North Sumatera Using Discriminant Analysis

Science.gov (United States)

Nasution, D. H.; Bangun, P.; Sitepu, H. R.

2018-04-01

In Indonesia, especially North Sumatera, the problem of poverty is one of the fundamental problems that become the focus of government both central and local government. Although the poverty rate decreased but the fact is there are many people who are poor. Poverty happens covers several aspects such as education, health, demographics, and also structural and cultural. This research will discuss about several factors such as population density, Unemployment Rate, GDP per capita ADHK, ADHB GDP per capita, economic growth and life expectancy that affect poverty in Indonesia. To determine the factors that most influence and differentiate the level of poverty of the Regency/City North Sumatra used discriminant analysis method. Discriminant analysis is one multivariate analysis technique are used to classify the data into a group based on the dependent variable and independent variable. Using discriminant analysis, it is evident that the factor affecting poverty is Unemployment Rate.

DISCRIMINANT ANALYSIS OF BANK PROFITABILITY LEVELS

Directory of Open Access Journals (Sweden)

Ante Rozga

2013-02-01

Full Text Available Discriminant analysis has been employed in this paper in order to identify and explain key features of bank profitability levels. Bank profitability is set up in the form of two categorical variables: profit or loss recorded and above or below average return on equity. Predictor variables are selected from various groups of financial indicators usually included in the empirical work on microeconomic determinants of bank profitability. The data from the Croatian banking sector is analyzed using the Enter method. General recommendations for a more profitable business of banking found in the bank management literature and existing empirical framework such as rationalization of overhead costs, asset growth, increase of non-interest income by expanding scale and scope of financial products proved to be important for classification of banks in different profitability levels. A higher market share may bring additional advantages. Classification results, canonical correlation and Wilks’ Lambda test confirm statistical significance of research results. Altogether, discriminant analysis turns out to be a suitable statistical method for solving presented research problem and moving forward from the bankruptcy, credit rating or default issues in finance.

Selecting statistical models and variable combinations for optimal classification using otolith microchemistry.

Science.gov (United States)

Mercier, Lény; Darnaude, Audrey M; Bruguier, Olivier; Vasconcelos, Rita P; Cabral, Henrique N; Costa, Maria J; Lara, Monica; Jones, David L; Mouillot, David

2011-06-01

Reliable assessment of fish origin is of critical importance for exploited species, since nursery areas must be identified and protected to maintain recruitment to the adult stock. During the last two decades, otolith chemical signatures (or "fingerprints") have been increasingly used as tools to discriminate between coastal habitats. However, correct assessment of fish origin from otolith fingerprints depends on various environmental and methodological parameters, including the choice of the statistical method used to assign fish to unknown origin. Among the available methods of classification, Linear Discriminant Analysis (LDA) is the most frequently used, although it assumes data are multivariate normal with homogeneous within-group dispersions, conditions that are not always met by otolith chemical data, even after transformation. Other less constrained classification methods are available, but there is a current lack of comparative analysis in applications to otolith microchemistry. Here, we assessed stock identification accuracy for four classification methods (LDA, Quadratic Discriminant Analysis [QDA], Random Forests [RF], and Artificial Neural Networks [ANN]), through the use of three distinct data sets. In each case, all possible combinations of chemical elements were examined to identify the elements to be used for optimal accuracy in fish assignment to their actual origin. Our study shows that accuracy differs according to the model and the number of elements considered. Best combinations did not include all the elements measured, and it was not possible to define an ad hoc multielement combination for accurate site discrimination. Among all the models tested, RF and ANN performed best, especially for complex data sets (e.g., with numerous fish species and/or chemical elements involved). However, for these data, RF was less time-consuming and more interpretable than ANN, and far more efficient and less demanding in terms of assumptions than LDA or QDA

Classification of astrocyto-mas and meningiomas using statistical discriminant analysis on MRI data

International Nuclear Information System (INIS)

Siromoney, Anna; Prasad, G.N.S.; Raghuram, Lakshminarayan; Korah, Ipeson; Siromoney, Arul; Chandrasekaran, R.

2001-01-01

The objective of this study was to investigate the usefulness of Multivariate Discriminant Analysis for classifying two groups of primary brain tumours, astrocytomas and meningiomas, from Magnetic Resonance Images. Discriminant analysis is a multivariate technique concerned with separating distinct sets of objects and with allocating new objects to previously defined groups. Allocation or classification rules are usually developed from learning examples in a supervised learning environment. Data from signal intensity measurements in the multiple scan performed on each patient in routine clinical scanning was analysed using Fisher's Classification, which is one method of discriminant analysis

High-wavenumber FT-Raman spectroscopy for in vivo and ex vivo measurements of breast cancer

DEFF Research Database (Denmark)

Garcia-Flores, A. F.; Raniero, L.; Canevari, R. A.

2011-01-01

structures (2,800-3,100 cm(-1)) as well as in the broad band of water (3,100-3,550 cm(-1)) were observed in mean normal and cancer tissue spectra. The multivariate statistical analysis methods of principal components analysis (PCA) and linear discriminant analysis (LDA) were performed on all high...

Smoke detection

Energy Technology Data Exchange (ETDEWEB)

Warmack, Robert J. Bruce; Wolf, Dennis A.; Frank, Steven Shane

2017-10-17

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDA training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.

[Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].

Science.gov (United States)

Kleinhans, Sonja; Herrmann, Eva; Kohnen, Thomas; Bühren, Jens

2017-08-15

Background Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. Patients and Methods The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Results Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. Conclusion On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

LDA boost classification: boosting by topics

Science.gov (United States)

Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

2012-12-01

AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

Discrimination against Latina/os: A Meta-Analysis of Individual-Level Resources and Outcomes

Science.gov (United States)

Lee, Debbiesiu L.; Ahn, Soyeon

2012-01-01

This meta-analysis synthesizes the findings of 60 independent samples from 51 studies examining racial/ethnic discrimination against Latina/os in the United States. The purpose was to identify individual-level resources and outcomes that most strongly relate to discrimination. Discrimination against Latina/os significantly results in outcomes…

Calibration of a micromachined particle velocity microphone in a standing wave tube using a LDA photon-correlation technique

NARCIS (Netherlands)

Raangs, R.; Schlicke, Ted; Barham, Richard

2005-01-01

In this paper, a new method of calibrating an acoustic particle velocity sensor using laser Doppler anemometry (LDA) is discussed. The results were compared and were in good agreement with the results obtained by conventional methods, where the sensitivity of the microflown is obtained with the use

Discriminant Function Analysis as a Proof for Sexual Dimorphism ...

African Journals Online (AJOL)

Background: Forensic scientists study human skeleton in legal setting. Discriminant function analysis has become important in forensic anthropology. The aim of this study was to determine the sex of adolescent Yoruba ethnic group of Nigeria using iscriminant function analysis. Methodology: One thousand (500 males and ...

Detection of Genetically Modified Sugarcane by Using Terahertz Spectroscopy and Chemometrics

Science.gov (United States)

Liu, J.; Xie, H.; Zha, B.; Ding, W.; Luo, J.; Hu, C.

2018-03-01

A methodology is proposed to identify genetically modified sugarcane from non-genetically modified sugarcane by using terahertz spectroscopy and chemometrics techniques, including linear discriminant analysis (LDA), support vector machine-discriminant analysis (SVM-DA), and partial least squares-discriminant analysis (PLS-DA). The classification rate of the above mentioned methods is compared, and different types of preprocessing are considered. According to the experimental results, the best option is PLS-DA, with an identification rate of 98%. The results indicated that THz spectroscopy and chemometrics techniques are a powerful tool to identify genetically modified and non-genetically modified sugarcane.

Prediction of Potential Hit Song and Musical Genre Using Artificial Neural Networks

Science.gov (United States)

Monterola, Christopher; Abundo, Cheryl; Tugaff, Jeric; Venturina, Lorcel Ericka

Accurately quantifying the goodness of music based on the seemingly subjective taste of the public is a multi-million industry. Recording companies can make sound decisions on which songs or artists to prioritize if accurate forecasting is achieved. We extract 56 single-valued musical features (e.g. pitch and tempo) from 380 Original Pilipino Music (OPM) songs (190 are hit songs) released from 2004 to 2006. Based on an effect size criterion which measures a variable's discriminating power, the 20 highest ranked features are fed to a classifier tasked to predict hit songs. We show that regardless of musical genre, a trained feed-forward neural network (NN) can predict potential hit songs with an average accuracy of ΦNN = 81%. The accuracy is about +20% higher than those of standard classifiers such as linear discriminant analysis (LDA, ΦLDA = 61%) and classification and regression trees (CART, ΦCART = 57%). Both LDA and CART are above the proportional chance criterion (PCC, ΦPCC = 50%) but are slightly below the suggested acceptable classifier requirement of 1.25*ΦPCC = 63%. Utilizing a similar procedure, we demonstrate that different genres (ballad, alternative rock or rock) of OPM songs can be automatically classified with near perfect accuracy using LDA or NN but only around 77% using CART.

Analyses of absorption distribution of a rubidium cell side-pumped by a Laser-Diode-Array (LDA)

Science.gov (United States)

Yu, Hang; Han, Juhong; Rong, Kepeng; Wang, Shunyan; Cai, He; An, Guofei; Zhang, Wei; Yu, Qiang; Wu, Peng; Wang, Hongyuan; Wang, You

2018-01-01

A diode-pumped alkali laser (DPAL) has been regarded as one of the most potential candidates to achieve high power performances of next generation. In this paper, we investigate the physical properties of a rubidium cell side-pumped by a Laser-Diode-Array (LDA) in this study. As the saturated concentration of a gain medium inside a vapor cell is extremely sensitive to the temperature, the populations of every energy-level of the atomic alkali are strongly relying on the vapor temperature. Thus, the absorption characteristics of a DPAL are mainly dominated by the temperature distribution. In this paper, the temperature, absorption, and lasing distributions in the cross-section of a rubidium cell side-pumped by a LDA are obtained by means of a complicated mathematic procedure. Based on the original end-pumped mode we constructed before, a novel one-direction side-pumped theoretical mode has been established to explore the distribution properties in the transverse section of a rubidium vapor cell by combining the procedures of heat transfer and laser kinetics together. It has been thought the results might be helpful for design of a side-pumped configuration in a high-powered DPAL.

Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

Science.gov (United States)

Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

2016-01-01

Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

Measurement of mist cooling of PWR during LOCA by LDA

International Nuclear Information System (INIS)

Lee, S.L.; Sheen, H.J.; Issapour, I.

1985-01-01

The prediction of temperature distribution and heat transfer within rod bundles during the refill and reflood phase of a LOCA (loss of coolant accident) is of critical importance for determining the location and size of blockages due to clad deformation in a pressurized water reactor (PWR). Mist cooling by small droplets generated from large droplets on hitting grid spacers has been suggested as one of the most important heat transfer mechanisms which are responsible for the development of this temperature transient. The questions to be asked are whether such small droplets indeed exist and, if so, how are they related to the cooling of the fuel rods. Hereby reported is the result of a direct experimental investigation on these questions by a special laser-Doppler anemometry (LDA) particle sizing technique together with temperature measurements of the rod claddings and flow in the subchannel

Enamel surface topography analysis for diet discrimination. A methodology to enhance and select discriminative parameters

Science.gov (United States)

Francisco, Arthur; Blondel, Cécile; Brunetière, Noël; Ramdarshan, Anusha; Merceron, Gildas

2018-03-01

Tooth wear and, more specifically, dental microwear texture is a dietary proxy that has been used for years in vertebrate paleoecology and ecology. DMTA, dental microwear texture analysis, relies on a few parameters related to the surface complexity, anisotropy and heterogeneity of the enamel facets at the micrometric scale. Working with few but physically meaningful parameters helps in comparing published results and in defining levels for classification purposes. Other dental microwear approaches are based on ISO parameters and coupled with statistical tests to find the more relevant ones. The present study roughly utilizes most of the aforementioned parameters in their more or less modified form. But more than parameters, we here propose a new approach: instead of a single parameter characterizing the whole surface, we sample the surface and thus generate 9 derived parameters in order to broaden the parameter set. The identification of the most discriminative parameters is performed with an automated procedure which is an extended and refined version of the workflows encountered in some studies. The procedure in its initial form includes the most common tools, like the ANOVA and the correlation analysis, along with the required mathematical tests. The discrimination results show that a simplified form of the procedure is able to more efficiently identify the desired number of discriminative parameters. Also highlighted are some trends like the relevance of working with both height and spatial parameters, as well as the potential benefits of dimensionless surfaces. On a set of 45 surfaces issued from 45 specimens of three modern ruminants with differences in feeding preferences (grazing, leaf-browsing and fruit-eating), it is clearly shown that the level of wear discrimination is improved with the new methodology compared to the other ones.

Classification of cultivated mussels from Galicia (Northwest Spain) with European Protected Designation of Origin using trace element fingerprint and chemometric analysis

International Nuclear Information System (INIS)

Costas-Rodriguez, M.; Lavilla, I.; Bendicho, C.

2010-01-01

Inductively coupled plasma-mass spectrometry (ICP-MS) in combination with different supervised chemometric approaches has been used to classify cultivated mussels in Galicia (Northwest of Spain) under the European Protected Designation of Origin (PDO). 158 mussel samples, collected in the five rias on the basis of the production, along with minor and trace elements, including high field strength elements (HFSEs) and rare earth elements (REEs), were used with this aim. The classification of samples was achieved according to their origin: Galician vs. other regions (from Tarragona, Spain, and Ethang de Thau, France) and between the Galician Rias. The ability of linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA) and artificial neural network (ANN) to classify the samples was investigated. Correct assignations for Galician and non-Galician samples were obtained when LDA and SIMCA were used. ANNs were more effective when a classification according to the ria of origin was to be applied.

Robustness of movement detection techniques from motor execution

DEFF Research Database (Denmark)

Aliakbaryhosseinabadi, Susan; Jiang, Ning; Petrini, Laura

2015-01-01

subjects completed a set of movement executions prior to and following the oddball paradigm. The locality preserving projection followed by the linear discriminant analysis (LPP-LDA) and the matched-filter (MF) technique were applied offline for detection of movement. Results show that LPP...

Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines

International Nuclear Information System (INIS)

Li Jiazhong; Liu Huanxiang; Yao Xiaojun; Liu Mancang; Hu Zhide; Fan Botao

2007-01-01

The least-squares support vector machines (LS-SVMs), as an effective modified algorithm of support vector machine, was used to build structure-activity relationship (SAR) models to classify the oxindole-based inhibitors of cyclin-dependent kinases (CDKs) based on their activity. Each compound was depicted by the structural descriptors that encode constitutional, topological, geometrical, electrostatic and quantum-chemical features. The forward-step-wise linear discriminate analysis method was used to search the descriptor space and select the structural descriptors responsible for activity. The linear discriminant analysis (LDA) and nonlinear LS-SVMs method were employed to build classification models, and the best results were obtained by the LS-SVMs method with prediction accuracy of 100% on the test set and 90.91% for CDK1 and CDK2, respectively, as well as that of LDA models 95.45% and 86.36%. This paper provides an effective method to screen CDKs inhibitors

Anti-discrimination Analysis Using Privacy Attack Strategies

KAUST Repository

Ruggieri, Salvatore

2014-09-15

Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Frèchet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.

A novel R-package graphic user interface for the analysis of metabonomic profiles

Directory of Open Access Journals (Sweden)

Villa Palmira

2009-10-01

Full Text Available Abstract Background Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries. Results The package offers the following options: Raw 1-dimensional spectra processing: phase, baseline correction and normalization. Importing processed spectra. Including/excluding spectral ranges, optional binning and bucketing, detection and alignment of peaks. Sorting of metabolites based on their ability to discriminate, metabolite selection, and outlier identification. Multivariate unsupervised analysis: principal components analysis (PCA. Multivariate supervised analysis: partial least squares (PLS, linear discriminant analysis (LDA, k-nearest neighbor classification. Neural networks. Visualization and overlapping of spectra. Plot values of the chemical shift position for different samples. Furthermore, the "Metabonomic" GUI includes a console to enable other kinds of analyses and to take advantage of all R statistical tools. Conclusion We made complex multivariate analysis user-friendly for both experienced and novice users, which could help to expand the use of NMR-based metabonomics.

Harassment and discrimination in medical training: a systematic review and meta-analysis.

Science.gov (United States)

Fnais, Naif; Soobiah, Charlene; Chen, Maggie Hong; Lillie, Erin; Perrier, Laure; Tashkhandi, Mariam; Straus, Sharon E; Mamdani, Muhammad; Al-Omran, Mohammed; Tricco, Andrea C

2014-05-01

Harassment and discrimination include a wide range of behaviors that medical trainees perceive as being humiliating, hostile, or abusive. To understand the significance of such mistreatment and to explore potential preventive strategies, the authors conducted a systematic review and meta-analysis to examine the prevalence, risk factors, and sources of harassment and discrimination among medical trainees. In 2011, the authors identified relevant studies by searching MEDLINE and EMBASE, scanning reference lists of relevant studies, and contacting experts. They included studies that reported the prevalence, risk factors, and sources of harassment and discrimination among medical trainees. Two reviewers independently screened all articles and abstracted study and participant characteristics and study results. The authors assessed the methodological quality in individual studies using the Newcastle-Ottawa Scale. They also conducted a meta-analysis. The authors included 57 cross-sectional and 2 cohort studies in their review. The meta-analysis of 51 studies demonstrated that 59.4% of medical trainees had experienced at least one form of harassment or discrimination during their training (95% confidence interval [CI]: 52.0%-66.7%). Verbal harassment was the most commonly cited form of harassment (prevalence: 63.0%; 95% CI: 54.8%-71.2%). Consultants were the most commonly cited source of harassment and discrimination, followed by patients or patients' families (34.4% and 21.9%, respectively). This review demonstrates the surprisingly high prevalence of harassment and discrimination among medical trainees that has not declined over time. The authors recommend both drafting policies and promoting cultural change within academic institutions to prevent future abuse.

[Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

Science.gov (United States)

Zhou, Jinzhi; Tang, Xiaofang

2015-08-01

In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis.

Science.gov (United States)

Cao, Longlong; Guo, Shuixia; Xue, Zhimin; Hu, Yong; Liu, Haihong; Mwansisya, Tumbwene E; Pu, Weidan; Yang, Bo; Liu, Chang; Feng, Jianfeng; Chen, Eric Y H; Liu, Zhening

2014-02-01

Aberrant brain functional connectivity patterns have been reported in major depressive disorder (MDD). It is unknown whether they can be used in discriminant analysis for diagnosis of MDD. In the present study we examined the efficiency of discriminant analysis of MDD by individualized computer-assisted diagnosis. Based on resting-state functional magnetic resonance imaging data, a new approach was adopted to investigate functional connectivity changes in 39 MDD patients and 37 well-matched healthy controls. By using the proposed feature selection method, we identified significant altered functional connections in patients. They were subsequently applied to our analysis as discriminant features using a support vector machine classification method. Furthermore, the relative contribution of functional connectivity was estimated. After subset selection of high-dimension features, the support vector machine classifier reached up to approximately 84% with leave-one-out training during the discrimination process. Through summarizing the classification contribution of functional connectivities, we obtained four obvious contribution modules: inferior orbitofrontal module, supramarginal gyrus module, inferior parietal lobule-posterior cingulated gyrus module and middle temporal gyrus-inferior temporal gyrus module. The experimental results demonstrated that the proposed method is effective in discriminating MDD patients from healthy controls. Functional connectivities might be useful as new biomarkers to assist clinicians in computer auxiliary diagnosis of MDD. © 2013 The Authors. Psychiatry and Clinical Neurosciences © 2013 Japanese Society of Psychiatry and Neurology.

Structural and electronic properties of Pb-doped Bi{sub 2}Sr{sub 2}Ca{sub 2}Cu{sub 3}O{sub 10}: Comparison of LDA and GGA calculations

Energy Technology Data Exchange (ETDEWEB)

Camargo-Martínez, J.A., E-mail: jcamargo@unitropico.edu.co [Grupo de Investigación en Ciencias Básicas, Aplicación e Innovación - CIBAIN, Fundación Universitaria Internacional del Trópico Americano– Unitrópico, Yopal, Casanare (Colombia); Martínez-Pieschacón, D.J. [Departamento de Ciencias Básicas, Universidad Santo Tomás, Tunja, Boyaca (Colombia); Baquero, R. [Departamento de Física, CINVESTAV-IPN, Av. IPN 2508, 07360, México (Mexico)

2017-04-15

Highlights: • We present for the first time the effects of Pb doping on structural and electronic properties of Bi{sub 2}Sr{sub 2}Ca{sub 2}Cu{sub 3}O{sub 10} (Bi2223) using GGA, compared with LDA results. • We found the Pb concentration for which the Bi–O pockets disappear from the Fermi Surface in the Bi2223 compound using GGA and LDA, respectively. - Abstract: We use Density Functional Theory to study the effects on the crystal structure and the electronic band structure of substituting Pb for Bi in Bi{sub 2}Sr{sub 2}Ca{sub 2}Cu{sub 3}O{sub 10}. We further use the Local Density Approximation (LDA) and the Generalized Gradient Approximation (GGA). The Virtual Crystal Approximation (VCA) was used to account for the substitution. We found that GGA reproduces better the lattice parameters although in both cases the internal coordinates were reproduced with some uncertainties. We further looked at the behavior of the so called Bi–O pockets, some electronic states that originate on the Bi–O planes and that appear on the Fermi surface (FS) in contradiction to the experimental evidence. We found that LDA and GGA differ on that subject. With 26% Pb and using LDA, the Bi–O pockets run away from the FS. But when GGA is used, it is needed up to 35% Pb to make the Bi–O pockets disappear from the FS. In the last case, once the Bi–O pockets are removed from the FS, we get a very good agreement with angular resolved photo-emission (ARPES) and nuclear magnetic resonance (NMR) experiments.

Applicability of supervised discriminant analysis models to analyze astigmatism clinical trial data

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Sedghipour MR

2012-09-01

Full Text Available Mohammad Reza Sedghipour,1 Homayoun Sadeghi-Bazargani2,31Nikoukari Ophthalmology University Hospital, Tabriz, Iran; 2Department of Statistics and Epidemiology, Neuroscience Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; 3Department of Public Health Sciences, Karolinska Institute, Stockholm, SwedenBackground: In astigmatism clinical trials where more complex measurements are common, especially in nonrandomized small sized clinical trials, there is a demand for the development and application of newer statistical methods.Methods: The source data belonged to a project on astigmatism treatment. Data were used regarding a total of 296 eyes undergoing different astigmatism treatment modalities: wavefront-guided photorefractive keratectomy, cross-cylinder photorefractive keratectomy, and monotoric (single photorefractive keratectomy. Astigmatism analysis was primarily done using the Alpins method. Prior to fitting partial least squares regression discriminant analysis, a preliminary principal component analysis was done for data overview. Through fitting the partial least squares regression discriminant analysis statistical method, various model validity and predictability measures were assessed.Results: The model found the patients treated by the wavefront method to be different from the two other treatments both in baseline and outcome measures. Also, the model found that patients treated with the cross-cylinder method versus the single method didn't appear to be different from each other. This analysis provided an opportunity to compare the three methods while including a substantial number of baseline and outcome variables.Conclusion: Partial least squares regression discriminant analysis had applicability for the statistical analysis of astigmatism clinical trials and it may be used as an adjunct or alternative analysis method in small sized clinical trials.Keywords: astigmatism, regression, partial least squares regression

Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

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Ting-quan Deng

2014-01-01

Full Text Available In the visual tracking scenarios, if there are multiple objects, due to the interference of similar objects, tracking may fail in the progress of occlusion to separation. To address this problem, this paper proposed a visual tracking algorithm with discrimination through multimanifold learning. Color-gradient-based feature tensor was used to describe object appearance for accommodation of partial occlusion. A prior multimanifold tensor dataset is established through the template matching tracking algorithm. For the purpose of discrimination, tensor distance was defined to determine the intramanifold and intermanifold neighborhood relationship in multimanifold space. Then multimanifold discriminate analysis was employed to construct multilinear projection matrices of submanifolds. Finally, object states were obtained by combining with sequence inference. Meanwhile, the multimanifold dataset and manifold learning embedded projection should be updated online. Experiments were conducted on two real visual surveillance sequences to evaluate the proposed algorithm with three state-of-the-art tracking methods qualitatively and quantitatively. Experimental results show that the proposed algorithm can achieve effective and robust effect in multi-similar-object mutual occlusion scenarios.

Discriminant analysis on the treatment results of interstitial radium tongue implants

International Nuclear Information System (INIS)

Hoshina, Masao; Shibuya, Hitoshi; Horiuchi, Jun-Ichi; Matsubara, Sho; Suzuki, Soji; Takeda, Masamune

1989-01-01

Discriminant analysis was carried out for 48 tongue cancer patients who were treated with radium single-plane implantation. The 48 patients were grouped into 32 successfully cured without complications, five successfully cured with complications, six successfully cured but requiring additional boost therapy and five with local recurrence. To evaluate the relation between the dose distribution and the local treatment results, the analysis was based on a volume-dose relationship. The functions introduced by this discriminant analysis were linear, and the parameters used were modal dose, average dose and shape factors of histograms. Each group of treatment results had a correction rate of >80%, except for the successfully cured group with ulcers. The discriminant functions were useful as an index to obtain a final clinical treatment result at the early time of implantation, and these functions could be used as a criterion for the optimal treatment of tongue carcinoma. We were also able to recognize the limitation of the actual arrangement of sources in the single-plane implant. (author)

Use of linear discriminant function analysis in seed morphotype ...

African Journals Online (AJOL)

Use of linear discriminant function analysis in seed morphotype relationship study in 31 ... Data were collected on 100-seed weight, seed length and seed width. ... to the Mesoamerican gene pool, comprising the cultigroups Sieva-Big Lima, ...

Voltammetric electronic tongue and support vector machines for identification of selected features in Mexican coffee.

Science.gov (United States)

Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

2014-09-24

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure.

Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia

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Eloísa Urrechaga

2013-01-01

Full Text Available Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA to the differential diagnosis of microcytic anemia. Methods. The training group was composed of 200 β-thalassemia carriers, 65 α-thalassemia carriers, 170 iron deficiency anemia (IDA, and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC, hemoglobin (Hb, mean cell volume (MCV, mean cell hemoglobin (MCH, and RBC distribution width (RDW. The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia. Results. For classifying patients into two groups (genetic anemia and acquired anemia, only one function was needed; 87.9% β-thalassemia carriers, and 83.3% α-thalassemia carriers, and 72.1% in the mixed group were correctly classified. Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia.

A Computational Discriminability Analysis on Twin Fingerprints

Science.gov (United States)

Liu, Yu; Srihari, Sargur N.

Sharing similar genetic traits makes the investigation of twins an important study in forensics and biometrics. Fingerprints are one of the most commonly found types of forensic evidence. The similarity between twins’ prints is critical establish to the reliability of fingerprint identification. We present a quantitative analysis of the discriminability of twin fingerprints on a new data set (227 pairs of identical twins and fraternal twins) recently collected from a twin population using both level 1 and level 2 features. Although the patterns of minutiae among twins are more similar than in the general population, the similarity of fingerprints of twins is significantly different from that between genuine prints of the same finger. Twins fingerprints are discriminable with a 1.5%~1.7% higher EER than non-twins. And identical twins can be distinguished by examine fingerprint with a slightly higher error rate than fraternal twins.

Perceived Discrimination in LGBTIQ Discourse: A Typology of Verbal Discrimination

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Sol Rojas Lizana

2014-08-01

Full Text Available New within the field of Discourse Analysis, Perceived Discrimination (PD is the study of discourse that focuses on the perspective of the victims of discrimination. This article explores the experiences of verbal discrimination as reported by eighteen LGBTIQ participants during semi-structured, co-constructed interviews. Data were classified in order to develop a taxonomy of discrimination based on Mellor’s (2003, 2004. This taxonomy foregrounds two types of discrimination: verbal and behavioural. In this paper, I exemplify the forms of verbal discrimination encountered and offer an analysis of the discourse used in the construction of the experiences and of the effects reported. The results show that verbal discrimination is an overt phenomenon and that participants are stressed by the ever present possibility of facing it. Verbal discrimination is mainly triggered by a perceived transgression to the normalised standards of people’s behaviour, movements and look in a heterosexist society. It presents three subtypes: name calling, abuse and remarks. These subtypes are described through the analysis of keywords, effects and expressions (such as faggot, gay, dyke, queer, the pronoun ‘it’, religious comments and other remarks. The type of discrimination used was associated with the level of acquaintance perpetrators have with the experiencers; that is, name calling was used by people unknown to the victims while abuse and remarks by acquaintances and family members. Participants resorted to several discursive strategies to convey their intentions. They used mitigation strategies when wanting to minimize the experience, hedging and repetition were used for emphasis, and to convey urgency and pervasiveness. Metaphorical expressions related to internal or external injuries were also used to express the powerful effect of verbal discrimination on people.

Discrimination based on HIV/AIDS status: A comparative analysis of ...

African Journals Online (AJOL)

Discrimination based on HIV/AIDS status: A comparative analysis of the Nigerian court's decision in Festus Odaife & Ors v Attorney General of the Federation & Ors with other Commonwealth jurisdictions.

Non-Discrimination à la Cour: the ECJ’s (lack of) Comparability Analysis in Direct Tax Cases

NARCIS (Netherlands)

Wattel, P.

2015-01-01

The ECJ’s discrimination analysis in direct tax cases is inconsistent. It sometimes creates discrimination, condemns non-existent discrimination or fails to address discrimination. Only one comparability standard makes sense: to be (subject to tax) or not to be (subject to tax). The ECJ is not

Determination of authenticity, regional origin, and vintage of Slovenian wines using a combination of IRMS and SNIF-NMR analyses.

Science.gov (United States)

Ogrinc, N; Kosir, I J; Kocjancic, M; Kidric, J

2001-03-01

The authenticity and geographical origin of wines produced in Slovenia were investigated by a combination of IRMS and SNIF-NMR methods. A total of 102 grape samples of selected wines were carefully collected in three different wine-growing regions of Slovenia in 1996, 1997, and 1998. The stable isotope data were evaluated using principal component analysis (PCA) and linear discriminant analysis (LDA). The isotopic ratios to discriminate between coastal and continental regions are the deuterium/hydrogen isotopic ratio of the methylene site in the ethanol molecule (D/H)(II) and delta(13)C values; including also delta(18)O values in the PCA and LDA made possible separation between the two continental regions Drava and Sava. It was found that delta(18)O values are modified by the meteorological events during grape ripening and harvest. The usefulness of isotopic parameters for detecting adulteration or watering and to assess the geographical origin of wines is improved only when they are used concurrently.

Authentication of virgin olive oil by a novel curve resolution approach combined with visible spectroscopy.

Science.gov (United States)

Ferreiro-González, Marta; Barbero, Gerardo F; Álvarez, José A; Ruiz, Antonio; Palma, Miguel; Ayuso, Jesús

2017-04-01

Adulteration of olive oil is not only a major economic fraud but can also have major health implications for consumers. In this study, a combination of visible spectroscopy with a novel multivariate curve resolution method (CR), principal component analysis (PCA) and linear discriminant analysis (LDA) is proposed for the authentication of virgin olive oil (VOO) samples. VOOs are well-known products with the typical properties of a two-component system due to the two main groups of compounds that contribute to the visible spectra (chlorophylls and carotenoids). Application of the proposed CR method to VOO samples provided the two pure-component spectra for the aforementioned families of compounds. A correlation study of the real spectra and the resolved component spectra was carried out for different types of oil samples (n=118). LDA using the correlation coefficients as variables to discriminate samples allowed the authentication of 95% of virgin olive oil samples. Copyright © 2016 Elsevier Ltd. All rights reserved.

Discriminant analysis of normal and malignant breast tissue based upon INAA investigation of elemental concentration

International Nuclear Information System (INIS)

Kwanhoong Ng; Senghuat Ong; Bradley, D.A.; Laimeng Looi

1997-01-01

Discriminant analysis of six trace element concentrations measured by instrumental neutron activation analysis (INAA) in 26 paired-samples of malignant and histologically normal human breast tissues shows the technique to be a potentially valuable clinical tool for making malignant-normal classification. Nonparametric discriminant analysis is performed for the data obtained. Linear and quadratic discriminant analyses are also carried out for comparison. For this data set a formal analysis shows that the elements which may be useful in distinguishing between malignant and normal tissues are Ca, Rb and Br, providing correct classification for 24 out of 26 normal samples and 22 out of 26 malignant samples. (Author)

Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) in the discrimination of normal and oral cancer blood plasma

Science.gov (United States)

Pachaiappan, Rekha; Prakasarao, Aruna; Singaravelu, Ganesan

2017-02-01

Oral cancer is the most frequent type of cancer that occurs with 75000 to 80000 new cases reported every year in India. The carcinogens from tobacco and related products are the main cause for the oral cancer. ATR-FTIR method is label free, fast and cost-effective diagnostic method would allow for rapid diagnostic results in earlier stages by the minimal chemical changes occur in the biological metabolites available in the blood plasma. The present study reports the use of ATR-FTIR data with advanced statistical model (LDA-ANN) in the diagnosis of oral cancer from normal with better accuracy. The infrared spectra were acquired on ATR-FTIR Jasco spectrophotometer at 4 cm-1 resolution, 30 scans, in the 1800-900 cm-1 spectral range. Each sample had 5 spectra recorded from each blood plasma sample. The spectral data were routed through the multilayer perception of artificial neural network to evaluate for the statistical efficacy. Among the spectral data it was found that amide II (1486 cm-1) and lipid (1526 cm-1) affords about 90 % in the discrimination between groups using LDA. These preliminary results indicate that ATR-FTIR is useful to differentiate normal subject from oral cancer patients using blood plasma.

Applicability of supervised discriminant analysis models to analyze astigmatism clinical trial data.

Science.gov (United States)

Sedghipour, Mohammad Reza; Sadeghi-Bazargani, Homayoun

2012-01-01

In astigmatism clinical trials where more complex measurements are common, especially in nonrandomized small sized clinical trials, there is a demand for the development and application of newer statistical methods. The source data belonged to a project on astigmatism treatment. Data were used regarding a total of 296 eyes undergoing different astigmatism treatment modalities: wavefront-guided photorefractive keratectomy, cross-cylinder photorefractive keratectomy, and monotoric (single) photorefractive keratectomy. Astigmatism analysis was primarily done using the Alpins method. Prior to fitting partial least squares regression discriminant analysis, a preliminary principal component analysis was done for data overview. Through fitting the partial least squares regression discriminant analysis statistical method, various model validity and predictability measures were assessed. The model found the patients treated by the wavefront method to be different from the two other treatments both in baseline and outcome measures. Also, the model found that patients treated with the cross-cylinder method versus the single method didn't appear to be different from each other. This analysis provided an opportunity to compare the three methods while including a substantial number of baseline and outcome variables. Partial least squares regression discriminant analysis had applicability for the statistical analysis of astigmatism clinical trials and it may be used as an adjunct or alternative analysis method in small sized clinical trials.

Comparing GW+DMFT and LDA+DMFT for the testbed material SrVO{sub 3}

Energy Technology Data Exchange (ETDEWEB)

Taranto, Ciro; Toschi, Alessandro; Held, Karsten [Institute for Solid State Physics, Vienna University of Technology (Austria); Kaltak, Merzuk; Kresse, Georg [University of Vienna, Faculty of Physics and Center for Computational Materials Science (Austria); Parragh, Nicolaus; Sangiovanni, Giorgio [Institut fuer Theoretische Physik und Astrophysik, Universitaet Wuerzburg (Germany)

2013-07-01

We have implemented the GW+dynamical mean field theory (DMFT) approach in the Vienna ab initio simulation package. Employing the interaction values obtained from the locally unscreened random phase approximation (RPA), we compare GW+DMFT and LDA+DMFT against each other and against experiment for SrVO{sub 3}. We observed a partial compensation of stronger electronic correlations due to the reduced GW bandwidth and weaker correlations due to a larger screening of the RPA interaction, so that the obtained spectra are quite similar and well agree with experiment. Noteworthily, the GW+DMFT better reproduces the position of the lower Hubbard side band.

Social Status Correlates of Reporting Racial Discrimination and Gender Discrimination among Racially Diverse Women

Science.gov (United States)

Ro, Annie E.; Choi, Kyung-Hee

2009-01-01

The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in Northern California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design. PMID:19485231

Social status correlates of reporting gender discrimination and racial discrimination among racially diverse women.

Science.gov (United States)

Ro, Annie E; Choi, Kyung-Hee

2009-01-01

The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in North California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design.

Discrimination of handlebar grip samples by fourier transform infrared microspectroscopy analysis and statistics

Directory of Open Access Journals (Sweden)

Zeyu Lin

2017-01-01

Full Text Available In this paper, the authors presented a study on the discrimination of handlebar grip samples, to provide effective forensic science service for hit and run traffic cases. 50 bicycle handlebar grip samples, 49 electric bike handlebar grip samples, and 96 motorcycle handlebar grip samples have been randomly collected by the local police in Beijing (China. Fourier transform infrared microspectroscopy (FTIR was utilized as analytical technology. Then, target absorption selection, data pretreatment, and discrimination of linked samples and unlinked samples were chosen as three steps to improve the discrimination of FTIR spectrums collected from different handlebar grip samples. Principal component analysis and receiver operating characteristic curve were utilized to evaluate different data selection methods and different data pretreatment methods, respectively. It is possible to explore the evidential value of handlebar grip residue evidence through instrumental analysis and statistical treatments. It will provide a universal discrimination method for other forensic science samples as well.

Quark/gluon jet discrimination: a reproducible analysis using R

CERN Multimedia

CERN. Geneva

2017-01-01

The power to discriminate between light-quark jets and gluon jets would have a huge impact on many searches for new physics at CERN and beyond. This talk will present a walk-through of the development of a prototype machine learning classifier for differentiating between quark and gluon jets at experiments like those at the Large Hadron Collider at CERN. A new fast feature selection method that combines information theory and graph analytics will be outlined. This method has found new variables that promise significant improvements in discrimination power. The prototype jet tagger is simple, interpretable, parsimonious, and computationally extremely cheap, and therefore might be suitable for use in trigger systems for real-time data processing. Nested stratified k-fold cross validation was used to generate robust estimates of model performance. The data analysis was performed entirely in the R statistical programming language, and is fully reproducible. The entire analysis workflow is data-driven, automated a...

Research on n-γ discrimination method based on spectrum gradient analysis of signals

International Nuclear Information System (INIS)

Luo Xiaoliang; Liu Guofu; Yang Jun; Wang Yueke

2013-01-01

Having discovered that there are distinct differences between the spectrum gradient of the output neutron and γ-ray signal from liquid scintillator detectors, this paper presented a n-γ discrimination method called spectrum gradient analysis (SGA) based on frequency-domain features of the pulse signals. The basic principle and feasibility of SGA method were discussed and the validity of n-γ discrimination results of SGA was verified by the associated particle neutron flight experiment. The discrimination performance of SGA was evaluated under different conditions of sampling rates ranging from 5 G/s to 250 M/s. The results show that SGA method exhibits insensitivity to noise, strong anti-interference ability, stable discrimination performance and lower amount of calculation in contrast with time-domain n-γ discrimination methods. (authors)

Characterization and noninvasive diagnosis of bladder cancer with serum surface enhanced Raman spectroscopy and genetic algorithms

Science.gov (United States)

Li, Shaoxin; Li, Linfang; Zeng, Qiuyao; Zhang, Yanjiao; Guo, Zhouyi; Liu, Zhiming; Jin, Mei; Su, Chengkang; Lin, Lin; Xu, Junfa; Liu, Songhao

2015-05-01

This study aims to characterize and classify serum surface-enhanced Raman spectroscopy (SERS) spectra between bladder cancer patients and normal volunteers by genetic algorithms (GAs) combined with linear discriminate analysis (LDA). Two group serum SERS spectra excited with nanoparticles are collected from healthy volunteers (n = 36) and bladder cancer patients (n = 55). Six diagnostic Raman bands in the regions of 481-486, 682-687, 1018-1034, 1313-1323, 1450-1459 and 1582-1587 cm-1 related to proteins, nucleic acids and lipids are picked out with the GAs and LDA. By the diagnostic models built with the identified six Raman bands, the improved diagnostic sensitivity of 90.9% and specificity of 100% were acquired for classifying bladder cancer patients from normal serum SERS spectra. The results are superior to the sensitivity of 74.6% and specificity of 97.2% obtained with principal component analysis by the same serum SERS spectra dataset. Receiver operating characteristic (ROC) curves further confirmed the efficiency of diagnostic algorithm based on GA-LDA technique. This exploratory work demonstrates that the serum SERS associated with GA-LDA technique has enormous potential to characterize and non-invasively detect bladder cancer through peripheral blood.

Gas Classification Using Combined Features Based on a Discriminant Analysis for an Electronic Nose

Directory of Open Access Journals (Sweden)

Sang-Il Choi

2016-01-01

Full Text Available This paper proposes a gas classification method for an electronic nose (e-nose system, for which combined features that have been configured through discriminant analysis are used. First, each global feature is extracted from the entire measurement section of the data samples, while the same process is applied to the local features of the section that corresponds to the stabilization, exposure, and purge stages. The discriminative information amounts in the individual features are then measured based on the discriminant analysis, and the combined features are subsequently composed by selecting the features that have a large amount of discriminative information. Regarding a variety of volatile organic compound data, the results of the experiment show that, in a noisy environment, the proposed method exhibits classification performance that is relatively excellent compared to the other feature types.

Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

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ZHANG Long

2015-09-01

Full Text Available Near infrared reflectance spectroscopy (NIRS, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis (PLS-DA to discriminate the transgenic (TCTP and mi166 and wild type (Zhonghua 11 rice. Furthermore, rice lines transformed with protein gene (OsTCTP and regulation gene (Osmi166 were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000–8 000 cm-1 and 4 000–10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000–10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000–10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

Identification of roselle varieties through simple discriminating physicochemical characteristics using multivariate analysis

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Alé KANE

2018-01-01

Full Text Available Abstract The objective of this work is to study the feasibility of a more objective and rigorous classification of the calices of Hibiscus sabdariffa based on their physicochemical profile. To do so, 19 analyses were carried out on 4 varieties of calices cultivated in Senegal: Vimto, Koor, Thaï and CLT92. Principal component analysis results showed that 15 physicochemical and biochemical parameters could be potentially used to discriminate the varieties of calices. Polyphenolic and anthocyanin contents were anti-correlated to protein content and could be used to differentiate the Vimto/CLT92 and the Koor/Thaï varieties. Within these two clusters, pH and lipid content could discriminate each variety. Finally, factorial discriminant analysis showed that total anthocyanin content, lipid content and chromaticity C* were the 3 parameters enabling the most efficient classification of calices according to variety and led to 100% classification accuracy.

An Application of Monte-Carlo-Based Sensitivity Analysis on the Overlap in Discriminant Analysis

Directory of Open Access Journals (Sweden)

S. Razmyan

2012-01-01

Full Text Available Discriminant analysis (DA is used for the measurement of estimates of a discriminant function by minimizing their group misclassifications to predict group membership of newly sampled data. A major source of misclassification in DA is due to the overlapping of groups. The uncertainty in the input variables and model parameters needs to be properly characterized in decision making. This study combines DEA-DA with a sensitivity analysis approach to an assessment of the influence of banks’ variables on the overall variance in overlap in a DA in order to determine which variables are most significant. A Monte-Carlo-based sensitivity analysis is considered for computing the set of first-order sensitivity indices of the variables to estimate the contribution of each uncertain variable. The results show that the uncertainties in the loans granted and different deposit variables are more significant than uncertainties in other banks’ variables in decision making.

Dimensional Analysis with space discrimination applied to Fickian difussion phenomena

International Nuclear Information System (INIS)

Diaz Sanchidrian, C.; Castans, M.

1989-01-01

Dimensional Analysis with space discrimination is applied to Fickian difussion phenomena in order to transform its partial differen-tial equations into ordinary ones, and also to obtain in a dimensionl-ess fom the Ficks second law. (Author)

Authenticity assessment of banknotes using portable near infrared spectrometer and chemometrics.

Science.gov (United States)

da Silva Oliveira, Vanessa; Honorato, Ricardo Saldanha; Honorato, Fernanda Araújo; Pereira, Claudete Fernandes

2018-05-01

Spectra recorded using a portable near infrared (NIR) spectrometer, Soft Independent Modeling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) associated to Successive Projections Algorithm (SPA) models were applied to identify counterfeit and authentic Brazilian Real (R$20, R$50 and R$100) banknotes, enabling a simple field analysis. NIR spectra (950-1650nm) were recorded from seven different areas of the banknotes (two with fluorescent ink, one over watermark, three with intaglio printing process and one over the serial numbers with typography printing). SIMCA and SPA-LDA models were built using 1st derivative preprocessed spectral data from one of the intaglio areas. For the SIMCA models, all authentic (300) banknotes were correctly classified and the counterfeits (227) were not classified. For the two classes SPA-LDA models (authentic and counterfeit currencies), all the test samples were correctly classified into their respective class. The number of selected variables by SPA varied from two to nineteen for R$20, R$50 and R$100 currencies. These results show that the use of the portable near-infrared with SIMCA or SPA-LDA models can be a completely effective, fast, and non-destructive way to identify authenticity of banknotes as well as permitting field analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

Fish otoliths analysis by PIXE: application to stock discrimination

International Nuclear Information System (INIS)

Arai, Nobuaki; Takai, Noriyuki; Sakamoto, Wataru; Yoshida, Koji; Maeda, Kuniko.

1996-01-01

Fish otoliths are continuously deposited from fish birth to its death along with encoding environmental information. In order to decode the information, PIXE was adopted as trace elemental analysis of the otoliths. Strontium to calcium concentration ratios of red sea bream otoliths varied among rearing stations. The Sr/Ca ratios of Lake Biwa catfishes also varied between male and female and among fishing grounds. The PIXE analysis was applied to the fish stock discrimination. (author)

The intersectionality of discrimination attributes and bullying among youth: an applied latent class analysis.

Science.gov (United States)

Garnett, Bernice Raveche; Masyn, Katherine E; Austin, S Bryn; Miller, Matthew; Williams, David R; Viswanath, Kasisomayajula

2014-08-01

Discrimination is commonly experienced among adolescents. However, little is known about the intersection of multiple attributes of discrimination and bullying. We used a latent class analysis (LCA) to illustrate the intersections of discrimination attributes and bullying, and to assess the associations of LCA membership to depressive symptoms, deliberate self harm and suicidal ideation among a sample of ethnically diverse adolescents. The data come from the 2006 Boston Youth Survey where students were asked whether they had experienced discrimination based on four attributes: race/ethnicity, immigration status, perceived sexual orientation and weight. They were also asked whether they had been bullied or assaulted for these attributes. A total of 965 (78%) students contributed to the LCA analytic sample (45% Non-Hispanic Black, 29% Hispanic, 58% Female). The LCA revealed that a 4-class solution had adequate relative and absolute fit. The 4-classes were characterized as: low discrimination (51%); racial discrimination (33%); sexual orientation discrimination (7%); racial and weight discrimination with high bullying (intersectional class) (7%). In multivariate models, compared to the low discrimination class, individuals in the sexual orientation discrimination class and the intersectional class had higher odds of engaging in deliberate self-harm. Students in the intersectional class also had higher odds of suicidal ideation. All three discrimination latent classes had significantly higher depressive symptoms compared to the low discrimination class. Multiple attributes of discrimination and bullying co-occur among adolescents. Research should consider the co-occurrence of bullying and discrimination.

Linear discriminant analysis of structure within African eggplant 'Shum'

African Journals Online (AJOL)

A MANOVA preceded linear discriminant analysis, to model each of 61 variables, as predicted by clusters and experiment to filter out non-significant traits. Four distinct clusters emerged, with a cophenetic relation coefficient of 0.87 (P<0.01). Canonical variates that best predicted the observed clusters include petiole length, ...

Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis

OpenAIRE

Oyewola, David; Hakimi, Danladi; Adeboye, Kayode; Shehu, Musa Danjuma

2017-01-01

Breast cancer is one of thecauses of female death in the world. Mammography  is commonly  used for  distinguishing  malignant tumors  from benign  ones. In this research,  a mammographic  diagnostic method  is  presented for breast  cancer  biopsy outcome  predictions  using  fivemachine learning which includes: Logistic Regression(LR), Linear DiscriminantAnalysis(LDA), Quadratic Discriminant Analysis(QDA), Random Forest(RF) andSupport  Vector Machine(SVM)  classification.  The testing result...

Classification of Astaxanthin Colouration of Salmonid Fish using Spectral Imaging and Tricolour Measurement

DEFF Research Database (Denmark)

Ljungqvist, Martin Georg; Dissing, Bjørn Skovlund; Nielsen, Michael Engelbrecht

capturing, tricolour CIELAB measurement, and manual SalmoFan inspection. Furthermore it was tested whether the best predictions come from measurements of the steak or the fillet of the fish. Methods used for classication were linear discriminant analysis (LDA), quadratic discriminant analysis (QDA......The goal of this study was to investigate if it is possible to differentiate between rainbow trout (Oncorhynchus mykiss) having been fed with natural or synthetic astaxanthin. Three different techniques were used for visual inspection of the surface colour of the fish meat: multi-spectral image...

Acoustic modeling for emotion recognition

CERN Document Server

Anne, Koteswara Rao; Vankayalapati, Hima Deepthi

2015-01-01

 This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.

Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease.

Directory of Open Access Journals (Sweden)

Robin Wolz

Full Text Available The role of structural brain magnetic resonance imaging (MRI is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD. This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC, 238 stable mild cognitive impairment (S-MCI, 167 MCI to AD progressors (P-MCI, 198 AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA and support vector machines (SVM. The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification, 64%/66% (S-MCI/P-MCI and 82%/76% (HC/P-MCI with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD, 67%/69% (S-MCI/P-MCI and 86%/82% (HC/P-MCI. Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.

Chemometric analysis for discrimination of extra virgin olive oils from whole and stoned olive pastes.

Science.gov (United States)

De Luca, Michele; Restuccia, Donatella; Clodoveo, Maria Lisa; Puoci, Francesco; Ragno, Gaetano

2016-07-01

Chemometric discrimination of extra virgin olive oils (EVOO) from whole and stoned olive pastes was carried out by using Fourier transform infrared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach. Four Italian commercial EVOO brands, all in both whole and stoned version, were considered in this study. The adopted chemometric methodologies were able to describe the different chemical features in phenolic and volatile compounds contained in the two types of oil by using unspecific IR spectral information. Principal component analysis (PCA) was employed in cluster analysis to capture data patterns and to highlight differences between technological processes and EVOO brands. The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extraction procedures. Discriminant analysis was extended to the evaluation of possible adulteration by addition of aliquots of oil from whole paste to the most valuable oil from stoned olives. The statistical parameters from external validation of all the PLS models were very satisfactory, with low root mean square error of prediction (RMSEP) and relative error (RE%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction Model of Collapse Risk Based on Information Entropy and Distance Discriminant Analysis Method

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Hujun He

2017-01-01

Full Text Available The prediction and risk classification of collapse is an important issue in the process of highway construction in mountainous regions. Based on the principles of information entropy and Mahalanobis distance discriminant analysis, we have produced a collapse hazard prediction model. We used the entropy measure method to reduce the influence indexes of the collapse activity and extracted the nine main indexes affecting collapse activity as the discriminant factors of the distance discriminant analysis model (i.e., slope shape, aspect, gradient, and height, along with exposure of the structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate, and degree of rock weathering. We employ postearthquake collapse data in relation to construction of the Yingxiu-Wolong highway, Hanchuan County, China, as training samples for analysis. The results were analyzed using the back substitution estimation method, showing high accuracy and no errors, and were the same as the prediction result of uncertainty measure. Results show that the classification model based on information entropy and distance discriminant analysis achieves the purpose of index optimization and has excellent performance, high prediction accuracy, and a zero false-positive rate. The model can be used as a tool for future evaluation of collapse risk.

NBLDA: negative binomial linear discriminant analysis for RNA-Seq data.

Science.gov (United States)

Dong, Kai; Zhao, Hongyu; Tong, Tiejun; Wan, Xiang

2016-09-13

RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statistical methods that have been developed for microarray data can be applied to RNA-Seq data, they are not ideal due to the discrete nature of RNA-Seq data. The Poisson distribution and negative binomial distribution are commonly used to model count data. Recently, Witten (Annals Appl Stat 5:2493-2518, 2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson assumption may not be as appropriate as the negative binomial distribution when biological replicates are available and in the presence of overdispersion (i.e., when the variance is larger than or equal to the mean). However, it is more complicated to model negative binomial variables because they involve a dispersion parameter that needs to be estimated. In this paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data. By Bayes' rule, we construct the classifier by fitting a negative binomial model, and propose some plug-in rules to estimate the unknown parameters in the classifier. The relationship between the negative binomial classifier and the Poisson classifier is explored, with a numerical investigation of the impact of dispersion on the discriminant score. Simulation results show the superiority of our proposed method. We also analyze two real RNA-Seq data sets to demonstrate the advantages of our method in real-world applications. We have developed a new classifier using the negative binomial model for RNA-seq data classification. Our simulation results show that our proposed classifier has a better performance than existing works. The proposed classifier can serve as an effective tool for classifying RNA-seq data. Based on the comparison results, we have provided some guidelines for scientists to decide which method should be used in the discriminant analysis of RNA-Seq data

Volatile-Compound Fingerprinting by Headspace-Gas-Chromatography Ion-Mobility Spectrometry (HS-GC-IMS) as a Benchtop Alternative to 1H NMR Profiling for Assessment of the Authenticity of Honey.

Science.gov (United States)

Gerhardt, Natalie; Birkenmeier, Markus; Schwolow, Sebastian; Rohn, Sascha; Weller, Philipp

2018-02-06

This work describes a simple approach for the untargeted profiling of volatile compounds for the authentication of the botanical origins of honey based on resolution-optimized HS-GC-IMS combined with optimized chemometric techniques, namely PCA, LDA, and kNN. A direct comparison of the PCA-LDA models between the HS-GC-IMS and 1 H NMR data demonstrated that HS-GC-IMS profiling could be used as a complementary tool to NMR-based profiling of honey samples. Whereas NMR profiling still requires comparatively precise sample preparation, pH adjustment in particular, HS-GC-IMS fingerprinting may be considered an alternative approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. It was demonstrated that all tested honey samples could be distinguished on the basis of their botanical origins. Loading plots revealed the volatile compounds responsible for the differences among the monofloral honeys. The HS-GC-IMS-based PCA-LDA model was composed of two linear functions of discrimination and 10 selected PCs that discriminated canola, acacia, and honeydew honeys with a predictive accuracy of 98.6%. Application of the LDA model to an external test set of 10 authentic honeys clearly proved the high predictive ability of the model by correctly classifying them into three variety groups with 100% correct classifications. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other food types.

Correlation effects and spin-orbit interaction in Sr{sub 3}Ru{sub 2}O{sub 7}: LDA+DMFT study

Energy Technology Data Exchange (ETDEWEB)

Gorelov, Evgeny; Zhang, Guoren; Pavarini, Eva [IAS-3, Forschungszentrum Juelich, 52425 Juelich (Germany)

2013-07-01

The layered ruthenates of the Ruddlesden-Popper family Sr{sub n+1}Ru{sub n}O{sub 3n+1} are interesting examples of strongly correlated transition metal compounds. Due to competing kinetic and Coulomb energies, that are of the same order for Ru 4d electrons, these compounds have very rich phase diagram, including Mott-insulator, ferro- and meta-magnetic phases. Among layered ruthenates the bilayered compound Sr{sub 3}Ru{sub 2}O{sub 7} is particularly interesting. It is known to be a paramagnetic metal close to ferro-magnetism and exhibits a metamagnetic behavior in external magnetic field. By using the LDA+DMFT (local-density approximation + dynamical mean-field theory) approach, we study magnetic properties and electron mass renormalization due to correlation effects. In our LDA+DMFT scheme we use maximally-localized Wannier orbitals obtained from Linearized Augmented Plane Wave (LAPW) calculations to build a low-energy Hubbard model for the Ru d bands; we use the weak-coupling CT-quantum Monte Carlo method to solve the quantum impurity problem. We take into account the full rotationally-invariant Coulomb interaction, as well as full on-site self-energy matrix in orbital space with spin-orbit coupling.

Hierarchical Discriminant Analysis

Directory of Open Access Journals (Sweden)

Di Lu

2018-01-01

Full Text Available The Internet of Things (IoT generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA. It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

Discriminant analysis of Social Work’s performance in licensure examination

Directory of Open Access Journals (Sweden)

Jonel R. Alonzo

2017-12-01

Full Text Available Many research studies have examined academic factors as predictors of success in licensure examination. The purpose of this descriptive discriminant analysis was to explore possible factors in passing social work licensure examination. Data were examined from academic records of 69 (37 passed and 32 failed Social Work graduates of the University of Mindanao who took Social Work Licensure Examination 2014. This can be used as a basis of Social Work program in planning and administering strategies to improve its national passing rates. Discriminant analysis was employed along five academic factors which are Human Behavior and Social Environment (HBSE, Social Work Programs and Policies (SWPP, Social Work Methods (SWM, Field Practice (FP and Grade Point Average (GPA. The analysis generated three significant predictors accounting for 76.22% of between group variability. The function had a hit ratio of 100%. Structure matrix revealed that three cluster subjects were identified as good factors of passing the social work licensure examination: HBSE, SWPP and SWM had a correlation value of 0.713, 0.768 and 0.840, respectively.

Discriminant analysis in Polish manufacturing sector performance assessment

Directory of Open Access Journals (Sweden)

Józef Dziechciarz

2004-01-01

Full Text Available This is a presentation of the preliminary results of a larger project on the determination of the attractiveness of manufacturing branches. Results of the performance assessment of Polish manufacturing branches in 2000 (section D „Manufacturing” – based on NACE – Nomenclatures des Activites de Communite Europeene are shown. In the research, the classical (Fisher’s linear discriminant analysis technique was used for the analysis of the profit generation ability by the firms belonging to a certain production branch. For estimation, the data describing group level was used – for cross-validation, the classes data.

Computational chemistry approach for the early detection of drug-induced idiosyncratic liver toxicity.

Science.gov (United States)

Cruz-Monteagudo, Maykel; Cordeiro, M Natália D S; Borges, Fernanda

2008-03-01

Idiosyncratic drug toxicity (IDT), considered as a toxic host-dependent event, with an apparent lack of dose response relationship, is usually not predictable from early phases of clinical trials, representing a particularly confounding complication in drug development. Albeit a rare event (usually approach proposed in the present study, can play an important role in addressing IDT in early drug discovery. We report for the first time a systematic evaluation of classification models to predict idiosyncratic hepatotoxicity based on linear discriminant analysis (LDA), artificial neural networks (ANN), and machine learning algorithms (OneR) in conjunction with a 3D molecular structure representation and feature selection methods. These modeling techniques (LDA, feature selection to prevent over-fitting and multicollinearity, ANN to capture nonlinear relationships in the data, as well as the simple OneR classifier) were found to produce QSTR models with satisfactory internal cross-validation statistics and predictivity on an external subset of chemicals. More specifically, the models reached values of accuracy/sensitivity/specificity over 84%/78%/90%, respectively in the training series along with predictivity values ranging from ca. 78 to 86% of correctly classified drugs. An LDA-based desirability analysis was carried out in order to select the levels of the predictor variables needed to trigger the more desirable drug, i.e. the drug with lower potential for idiosyncratic hepatotoxicity. Finally, two external test sets were used to evaluate the ability of the models in discriminating toxic from nontoxic structurally and pharmacologically related drugs and the ability of the best model (LDA) in detecting potential idiosyncratic hepatotoxic drugs, respectively. The computational approach proposed here can be considered as a useful tool in early IDT prognosis.

Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification

Science.gov (United States)

Teye, Ernest; Huang, Xingyi; Dai, Huang; Chen, Quansheng

2013-10-01

Quick, accurate and reliable technique for discrimination of cocoa beans according to geographical origin is essential for quality control and traceability management. This current study presents the application of Near Infrared Spectroscopy technique and multivariate classification for the differentiation of Ghana cocoa beans. A total of 194 cocoa bean samples from seven cocoa growing regions were used. Principal component analysis (PCA) was used to extract relevant information from the spectral data and this gave visible cluster trends. The performance of four multivariate classification methods: Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Back propagation artificial neural network (BPANN) and Support vector machine (SVM) were compared. The performances of the models were optimized by cross validation. The results revealed that; SVM model was superior to all the mathematical methods with a discrimination rate of 100% in both the training and prediction set after preprocessing with Mean centering (MC). BPANN had a discrimination rate of 99.23% for the training set and 96.88% for prediction set. While LDA model had 96.15% and 90.63% for the training and prediction sets respectively. KNN model had 75.01% for the training set and 72.31% for prediction set. The non-linear classification methods used were superior to the linear ones. Generally, the results revealed that NIR Spectroscopy coupled with SVM model could be used successfully to discriminate cocoa beans according to their geographical origins for effective quality assurance.

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

Science.gov (United States)

Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

2017-10-12

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

Anti-discrimination Analysis Using Privacy Attack Strategies

KAUST Repository

Ruggieri, Salvatore; Hajian, Sara; Kamiran, Faisal; Zhang, Xiangliang

2014-01-01

Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination

Classification of Fusarium-Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis

Directory of Open Access Journals (Sweden)

Jongguk Lim

2017-09-01

Full Text Available The purpose of this study is to use near-infrared reflectance (NIR spectroscopy equipment to nondestructively and rapidly discriminate Fusarium-infected hulled barley. Both normal hulled barley and Fusarium-infected hulled barley were scanned by using a NIR spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra obtained for Fusarium discrimination and the multivariate analysis method of partial least squares discriminant analysis (PLS-DA was used for discriminant prediction. The PLS-DA prediction model developed by applying the second-order derivative pretreatment to the reflectance spectra obtained from the side of hulled barley without crease achieved 100% accuracy in discriminating the normal hulled barley and the Fusarium-infected hulled barley. These results demonstrated the feasibility of rapid discrimination of the Fusarium-infected hulled barley by combining multivariate analysis with the NIR spectroscopic technique, which is utilized as a nondestructive detection method.

Phylogenetic comparative methods complement discriminant function analysis in ecomorphology.

Science.gov (United States)

Barr, W Andrew; Scott, Robert S

2014-04-01

In ecomorphology, Discriminant Function Analysis (DFA) has been used as evidence for the presence of functional links between morphometric variables and ecological categories. Here we conduct simulations of characters containing phylogenetic signal to explore the performance of DFA under a variety of conditions. Characters were simulated using a phylogeny of extant antelope species from known habitats. Characters were modeled with no biomechanical relationship to the habitat category; the only sources of variation were body mass, phylogenetic signal, or random "noise." DFA on the discriminability of habitat categories was performed using subsets of the simulated characters, and Phylogenetic Generalized Least Squares (PGLS) was performed for each character. Analyses were repeated with randomized habitat assignments. When simulated characters lacked phylogenetic signal and/or habitat assignments were random, ecomorphology. Copyright © 2013 Wiley Periodicals, Inc.

Analysis of Financial Ratio to Distinguish Indonesia Joint Venture General Insurance Company Performance using Discriminant Analysis

Directory of Open Access Journals (Sweden)

Subiakto Soekarno

2012-01-01

Full Text Available Insurance industry stands as a service business that plays a significant role in Indonesiaeconomical condition. The development of insurance industry in Indonesia, both of generalinsurance and life insurance, has increased very fast. The general insurance industry itselfdivided into two major players which are local private company and Joint Venture Company.Lately, the use of statistical techniques and financial ratios models to asses financial institutionsuch as insurance company have been used as one of the appropriate combination inpredicting the performance of an industry. This research aims to distinguish between JointVenture General Insurance Companies that have a good performance and those who are lessperforming well using Discriminant Analysis. Further, the findings led that DiscriminantAnalysis is able to distinguish Joint Venture General Insurance Companies that have a goodperformance and those who are not performing well. There are also six ratios which are RBC,Technical Reserve to Investment Ratio, Debt Ratio, Return on Equity, Loss Ratio, and ExpenseRatio that stand as the most influential ratios to distinguish the performance of joint venturegeneral insurance companies. In addition, the result suggest business people to be concernedtoward those six ratios, to increase their companies’ performance.Key words: general insurance, financial ratio, discriminant analysis

Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

Science.gov (United States)

Ahmadi, Shiva; Mani-Varnosfaderani, Ahmad; Habibi, Biuck

2018-04-20

Motor oil classification is important for quality control and the identification of oil adulteration. In thiswork, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

Aided diagnosis methods of breast cancer based on machine learning

Science.gov (United States)

Zhao, Yue; Wang, Nian; Cui, Xiaoyu

2017-08-01

In the field of medicine, quickly and accurately determining whether the patient is malignant or benign is the key to treatment. In this paper, K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression were applied to predict the classification of thyroid,Her-2,PR,ER,Ki67,metastasis and lymph nodes in breast cancer, in order to recognize the benign and malignant breast tumors and achieve the purpose of aided diagnosis of breast cancer. The results showed that the highest classification accuracy of LDA was 88.56%, while the classification effect of KNN and Logistic Regression were better than that of LDA, the best accuracy reached 96.30%.

Fast mental states decoding in mixed reality.

Science.gov (United States)

De Massari, Daniele; Pacheco, Daniel; Malekshahi, Rahim; Betella, Alberto; Verschure, Paul F M J; Birbaumer, Niels; Caria, Andrea

2014-01-01

The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

Science.gov (United States)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis.

Science.gov (United States)

Blasco, H; Błaszczyński, J; Billaut, J C; Nadal-Desbarats, L; Pradat, P F; Devos, D; Moreau, C; Andres, C R; Emond, P; Corcia, P; Słowiński, R

2015-02-01

Metabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients. Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS. DRSA provides

Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

DEFF Research Database (Denmark)

Han, Xixuan; Clemmensen, Line Katrine Harder

2015-01-01

We propose a general technique for obtaining sparse solutions to generalized eigenvalue problems, and call it Regularized Generalized Eigen-Decomposition (RGED). For decades, Fisher's discriminant criterion has been applied in supervised feature extraction and discriminant analysis, and it is for...

Electrochemical approach for acute myocardial infarction diagnosis based on direct antibodies-free analysis of human blood plasma.

Science.gov (United States)

Suprun, Elena V; Saveliev, Anatoly A; Evtugyn, Gennady A; Lisitsa, Alexander V; Bulko, Tatiana V; Shumyantseva, Victoria V; Archakov, Alexander I

2012-03-15

A novel direct antibodies-free electrochemical approach for acute myocardial infarction (AMI) diagnosis has been developed. For this purpose, a combination of the electrochemical assay of plasma samples with chemometrics was proposed. Screen printed carbon electrodes modified with didodecyldimethylammonium bromide were used for plasma charactrerization by cyclic (CV) and square wave voltammetry and square wave (SWV) voltammetry. It was shown that the cathodic peak in voltammograms at about -250 mV vs. Ag/AgCl can be associated with AMI. In parallel tests, cardiac myoglobin and troponin I, the AMI biomarkers, were determined in each sample by RAMP immunoassay. The applicability of the electrochemical testing for AMI diagnostics was confirmed by statistical methods: generalized linear model (GLM), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), artificial neural net (multi-layer perception, MLP), and support vector machine (SVM), all of which were created to obtain the "True-False" distribution prediction where "True" and "False" are, respectively, positive and negative decision about an illness event. Copyright © 2011 Elsevier B.V. All rights reserved.

Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification.

Science.gov (United States)

Wen, Zaidao; Hou, Biao; Jiao, Licheng

2017-05-03

Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

Authentication of fattening diet of Iberian pigs according to their volatile compounds profile from raw subcutaneous fat.

Science.gov (United States)

Narváez-Rivas, M; Pablos, F; Jurado, J M; León-Camacho, M

2011-02-01

The composition of volatile components of subcutaneous fat from Iberian pig has been studied. Purge and trap gas chromatography-mass spectrometry has been used. The composition of the volatile fraction of subcutaneous fat has been used for authentication purposes of different types of Iberian pig fat. Three types of this product have been considered, montanera, extensive cebo and intensive cebo. With classification purposes, several pattern recognition techniques have been applied. In order to find out possible tendencies in the sample distribution as well as the discriminant power of the variables, principal component analysis was applied as visualisation technique. Linear discriminant analysis (LDA) and soft independent modelling by class analogy (SIMCA) were used to obtain suitable classification models. LDA and SIMCA allowed the differentiation of three fattening diets by using the contents in 2,2,4,6,6-pentamethyl-heptane, m-xylene, 2,4-dimethyl-heptane, 6-methyl-tridecane, 1-methoxy-2-propanol, isopropyl alcohol, o-xylene, 3-ethyl-2,2-dimethyl-oxirane, 2,6-dimethyl-undecane, 3-methyl-3-pentanol and limonene.

Risk of losing remission, low disease activity or radiographic progression in case of bDMARD discontinuation or tapering in rheumatoid arthritis: systematic analysis of the literature and meta-analysis.

Science.gov (United States)

Henaux, Sophie; Ruyssen-Witrand, Adeline; Cantagrel, Alain; Barnetche, Thomas; Fautrel, Bruno; Filippi, Nathalie; Lukas, Cédric; Raffeiner, Bernd; Rossini, Maurizio; Degboé, Yannick; Constantin, Arnaud

2018-04-01

To assess the risk of losing remission, low disease activity (LDA) or radiographic progression in the case of (1) discontinuing or (2) tapering doses of biological disease-modifying antirheumatic drugs (bDMARDs) compared with continuation of the initial treatment regimen in rheumatoid arthritis (RA) patients with remission or LDA. A systematic literature analysis was carried out through May 2017 on the PubMed, Embase, Cochrane and international congress databases, selecting controlled trials comparing bDMARDs discontinuation/tapering versus continuation in RA patients with remission or LDA. The meta-analysis assessed the risk ratio (RR) and 95% CI of losing remission or LDA and the risk of radiographic progression after (1) discontinuing and (2) tapering doses of bDMARDs versus continuing the initial treatment. The meta-analysis comparing bDMARDs discontinuation versus continuation performed on nine trials showed an increased risk of losing remission (RR (95% CI)=1.97(1.43 to 2.73), P<0.0001) or LDA (RR (95% CI)=2.24(1.52 to 3.30), P<0.0001) and an increased risk of radiographic progression (RR (95% CI)=1.09(1.02 to 1.17), P=0.01) in case of bDMARD discontinuation. The meta-analysis comparing bDMARDs tapering versus continuation performed on 11 trials showed an increased risk of losing remission (RR (95% CI)=1.23(1.06 to 1.42), P=0.006) but no increased risk of losing LDA (RR (95% CI)=1.02 (0.85 to 1.23), P=0.81) nor any increased risk of radiographic progression (RR (95% CI)=1.09(0.94 to 1.26), P=0.26) in case of bDMARD tapering. Discontinuation of bDMARDs leads to an increased risk of losing remission or LDA and radiographic progression, while tapering doses of bDMARDs does not increase the risk of relapse (LDA) or radiographic progression, even though there is an increased risk of losing remission. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted

Energetic, electronic and optical properties of lanthanide doped TiO2: An ab initio LDA+U study

CSIR Research Space (South Africa)

Mulwa, WM

2016-05-01

Full Text Available potential, J. Chem. Phys. 118 (2003) 8207. doi:10.1063/1.1564060. [23] X. Ren, Beyond LDA and GGA - Tackling exact exchange , hybrid functional , MP2 , and RPA with numeric atom-centered orbitals The Fritz-Haber-Institute ab initio molecular simulations.... Calzolari, A. Ruini, A. Catellani, Anchor Group versus Conjugation: Toward the Gap-State Engineering of Functionalized ZnO (101̅0) Surface for Optoelectronic Applications, J. Am. Chem. Soc. 133 (2011) 5893–5899. [36] R. Gillen, S.J. Clark, J. Robertson...

Differentiation of free-ranging chicken using discriminant analysis of phenotypic traits

Directory of Open Access Journals (Sweden)

Raed M. Al-Atiyat

Full Text Available ABSTRACT In this study, we investigated the differentiation of five different chicken ecotypes - Center, North, South, West, and East - of Saudi Arabia using discriminate analysis. The analysis was based on nine important morphological and phenotypic traits: body color, beak color, earlobe color, eye color, shank color, comb color, comb type, comb size, and feather distribution. There was a strong significant relationship between the phenotype and effect of geographic height in terms of comb type and earlobe color in males as well as body, beak, eye, and shank color. In particular, the comb type and earlobe color differentiated the ecotypes of males. Among the females, the beak, earlobe, eye, shank color, and feather distribution had more differentiating power. Moreover, the discriminant analysis revealed that the five ecotypes were grouped into three clusters; the Center and the North in one cluster, the West and the South ecotypes in the second for males, and the East ecotype in the last cluster. The female dendogram branching was similar to the male dendrogram branching, except that the Center ecotype was grouped with the North instead of the South. The East ecotype was highly discriminated from the other ecotypes. Nevertheless, the potential of recent individual migration between ecotypes was also noted. Accordingly, the results of the utilized traits in this study might be effective in characterization and conservation of the genetic resources of the Saudi chicken.

The Discriminant Analysis Flare Forecasting System (DAFFS)

Science.gov (United States)

Leka, K. D.; Barnes, Graham; Wagner, Eric; Hill, Frank; Marble, Andrew R.

2016-05-01

The Discriminant Analysis Flare Forecasting System (DAFFS) has been developed under NOAA/Small Business Innovative Research funds to quantitatively improve upon the NOAA/SWPC flare prediction. In the Phase-I of this project, it was demonstrated that DAFFS could indeed improve by the requested 25% most of the standard flare prediction data products from NOAA/SWPC. In the Phase-II of this project, a prototype has been developed and is presently running autonomously at NWRA.DAFFS uses near-real-time data from NOAA/GOES, SDO/HMI, and the NSO/GONG network to issue both region- and full-disk forecasts of solar flares, based on multi-variable non-parametric Discriminant Analysis. Presently, DAFFS provides forecasts which match those provided by NOAA/SWPC in terms of thresholds and validity periods (including 1-, 2-, and 3- day forecasts), although issued twice daily. Of particular note regarding DAFFS capabilities are the redundant system design, automatically-generated validation statistics and the large range of customizable options available. As part of this poster, a description of the data used, algorithm, performance and customizable options will be presented, as well as a demonstration of the DAFFS prototype.DAFFS development at NWRA is supported by NOAA/SBIR contracts WC-133R-13-CN-0079 and WC-133R-14-CN-0103, with additional support from NASA contract NNH12CG10C, plus acknowledgment to the SDO/HMI and NSO/GONG facilities and NOAA/SWPC personnel for data products, support, and feedback. DAFFS is presently ready for Phase-III development.

DISCRIMINATION OF WOMEN IN THE LABOUR MARKET OF SR AND MODELS OF DISCRIMINATION

OpenAIRE

Ján Vravec; Radovan Baèík

2012-01-01

IThe paper deals with the problem of women’s discrimination in the labour market. Significant differences, among women and men in the labour market, are especially in unemployment rate in reward system, and high horizontal and vertical segregation of women. The aspects of discrimination arise despite of existing legislation, which gender discrimination strictly prohibits. An analysis of arguments, consequences and models of women’s discrimination in the labour market can significantly help to...

Plano de marketing para a venda de camiões Volvo em Portugal – Auto Sueco, Lda.

OpenAIRE

Martins, José Nuno da Rocha

2014-01-01

O principal objetivo deste estudo foi o desenvolvimento de um plano de marketing para a venda de camiões Volvo em Portugal. O estudo pretende dotar a Auto Sueco Lda. de ferramentas que lhe permitam melhorar a performance no negócio da venda de camiões Volvo em Portugal. Nesse sentido, o plano começa por fazer uma caracterização detalhada da envolvente externa, bem como elaborar um diagnóstico da situação interna da empresa. Mediante esta análise, são enumerados os pontos que a empresa deve...

WOMEN RESISTANCE TOWARD DISCRIMINATIONS: A MODERN LITERARY WORK ANALYSIS ON FEMINISM REVIEW IN BEKISAR MERAH

Directory of Open Access Journals (Sweden)

Mujiono .

2016-02-01

Full Text Available This study was conducted to discover the discriminations against women in the Bekisar Merah novel and how they formulate resistance to those discriminations. To address the above objective, this study used descriptive qualitative research design with a feminism approach. Source of the data in this study was the second edition of Bekisar Merah novel written by Ahmad Tohari. The data included were words, phrases, sentences, and paragraphs on Bekisar Merah which portray womens discrimination toward Lasi, the women figure in the novel, and power types formulated by her who resisted the discrimination. To analyze the data, content analysis was applied. Triangulation was used to ensure the trustworthiness of the data. The result of the study showed eight forms of discriminations and three resistances. The discriminations were domestic abuse, molestation, gender harassment, seduction behavior, imposition, coercion, bribery, and subordination. The resistances were physically, mentally, and verbally.

Discrimination of Aurantii Fructus Immaturus and Fructus Poniciri Trifoliatae Immaturus by Flow Injection UV Spectroscopy (FIUV) and 1H NMR using Partial Least-squares Discriminant Analysis (PLS-DA)

Science.gov (United States)

Two simple fingerprinting methods, flow-injection UV spectroscopy (FIUV) and 1H nuclear magnetic resonance (NMR), for discrimination of Aurantii FructusImmaturus and Fructus Poniciri TrifoliataeImmaturususing were described. Both methods were combined with partial least-squares discriminant analysis...

Application of mass spectrometry based electronic nose and chemometrics for fingerprinting radiation treatment

International Nuclear Information System (INIS)

Gupta, Sumit; Variyar, Prasad S.; Sharma, Arun

2015-01-01

Volatile compounds were isolated from apples and grapes employing solid phase micro extraction (SPME) and subsequently analyzed by GC/MS equipped with a transfer line without stationary phase. Single peak obtained was integrated to obtain total mass spectrum of the volatile fraction of samples. A data matrix having relative abundance of all mass-to-charge ratios was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) to identify radiation treatment. PCA results suggested that there is sufficient variability between control and irradiated samples to build classification models based on supervised techniques. LDA successfully aided in segregating control from irradiated samples at all doses (0.1, 0.25, 0.5, 1.0, 1.5, 2.0 kGy). SPME-MS with chemometrics was successfully demonstrated as simple screening method for radiation treatment. - Highlights: • Total mass spectra obtained from HS-MS for control and irradiated fruits. • Grapes and apples are chosen for present study. • Total mass spectrum was analyzed by two chemometric techniques (PCA and LDA). • Successful segregation of control and irradiated samples achieved using chemometrics

Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review.

Science.gov (United States)

Fusco, Roberta; Sansone, Mario; Filice, Salvatore; Carone, Guglielmo; Amato, Daniela Maria; Sansone, Carlo; Petrillo, Antonella

2016-01-01

We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), and Bayesian classifiers (BC), and features used for classification are described. The findings of a systematic review of 26 studies are presented. The sensitivity and specificity are respectively 91 and 83 % for ANN, 85 and 82 % for SVM, 96 and 85 % for LDA, 92 and 87 % for TC, and 82 and 85 % for BC. The sensitivity and specificity are respectively 82 and 74 % for dynamic features, 93 and 60 % for morphological features, 88 and 81 % for textural features, 95 and 86 % for a combination of dynamic and morphological features, and 88 and 84 % for a combination of dynamic, morphological, and other features. LDA and TC have the best performance. A combination of dynamic and morphological features gives the best performance.

Palm vein recognition based on directional empirical mode decomposition

Science.gov (United States)

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

2014-04-01

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the 2LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based 2LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

Women ministers' experiences of gender discrimination in the Lutheran Church : a discourse analysis

OpenAIRE

2011-01-01

M.A. The aim of this psychological study was to uncover women minister’s experiences of gender discrimination in the Lutheran Church by using a discourse analysis. Three female participants, who are involved in ministry in the Lutheran Church, were interviewed about their experiences and perceptions of gender discrimination. The resultant texts were analysed using Parker’s (2005) steps to discourse analytic reading. The discourses that were discovered indicate that power struggles are prev...

A new kernel discriminant analysis framework for electronic nose recognition

International Nuclear Information System (INIS)

Zhang, Lei; Tian, Feng-Chun

2014-01-01

Graphical abstract: - Highlights: • This paper proposes a new discriminant analysis framework for feature extraction and recognition. • The principle of the proposed NDA is derived mathematically. • The NDA framework is coupled with kernel PCA for classification. • The proposed KNDA is compared with state of the art e-Nose recognition methods. • The proposed KNDA shows the best performance in e-Nose experiments. - Abstract: Electronic nose (e-Nose) technology based on metal oxide semiconductor gas sensor array is widely studied for detection of gas components. This paper proposes a new discriminant analysis framework (NDA) for dimension reduction and e-Nose recognition. In a NDA, the between-class and the within-class Laplacian scatter matrix are designed from sample to sample, respectively, to characterize the between-class separability and the within-class compactness by seeking for discriminant matrix to simultaneously maximize the between-class Laplacian scatter and minimize the within-class Laplacian scatter. In terms of the linear separability in high dimensional kernel mapping space and the dimension reduction of principal component analysis (PCA), an effective kernel PCA plus NDA method (KNDA) is proposed for rapid detection of gas mixture components by an e-Nose. The NDA framework is derived in this paper as well as the specific implementations of the proposed KNDA method in training and recognition process. The KNDA is examined on the e-Nose datasets of six kinds of gas components, and compared with state of the art e-Nose classification methods. Experimental results demonstrate that the proposed KNDA method shows the best performance with average recognition rate and total recognition rate as 94.14% and 95.06% which leads to a promising feature extraction and multi-class recognition in e-Nose

Meta-analysis of field experiments shows no change in racial discrimination in hiring over time.

Science.gov (United States)

Quillian, Lincoln; Pager, Devah; Hexel, Ole; Midtbøen, Arnfinn H

2017-10-10

This study investigates change over time in the level of hiring discrimination in US labor markets. We perform a meta-analysis of every available field experiment of hiring discrimination against African Americans or Latinos ( n = 28). Together, these studies represent 55,842 applications submitted for 26,326 positions. We focus on trends since 1989 ( n = 24 studies), when field experiments became more common and improved methodologically. Since 1989, whites receive on average 36% more callbacks than African Americans, and 24% more callbacks than Latinos. We observe no change in the level of hiring discrimination against African Americans over the past 25 years, although we find modest evidence of a decline in discrimination against Latinos. Accounting for applicant education, applicant gender, study method, occupational groups, and local labor market conditions does little to alter this result. Contrary to claims of declining discrimination in American society, our estimates suggest that levels of discrimination remain largely unchanged, at least at the point of hire.

Potential of non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy

Science.gov (United States)

Huang, Shaohua; Wang, Lan; Chen, Weisheng; Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Li, Buhong; Chen, Rong

2014-11-01

Non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy (SERS) analysis was presented. Urine SERS spectra were measured on esophagus cancer patients (n = 56) and healthy volunteers (n = 36) for control analysis. Tentative assignments of the urine SERS spectra indicated some interesting esophagus cancer-specific biomolecular changes, including a decrease in the relative content of urea and an increase in the percentage of uric acid in the urine of esophagus cancer patients compared to that of healthy subjects. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to analyze and differentiate the SERS spectra between normal and esophagus cancer urine. The diagnostic algorithms utilizing a multivariate analysis method achieved a diagnostic sensitivity of 89.3% and specificity of 83.3% for separating esophagus cancer samples from normal urine samples. These results from the explorative work suggested that silver nano particle-based urine SERS analysis coupled with PCA-LDA multivariate analysis has potential for non-invasive detection of esophagus cancer.

Potential of non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy

International Nuclear Information System (INIS)

Huang, Shaohua; Wang, Lan; Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Li, Buhong; Chen, Rong; Chen, Weisheng

2014-01-01

Non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy (SERS) analysis was presented. Urine SERS spectra were measured on esophagus cancer patients (n = 56) and healthy volunteers (n = 36) for control analysis. Tentative assignments of the urine SERS spectra indicated some interesting esophagus cancer-specific biomolecular changes, including a decrease in the relative content of urea and an increase in the percentage of uric acid in the urine of esophagus cancer patients compared to that of healthy subjects. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to analyze and differentiate the SERS spectra between normal and esophagus cancer urine. The diagnostic algorithms utilizing a multivariate analysis method achieved a diagnostic sensitivity of 89.3% and specificity of 83.3% for separating esophagus cancer samples from normal urine samples. These results from the explorative work suggested that silver nano particle-based urine SERS analysis coupled with PCA–LDA multivariate analysis has potential for non-invasive detection of esophagus cancer. (letter)

Application of mass spectrometry based electronic nose and chemometrics for fingerprinting radiation treatment

Science.gov (United States)

Gupta, Sumit; Variyar, Prasad S.; Sharma, Arun

2015-01-01

Volatile compounds were isolated from apples and grapes employing solid phase micro extraction (SPME) and subsequently analyzed by GC/MS equipped with a transfer line without stationary phase. Single peak obtained was integrated to obtain total mass spectrum of the volatile fraction of samples. A data matrix having relative abundance of all mass-to-charge ratios was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) to identify radiation treatment. PCA results suggested that there is sufficient variability between control and irradiated samples to build classification models based on supervised techniques. LDA successfully aided in segregating control from irradiated samples at all doses (0.1, 0.25, 0.5, 1.0, 1.5, 2.0 kGy). SPME-MS with chemometrics was successfully demonstrated as simple screening method for radiation treatment.

Discrimination analysis of ononis repens and ononis spinosa of the ...

African Journals Online (AJOL)

Discrimination analysis of ononis repens and ononis spinosa of the British Isles. CE Stephens. Abstract. No Abstract. Journal of the Ghana Association Vol. 2 (3) 1999: pp.88-94. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT · http://dx.doi.org/10.4314/jgsa.v2i3.17997.

Discrimination of honeys using colorimetric sensor arrays, sensory analysis and gas chromatography techniques.

Science.gov (United States)

Tahir, Haroon Elrasheid; Xiaobo, Zou; Xiaowei, Huang; Jiyong, Shi; Mariod, Abdalbasit Adam

2016-09-01

Aroma profiles of six honey varieties of different botanical origins were investigated using colorimetric sensor array, gas chromatography-mass spectrometry (GC-MS) and descriptive sensory analysis. Fifty-eight aroma compounds were identified, including 2 norisoprenoids, 5 hydrocarbons, 4 terpenes, 6 phenols, 7 ketones, 9 acids, 12 aldehydes and 13 alcohols. Twenty abundant or active compounds were chosen as key compounds to characterize honey aroma. Discrimination of the honeys was subsequently implemented using multivariate analysis, including hierarchical clustering analysis (HCA) and principal component analysis (PCA). Honeys of the same botanical origin were grouped together in the PCA score plot and HCA dendrogram. SPME-GC/MS and colorimetric sensor array were able to discriminate the honeys effectively with the advantages of being rapid, simple and low-cost. Moreover, partial least squares regression (PLSR) was applied to indicate the relationship between sensory descriptors and aroma compounds. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

Science.gov (United States)

Liu, Yan; Salvendy, Gavriel

2009-05-01

This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.

Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system.

Science.gov (United States)

Wang, Dongcui; Mo, Fongming; Zhang, Yangde; Yang, Chao; Liu, Jun; Chen, Zhencheng; Zhao, Jinfeng

2015-01-01

In a previous study (unpublished), Emotiv headset was validated for capturing event-related potentials (ERPs) from normal subjects. In the present follow-up study, the signal quality of Emotiv headset was tested by the accuracy rate of discriminating Major Depressive Disorder (MDD) patients from the normal subjects. ERPs of 22 MDD patients and 15 normal subjects were induced by an auditory oddball task and the amplitude of N1, N2 and P3 of ERP components were specifically analyzed. The features of ERPs were statistically investigated. It is found that Emotiv headset is capable of discriminating the abnormal N1, N2 and P3 components in MDD patients. Relief-F algorithm was applied to all features for feature selection. The selected features were then input to a linear discriminant analysis (LDA) classifier with leave-one-out cross-validation to characterize the ERP features of MDD. 127 possible combinations out of the selected 7 ERP features were classified using LDA. The best classification accuracy was achieved to be 89.66%. These results suggest that MDD patients are identifiable from normal subjects by ERPs measured by Emotiv headset.

Global classification of human facial healthy skin using PLS discriminant analysis and clustering analysis.

Science.gov (United States)

Guinot, C; Latreille, J; Tenenhaus, M; Malvy, D J

2001-04-01

Today's classifications of healthy skin are predominantly based on a very limited number of skin characteristics, such as skin oiliness or susceptibility to sun exposure. The aim of the present analysis was to set up a global classification of healthy facial skin, using mathematical models. This classification is based on clinical, biophysical skin characteristics and self-reported information related to the skin, as well as the results of a theoretical skin classification assessed separately for the frontal and the malar zones of the face. In order to maximize the predictive power of the models with a minimum of variables, the Partial Least Square (PLS) discriminant analysis method was used. The resulting PLS components were subjected to clustering analyses to identify the plausible number of clusters and to group the individuals according to their proximities. Using this approach, four PLS components could be constructed and six clusters were found relevant. So, from the 36 hypothetical combinations of the theoretical skin types classification, we tended to a strengthened six classes proposal. Our data suggest that the association of the PLS discriminant analysis and the clustering methods leads to a valid and simple way to classify healthy human skin and represents a potentially useful tool for cosmetic and dermatological research.

A chromatochemometric approach for evaluating and selecting the perfume maceration time.

Science.gov (United States)

López-Nogueroles, Marina; Chisvert, Alberto; Salvador, Amparo

2010-04-30

A chemometric treatment of the data obtained by gas chromatography (GC) with flame ionization detector (FID) has been proposed to study the maceration time involved in perfumes manufacture with the final purpose of reducing this time but preserving the organoleptic characteristics of the perfume that is being elaborated. In this sense, GC-FID chromatograms were used as a fingerprint of perfume samples subjected to different maceration times, and data were treated by linear discriminant analysis (LDA), by comparing to a set of samples known to be macerated or not, which were used as calibration objects. The GC-FID methodology combined with the treatment of data by LDA has been applied successfully to seven different perfumes. The constructed LDA models exhibited excellent Wilks' lambdas (0.013-0.118, depending on the perfume), and up to a reduction of 57% has been achieved with respect to the maceration time initially established. 2010 Elsevier B.V. All rights reserved.

Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry

Directory of Open Access Journals (Sweden)

Massimo Rutolo

2014-08-01

Full Text Available Soft rot is a commonly occurring potato tuber disease that each year causes substantial losses to the food industry. Here, we explore the possibility of early detection of the disease via gas/vapor analysis, in a laboratory environment, using a recent technology known as FAIMS (Field Asymmetric Ion Mobility Spectrometry. In this work, tubers were inoculated with a bacterium causing the infection, Pectobacterium carotovorum, and stored within set environmental conditions in order to manage disease progression. They were compared with controls stored in the same conditions. Three different inoculation time courses were employed in order to obtain diseased potatoes showing clear signs of advanced infection (for standard detection and diseased potatoes with no apparent evidence of infection (for early detection. A total of 156 samples were processed by PCA (Principal Component Analysis and k-means clustering. Results show a clear discrimination between controls and diseased potatoes for all experiments with no difference among observations from standard and early detection. Further analysis was carried out by means of a statistical model based on LDA (Linear Discriminant Analysis that showed a high classification accuracy of 92.1% on the test set, obtained via a LOOCV (leave-one out cross-validation.

Isokinetic evaluation of knee muscles in soccer players: discriminant analysis

Directory of Open Access Journals (Sweden)

Bruno Fles Mazuquin

2015-10-01

Full Text Available ABSTRACTIntroduction:Muscle activity in soccer players can be measured by isokinetic dynamometer, which is a reliable tool for assessing human performance.Objectives:To perform isokinetic analyses and to determine which variables differentiate the under-17 (U17 soccer category from the professional (PRO.Methods:Thirty four players were assessed (n=17 for each category. The isokinetic variables used for the knee extension-flexion analysis were: peak torque (Nm, total work (J, average power (W, angle of peak torque (deg., agonist/ antagonist ratio (%, measured for three velocities (60°/s, 120°/s and 300°/s, with each series containing five repetitions. Three Wilks' Lambda discriminant analyses were performed, to identify which variables were more significant for the definition of each of the categories.Results:The discriminative variables at 60°/s in the PRO category were: extension peak torque, flexion total work, extension average power and agonist/antagonist ratio; and for the U17s were: extension total work, flexion peak torque and flexion average power. At 120°/s for the PRO category the discriminant variables were: flexion peak torque and extension average power; for the U17s they were: extension total work and flexion average power. Finally at 300°/s, the variables found in the PRO and U17 categories respectively were: extension average power and extension total work.Conclusion:Isokinetic variables for flexion and extension knee muscles were able to significantly discriminate between PRO and U17 soccer players.

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

Science.gov (United States)

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy.

Science.gov (United States)

Zhu, Ying; Tan, Tuck Lee

2016-04-15

An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects. Copyright © 2016 Elsevier B.V. All rights reserved.

Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy

Science.gov (United States)

Zhu, Ying; Tan, Tuck Lee

2016-04-01

An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

Is it really organic? – Multi-isotopic analysis as a tool to discriminate between organic and conventional plants

DEFF Research Database (Denmark)

Laursen, K.H.; Mihailova, A.; Kelly, S.D.

2013-01-01

for discrimination of organically and conventionally grown plants. The study was based on wheat, barley, faba bean and potato produced in rigorously controlled long-term field trials comprising 144 experimental plots. Nitrogen isotope analysis revealed the use of animal manure, but was unable to discriminate between......Novel procedures for analytical authentication of organic plant products are urgently needed. Here we present the first study encompassing stable isotopes of hydrogen, carbon, nitrogen, oxygen, magnesium and sulphur as well as compound-specific nitrogen and oxygen isotope analysis of nitrate...... plants that were fertilised with synthetic nitrogen fertilisers or green manures from atmospheric nitrogen fixing legumes. This limitation was bypassed using oxygen isotope analysis of nitrate in potato tubers, while hydrogen isotope analysis allowed complete discrimination of organic and conventional...

Cross coherence independent component analysis in resting and action states EEG discrimination

International Nuclear Information System (INIS)

Almurshedi, A; Ismail, A K

2014-01-01

Cross Coherence time frequency transform and independent component analysis (ICA) method were used to analyse the electroencephalogram (EEG) signals in resting and action states during open and close eyes conditions. From the topographical scalp distributions of delta, theta, alpha, and beta power spectrum can clearly discriminate between the signal when the eyes were open or closed, but it was difficult to distinguish between resting and action states when the eyes were closed. In open eyes condition, the frontal area (Fp1, Fp2) was activated (higher power) in delta and theta bands whilst occipital (O1, O2) and partial (P3, P4, Pz) area of brain was activated alpha band in closed eyes condition. The cross coherence method of time frequency analysis is capable of discrimination between rest and action brain signals in closed eyes condition

A Comparative Analysis of the Evolution of Gender Wage Discrimination: Spain Versus Galicia

OpenAIRE

Pena-Boquete, Yolanda

2006-01-01

The aim of this paper is to analyze the degree of female wage discrimination in the Spanish region of Galicia relative to the rest of Spain. The analysis starts from an established fact: women's average earnings are lower than men's. First, we try to show the causes behind this wage differential. Next, we discuss the evolution of the wage gap between 1995 and 2002, in order to bring some light on the factors potentially accounting for wage discrimination persistence in Galicia and Spain. We w...

A Comparative Analysis of the Evolution of Gender Wage Discrimination: Spain Versus Galicia.

OpenAIRE

Yolanda Pena-Boquete

2006-01-01

The aim of this paper is to analyze the degree of female wage discrimination in the Spanish region of Galicia relative to the rest of Spain. The analysis starts from an established fact: women’s average earnings are lower than men’s. First, we try to show the causes behind this wage differential. Next, we discuss the evolution of the wage gap between 1995 and 2002, in order to bring some light on the factors potentially accounting for wage discrimination persistence in Galicia and Spain. We w...

Theoretical remarks on the statistics of three discriminants in Piety's automated signature analysis of PSD [Power Spectral Density] data

International Nuclear Information System (INIS)

Behringer, K.; Spiekerman, G.

1984-01-01

Piety (1977) proposed an automated signature analysis of power spectral density data. Eight statistical decision discriminants are introduced. For nearly all the discriminants, improved confidence statements can be made. The statistical characteristics of the last three discriminants, which are applications of non-parametric tests, are considered. (author)

Hidrocarbonetos policíclicos aromáticos (HPAS em cachaça, rum, uísque e álcool combustível Polycyclic aromatic hydrocarbons (PAHS in cachaça, rum, whiskey and alcohol fuel

Directory of Open Access Journals (Sweden)

Carlos Alexandre Galinaro

2009-01-01

Full Text Available The concentration of 15 polycyclic aromatic hydrocarbons (PAHs in 57 samples of distillates (cachaça, rum, whiskey, and alcohol fuel has been determined by HPLC-Fluorescence detection. The quantitative analytical profile of PAHs treated by Partial Least Square - Discriminant Analysis (PLS-DA provided a good classification of the studied spirits based on their PAHs content. Additionally, the classification of the sugar cane derivatives according to the harvest practice was obtained treating the analytical data by Linear Discriminant Analysis (LDA, using naphthalene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, benz[b]fluoranthene, and benz[g,h,i]perylene, as a chemical descriptors.

Discriminant analysis method to determine the power of the boys 11-12 year

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Mirosława Cieślicka

2016-10-01

Full Text Available Purpose: To determine the model of power in boys 11-12 years old. Material and methods: To achieve the objectives, the following methods: analysis of scientific literature, statistical methods for analysis of results. The study involved 35 boys 11 year (n = 35 and 32 boys 12 year (n = 32. Results: Analysis of the results shows that the statistical significance of differences in the test results of boys 11 and 12 years there has been research jump from the place of execution and the amount of squats (the amount of execution time (p <0.001, p <0. Conclusions: Structural factors discriminant function suggest that more attention is paid to training of speed and endurance, the more likely to increase the force to prepare the boys. The canonical discriminant function can  be used to assess and forecast the development of motor skills in boys.

Face sketch recognition based on edge enhancement via deep learning

Science.gov (United States)

Xie, Zhenzhu; Yang, Fumeng; Zhang, Yuming; Wu, Congzhong

2017-11-01

In this paper,we address the face sketch recognition problem. Firstly, we utilize the eigenface algorithm to convert a sketch image into a synthesized sketch face image. Subsequently, considering the low-level vision problem in synthesized face sketch image .Super resolution reconstruction algorithm based on CNN(convolutional neural network) is employed to improve the visual effect. To be specific, we uses a lightweight super-resolution structure to learn a residual mapping instead of directly mapping the feature maps from the low-level space to high-level patch representations, which making the networks are easier to optimize and have lower computational complexity. Finally, we adopt LDA(Linear Discriminant Analysis) algorithm to realize face sketch recognition on synthesized face image before super resolution and after respectively. Extensive experiments on the face sketch database(CUFS) from CUHK demonstrate that the recognition rate of SVM(Support Vector Machine) algorithm improves from 65% to 69% and the recognition rate of LDA(Linear Discriminant Analysis) algorithm improves from 69% to 75%.What'more,the synthesized face image after super resolution can not only better describer image details such as hair ,nose and mouth etc, but also improve the recognition accuracy effectively.

Hard and soft age discrimination: the dual nature of workplace discrimination.

Science.gov (United States)

Stypinska, Justyna; Turek, Konrad

2017-03-01

The paper concentrates on the problem of age discrimination in the labour market and the way it can be conceptualised and measured in a multi-disciplinary way. The approach proposed here combines two understandings of age discrimination-a sociological and legal one, what allows for a fuller and expanded understanding of ageism in the workplace. At the heart of the study is a survey carried out in Poland with a sample of 1000 men and women aged 45-65 years. The study takes a deeper and innovative look into the issue of age discrimination in employment. Confirmatory factor analysis with WLSMV estimation and logistic regressions were used to test the hypotheses. The study shows that age discrimination in labour market can take on different forms: hard and soft, where the hard type of age discrimination mirrors the legally prohibited types of behaviours and those which relate to the actual decisions of employers which can impact on the employee's career development. The soft discrimination corresponds with those occurrences, which are not inscribed in the legal system per se, are occurring predominantly in the interpersonal sphere, but can nevertheless have negative consequences. Soft discrimination was experienced more often (28.6% of respondents) than hard discrimination (15.7%) with higher occurrences among women, persons in precarious job situation or residents of urban areas. The role of education was not confirmed to influence the levels of perceived age discrimination.

Studies in genetic discrimination. Final progress report

Energy Technology Data Exchange (ETDEWEB)

1994-06-01

We have screened 1006 respondents in a study of genetic discrimination. Analysis of these responses has produced evidence of the range of institutions engaged in genetic discrimination and demonstrates the impact of this discrimination on the respondents to the study. We have found that both ignorance and policy underlie genetic discrimination and that anti-discrimination laws are being violated.

Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW.

Science.gov (United States)

Stewart, C M; Newlands, S D; Perachio, A A

2004-12-01

Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.

Latent Dirichlet Allocation (LDA) Model and kNN Algorithm to Classify Research Project Selection

Science.gov (United States)

Safi’ie, M. A.; Utami, E.; Fatta, H. A.

2018-03-01

Universitas Sebelas Maret has a teaching staff more than 1500 people, and one of its tasks is to carry out research. In the other side, the funding support for research and service is limited, so there is need to be evaluated to determine the Research proposal submission and devotion on society (P2M). At the selection stage, research proposal documents are collected as unstructured data and the data stored is very large. To extract information contained in the documents therein required text mining technology. This technology applied to gain knowledge to the documents by automating the information extraction. In this articles we use Latent Dirichlet Allocation (LDA) to the documents as a model in feature extraction process, to get terms that represent its documents. Hereafter we use k-Nearest Neighbour (kNN) algorithm to classify the documents based on its terms.

A liquid-liquid transition in supercooled aqueous solution related to the HDA-LDA transition

Science.gov (United States)

Woutersen, Sander; Ensing, Bernd; Hilbers, Michiel; Zhao, Zuofeng; Angell, C. Austen

2018-03-01

Simulations and theory suggest that the thermodynamic anomalies of water may be related to a phase transition between two supercooled liquid states, but so far this phase transition has not been observed experimentally because of preemptive ice crystallization. We used calorimetry, infrared spectroscopy, and molecular dynamics simulations to investigate a water-rich hydrazinium trifluoroacetate solution in which the local hydrogen bond structure surrounding a water molecule resembles that in neat water at elevated pressure, but which does not crystallize upon cooling. Instead, this solution underwent a sharp, reversible phase transition between two homogeneous liquid states. The hydrogen-bond structures of these two states are similar to those established for high- and low-density amorphous (HDA and LDA) water. Such structural similarity supports theories that predict a similar sharp transition in pure water under pressure if ice crystallization could be suppressed.

Analysis of physiological signals for recognition of boredom, pain, and surprise emotions.

Science.gov (United States)

Jang, Eun-Hye; Park, Byoung-Jun; Park, Mi-Sook; Kim, Sang-Hyeob; Sohn, Jin-Hun

2015-06-18

The aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals. Three emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions. The result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7% is obtained by using DFA. This study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.

Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis.

Science.gov (United States)

Martins, Angélica Rocha; Talhavini, Márcio; Vieira, Maurício Leite; Zacca, Jorge Jardim; Braga, Jez Willian Batista

2017-08-15

The discrimination of whisky brands and counterfeit identification were performed by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA). In the proposed method all spectra were obtained with no sample preparation. The discrimination models were built with the employment of seven whisky brands: Red Label, Black Label, White Horse, Chivas Regal (12years), Ballantine's Finest, Old Parr and Natu Nobilis. The method was validated with an independent test set of authentic samples belonging to the seven selected brands and another eleven brands not included in the training samples. Furthermore, seventy-three counterfeit samples were also used to validate the method. Results showed correct classification rates for genuine and false samples over 98.6% and 93.1%, respectively, indicating that the method can be helpful for the forensic analysis of whisky samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

A novel electroencephalographic analysis method discriminates alcohol effects from those of other sedative/hypnotics.

Science.gov (United States)

Steffensen, Scott C; Lee, Rong-Sheng; Henriksen, Steven J; Packer, Thomas L; Cook, Daniel R

2002-04-15

Here we describe a mathematical and statistical signal processing strategy termed event resolution imaging (ERI). Our principal objective was to determine if the acute intoxicating effects of ethanol on spontaneous EEG activity could be discriminated from those of other sedative/hypnotics. We employed ERI to combine and integrate standard analysis methods to learn multiple signal features of time-varying EEG signals. We recorded cortical EEG, electromyographic activity, and motor activity during intravenous administration of saline, ethanol (1.0 g/kg), chlordiazepoxide (10 mg/kg), pentobarbital (6 mg/kg), heroin (0.3 mg/kg), and methamphetamine (2 mg/kg) administered on separate days in six rats. A blind treatment of one of the drugs was readministered to validate the efficacy of ERI analysis. Significant changes in spontaneous EEG activity produced by all five drugs were detected by ERI analysis with a time resolution of 5-10 s. ERI analysis of spontaneous EEG activity also discriminated, with 90-95% accuracy, an ataxic dose of ethanol versus equivalent ataxic doses of chlordiazepoxide or pentobarbital, as well as the effects of saline, a reinforcing dose of heroin, or a locomotor activating dose of methamphetamine. ERI correctly matched the 'blind drug' as ethanol. These findings indicate that ERI analysis can detect the central nervous system effects of various psychoactive drugs and accurately discriminate the electrocortical effects of select sedative/hypnotics, with similar behavioral endpoints, but with dissimilar mechanisms of action.

Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

Directory of Open Access Journals (Sweden)

Fubiao Feng

2017-03-01

Full Text Available Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA.

Study on discrimination of oral cancer from normal using blood plasma based on fluorescence steady and excited state at excitation wavelength 280 nm

Science.gov (United States)

Rekha, Pachaiappan; Aruna, Prakasa Rao; Ganesan, Singaravelu

2016-03-01

Many research works based on fluorescence spectroscopy have proven its potential in the diagnosis of various diseases using the spectral signatures of the native key fluorophores such as tryptophan, tyrosine, collagen, NADH, FAD and porphyrin. These fluorophores distribution, concentration and their conformation may be changed depending upon the pathological and metabolic conditions of cells and tissues. In this study, we have made an attempt to characterize the blood plasma of normal subject and oral cancer patients by native fluorescence spectroscopy at 280 nm excitation. Further, the fluorescence data were analyzed by employing the multivariate statistical method - linear discriminant analyses (LDA) using leaves one out cross validation method. The results illustrate the potential of fluorescence spectroscopy technique in the diagnosis of oral cancer using blood plasma.

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

Science.gov (United States)

Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

2016-12-01

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fast differential pulse discriminator-counter

International Nuclear Information System (INIS)

Shelevoj, K.D.

1985-01-01

The flowsheet of a differential pulse discriminator counter is described; the result of discrimination here is independent from the shape of the input pulse. Rate of the analysis of input pulses with minimum amplitude up to 0.3 mV coming out from the photomultiplier makes up 220 MHz. The flowsheet of the discriminator used in the system of photon counting for atmosphere probing is presented

An Analysis of Discrimination by Real Estate Brokers.

Science.gov (United States)

Yinger, John

This paper focuses on designing policies to eliminate discrimination in the sale of single-family houses by analyzing the behavior of the agents who actually do most of the discriminating, namely real estate agents. Discriminatory practices are said to be supported by policies of house builders, lending institutions, and government, and by the…

A Robust Multimodal Bio metric Authentication Scheme with Voice and Face Recognition

International Nuclear Information System (INIS)

Kasban, H.

2017-01-01

This paper proposes a multimodal biometric scheme for human authentication based on fusion of voice and face recognition. For voice recognition, three categories of features (statistical coefficients, cepstral coefficients and voice timbre) are used and compared. The voice identification modality is carried out using Gaussian Mixture Model (GMM). For face recognition, three recognition methods (Eigenface, Linear Discriminate Analysis (LDA), and Gabor filter) are used and compared. The combination of voice and face biometrics systems into a single multimodal biometrics system is performed using features fusion and scores fusion. This study shows that the best results are obtained using all the features (cepstral coefficients, statistical coefficients and voice timbre features) for voice recognition, LDA face recognition method and scores fusion for the multimodal biometrics system

Decoding magnetoencephalographic rhythmic activity using spectrospatial information.

Science.gov (United States)

Kauppi, Jukka-Pekka; Parkkonen, Lauri; Hari, Riitta; Hyvärinen, Aapo

2013-12-01

We propose a new data-driven decoding method called Spectral Linear Discriminant Analysis (Spectral LDA) for the analysis of magnetoencephalography (MEG). The method allows investigation of changes in rhythmic neural activity as a result of different stimuli and tasks. The introduced classification model only assumes that each "brain state" can be characterized as a combination of neural sources, each of which shows rhythmic activity at one or several frequency bands. Furthermore, the model allows the oscillation frequencies to be different for each such state. We present decoding results from 9 subjects in a four-category classification problem defined by an experiment involving randomly alternating epochs of auditory, visual and tactile stimuli interspersed with rest periods. The performance of Spectral LDA was very competitive compared with four alternative classifiers based on different assumptions concerning the organization of rhythmic brain activity. In addition, the spectral and spatial patterns extracted automatically on the basis of trained classifiers showed that Spectral LDA offers a novel and interesting way of analyzing spectrospatial oscillatory neural activity across the brain. All the presented classification methods and visualization tools are freely available as a Matlab toolbox. © 2013.

LDA-Based Unified Topic Modeling for Similar TV User Grouping and TV Program Recommendation.

Science.gov (United States)

Pyo, Shinjee; Kim, Eunhui; Kim, Munchurl

2015-08-01

Social TV is a social media service via TV and social networks through which TV users exchange their experiences about TV programs that they are viewing. For social TV service, two technical aspects are envisioned: grouping of similar TV users to create social TV communities and recommending TV programs based on group and personal interests for personalizing TV. In this paper, we propose a unified topic model based on grouping of similar TV users and recommending TV programs as a social TV service. The proposed unified topic model employs two latent Dirichlet allocation (LDA) models. One is a topic model of TV users, and the other is a topic model of the description words for viewed TV programs. The two LDA models are then integrated via a topic proportion parameter for TV programs, which enforces the grouping of similar TV users and associated description words for watched TV programs at the same time in a unified topic modeling framework. The unified model identifies the semantic relation between TV user groups and TV program description word groups so that more meaningful TV program recommendations can be made. The unified topic model also overcomes an item ramp-up problem such that new TV programs can be reliably recommended to TV users. Furthermore, from the topic model of TV users, TV users with similar tastes can be grouped as topics, which can then be recommended as social TV communities. To verify our proposed method of unified topic-modeling-based TV user grouping and TV program recommendation for social TV services, in our experiments, we used real TV viewing history data and electronic program guide data from a seven-month period collected by a TV poll agency. The experimental results show that the proposed unified topic model yields an average 81.4% precision for 50 topics in TV program recommendation and its performance is an average of 6.5% higher than that of the topic model of TV users only. For TV user prediction with new TV programs, the average

Frontotemporal Dysfunction in Amyotrophic Lateral Sclerosis: A Discriminant Function Analysis.

Science.gov (United States)

Nidos, Andreas; Kasselimis, Dimitrios S; Simos, Panagiotis G; Rentzos, Michael; Alexakis, Theodoros; Zalonis, Ioannis; Zouvelou, Vassiliki; Potagas, Constantin; Evdokimidis, Ioannis; Woolley, Susan C

2016-01-01

There is growing evidence for extramotor dysfunction (EMd) in amyotrophic lateral sclerosis (ALS), with a reported prevalence of up to 52%. In the present study, we explore the clinical utility of a brief neuropsychological battery for the investigation of cognitive, behavioral, and language deficits in patients with ALS. Thirty-four consecutive ALS patients aged 44-89 years were tested with a brief neuropsychological battery, including executive, behavioral, and language measures. Patients were initially classified as EMd or non-EMd based on their scores on the frontal assessment battery (FAB). Between-group comparisons revealed significant differences in all measures (p < 0.01). Discriminant analysis resulted in a single canonical function, with all tests serving as significant predictors. This function agreed with the FAB in 13 of 17 patients screened as EMd and identified extramotor deficits in 2 additional patients. Overall sensitivity and specificity estimates against FAB were 88.2%. We stress the importance of discriminant function analysis in clinical neuropsychological assessment and argue that the proposed neuropsychological battery may be of clinical value, especially when the option of extensive and comprehensive neuropsychological testing is limited. The psychometric validity of an ALS-frontotemporal dementia diagnosis using neuropsychological tests is also discussed. © 2015 S. Karger AG, Basel.

Non-invasive optical detection of esophagus cancer based on urine surface-enhanced Raman spectroscopy

Science.gov (United States)

Huang, Shaohua; Wang, Lan; Chen, Weiwei; Lin, Duo; Huang, Lingling; Wu, Shanshan; Feng, Shangyuan; Chen, Rong

2014-09-01

A surface-enhanced Raman spectroscopy (SERS) approach was utilized for urine biochemical analysis with the aim to develop a label-free and non-invasive optical diagnostic method for esophagus cancer detection. SERS spectrums were acquired from 31 normal urine samples and 47 malignant esophagus cancer (EC) urine samples. Tentative assignments of urine SERS bands demonstrated esophagus cancer specific changes, including an increase in the relative amounts of urea and a decrease in the percentage of uric acid in the urine of normal compared with EC. The empirical algorithm integrated with linear discriminant analysis (LDA) were employed to identify some important urine SERS bands for differentiation between healthy subjects and EC urine. The empirical diagnostic approach based on the ratio of the SERS peak intensity at 527 to 1002 cm-1 and 725 to 1002 cm-1 coupled with LDA yielded a diagnostic sensitivity of 72.3% and specificity of 96.8%, respectively. The area under the receive operating characteristic (ROC) curve was 0.954, which further evaluate the performance of the diagnostic algorithm based on the ratio of the SERS peak intensity combined with LDA analysis. This work demonstrated that the urine SERS spectra associated with empirical algorithm has potential for noninvasive diagnosis of esophagus cancer.

Using discriminant analysis for credit decision

Directory of Open Access Journals (Sweden)

Gheorghiţa DINCĂ

2015-12-01

Full Text Available This paper follows to highlight the link between the results obtained applying discriminant analysis and lending decision. For this purpose, we have carried out the research on a sample of 24 Romanian private companies, pertaining to 12 different economic sectors, from I and II categories of Bucharest Stock Exchange, for the period 2010-2012. Our study works with two popular bankruptcy risk’s prediction models, the Altman model and the Anghel model. We have double-checked and confirmed the results of our research by comparing the results from applying the two fore-mentioned models as well as by checking existing debt commitments of each analyzed company to credit institutions during the 2010-2012 period. The aim of this paper was the classification of studied companies into potential bankrupt and non-bankrupt, to assist credit institutions in their decision to grant credit, understanding the approval or rejection algorithm of loan applications and even help potential investors in these ompanies.

INCOME INEQUALITY IN SOME MAJOR EUROPEAN UNION ECONOMIES A DISCRIMINANT ANALYSIS

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JYOTIRMAYEE KAR

2012-12-01

Full Text Available This exercise is an attempt to assess the importance of some social, economic, demographic and infrastructural factors which account for the prevailing income inequality across some of the EU countries. Using discriminant analysis the study suggests that crime recorded by police is the most important predictor in discriminating between the group of countries with relatively more equitable distribution of income from those with less. This variable is followed by number of students in the country. Reduction in the level of crime and improvement in the student strength could help in reducing income inequality. Quite intuitively, improvement in all the economic factors like GDP per capita and agricultural index will help to reduce income inequality. Identical is the case of the demographic factors. This calls for implementation of developmental policies towards improvement in these areas.

Enlarge the training set based on inter-class relationship for face recognition from one image per person.

Science.gov (United States)

Li, Qin; Wang, Hua Jing; You, Jane; Li, Zhao Ming; Li, Jin Xue

2013-01-01

In some large-scale face recognition task, such as driver license identification and law enforcement, the training set only contains one image per person. This situation is referred to as one sample problem. Because many face recognition techniques implicitly assume that several (at least two) images per person are available for training, they cannot deal with the one sample problem. This paper investigates principal component analysis (PCA), Fisher linear discriminant analysis (LDA), and locality preserving projections (LPP) and shows why they cannot perform well in one sample problem. After that, this paper presents four reasons that make one sample problem itself difficult: the small sample size problem; the lack of representative samples; the underestimated intra-class variation; and the overestimated inter-class variation. Based on the analysis, this paper proposes to enlarge the training set based on the inter-class relationship. This paper also extends LDA and LPP to extract features from the enlarged training set. The experimental results show the effectiveness of the proposed method.

Non-invasive optical detection of HBV based on serum surface-enhanced Raman spectroscopy

Science.gov (United States)

Zheng, Zuci; Wang, Qiwen; Weng, Cuncheng; Lin, Xueliang; Lin, Yao; Feng, Shangyuan

2016-10-01

An optical method of surface-enhanced Raman spectroscopy (SERS) was developed for non-invasive detection of hepatitis B surface virus (HBV). Hepatitis B virus surface antigen (HBsAg) is an established serological marker that is routinely used for the diagnosis of acute or chronic hepatitis B virus(HBV) infection. Utilizing SERS to analyze blood serum for detecting HBV has not been reported in previous literature. SERS measurements were performed on two groups of serum samples: one group for 50 HBV patients and the other group for 50 healthy volunteers. Blood serum samples are collected from healthy control subjects and patients diagnosed with HBV. Furthermore, principal components analysis (PCA) combined with linear discriminant analysis (LDA) were employed to differentiate HBV patients from healthy volunteer and achieved sensitivity of 80.0% and specificity of 74.0%. This exploratory work demonstrates that SERS serum analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of HBV.

Differentiation of Chinese rice wines from different wineries based on mineral elemental fingerprinting.

Science.gov (United States)

Shen, Fei; Wu, Jian; Ying, Yibin; Li, Bobin; Jiang, Tao

2013-12-15

Discrimination of Chinese rice wines from three well-known wineries ("Guyuelongshan", "Kuaijishan", and "Pagoda") in China has been carried out according to mineral element contents in this study. Nineteen macro and trace mineral elements (Na, Mg, Al, K, Ca, Mn, Fe, Cu, Zn, V, Cr, Co, Ni, As, Se, Mo, Cd, Ba and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in 117 samples. Then the experimental data were subjected to analysis of variance (ANOVA) and principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Stepwise linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA) were applied to develop classification models and achieved correct classified rates of 100% and 97.4% for the prediction sample set, respectively. The discrimination could be attributed to different raw materials (mainly water) and elaboration processes employed. The results indicate that the element compositions combined with multivariate analysis can be used as fingerprinting techniques to protect prestigious wineries and enable the authenticity of Chinese rice wine. Copyright © 2013 Elsevier Ltd. All rights reserved.

Analysis of pulse-shape discrimination techniques for BC501A using GHz digital signal processing

International Nuclear Information System (INIS)

Rooney, B.D.; Dinwiddie, D.R.; Nelson, M.A.; Rawool-Sullivan, Mohini W.

2001-01-01

A comparison study of pulse-shape analysis techniques was conducted for a BC501A scintillator using digital signal processing (DSP). In this study, output signals from a preamplifier were input directly into a 1 GHz analog-to-digital converter. The digitized data obtained with this method was post-processed for both pulse-height and pulse-shape information. Several different analysis techniques were evaluated for neutron and gamma-ray pulse-shape discrimination. It was surprising that one of the simplest and fastest techniques resulted in some of the best pulse-shape discrimination results. This technique, referred to here as the Integral Ratio technique, was able to effectively process several thousand detector pulses per second. This paper presents the results and findings of this study for various pulse-shape analysis techniques with digitized detector signals.

Chemometric and Statistical Analyses of ToF-SIMS Spectra of Increasingly Complex Biological Samples

Energy Technology Data Exchange (ETDEWEB)

Berman, E S; Wu, L; Fortson, S L; Nelson, D O; Kulp, K S; Wu, K J

2007-10-24

Characterizing and classifying molecular variation within biological samples is critical for determining fundamental mechanisms of biological processes that will lead to new insights including improved disease understanding. Towards these ends, time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to examine increasingly complex samples of biological relevance, including monosaccharide isomers, pure proteins, complex protein mixtures, and mouse embryo tissues. The complex mass spectral data sets produced were analyzed using five common statistical and chemometric multivariate analysis techniques: principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), soft independent modeling of class analogy (SIMCA), and decision tree analysis by recursive partitioning. PCA was found to be a valuable first step in multivariate analysis, providing insight both into the relative groupings of samples and into the molecular basis for those groupings. For the monosaccharides, pure proteins and protein mixture samples, all of LDA, PLSDA, and SIMCA were found to produce excellent classification given a sufficient number of compound variables calculated. For the mouse embryo tissues, however, SIMCA did not produce as accurate a classification. The decision tree analysis was found to be the least successful for all the data sets, providing neither as accurate a classification nor chemical insight for any of the tested samples. Based on these results we conclude that as the complexity of the sample increases, so must the sophistication of the multivariate technique used to classify the samples. PCA is a preferred first step for understanding ToF-SIMS data that can be followed by either LDA or PLSDA for effective classification analysis. This study demonstrates the strength of ToF-SIMS combined with multivariate statistical and chemometric techniques to classify increasingly complex biological samples

How discriminating are discriminative instruments?

Science.gov (United States)

Hankins, Matthew

2008-05-27

The McMaster framework introduced by Kirshner & Guyatt is the dominant paradigm for the development of measures of health status and health-related quality of life (HRQL). The framework defines the functions of such instruments as evaluative, predictive or discriminative. Evaluative instruments are required to be sensitive to change (responsiveness), but there is no corresponding index of the degree to which discriminative instruments are sensitive to cross-sectional differences. This paper argues that indices of validity and reliability are not sufficient to demonstrate that a discriminative instrument performs its function of discriminating between individuals, and that the McMaster framework would be augmented by the addition of a separate index of discrimination. The coefficient proposed by Ferguson (Delta) is easily adapted to HRQL instruments and is a direct, non-parametric index of the degree to which an instrument distinguishes between individuals. While Delta should prove useful in the development and evaluation of discriminative instruments, further research is required to elucidate the relationship between the measurement properties of discrimination, reliability and responsiveness.

How discriminating are discriminative instruments?

Directory of Open Access Journals (Sweden)

Hankins Matthew

2008-05-01

Full Text Available Abstract The McMaster framework introduced by Kirshner & Guyatt is the dominant paradigm for the development of measures of health status and health-related quality of life (HRQL. The framework defines the functions of such instruments as evaluative, predictive or discriminative. Evaluative instruments are required to be sensitive to change (responsiveness, but there is no corresponding index of the degree to which discriminative instruments are sensitive to cross-sectional differences. This paper argues that indices of validity and reliability are not sufficient to demonstrate that a discriminative instrument performs its function of discriminating between individuals, and that the McMaster framework would be augmented by the addition of a separate index of discrimination. The coefficient proposed by Ferguson (Delta is easily adapted to HRQL instruments and is a direct, non-parametric index of the degree to which an instrument distinguishes between individuals. While Delta should prove useful in the development and evaluation of discriminative instruments, further research is required to elucidate the relationship between the measurement properties of discrimination, reliability and responsiveness.

Determination of authenticity of brand perfume using electronic nose prototypes

International Nuclear Information System (INIS)

Gebicki, Jacek; Szulczynski, Bartosz; Kaminski, Marian

2015-01-01

The paper presents the practical application of an electronic nose technique for fast and efficient discrimination between authentic and fake perfume samples. Two self-built electronic nose prototypes equipped with a set of semiconductor sensors were employed for that purpose. Additionally 10 volunteers took part in the sensory analysis. The following perfumes and their fake counterparts were analysed: Dior—Fahrenheit, Eisenberg—J’ose, YSL—La nuit de L’homme, 7 Loewe and Spice Bomb. The investigations were carried out using the headspace of the aqueous solutions. Data analysis utilized multidimensional techniques: principle component analysis (PCA), linear discrimination analysis (LDA), k-nearest neighbour (k-NN). The results obtained confirmed the legitimacy of the electronic nose technique as an alternative to the sensory analysis as far as the determination of authenticity of perfume is concerned. (paper)

Determination of authenticity of brand perfume using electronic nose prototypes

Science.gov (United States)

Gebicki, Jacek; Szulczynski, Bartosz; Kaminski, Marian

2015-12-01

The paper presents the practical application of an electronic nose technique for fast and efficient discrimination between authentic and fake perfume samples. Two self-built electronic nose prototypes equipped with a set of semiconductor sensors were employed for that purpose. Additionally 10 volunteers took part in the sensory analysis. The following perfumes and their fake counterparts were analysed: Dior—Fahrenheit, Eisenberg—J’ose, YSL—La nuit de L’homme, 7 Loewe and Spice Bomb. The investigations were carried out using the headspace of the aqueous solutions. Data analysis utilized multidimensional techniques: principle component analysis (PCA), linear discrimination analysis (LDA), k-nearest neighbour (k-NN). The results obtained confirmed the legitimacy of the electronic nose technique as an alternative to the sensory analysis as far as the determination of authenticity of perfume is concerned.

Gas Identification Using Passive UHF RFID Sensor Platform

Directory of Open Access Journals (Sweden)

Muhammad Ali AKBAR

2015-11-01

Full Text Available The concept of passive Radio Frequency Identification (RFID sensor tag is introduced to remove the dependency of current RFID platforms on battery life. In this paper, a gas identification system is presented using passive RFID sensor tag along with the processing unit. The RFID system is compliant to Electronics Product Code Generation 2 (EPC-Gen2 protocol in 902-928 MHz ISM band. Whereas the processing unit is implemented and analyzed in software and hardware platforms. The software platform uses MATLAB, whereas a High Level Synthesis (HLS tool is used to implement the processing unit on a Zynq platform. Moreover, two sets of different gases are used along with Principal Component Analysis (PCA and Linear Discriminant Analysis (LDA based feature reduction approaches to analyze in detail the best feature reduction approach for efficient classification of gas data. It is found that for the first set of gases, 90 % gases are identified using first three principal components, which is 7 % more efficient than LDA. However in terms of hardware overhead, LDA requires 50 % less hardware resources than PCA. The classification results for the second set of gases reveal that 91 % of gas classification is obtained using LDA and first four PCA, while LDA requires 52 % less hardware resources than PCA. The RFID tag used for transmission is implemented in 0.13 µm CMOS process, with simulated average power consumption of 2.6 µW from 1.2 V supply. ThingMagic M6e embedded reader is used for RFID platform implementation. It shows an output power of 31.5 dBm which allows a read range up to 9 meters.

Using discriminant analysis as a nucleation event classification method

Directory of Open Access Journals (Sweden)

S. Mikkonen

2006-01-01

Full Text Available More than three years of measurements of aerosol size-distribution and different gas and meteorological parameters made in Po Valley, Italy were analysed for this study to examine which of the meteorological and trace gas variables effect on the emergence of nucleation events. As the analysis method, we used discriminant analysis with non-parametric Epanechnikov kernel, included in non-parametric density estimation method. The best classification result in our data was reached with the combination of relative humidity, ozone concentration and a third degree polynomial of radiation. RH appeared to have a preventing effect on the new particle formation whereas the effects of O3 and radiation were more conductive. The concentration of SO2 and NO2 also appeared to have significant effect on the emergence of nucleation events but because of the great amount of missing observations, we had to exclude them from the final analysis.

Optical spectroscopic analysis for the discrimination of extra-virgin olive-oil (Conference Presentation)

Science.gov (United States)

McReynolds, Naomi; Auñón Garcia, Juan M.; Guengerich, Zoe; Smith, Terry K.; Dholakia, Kishan

2017-02-01

We present an optical spectroscopic technique, making use of both Raman signals and fluorescence spectroscopy, for the identification of five brands of commercially available extra-virgin olive-oil (EVOO). We demonstrate our technique on both a `bulk-optics' free-space system and a compact device. Using the compact device, which is capable of recording both Raman and fluorescence signals, we achieved an average sensitivity and specificity of 98.4% and 99.6% for discrimination, respectively. Our approach demonstrates that both Raman and fluorescence spectroscopy can be used for portable discrimination of EVOOs which obviates the need to use centralised laboratories and opens up the prospect of in-field testing. This technique may enable detection of EVOO that has undergone counterfeiting or adulteration. One of the main challenges facing Raman spectroscopy for use in quality control of EVOOs is that the oxidation of EVOO, which naturally occurs due to aging, causes shifts in Raman spectra with time, which implies regular retraining would be necessary. We present a potential method of analysis to minimize the effect that aging has on discrimination efficiency; we show that by discarding the first principal component, which contains information on the variations due to oxidation, we can improve discrimination efficiency thus improving the robustness of our technique.

Field Spectroscopy in the VNIR-SWIR Region to Discriminate between Mediterranean Native Plants and Exotic-Invasive Shrubs Based on Leaf Tannin Content

Directory of Open Access Journals (Sweden)

Jan Rudolf Karl Lehmann

2015-01-01

Full Text Available The invasive shrub, Acacia longifolia, native to southeastern Australia, has a negative impact on vegetation and ecosystem functioning in Portuguese dune ecosystems. In order to spectrally discriminate A. longifolia from other non-native and native species, we developed a classification model based on leaf reflectance spectra (350–2500 nm and condensed leaf tannin content. High variation of leaf tannin content is common for Mediterranean shrub and tree species, in particular between N-fixing and non-N-fixing species, as well as within the genus, Acacia. However, variation in leaf tannin content has not been studied in coastal dune ecosystems in southwest Portugal. We hypothesized that condensed tannin concentration varies significantly across species, further allowing for distinguishing invasive, nitrogen-fixing A. longifolia from other vegetation based on leaf spectral reflectance data. Spectral field measurements were carried out using an ASD FieldSpec FR spectroradiometer attached to an ASD leaf clip in order to collect 750 in situ leaf reflectance spectra of seven frequent plant species at three study sites in southwest Portugal. We applied partial least squares (PLS regression to predict the obtained leaf reflectance spectra of A. longifolia individuals to their corresponding tannin concentration. A. longifolia had the lowest tannin concentration of all investigated species. Four wavelength regions (675–710 nm, 1060–1170 nm, 1360–1450 nm and 1630–1740 nm were identified as being highly correlated with tannin concentration. A spectra-based classification model of the different plant species was calculated using a principal component analysis-linear discriminant analysis (PCA-LDA. The best prediction of A. longifolia was achieved by using wavelength regions between 1360–1450 nm and 1630–1740 nm, resulting in a user’s accuracy of 98.9%. In comparison, selecting the entire wavelength range, the best user accuracy only reached 86

Analysis of the discriminative methods for diagnosis of benign and malignant solitary pulmonary nodules based on serum markers.

Science.gov (United States)

Wang, Wanping; Liu, Mingyue; Wang, Jing; Tian, Rui; Dong, Junqiang; Liu, Qi; Zhao, Xianping; Wang, Yuanfang

2014-01-01

Screening indexes of tumor serum markers for benign and malignant solitary pulmonary nodules (SPNs) were analyzed to find the optimum method for diagnosis. Enzyme-linked immunosorbent assays, an automatic immune analyzer and radioimmunoassay methods were used to examine the levels of 8 serum markers in 164 SPN patients, and the sensitivity for differential diagnosis of malignant or benign SPN was compared for detection using a single plasma marker or a combination of markers. The results for serological indicators that closely relate to benign and malignant SPNs were screened using the Fisher discriminant analysis and a non-conditional logistic regression analysis method, respectively. The results were then verified by the k-means clustering analysis method. The sensitivity when using a combination of serum markers to detect SPN was higher than that using a single marker. By Fisher discriminant analysis, cytokeratin 19 fragments (CYFRA21-1), carbohydrate antigen 125 (CA125), squamous cell carcinoma antigen (SCC) and breast cancer antigen (CA153), which relate to the benign and malignant SPNs, were screened. Through non-conditional logistic regression analysis, CYFRA21-1, SCC and CA153 were obtained. Using the k-means clustering analysis, the cophenetic correlation coefficient (0.940) obtained by the Fisher discriminant analysis was higher than that obtained with logistic regression analysis (0.875). This study indicated that the Fisher discriminant analysis functioned better in screening out serum markers to recognize the benign and malignant SPN. The combined detection of CYFRA21-1, CA125, SCC and CA153 is an effective way to distinguish benign and malignant SPN, and will find an important clinical application in the early diagnosis of SPN. © 2014 S. Karger GmbH, Freiburg.

Relatório de Estágio Curricular – Moveis Carlos Alfredo Barros da Silva, Lda (Rebordosa/Gandra)

OpenAIRE

Pimenta, Rita

2013-01-01

O estágio teve início no dia 17 de Junho e terminou a 9 de Agosto. Decorreu na empresa Moveis Carlos Alfredo. LDA, Wewood. Foram realizados vários trabalhos, alguns deles realizados em simultâneo. O primeiro trabalho consistia na modelação de duas mesas de jantar, o segundo consistiu na modelação de varias peças, a pedido de vários clientes individuais, enquanto desenvolvia este, tinha em mãos também a modelação 2D de algumas peças da empresa. O quarto trabalho consistia na conceção de ...

Chemometric classification of Chinese lager beers according to manufacturer based on data fusion of fluorescence, UV and visible spectroscopies.

Science.gov (United States)

Tan, Jin; Li, Rong; Jiang, Zi-Tao

2015-10-01

We report an application of data fusion for chemometric classification of 135 canned samples of Chinese lager beers by manufacturer based on the combination of fluorescence, UV and visible spectroscopies. Right-angle synchronous fluorescence spectra (SFS) at three wavelength difference Δλ=30, 60 and 80 nm and visible spectra in the range 380-700 nm of undiluted beers were recorded. UV spectra in the range 240-400 nm of diluted beers were measured. A classification model was built using principal component analysis (PCA) and linear discriminant analysis (LDA). LDA with cross-validation showed that the data fusion could achieve 78.5-86.7% correct classification (sensitivity), while those rates using individual spectroscopies ranged from 42.2% to 70.4%. The results demonstrated that the fluorescence, UV and visible spectroscopies complemented each other, yielding higher synergic effect. Copyright © 2015 Elsevier Ltd. All rights reserved.

Detection of non-milk fat in milk fat by gas chromatography and linear discriminant analysis.

Science.gov (United States)

Gutiérrez, R; Vega, S; Díaz, G; Sánchez, J; Coronado, M; Ramírez, A; Pérez, J; González, M; Schettino, B

2009-05-01

Gas chromatography was utilized to determine triacylglycerol profiles in milk and non-milk fat. The values of triacylglycerol were subjected to linear discriminant analysis to detect and quantify non-milk fat in milk fat. Two groups of milk fat were analyzed: A) raw milk fat from the central region of Mexico (n = 216) and B) ultrapasteurized milk fat from 3 industries (n = 36), as well as pork lard (n = 2), bovine tallow (n = 2), fish oil (n = 2), peanut (n = 2), corn (n = 2), olive (n = 2), and soy (n = 2). The samples of raw milk fat were adulterated with non-milk fats in proportions of 0, 5, 10, 15, and 20% to form 5 groups. The first function obtained from the linear discriminant analysis allowed the correct classification of 94.4% of the samples with levels <10% of adulteration. The triacylglycerol values of the ultrapasteurized milk fats were evaluated with the discriminant function, demonstrating that one industry added non-milk fat to its product in 80% of the samples analyzed.

Patient-Specific Variations in Biomarkers across Gingivitis and Periodontitis

Science.gov (United States)

Nagarajan, Radhakrishnan; Miller, Craig S.; Dawson, Dolph; Al-Sabbagh, Mohanad; Ebersole, Jeffrey L.

2015-01-01

This study investigates the use of saliva, as an emerging diagnostic fluid in conjunction with classification techniques to discern biological heterogeneity in clinically labelled gingivitis and periodontitis subjects (80 subjects; 40/group) A battery of classification techniques were investigated as traditional single classifier systems as well as within a novel selective voting ensemble classification approach (SVA) framework. Unlike traditional single classifiers, SVA is shown to reveal patient-specific variations within disease groups, which may be important for identifying proclivity to disease progression or disease stability. Salivary expression profiles of IL-1ß, IL-6, MMP-8, and MIP-1α from 80 patients were analyzed using four classification algorithms (LDA: Linear Discriminant Analysis [LDA], Quadratic Discriminant Analysis [QDA], Naïve Bayes Classifier [NBC] and Support Vector Machines [SVM]) as traditional single classifiers and within the SVA framework (SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM). Our findings demonstrate that performance measures (sensitivity, specificity and accuracy) of traditional classification as single classifier were comparable to that of the SVA counterparts using clinical labels of the samples as ground truth. However, unlike traditional single classifier approaches, the normalized ensemble vote-counts from SVA revealed varying proclivity of the subjects for each of the disease groups. More importantly, the SVA identified a subset of gingivitis and periodontitis samples that demonstrated a biological proclivity commensurate with the other clinical group. This subset was confirmed across SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM. Heatmap visualization of their ensemble sets revealed lack of consensus between these subsets and the rest of the samples within the respective disease groups indicating the unique nature of the patients in these subsets. While the source of variation is not known, the results presented clearly elucidate the

[Development of Tianma HPLC fingerprint and discriminant analysis].

Science.gov (United States)

Xiao, Jia-Jia; Huang, Hong; Lei, You-Cheng; Lin, Ting-Wen; Ma, Yue; Zhang, Jing; Zhang, Xing-Guo; Zhang, Da-Quan; Lv, Guang-Hua

2017-07-01

Tianma(the tuber of Gastrodia eleta) is a widely used and pricy Chinese herb. Its counterfeits are often found in herbal markets, which are the plant materials with similar macroscopic characteristics of Tianma. Moreover, the prices of Winter Tianma(cultivated Tianma) and Spring Tianma(mostly wild Tianma) have significant difference. However, it is difficult to identify the true or false, good or bad quality of Tianma samples. Thus, a total of 48 Tianma samples with different characteristics(including Winter Tianma, Spring Tianma, slice, powder, etc.) and 9 plant species 10 samples of Tianma counterfeits were collected and analyzed by HPLC-DAD-MS techniques. After optimizing the procedure of sample preparation, chromatographic and mass-spectral conditions, the HPLC chromatograms of all those samples were collected and compared. The similarities and Fisher discriminant analysis were further conducted between the HPLC chromatograms of Tianma and counterfeit, Winter Tianma and Spring Tianma. The results showed the HPLC chromatograms of 48 Tianma samples were similar at the correlation coefficient more than 0.848(n=48). Their mean chromatogram was simulated and used as Tianma HPLC fingerprint. There were 11 common peaks on the HPLC chromatograms of Tianma, in which 6 main peaks were chosen as characteristic peaks and identified as gastrodin, p-hydroxybenzyl alcohol, parishin A, parishin B, parishin C, parishin E, respectively by comparison of the retention time, UV and MS data with those of standard chemical compounds. All the six chemical compounds are bioactive in Tianma. However, the HPLC chromatograms of the 10 counterfeit samples were significantly different from Tianma fingerprint. The correlation coefficients between HPLC fingerprints of Tianma with the HPLC chromatograms of counterfeits were less than 0.042 and the characteristic peaks were not observed on the HPLC chromatograms of these counterfeit samples. It indicated the true or false Tianma can be

Linear discriminant analysis of character sequences using occurrences of words

KAUST Repository

Dutta, Subhajit; Chaudhuri, Probal; Ghosh, Anil

2014-01-01

Classification of character sequences, where the characters come from a finite set, arises in disciplines such as molecular biology and computer science. For discriminant analysis of such character sequences, the Bayes classifier based on Markov models turns out to have class boundaries defined by linear functions of occurrences of words in the sequences. It is shown that for such classifiers based on Markov models with unknown orders, if the orders are estimated from the data using cross-validation, the resulting classifier has Bayes risk consistency under suitable conditions. Even when Markov models are not valid for the data, we develop methods for constructing classifiers based on linear functions of occurrences of words, where the word length is chosen by cross-validation. Such linear classifiers are constructed using ideas of support vector machines, regression depth, and distance weighted discrimination. We show that classifiers with linear class boundaries have certain optimal properties in terms of their asymptotic misclassification probabilities. The performance of these classifiers is demonstrated in various simulated and benchmark data sets.

Linear discriminant analysis of character sequences using occurrences of words

KAUST Repository

Dutta, Subhajit

2014-02-01

Classification of character sequences, where the characters come from a finite set, arises in disciplines such as molecular biology and computer science. For discriminant analysis of such character sequences, the Bayes classifier based on Markov models turns out to have class boundaries defined by linear functions of occurrences of words in the sequences. It is shown that for such classifiers based on Markov models with unknown orders, if the orders are estimated from the data using cross-validation, the resulting classifier has Bayes risk consistency under suitable conditions. Even when Markov models are not valid for the data, we develop methods for constructing classifiers based on linear functions of occurrences of words, where the word length is chosen by cross-validation. Such linear classifiers are constructed using ideas of support vector machines, regression depth, and distance weighted discrimination. We show that classifiers with linear class boundaries have certain optimal properties in terms of their asymptotic misclassification probabilities. The performance of these classifiers is demonstrated in various simulated and benchmark data sets.

Contributions to sensitivity analysis and generalized discriminant analysis; Contributions a l'analyse de sensibilite et a l'analyse discriminante generalisee

Energy Technology Data Exchange (ETDEWEB)

Jacques, J

2005-12-15

Two topics are studied in this thesis: sensitivity analysis and generalized discriminant analysis. Global sensitivity analysis of a mathematical model studies how the output variables of this last react to variations of its inputs. The methods based on the study of the variance quantify the part of variance of the response of the model due to each input variable and each subset of input variables. The first subject of this thesis is the impact of a model uncertainty on results of a sensitivity analysis. Two particular forms of uncertainty are studied: that due to a change of the model of reference, and that due to the use of a simplified model with the place of the model of reference. A second problem was studied during this thesis, that of models with correlated inputs. Indeed, classical sensitivity indices not having significance (from an interpretation point of view) in the presence of correlation of the inputs, we propose a multidimensional approach consisting in expressing the sensitivity of the output of the model to groups of correlated variables. Applications in the field of nuclear engineering illustrate this work. Generalized discriminant analysis consists in classifying the individuals of a test sample in groups, by using information contained in a training sample, when these two samples do not come from the same population. This work extends existing methods in a Gaussian context to the case of binary data. An application in public health illustrates the utility of generalized discrimination models thus defined. (author)

Sex assessment from carpals bones: discriminant function analysis in a contemporary Mexican sample.

Science.gov (United States)

Mastrangelo, Paola; De Luca, Stefano; Sánchez-Mejorada, Gabriela

2011-06-15

Sex assessment is one of the first essential steps in human identification, in both medico-legal cases and bio-archaeological contexts. Fragmentary human remains compromised by different types of burial or physical insults may frustrate the use of the traditional sex estimation methods, such as the analysis of the skull and pelvis. Currently, the application of discriminant functions to sex unidentified skeletal remains is steadily increasing. However, several studies have demonstrated that, due to variation in size and patterns of sexual dimorphism, discriminant functions are population-specific. In this study, in order to improve sex assessment from skeletal remains and to establish population-specific discriminant functions, the diagnostic values of the carpal bones were considered. A sample of 136 individuals (78 males, 58 females) of known sex and age was analyzed. They belong to a contemporary identified collection from the Laboratory of Physical Anthropology, Faculty of Medicine, UNAM (Universidad Nacional Autónoma de México, Mexico City). The age of the individuals ranged between 25 and 85 years. Between four and nine measurements of each carpal bone were taken. Independent t-tests confirm that all carpals are sexually dimorphic. Univariate measurements produce accuracy levels that range from 61.8% to 90.8%. Classification accuracies ranged between 81.3% and 92.3% in the multivariate stepwise discriminant analysis. In addition, intra- and inter-observer error tests were performed. These indicated that replication of measurements was satisfactory for the same observer over time and between observers. These results suggest that carpal bones can be used for assessing sex in both forensic and bio-archaeological identification procedures and that bone dimensions are population specific. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Asymptotic performance of regularized quadratic discriminant analysis based classifiers

KAUST Repository

Elkhalil, Khalil

2017-12-13

This paper carries out a large dimensional analysis of the standard regularized quadratic discriminant analysis (QDA) classifier designed on the assumption that data arise from a Gaussian mixture model. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that depends only on the covariances and means associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized QDA and can be used to determine the optimal regularization parameter that minimizes the misclassification error probability. Despite being valid only for Gaussian data, our theoretical findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from popular real data bases, thereby making an interesting connection between theory and practice.

Near-infrared-excited confocal Raman spectroscopy advances in vivo diagnosis of cervical precancer.

Science.gov (United States)

Duraipandian, Shiyamala; Zheng, Wei; Ng, Joseph; Low, Jeffrey J H; Ilancheran, Arunachalam; Huang, Zhiwei

2013-06-01

Raman spectroscopy is a unique optical technique that can probe the changes of vibrational modes of biomolecules associated with tissue premalignant transformation. This study evaluates the clinical utility of confocal Raman spectroscopy over near-infrared (NIR) autofluorescence (AF) spectroscopy and composite NIR AF/Raman spectroscopy for improving early diagnosis of cervical precancer in vivo at colposcopy. A rapid NIR Raman system coupled with a ball-lens fiber-optic confocal Raman probe was utilized for in vivo NIR AF/Raman spectral measurements of the cervix. A total of 1240 in vivo Raman spectra [normal (n=993), dysplasia (n=247)] were acquired from 84 cervical patients. Principal components analysis (PCA) and linear discriminant analysis (LDA) together with a leave-one-patient-out, cross-validation method were used to extract the diagnostic information associated with distinctive spectroscopic modalities. The diagnostic ability of confocal Raman spectroscopy was evaluated using the PCA-LDA model developed from the significant principal components (PCs) [i.e., PC4, 0.0023%; PC5, 0.00095%; PC8, 0.00022%, (p<0.05)], representing the primary tissue Raman features (e.g., 854, 937, 1095, 1253, 1311, 1445, and 1654 cm(-1)). Confocal Raman spectroscopy coupled with PCA-LDA modeling yielded the diagnostic accuracy of 84.1% (a sensitivity of 81.0% and a specificity of 87.1%) for in vivo discrimination of dysplastic cervix. The receiver operating characteristic curves further confirmed that the best classification was achieved using confocal Raman spectroscopy compared to the composite NIR AF/Raman spectroscopy or NIR AF spectroscopy alone. This study illustrates that confocal Raman spectroscopy has great potential to improve early diagnosis of cervical precancer in vivo during clinical colposcopy.

Characterization and Differentiation of Petroleum-Derived Products by E-Nose Fingerprints

Directory of Open Access Journals (Sweden)

Marta Ferreiro-González

2017-11-01

Full Text Available Characterization of petroleum-derived products is an area of continuing importance in environmental science, mainly related to fuel spills. In this study, a non-separative analytical method based on E-Nose (Electronic Nose is presented as a rapid alternative for the characterization of several different petroleum-derived products including gasoline, diesel, aromatic solvents, and ethanol samples, which were poured onto different surfaces (wood, cork, and cotton. The working conditions about the headspace generation were 145 °C and 10 min. Mass spectroscopic data (45–200 m/z combined with chemometric tools such as hierarchical cluster analysis (HCA, later principal component analysis (PCA, and finally linear discriminant analysis (LDA allowed for a full discrimination of the samples. A characteristic fingerprint for each product can be used for discrimination or identification. The E-Nose can be considered as a green technique, and it is rapid and easy to use in routine analysis, thus providing a good alternative to currently used methods.

Microaggressions, Discrimination, and Phenotype among African Americans: A Latent Class Analysis of the Impact of Skin Tone and BMI.

Science.gov (United States)

Keith, Verna M; Nguyen, Ann W; Taylor, Robert Joseph; Mouzon, Dawne M; Chatters, Linda M

2017-05-01

Data from the 2001-2003National Survey of American Life are used to investigate the effects of phenotype on everyday experiences with discrimination among African Americans (N=3343). Latent class analysis is used to identify four classes of discriminatory treatment: 1) low levels of discrimination, 2) disrespect and condescension, 3) character-based discrimination, and 4) high levels of discrimination. We then employ latent class multinomial logistic regression to evaluate the association between skin tone and body weight and these four classes of discrimination. Designating the low level discrimination class as the reference group, findings revealed that respondents with darker skin were more likely to be classified into the disrespect/condescension and the high level microaggression types. BMI was unrelated to the discrimination type, although there was a significant interaction effect between gender and BMI. BMI was strongly and positively associated with membership in the disrespect and condescension type among men but not among women. These findings indicate that skin tone and body weight are two phenotypic characteristics that influence the type and frequency of discrimination experienced by African Americans.

Revealing discriminating power of the elements in edible sea salts: Line-intensity correlation analysis from laser-induced plasma emission spectra

Energy Technology Data Exchange (ETDEWEB)

Lee, Yonghoon, E-mail: yhlee@mokpo.ac.kr [Department of Chemistry, Mokpo National University, Jeonnam 534-729 (Korea, Republic of); Ham, Kyung-Sik [Department of Food Engineering, Mokpo National University, Jeonnam 534-729 (Korea, Republic of); Han, Song-Hee [Division of Maritime Transportation System, Mokpo National Maritime University, Jeonnam 530-729 (Korea, Republic of); Yoo, Jonghyun, E-mail: jyoo@appliedspectra.com [Applied Spectra, Inc., 46665 Fremont Boulevard, Fremont, CA 94538 (United States); Jeong, Sungho [School of Mechatronics, Gwangju Institute of Science and Technology, Gwangju 500-712 (Korea, Republic of)

2014-11-01

We have investigated the discriminating power of the elements in edible sea salts using Laser-Induced Breakdown Spectroscopy (LIBS). For the ten different sea salts from South Korea, China, Japan, France, Mexico and New Zealand, LIBS spectra were recorded in the spectral range between 190 and 1040 nm, identifying the presence of Na, Cl, K, Ca, Mg, Li, Sr, Al, Si, Ti, Fe, C, O, N, and H. Intensity correlation analysis of the observed emission lines provided a valuable insight into the discriminating power of the different elements in the sea salts. The correlation analysis suggests that the elements with independent discrimination power can be categorized into three groups; those that represent dissolved ions in seawater (K, Li, and Mg), those that are associated with calcified particles (Ca and Sr), and those that are present in soils contained in the sea salts (Al, Si, Ti, and Fe). Classification models using a few emission lines selected based on the results from intensity correlation analysis and full broadband LIBS spectra were developed based on Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) and their performances were compared. Our results indicate that effective combination of a few emission lines can provide a dependable model for discriminating the edible sea salts and the performance is not much degraded from that based on the full broadband spectra. This can be rationalized by the intensity correlation results. - Highlights: • Broadband LIBS spectra of various edible sea salts were obtained. • Intensity correlation of emission lines of the elements in edible sea salts was analyzed. • The elements were categorized into three groups with independent discriminating power. • The effective combination of a few lines can provide dependable classification models.

Comparative analysis of face recognition techniques with illumination variation

International Nuclear Information System (INIS)

Jondhale, K C; Waghmare, L M

2010-01-01

Illumination variation is one of the major challenges in the face recognition. To deal with this problem, this paper presents comparative analysis of three different techniques. First, the DCT is employed to compensate for illumination variations in the logarithm domain. Since illumination variation lies mainly in the low frequency band, an appropriate number of DCT coefficients are truncated to reduce the variations under different lighting conditions. The nearest neighbor classifier based on Euclidean distance is employed for classification. Second, the performance of PCA is checked on normalized image. PCA is a technique used to reduce multidimensional data sets to a lower dimension for analysis. Third, LDA based methods gives a satisfactory result under controlled lighting condition. But its performance under large illumination variation is not satisfactory. So, the performance of LDA is checked on normalized image. Experimental results on the Yale B and ORL database show that the proposed approach of application of PCA and LDA on normalized dataset improves the performance significantly for the face images with large illumination variations.

Phonological experience modulates voice discrimination: Evidence from functional brain networks analysis.

Science.gov (United States)

Hu, Xueping; Wang, Xiangpeng; Gu, Yan; Luo, Pei; Yin, Shouhang; Wang, Lijun; Fu, Chao; Qiao, Lei; Du, Yi; Chen, Antao

2017-10-01

Numerous behavioral studies have found a modulation effect of phonological experience on voice discrimination. However, the neural substrates underpinning this phenomenon are poorly understood. Here we manipulated language familiarity to test the hypothesis that phonological experience affects voice discrimination via mediating the engagement of multiple perceptual and cognitive resources. The results showed that during voice discrimination, the activation of several prefrontal regions was modulated by language familiarity. More importantly, the same effect was observed concerning the functional connectivity from the fronto-parietal network to the voice-identity network (VIN), and from the default mode network to the VIN. Our findings indicate that phonological experience could bias the recruitment of cognitive control and information retrieval/comparison processes during voice discrimination. Therefore, the study unravels the neural substrates subserving the modulation effect of phonological experience on voice discrimination, and provides new insights into studying voice discrimination from the perspective of network interactions. Copyright © 2017. Published by Elsevier Inc.

Spatial discrimination and visual discrimination

DEFF Research Database (Denmark)

Haagensen, Annika M. J.; Grand, Nanna; Klastrup, Signe

2013-01-01

Two methods investigating learning and memory in juvenile Gottingen minipigs were evaluated for potential use in preclinical toxicity testing. Twelve minipigs were tested using a spatial hole-board discrimination test including a learning phase and two memory phases. Five minipigs were tested...... in a visual discrimination test. The juvenile minipigs were able to learn the spatial hole-board discrimination test and showed improved working and reference memory during the learning phase. Performance in the memory phases was affected by the retention intervals, but the minipigs were able to remember...... the concept of the test in both memory phases. Working memory and reference memory were significantly improved in the last trials of the memory phases. In the visual discrimination test, the minipigs learned to discriminate between the three figures presented to them within 9-14 sessions. For the memory test...

Using spectrotemporal indices to improve the fruit-tree crop classification accuracy

Science.gov (United States)

Peña, M. A.; Liao, R.; Brenning, A.

2017-06-01

This study assesses the potential of spectrotemporal indices derived from satellite image time series (SITS) to improve the classification accuracy of fruit-tree crops. Six major fruit-tree crop types in the Aconcagua Valley, Chile, were classified by applying various linear discriminant analysis (LDA) techniques on a Landsat-8 time series of nine images corresponding to the 2014-15 growing season. As features we not only used the complete spectral resolution of the SITS, but also all possible normalized difference indices (NDIs) that can be constructed from any two bands of the time series, a novel approach to derive features from SITS. Due to the high dimensionality of this "enhanced" feature set we used the lasso and ridge penalized variants of LDA (PLDA). Although classification accuracies yielded by the standard LDA applied on the full-band SITS were good (misclassification error rate, MER = 0.13), they were further improved by 23% (MER = 0.10) with ridge PLDA using the enhanced feature set. The most important bands to discriminate the crops of interest were mainly concentrated on the first two image dates of the time series, corresponding to the crops' greenup stage. Despite the high predictor weights provided by the red and near infrared bands, typically used to construct greenness spectral indices, other spectral regions were also found important for the discrimination, such as the shortwave infrared band at 2.11-2.19 μm, sensitive to foliar water changes. These findings support the usefulness of spectrotemporal indices in the context of SITS-based crop type classifications, which until now have been mainly constructed by the arithmetic combination of two bands of the same image date in order to derive greenness temporal profiles like those from the normalized difference vegetation index.

[Etiological analysis and establishment of a discriminant model for lower respiratory tract infections in hospitalized patients].

Science.gov (United States)

Chen, Y S; Lin, X H; Li, H R; Hua, Z D; Lin, M Q; Huang, W S; Yu, T; Lyu, H Y; Mao, W P; Liang, Y Q; Peng, X R; Chen, S J; Zheng, H; Lian, S Q; Hu, X L; Yao, X Q

2017-12-12

Objective: To analyze the pathogens of lower respiratory tract infection(LRTI) including bacterial, viral and mixed infection, and to establish a discriminant model based on clinical features in order to predict the pathogens. Methods: A total of 243 hospitalized patients with lower respiratory tract infections were enrolled in Fujian Provincial Hospital from April 2012 to September 2015. The clinical data and airway (sputum and/or bronchoalveolar lavage) samples were collected. Microbes were identified by traditional culture (for bacteria), loop-mediated isothermal amplification(LAMP) and gene sequencing (for bacteria and atypical pathogen), or Real-time quantitative polymerase chain reaction (Real-time PCR)for viruses. Finally, a discriminant model was established by using the discriminant analysis methods to help to predict bacterial, viral and mixed infections. Results: Pathogens were detected in 53.9% (131/243) of the 243 cases.Bacteria accounted for 23.5%(57/243, of which 17 cases with the virus, 1 case with Mycoplasma pneumoniae and virus), mainly Pseudomonas Aeruginosa and Klebsiella Pneumonia. Atypical pathogens for 4.9% (12/243, of which 3 cases with the virus, 1 case of bacteria and viruses), all were mycoplasma pneumonia. Viruses for 34.6% (84/243, of which 17 cases of bacteria, 3 cases with Mycoplasma pneumoniae, 1 case with Mycoplasma pneumoniae and bacteria) of the cases, mainly Influenza A virus and Human Cytomegalovirus, and other virus like adenovirus, human parainfluenza virus, respiratory syncytial virus, human metapneumovirus, human boca virus were also detected fewly. Seven parameters including mental status, using antibiotics prior to admission, complications, abnormal breath sounds, neutrophil alkaline phosphatase (NAP) score, pneumonia severity index (PSI) score and CRUB-65 score were enrolled after univariate analysis, and discriminant analysis was used to establish the discriminant model by applying the identified pathogens as the

Variations in students' perceived reasons for, sources of, and forms of in-school discrimination: A latent class analysis.

Science.gov (United States)

Byrd, Christy M; Carter Andrews, Dorinda J

2016-08-01

Although there exists a healthy body of literature related to discrimination in schools, this research has primarily focused on racial or ethnic discrimination as perceived and experienced by students of color. Few studies examine students' perceptions of discrimination from a variety of sources, such as adults and peers, their descriptions of the discrimination, or the frequency of discrimination in the learning environment. Middle and high school students in a Midwestern school district (N=1468) completed surveys identifying whether they experienced discrimination from seven sources (e.g., peers, teachers, administrators), for seven reasons (e.g., gender, race/ethnicity, religion), and in eight forms (e.g., punished more frequently, called names, excluded from social groups). The sample was 52% White, 15% Black/African American, 14% Multiracial, and 17% Other. Latent class analysis was used to cluster individuals based on reported sources of, reasons for, and forms of discrimination. Four clusters were found, and ANOVAs were used to test for differences between clusters on perceptions of school climate, relationships with teachers, perceptions that the school was a "good school," and engagement. The Low Discrimination cluster experienced the best outcomes, whereas an intersectional cluster experienced the most discrimination and the worst outcomes. The results confirm existing research on the negative effects of discrimination. Additionally, the paper adds to the literature by highlighting the importance of an intersectional approach to examining students' perceptions of in-school discrimination. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Three-dimensional passive sensing photon counting for object classification

Science.gov (United States)

Yeom, Seokwon; Javidi, Bahram; Watson, Edward

2007-04-01

In this keynote address, we address three-dimensional (3D) distortion-tolerant object recognition using photon-counting integral imaging (II). A photon-counting linear discriminant analysis (LDA) is discussed for classification of photon-limited images. We develop a compact distortion-tolerant recognition system based on the multiple-perspective imaging of II. Experimental and simulation results have shown that a low level of photons is sufficient to classify out-of-plane rotated objects.

Sustainable Production and Trade Discrimination: An Analysis of the WTO

Directory of Open Access Journals (Sweden)

María Alejandra Calle Saldarriaga

2018-02-01

Full Text Available This article aims to examine the legality of trade measures addressing environmental conditions of production (PPMs in the context of non-discrimination provisions under the General Agreement on Tariffs and Trade (GATT  and the Agreement on Technical Barriers to Trade (TBT Agreement.  It shows that the notion of de facto discrimination is still a sensitive subject in the analysis of origin-neutral measures, including those based on environmental PPMs. Much of the discussion regarding PPMs focuses on the issue of ‘like products’. The interpretation of ‘likeness’ has also served to classify PPMs into the two categories of product related and non-product related. Such distinction rests on how the PPM affects the final product. However, it is important to analyse to what extent these measures can accord less favourable treatment to like products. The author argues that this requires a competition analysis. This article also elucidates how depending upon the applicable law (the TBT Agreement or the GATT PPMs are likely to face different legal challenges, particularly in terms of less favourable treatment. The author also assesses the possibility of transposing concepts such as ‘legitimate regulatory distinctions’ stemming from the TBT jurisprudence into GATT cases involving PPMs, and whether there will be an additional ‘test’ for PPMs characterised as TBT measures. This article is based on an extensive literature review and doctrinal legal research

Detection and quantification of extra virgin olive oil adulteration by means of autofluorescence excitation-emission profiles combined with multi-way classification.

Science.gov (United States)

Durán Merás, Isabel; Domínguez Manzano, Jaime; Airado Rodríguez, Diego; Muñoz de la Peña, Arsenio

2018-02-01

Within olive oils, extra virgin olive oil is the highest quality and, in consequence, the most expensive one. Because of that, it is common that some merchants attempt to take economic advantage by mixing it up with other less expensive oils, like olive oil or olive pomace oil. In consequence, the characterization and authentication of extra virgin olive oils is a subject of great interest, both for industry and consumers. This paper reports the potential of front-face total fluorescence spectroscopy combined with second-order chemometric methods for the detection of extra virgin olive oils adulteration with other olive oils. Excitation-emission matrices (EEMs) of extra virgin olive oils and extra virgin olive oils adulterated with olive oils or with olive pomace oils were recorded using front-face fluorescence spectroscopy. The full information content in these fluorescence images was analyzed with the aid of unsupervised parallel factor analysis (PARAFAC), PARAFAC supervised by linear discriminant analysis (LDA-PARAFAC), and discriminant unfolded partial least-squares (DA-UPLS). The discriminant ability of LDA-PARAFAC was studied through the tridimensional plots of the canonical vectors, defining a surface separating the established categories. For DA-UPLS, the discriminant ability was established through the bidimensional plots of predicted values of calibration and validation samples, in order to assign each sample to a given class. The models demonstrated the possibility of detecting adulterations of extra virgin olive oils with percentages of around 15% and 3% of olive and olive pomace oils, respectively. Also, UPLS regression was used to quantify the adulteration level of extra virgin olive oils with olive oils or with olive pomace oils. Copyright © 2017 Elsevier B.V. All rights reserved.

Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

Science.gov (United States)

Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.

1984-01-01

An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

Classification of plum spirit drinks by synchronous fluorescence spectroscopy.

Science.gov (United States)

Sádecká, J; Jakubíková, M; Májek, P; Kleinová, A

2016-04-01

Synchronous fluorescence spectroscopy was used in combination with principal component analysis (PCA) and linear discriminant analysis (LDA) for the differentiation of plum spirits according to their geographical origin. A total of 14 Czech, 12 Hungarian and 18 Slovak plum spirit samples were used. The samples were divided in two categories: colorless (22 samples) and colored (22 samples). Synchronous fluorescence spectra (SFS) obtained at a wavelength difference of 60 nm provided the best results. Considering the PCA-LDA applied to the SFS of all samples, Czech, Hungarian and Slovak colorless samples were properly classified in both the calibration and prediction sets. 100% of correct classification was also obtained for Czech and Hungarian colored samples. However, one group of Slovak colored samples was classified as belonging to the Hungarian group in the calibration set. Thus, the total correct classifications obtained were 94% and 100% for the calibration and prediction steps, respectively. The results were compared with those obtained using near-infrared (NIR) spectroscopy. Applying PCA-LDA to NIR spectra (5500-6000 cm(-1)), the total correct classifications were 91% and 92% for the calibration and prediction steps, respectively, which were slightly lower than those obtained using SFS. Copyright © 2015 Elsevier Ltd. All rights reserved.

A pattern recognition approach to transistor array parameter variance

Science.gov (United States)

da F. Costa, Luciano; Silva, Filipi N.; Comin, Cesar H.

2018-06-01

The properties of semiconductor devices, including bipolar junction transistors (BJTs), are known to vary substantially in terms of their parameters. In this work, an experimental approach, including pattern recognition concepts and methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), was used to experimentally investigate the variation among BJTs belonging to integrated circuits known as transistor arrays. It was shown that a good deal of the devices variance can be captured using only two PCA axes. It was also verified that, though substantially small variation of parameters is observed for BJT from the same array, larger variation arises between BJTs from distinct arrays, suggesting the consideration of device characteristics in more critical analog designs. As a consequence of its supervised nature, LDA was able to provide a substantial separation of the BJT into clusters, corresponding to each transistor array. In addition, the LDA mapping into two dimensions revealed a clear relationship between the considered measurements. Interestingly, a specific mapping suggested by the PCA, involving the total harmonic distortion variation expressed in terms of the average voltage gain, yielded an even better separation between the transistor array clusters. All in all, this work yielded interesting results from both semiconductor engineering and pattern recognition perspectives.

Workplace discrimination: experiences of practicing physicians.

Science.gov (United States)

Coombs, Alice A Tolbert; King, Roderick K

2005-04-01

In response to a growing concern regarding physician discrimination in the workplace, this study was developed to: (1) describe the types of discrimination that exist for the practicing physician and (2) determine which groups of physicians are more likely to experience the various forms of discrimination. Surveys were mailed to 1930 practicing physicians in Massachusetts. Participants were asked if they had encountered discrimination, how significant the discrimination was against a specific group, the frequency of personal discrimination, and the type of discrimination. Factor analysis identified four types of discrimination: career advancement, punitive behaviors, practice barriers and hiring barriers. A total of 445 responses were received (a 24% response rate). Sixty-three percent of responding physicians had experienced some form of discrimination. Respondents were women (46%), racial/ethnic minorities (42%) and international medical graduates (IMGs) (40%). In addition, 26% of those classified as white were also IMGs. Over 60% of respondents believed discrimination against IMGs was very or somewhat significant. Almost 27% of males acknowledged that gender bias against females was very or somewhat significant. IMGs were more likely to indicate that discrimination against IMGs was significant in their current organization. Of U.S. medical graduates (USMGs) 44% reported that discrimination against IMGs in their current organization was significant. Nonwhites were more likely to report that discrimination based on race/ethnicity was significant. Nearly 29% of white respondents also believed that such discrimination was very or somewhat significant. Physicians practicing in academic, research, and private practice sectors experience discrimination based on gender, ethnic/racial, and IMG status.

Principal component analysis for neural electron/jet discrimination in highly segmented calorimeters

International Nuclear Information System (INIS)

Vassali, M.R.; Seixas, J.M.

2001-01-01

A neural electron/jet discriminator based on calorimetry is developed for the second-level trigger system of the ATLAS detector. As preprocessing of the calorimeter information, a principal component analysis is performed on each segment of the two sections (electromagnetic and hadronic) of the calorimeter system, in order to reduce significantly the dimension of the input data space and fully explore the detailed energy deposition profile, which is provided by the highly-segmented calorimeter system. It is shown that projecting calorimeter data onto 33 segmented principal components, the discrimination efficiency of the neural classifier reaches 98.9% for electrons (with only 1% of false alarm probability). Furthermore, restricting data projection onto only 9 components, an electron efficiency of 99.1% is achieved (with 3% of false alarm), which confirms that a fast triggering system may be designed using few components

Textural Maturity Analysis and Sedimentary Environment Discrimination Based on Grain Shape Data

Science.gov (United States)

Tunwal, M.; Mulchrone, K. F.; Meere, P. A.

2017-12-01

Morphological analysis of clastic sedimentary grains is an important source of information regarding the processes involved in their formation, transportation and deposition. However, a standardised approach for quantitative grain shape analysis is generally lacking. In this contribution we report on a study where fully automated image analysis techniques were applied to loose sediment samples collected from glacial, aeolian, beach and fluvial environments. A range of shape parameters are evaluated for their usefulness in textural characterisation of populations of grains. The utility of grain shape data in ranking textural maturity of samples within a given sedimentary environment is evaluated. Furthermore, discrimination of sedimentary environment on the basis of grain shape information is explored. The data gathered demonstrates a clear progression in textural maturity in terms of roundness, angularity, irregularity, fractal dimension, convexity, solidity and rectangularity. Textural maturity can be readily categorised using automated grain shape parameter analysis. However, absolute discrimination between different depositional environments on the basis of shape parameters alone is less certain. For example, the aeolian environment is quite distinct whereas fluvial, glacial and beach samples are inherently variable and tend to overlap each other in terms of textural maturity. This is most likely due to a collection of similar processes and sources operating within these environments. This study strongly demonstrates the merit of quantitative population-based shape parameter analysis of texture and indicates that it can play a key role in characterising both loose and consolidated sediments. This project is funded by the Irish Petroleum Infrastructure Programme (www.pip.ie)

Baseline drift effect on the performance of neutron and γ ray discrimination using frequency gradient analysis

International Nuclear Information System (INIS)

Liu Guofu; Luo Xiaoliang; Yang Jun; Lin Cunbao; Hu Qingqing; Peng Jinxian

2013-01-01

Frequency gradient analysis (FGA) effectively discriminates neutrons and γ rays by examining the frequency-domain features of the photomultiplier tube anode signal. This approach is insensitive to noise but is inevitably affected by the baseline drift similar to other pulse shape discrimination methods. The baseline drift effect is attributed to factors such as power line fluctuation, dark current, noise disturbances, hum, and pulse tail in front-end electronics. This effect needs to be elucidated and quantified before the baseline shift can be estimated and removed from the captured signal. Therefore, the effect of baseline shift on the discrimination performance of neutrons and γ rays with organic scintillation detectors using FGA is investigated in this paper. The relationship between the baseline shift and discrimination parameters of FGA is derived and verified by an experimental system consisting of an americium—beryllium source, a BC501A liquid scintillator detector, and a 5 GSample/s 8-bit oscilloscope. The theoretical and experimental results both show that the estimation of the baseline shift is necessary, and the removal of baseline drift from the pulse shapes can improve the discrimination performance of FGA. (authors)

Age and Workplace Discrimination in Lithuania

OpenAIRE

Braziene, Ruta

2017-01-01

This paper aims to disclose an expression of age and workplace discrimination in the Lithuanian labor market. The paper is discussing theoretical aspects of age discrimination and presents the results of the sociological survey research results carried out in 2014. The purpose of this paper is to disclose age and workplace discrimination at the Lithuanian labor market. Analysis of scientific literature and quantitative research results allows to state that older adults are experiencing discri...

Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics.

Science.gov (United States)

Melucci, Dora; Bendini, Alessandra; Tesini, Federica; Barbieri, Sara; Zappi, Alessandro; Vichi, Stefania; Conte, Lanfranco; Gallina Toschi, Tullia

2016-08-01

At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between "100% Italian" and "non-100% Italian" oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined. Copyright © 2016. Published by Elsevier Ltd.

Discrimination of bromodeoxyuridine labelled and unlabelled mitotic cells in flow cytometric bromodeoxyuridine/DNA analysis

DEFF Research Database (Denmark)

Jensen, P O; Larsen, J K; Christensen, I J

1994-01-01

Bromodeoxyuridine (BrdUrd) labelled and unlabelled mitotic cells, respectively, can be discriminated from interphase cells using a new method, based on immunocytochemical staining of BrdUrd and flow cytometric four-parameter analysis of DNA content, BrdUrd incorporation, and forward and orthogona...

Application of otolith shape analysis for stock discrimination and species identification of five goby species (Perciformes: Gobiidae) in the northern Chinese coastal waters

Science.gov (United States)

Yu, Xin; Cao, Liang; Liu, Jinhu; Zhao, Bo; Shan, Xiujuan; Dou, Shuozeng

2014-09-01

We tested the use of otolith shape analysis to discriminate between species and stocks of five goby species ( Ctenotrypauchen chinensis, Odontamblyopus lacepedii, Amblychaeturichthys hexanema, Chaeturichthys stigmatias, and Acanthogobius hasta) found in northern Chinese coastal waters. The five species were well differentiated with high overall classification success using shape indices (83.7%), elliptic Fourier coefficients (98.6%), or the combination of both methods (94.9%). However, shape analysis alone was only moderately successful at discriminating among the four stocks (Liaodong Bay, LD; Bohai Bay, BH; Huanghe (Yellow) River estuary HRE, and Jiaozhou Bay, JZ stocks) of A. hasta (50%-54%) and C. stigmatias (65.7%-75.8%). For these two species, shape analysis was moderately successful at discriminating the HRE or JZ stocks from other stocks, but failed to effectively identify the LD and BH stocks. A large number of otoliths were misclassified between the HRE and JZ stocks, which are geographically well separated. The classification success for stock discrimination was higher using elliptic Fourier coefficients alone (70.2%) or in combination with shape indices (75.8%) than using only shape indices (65.7%) in C. stigmatias whereas there was little difference among the three methods for A. hasta. Our results supported the common belief that otolith shape analysis is generally more effective for interspecific identification than intraspecific discrimination. Moreover, compared with shape indices analysis, Fourier analysis improves classification success during inter- and intra-species discrimination by otolith shape analysis, although this did not necessarily always occur in all fish species.

Early discrimination of nasopharyngeal carcinoma based on tissue deoxyribose nucleic acid surface-enhanced Raman spectroscopy analysis

Science.gov (United States)

Qiu, Sufang; Li, Chao; Lin, Jinyong; Xu, Yuanji; Lu, Jun; Huang, Qingting; Zou, Changyan; Chen, Chao; Xiao, Nanyang; Lin, Duo; Chen, Rong; Pan, Jianji; Feng, Shangyuan

2016-12-01

Surface-enhanced Raman spectroscopy (SERS) was employed to detect deoxyribose nucleic acid (DNA) variations associated with the development of nasopharyngeal carcinoma (NPC). Significant SERS spectral differences between the DNA extracted from early NPC, advanced NPC, and normal nasopharyngeal tissue specimens were observed at 678, 729, 788, 1337, 1421, 1506, and 1573 cm-1, which reflects the genetic variations in NPC. Principal component analysis combined with discriminant function analysis for early NPC discrimination yielded a diagnostic accuracy of 86.8%, 92.3%, and 87.9% for early NPC, advanced NPC, and normal nasopharyngeal tissue DNA, respectively. In this exploratory study, we demonstrated the potential of SERS for early detection of NPC based on the DNA molecular study of biopsy tissues.

Experienced discrimination in home mortgage lending

DEFF Research Database (Denmark)

Secchi, Davide; Seri, Raffaello

2017-01-01

This article proposes a framework for the analysis of experienced discrimination in home mortgages. It addresses the problem of home mortgage lending discrimination in one of the richest areas of northern Italy. Employees of a local hospital were interviewed to study their perception (or experien...

Perceived age discrimination in older adults.

Science.gov (United States)

Rippon, Isla; Kneale, Dylan; de Oliveira, Cesar; Demakakos, Panayotes; Steptoe, Andrew

2014-05-01

to examine perceived age discrimination in a large representative sample of older adults in England. this cross-sectional study of over 7,500 individuals used data from the fifth wave of the English Longitudinal Study of Ageing (ELSA), a longitudinal cohort study of men and women aged 52 years and older in England. Wave 5 asked respondents about the frequency of five everyday discriminatory situations. Participants who attributed any experiences of discrimination to their age were treated as cases of perceived age discrimination. Multivariable logistic regression analysis was used to estimate the odds ratios of experiencing perceived age discrimination in relation to selected sociodemographic factors. approximately a third (33.3%) of all respondents experienced age discrimination, rising to 36.8% in those aged 65 and over. Perceived age discrimination was associated with older age, higher education, lower levels of household wealth and being retired or not in employment. The correlates of age discrimination across the five discriminatory situations were similar. understanding age discrimination is vital if we are to develop appropriate policies and to target future interventions effectively. These findings highlight the scale of the challenge of age discrimination for older adults in England and illustrate that those groups are particularly vulnerable to this form of discrimination.

Investigation on n/γ discrimination methods for liquid scintillator detector

International Nuclear Information System (INIS)

Li Kuinian; Li Yang; Zhang Mei; Zhang Zhongbing; Li Binkang; Zhang Xiaodong; Liu Jun; Zhang Xianpeng

2014-01-01

To obtain the n/γ discrimination ability of different digital pulse shape discrimination methods, four methods (rising time method, charge comparison method, pulse gradient analysis and frequency gradient analysis) in americium-beryllium mixed radiation fields were demonstrated. The signals from EJ-301 and BC501A scintillator detectors were digitized using oscilloscope. A comparison was taken among the four discrimination methods. The discrimination results of the four methods in liquid scintillator detectors show that the rising time method is the best and it provides a good choice in real-time n/γ discrimination system. (authors)

Discrimination and Anti-discrimination in Denmark

DEFF Research Database (Denmark)

Olsen, Tore Vincents

The purpose of this report is to describe and analyse Danish anti-discrimination legislation and the debate about discrimination in Denmark in order to identify present and future legal challenges. The main focus is the implementation of the EU anti-discrimination directives in Danish law...

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

Energy Technology Data Exchange (ETDEWEB)

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen [Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

2014-05-15

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

International Nuclear Information System (INIS)

Jing, Yaqi; Meng, Qinghao; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen

2014-01-01

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively

Statistical analysis for discrimination of prompt gamma ray peak induced by high energy neutron: Monte Carlo simulation study

International Nuclear Information System (INIS)

Do-Kun Yoon; Joo-Young Jung; Tae Suk Suh; Seong-Min Han

2015-01-01

The purpose of this research is a statistical analysis for discrimination of prompt gamma ray peak induced by the 14.1 MeV neutron particles from spectra using Monte Carlo simulation. For the simulation, the information of 18 detector materials was used to simulate spectra by the neutron capture reaction. The discrimination of nine prompt gamma ray peaks from the simulation of each detector material was performed. We presented the several comparison indexes of energy resolution performance depending on the detector material using the simulation and statistics for the prompt gamma activation analysis. (author)

Predicting Insolvency : A comparison between discriminant analysis and logistic regression using principal components

OpenAIRE

Geroukis, Asterios; Brorson, Erik

2014-01-01

In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of ...

Synergistic effect of the simultaneous chemometric analysis of {sup 1}H NMR spectroscopic and stable isotope (SNIF-NMR, {sup 18}O, {sup 13}C) data: Application to wine analysis

Energy Technology Data Exchange (ETDEWEB)

Monakhova, Yulia B., E-mail: yul-monakhova@mail.ru [Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weissenburger Strasse 3, Karlsruhe 76187 (Germany); Bruker Biospin GmbH, Silberstreifen, Rheinstetten 76287 (Germany); Department of Chemistry, Saratov State University, Astrakhanskaya Street 83, Saratov 410012 (Russian Federation); Godelmann, Rolf [Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weissenburger Strasse 3, Karlsruhe 76187 (Germany); Hermann, Armin [Landesuntersuchungsamt -Institut für Lebensmittelchemie und Arzneimittelprüfung, Emy-Roeder-Straße 1, Mainz 55129 (Germany); Kuballa, Thomas [Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weissenburger Strasse 3, Karlsruhe 76187 (Germany); Cannet, Claire; Schäfer, Hartmut; Spraul, Manfred [Bruker Biospin GmbH, Silberstreifen, Rheinstetten 76287 (Germany); Rutledge, Douglas N. [AgroParisTech, UMR 1145, Ingénierie Procédés Aliments, 16 rue Claude Bernard, Paris F-75005 (France)

2014-06-23

Highlights: • {sup 1}H NMR profilings of 718 wines were fused with stable isotope analysis data (SNIF-NMR, {sup 18}O, {sup 13}C). • The best improvement was obtained for prediction of the geographical origin of wine. • Certain enhancement was also obtained for the year of vintage (from 88 to 97% for {sup 1}H NMR to 99% for the fused data). • Independent component analysis was used as an alternative chemometric tool for classification. - Abstract: It is known that {sup 1}H NMR spectroscopy represents a good tool for predicting the grape variety, the geographical origin, and the year of vintage of wine. In the present study we have shown that classification models can be improved when {sup 1}H NMR profiles are fused with stable isotope (SNIF-NMR, {sup 18}O, {sup 13}C) data. Variable selection based on clustering of latent variables was performed on {sup 1}H NMR data. Afterwards, the combined data of 718 wine samples from Germany were analyzed using linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), factorial discriminant analysis (FDA) and independent components analysis (ICA). Moreover, several specialized multiblock methods (common components and specific weights analysis (ComDim), consensus PCA and consensus PLS-DA) were applied to the data. The best improvement in comparison with {sup 1}H NMR data was obtained for prediction of the geographical origin (up to 100% for the fused data, whereas stable isotope data resulted only in 60–70% correct prediction and {sup 1}H NMR data alone in 82–89% respectively). Certain enhancement was obtained also for the year of vintage (from 88 to 97% for {sup 1}H NMR to 99% for the fused data), whereas in case of grape varieties improved models were not obtained. The combination of {sup 1}H NMR data with stable isotope data improves efficiency of classification models for geographical origin and vintage of wine and can be potentially used for other food products as well.

Discrimination of Geographical Origin of Asian Garlic Using Isotopic and Chemical Datasets under Stepwise Principal Component Analysis.

Science.gov (United States)

Liu, Tsang-Sen; Lin, Jhen-Nan; Peng, Tsung-Ren

2018-01-16

Isotopic compositions of δ 2 H, δ 18 O, δ 13 C, and δ 15 N and concentrations of 22 trace elements from garlic samples were analyzed and processed with stepwise principal component analysis (PCA) to discriminate garlic's country of origin among Asian regions including South Korea, Vietnam, Taiwan, and China. Results indicate that there is no single trace-element concentration or isotopic composition that can accomplish the study's purpose and the stepwise PCA approach proposed does allow for discrimination between countries on a regional basis. Sequentially, Step-1 PCA distinguishes garlic's country of origin among Taiwanese, South Korean, and Vietnamese samples; Step-2 PCA discriminates Chinese garlic from South Korean garlic; and Step-3 and Step-4 PCA, Chinese garlic from Vietnamese garlic. In model tests, countries of origin of all audit samples were correctly discriminated by stepwise PCA. Consequently, this study demonstrates that stepwise PCA as applied is a simple and effective approach to discriminating country of origin among Asian garlics. © 2018 American Academy of Forensic Sciences.

Logistic discriminant analysis of breast cancer using ultrasound measurement

International Nuclear Information System (INIS)

Abdolmaleki, P.; Mokhtari Dizaji, M.; Vahead, M.R.; Gity, M.

2004-01-01

Background: Logistic discriminant method was applied to differentiate malignant from benign in a group of patients with proved breast lesions of the basis of ultrasonic parameters. Materials and methods: Our database include 273 patients' ultrasonographic pictures consisting of 14 quantitative variables. The measured variables were ultrasound propagation velocity, acoustic impedance and attenuation coefficient at 10 MHz in breast lesions at 20, 25, 30 and 35 d ig c temperature, physical density and age. This database was randomly divided into the estimation of 201 and validation of 72 samples. The estimation samples were used to build the logistic discriminant model, and validation samples were used to validate the performance. Finally important criteria such as sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were evaluated. Results: Our results showed that the logistic discriminant method was able to classify correctly 67 out of 72 cases presented in the validation sample. The results indicate a remarkable diagnostic accuracy of 93%. Conclusion: A logistic discriminator approach is capable of predicting the probability of malignancy of breast cancer. Features from ultrasonic measurement on ultrasound imaging is used in this approach

Origin Discrimination of Osmanthus fragrans var. thunbergii Flowers using GC-MS and UPLC-PDA Combined with Multivariable Analysis Methods.

Science.gov (United States)

Zhou, Fei; Zhao, Yajing; Peng, Jiyu; Jiang, Yirong; Li, Maiquan; Jiang, Yuan; Lu, Baiyi

2017-07-01

Osmanthus fragrans flowers are used as folk medicine and additives for teas, beverages and foods. The metabolites of O. fragrans flowers from different geographical origins were inconsistent in some extent. Chromatography and mass spectrometry combined with multivariable analysis methods provides an approach for discriminating the origin of O. fragrans flowers. To discriminate the Osmanthus fragrans var. thunbergii flowers from different origins with the identified metabolites. GC-MS and UPLC-PDA were conducted to analyse the metabolites in O. fragrans var. thunbergii flowers (in total 150 samples). Principal component analysis (PCA), soft independent modelling of class analogy analysis (SIMCA) and random forest (RF) analysis were applied to group the GC-MS and UPLC-PDA data. GC-MS identified 32 compounds common to all samples while UPLC-PDA/QTOF-MS identified 16 common compounds. PCA of the UPLC-PDA data generated a better clustering than PCA of the GC-MS data. Ten metabolites (six from GC-MS and four from UPLC-PDA) were selected as effective compounds for discrimination by PCA loadings. SIMCA and RF analysis were used to build classification models, and the RF model, based on the four effective compounds (caffeic acid derivative, acteoside, ligustroside and compound 15), yielded better results with the classification rate of 100% in the calibration set and 97.8% in the prediction set. GC-MS and UPLC-PDA combined with multivariable analysis methods can discriminate the origin of Osmanthus fragrans var. thunbergii flowers. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Traceability of 'Limone di Siracusa PGI' by a multidisciplinary analytical and chemometric approach.

Science.gov (United States)

Amenta, M; Fabroni, S; Costa, C; Rapisarda, P

2016-11-15

Food traceability is increasingly relevant with respect to safety, quality and typicality issues. Lemon fruits grown in a typical lemon-growing area of southern Italy (Siracusa), have been awarded the PGI (Protected Geographical Indication) recognition as 'Limone di Siracusa'. Due to its peculiarity, consumers have an increasing interest about this product. The detection of potential fraud could be improved by using the tools linking the composition of this production to its typical features. This study used a wide range of analytical techniques, including conventional techniques and analytical approaches, such as spectral (NIR spectra), multi-elemental (Fe, Zn, Mn, Cu, Li, Sr) and isotopic ((13)C/(12)C, (18)O/(16)O) marker investigations, joined with multivariate statistical analysis, such as PLS-DA (Partial Least Squares Discriminant Analysis) and LDA (Linear Discriminant Analysis), to implement a traceability system to verify the authenticity of 'Limone di Siracusa' production. The results demonstrated a very good geographical discrimination rate. Copyright © 2016 Elsevier Ltd. All rights reserved.

The contribution of cluster and discriminant analysis to the classification of complex aquifer systems.

Science.gov (United States)

Panagopoulos, G P; Angelopoulou, D; Tzirtzilakis, E E; Giannoulopoulos, P

2016-10-01

This paper presents an innovated method for the discrimination of groundwater samples in common groups representing the hydrogeological units from where they have been pumped. This method proved very efficient even in areas with complex hydrogeological regimes. The proposed method requires chemical analyses of water samples only for major ions, meaning that it is applicable to most of cases worldwide. Another benefit of the method is that it gives a further insight of the aquifer hydrogeochemistry as it provides the ions that are responsible for the discrimination of the group. The procedure begins with cluster analysis of the dataset in order to classify the samples in the corresponding hydrogeological unit. The feasibility of the method is proven from the fact that the samples of volcanic origin were separated into two different clusters, namely the lava units and the pyroclastic-ignimbritic aquifer. The second step is the discriminant analysis of the data which provides the functions that distinguish the groups from each other and the most significant variables that define the hydrochemical composition of the aquifer. The whole procedure was highly successful as the 94.7 % of the samples were classified to the correct aquifer system. Finally, the resulted functions can be safely used to categorize samples of either unknown or doubtful origin improving thus the quality and the size of existing hydrochemical databases.

Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters

Energy Technology Data Exchange (ETDEWEB)

Riches, S.F.; Payne, G.S.; Morgan, V.A.; DeSouza, N.M. [Royal Marsden NHS Foundation Trust and Institute of Cancer Research, CRUK and EPSRC Cancer Imaging Centre, Sutton, Surrey (United Kingdom); Dearnaley, D. [Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Department of Urology and Department of Academic Radiotherapy, Sutton, Surrey (United Kingdom); Morgan, S. [The Ottawa Hospital Cancer Centre and the University of Ottawa, Division of Radiation Oncology, Ottawa, Ontario (Canada); Partridge, M. [The Institute of Cancer Research, Section of Radiotherapy and Imaging, Sutton, Surrey (United Kingdom); University of Oxford, The Gray Institute for Radiation Oncology and Biology, Oxford (United Kingdom); Livni, N. [Royal Marsden NHS Foundation Trust Chelsea, Department of Histopathology, London (United Kingdom); Ogden, C. [Royal Marsden NHS Foundation Trust Chelsea, Department of Urology, London (United Kingdom)

2015-05-01

The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist. Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T{sub 2}-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T{sub 2,} Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (K{sup trans},K{sub ep},V{sub e}), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer. Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively. The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data. (orig.)

Demographic Consequences of Gender Discrimination in China: Simulation Analysis of Policy Options.

Science.gov (United States)

Quanbao, Jiang; Shuzhuo, Li; Marcus W, Feldman

2011-08-01

The large number of missing females in China, a consequence of gender discrimination, is having and will continue to have a profound effect on the country's population development. In this paper, we analyze the causes of this gender discrimination in terms of institutions, culture and, economy, and suggest public policies that might help eliminate gender discrimination. Using a population simulation model, we study the effect of public policies on the sex ratio at birth and excess female child mortality, and the effect of gender discrimination on China's population development. We find that gender discrimination will decrease China's population size, number of births, and working age population, accelerate population aging and exacerbate the male marriage squeeze. These results provide theoretical support for suggesting that the government enact and implement public policies aimed at eliminating gender discrimination.

Attractor structure discriminates sleep states: recurrence plot analysis applied to infant breathing patterns.

Science.gov (United States)

Terrill, Philip Ian; Wilson, Stephen James; Suresh, Sadasivam; Cooper, David M; Dakin, Carolyn

2010-05-01

Breathing patterns are characteristically different between infant active sleep (AS) and quiet sleep (QS), and statistical quantifications of interbreath interval (IBI) data have previously been used to discriminate between infant sleep states. It has also been identified that breathing patterns are governed by a nonlinear controller. This study aims to investigate whether nonlinear quantifications of infant IBI data are characteristically different between AS and QS, and whether they may be used to discriminate between these infant sleep states. Polysomnograms were obtained from 24 healthy infants at six months of age. Periods of AS and QS were identified, and IBI data extracted. Recurrence quantification analysis (RQA) was applied to each period, and recurrence calculated for a fixed radius in the range of 0-8 in steps of 0.02, and embedding dimensions of 4, 6, 8, and 16. When a threshold classifier was trained, the RQA variable recurrence was able to correctly classify 94.3% of periods in a test dataset. It was concluded that RQA of IBI data is able to accurately discriminate between infant sleep states. This is a promising step toward development of a minimal-channel automatic sleep state classification system.

The Role of Discrimination in Care Postponement Among Trans-Feminine Individuals in the U.S. National Transgender Discrimination Survey.

Science.gov (United States)

Glick, Jennifer L; Theall, Katherine P; Andrinopoulos, Katherine M; Kendall, Carl

2018-04-01

This study examines the associations between discrimination experiences (types and locations) and care postponement among trans-feminine individuals in the United States. This secondary, cross-sectional study utilized a subset of the data from the National Transgender Discrimination Survey (n = 2248), specifically for trans-feminine individuals. In this analysis, we examined the relationship between discrimination and primary care postponement. Twenty-six percent (26.25%) of the study sample reported delaying preventive care due to fear of discrimination; 23.98%-46.66% of respondents reported past experiences of discrimination (setting dependent). Discrimination in health and non-health settings and different types of discrimination-being denied services, verbally harassed, or physically assaulted-were all significantly associated with delaying care; respondents reporting discrimination were up to 20 times more likely to postpone care. While discrimination at a health location had the strongest association with care postponement (adjusted odds ratio = 9.65, confidence interval = 7.60-12.24), discrimination in all non-health-related locations was also important. Individuals reporting discrimination in greater numbers of locations and multiple types of discrimination were more likely to postpone care. To promote preventive care-seeking, these results affirm the importance of interventions that promote discrimination-free environments for gender minorities.

Item validity vs. item discrimination index: a redundancy?

Science.gov (United States)

Panjaitan, R. L.; Irawati, R.; Sujana, A.; Hanifah, N.; Djuanda, D.

2018-03-01

In several literatures about evaluation and test analysis, it is common to find that there are calculations of item validity as well as item discrimination index (D) with different formula for each. Meanwhile, other resources said that item discrimination index could be obtained by calculating the correlation between the testee’s score in a particular item and the testee’s score on the overall test, which is actually the same concept as item validity. Some research reports, especially undergraduate theses tend to include both item validity and item discrimination index in the instrument analysis. It seems that these concepts might overlap for both reflect the test quality on measuring the examinees’ ability. In this paper, examples of some results of data processing on item validity and item discrimination index were compared. It would be discussed whether item validity and item discrimination index can be represented by one of them only or it should be better to present both calculations for simple test analysis, especially in undergraduate theses where test analyses were included.

PyLDM - An open source package for lifetime density analysis of time-resolved spectroscopic data.

Directory of Open Access Journals (Sweden)

Gabriel F Dorlhiac

2017-05-01

Full Text Available Ultrafast spectroscopy offers temporal resolution for probing processes in the femto- and picosecond regimes. This has allowed for investigation of energy and charge transfer in numerous photoactive compounds and complexes. However, analysis of the resultant data can be complicated, particularly in more complex biological systems, such as photosystems. Historically, the dual approach of global analysis and target modelling has been used to elucidate kinetic descriptions of the system, and the identity of transient species respectively. With regards to the former, the technique of lifetime density analysis (LDA offers an appealing alternative. While global analysis approximates the data to the sum of a small number of exponential decays, typically on the order of 2-4, LDA uses a semi-continuous distribution of 100 lifetimes. This allows for the elucidation of lifetime distributions, which may be expected from investigation of complex systems with many chromophores, as opposed to averages. Furthermore, the inherent assumption of linear combinations of decays in global analysis means the technique is unable to describe dynamic motion, a process which is resolvable with LDA. The technique was introduced to the field of photosynthesis over a decade ago by the Holzwarth group. The analysis has been demonstrated to be an important tool to evaluate complex dynamics such as photosynthetic energy transfer, and complements traditional global and target analysis techniques. Although theory has been well described, no open source code has so far been available to perform lifetime density analysis. Therefore, we introduce a python (2.7 based package, PyLDM, to address this need. We furthermore provide a direct comparison of the capabilities of LDA with those of the more familiar global analysis, as well as providing a number of statistical techniques for dealing with the regularization of noisy data.

Development and Validation of Discriminant Analysis Models for Student Loan Defaultees and Non-Defaultees.

Science.gov (United States)

Myers, Greeley; Siera, Steven

1980-01-01

Default on guaranteed student loans has been increasing. The use of discriminant analysis as a technique to identify "good" v "bad" student loans based on information available from the loan application is discussed. Research to test the ability of models to such predictions is reported. (Author/MLW)

Perceived Discrimination and Health: A Meta-Analytic Review

Science.gov (United States)

Pascoe, Elizabeth A.; Richman, Laura Smart

2009-01-01

Perceived discrimination has been studied with regard to its impact on several types of health effects. This meta-analysis provides a comprehensive account of the relationships between multiple forms of perceived discrimination and both mental and physical health outcomes. In addition, this meta-analysis examines potential mechanisms by which…

Micro-Raman spectroscopy of natural and synthetic indigo samples.

Science.gov (United States)

Vandenabeele, Peter; Moens, Luc

2003-02-01

In this work indigo samples from three different sources are studied by using Raman spectroscopy: the synthetic pigment and pigments from the woad (Isatis tinctoria) and the indigo plant (Indigofera tinctoria). 21 samples were obtained from 8 suppliers; for each sample 5 Raman spectra were recorded and used for further chemometrical analysis. Principal components analysis (PCA) was performed as data reduction method before applying hierarchical cluster analysis. Linear discriminant analysis (LDA) was implemented as a non-hierarchical supervised pattern recognition method to build a classification model. In order to avoid broad-shaped interferences from the fluorescence background, the influence of 1st and 2nd derivatives on the classification was studied by using cross-validation. Although chemically identical, it is shown that Raman spectroscopy in combination with suitable chemometric methods has the potential to discriminate between synthetic and natural indigo samples.

Demographic Consequences of Gender Discrimination in China: Simulation Analysis of Policy Options

Science.gov (United States)

Quanbao, Jiang; Marcus W., Feldman

2013-01-01

The large number of missing females in China, a consequence of gender discrimination, is having and will continue to have a profound effect on the country's population development. In this paper, we analyze the causes of this gender discrimination in terms of institutions, culture and, economy, and suggest public policies that might help eliminate gender discrimination. Using a population simulation model, we study the effect of public policies on the sex ratio at birth and excess female child mortality, and the effect of gender discrimination on China's population development. We find that gender discrimination will decrease China's population size, number of births, and working age population, accelerate population aging and exacerbate the male marriage squeeze. These results provide theoretical support for suggesting that the government enact and implement public policies aimed at eliminating gender discrimination. PMID:24363477

Discriminant analysis to predict the occurrence of ELMs in H-mode discharges

International Nuclear Information System (INIS)

Kardaun, O.J.W.F.; Itoh, S.; Itoh, K.; Kardaun, J.W.P.F.

1993-08-01

After an exposition of its theoretical background, discriminant analysis is applied to the H-mode confinement database to find the region in plasma parameter space in which H-mode with small ELMs (Edge Localized Modes) is likely to occur. The boundary of this region is determined by the condition that the probability of appearance of such a type of H-mode, as a function of the plasma parameters, should be (1) larger than some threshold value and (2) larger than the corresponding probability for other types of H-mode (i.e., H-mode without ELMs or with giant ELMs). In practice, the discrimination has been performed for the ASDEX, JET and JFT-2M tokamaks (a) using four instantaneous plasma parameters (injected power P inj , magnetic field B t , plasma current I p and line averaged electron density (n-bar e ) and (b) taking also memory effects of the plasma and the distance between the plasma and the wall into account, while using variables that are normalised with respect to machine size. Generally speaking, it is found that there is a substantial overlap between the region of H-mode with small ELMs and the region of the two other types of H-mode. However, the ELM-free and the giant ELM H-modes relatively rarely appear in the region, that, according to the analysis, is allocated to small ELMs. A reliable production of H-mode with only small ELMs seems well possible by choosing this regime in parameter space. In the present study, it was not attempted to arrive at a unified discrimination across the machines. So, projection from one machine to another remains difficult, and a reliable determination of the region where small ELMs occur still requires a training sample from the device under consideration. (author) 53 refs

Discrimination Issues in the Process of Personnel Selection

OpenAIRE

Krinitsyna, Zoya Vasilievna; Menshikova, Ekaterina Valentinovna

2015-01-01

The paper discusses the concept of employment discrimination and its types, depending on the causes. The analysis of problems of social (gender and age) and psychological (racial and religious affiliation, disability) discrimination is given. The consequences of discrimination from the perspective of the employee and the employer are considered. The unfavorable situation in Russia in terms of high levels of discrimination is shown. The main objective trend, which will lead inevitably to the r...

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

Science.gov (United States)

Herskovits, Edward H.; Gerring, Joan P.

2003-01-01

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

Bayes-Based Fault Discrimination in Wide Area Backup Protection

Directory of Open Access Journals (Sweden)

WANG, Z.

2012-02-01

Full Text Available Multivariate statistical analysis is an effective tool to finish the fault location for electric power system. In Bayesian discriminant analysis as a subbranch, by the research of several populations, one can calculate the conditional probability that some samples belong to these populations, and compare the corresponding probability. The sample will be classified as population with maximum probability. In this paper, based on Bayesian discriminant analysis principle, a great number of simulation examples have confirmed that the results of Bayesian fault discriminant in wide area backup protection are accurate and reliable.

Rapid discrimination of bergamot essential oil by paper spray mass spectrometry and chemometric analysis.

Science.gov (United States)

Taverna, Domenico; Di Donna, Leonardo; Mazzotti, Fabio; Tagarelli, Antonio; Napoli, Anna; Furia, Emilia; Sindona, Giovanni

2016-09-01

A novel approach for the rapid discrimination of bergamot essential oil from other citrus fruits oils is presented. The method was developed using paper spray mass spectrometry (PS-MS) allowing for a rapid molecular profiling coupled with a statistic tool for a precise and reliable discrimination between the bergamot complex matrix and other similar matrices, commonly used for its reconstitution. Ambient mass spectrometry possesses the ability to record mass spectra of ordinary samples, in their native environment, without sample preparation or pre-separation by creating ions outside the instrument. The present study reports a PS-MS method for the determination of oxygen heterocyclic compounds such as furocoumarins, psoralens and flavonoids present in the non-volatile fraction of citrus fruits essential oils followed by chemometric analysis. The volatile fraction of Bergamot is one of the most known and fashionable natural products, which found applications in flavoring industry as ingredient in beverages and flavored foodstuff. The development of the presented method employed bergamot, sweet orange, orange, cedar, grapefruit and mandarin essential oils. PS-MS measurements were carried out in full scan mode for a total run time of 2 min. The capability of PS-MS profiling to act as marker for the classification of bergamot essential oils was evaluated by using multivariate statistical analysis. Two pattern recognition techniques, linear discriminant analysis and soft independent modeling of class analogy, were applied to MS data. The cross-validation procedure has shown excellent results in terms of the prediction ability because both models have correctly classified all samples for each category. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Rapid classification of turmeric based on DNA fingerprint by near-infrared spectroscopy combined with moving window partial least squares-discrimination analysis

International Nuclear Information System (INIS)

Kasemsumran, Sumaporn; Suttiwijitpukdee, Nattaporn; Keeratinijakal, Vichein

2017-01-01

In this research, near-infrared (NIR) spectroscopy in combination with moving window partial least squares-discrimination analysis (MWPLS-DA) was utilized to discriminate the variety of turmeric based on DNA markers, which correlated to the quantity of curcuminoid. Curcuminoid was used as a marker compound in variety identification due to the most pharmacological properties of turmeric possessed from it. MWPLS-DA optimized informative NIR spectral regions for the fitting and prediction to {-1/1}-coded turmeric varieties, indicating variables in the development of latent variables in discrimination analysis. Consequently, MWPLS-DA benefited in the selection of combined informative NIR spectral regions of 1100 – 1260, 1300 – 1500 and 1880 – 2500 nm for classification modeling of turmeric variety with 148 calibration samples, and yielded the results better than that obtained from a partial least squares-discrimination analysis (PLS-DA) model built by using the whole NIR spectral region. An effective and rapid strategy of using NIR in combination with MWPLS-DA provided the best variety identification results of 100% in both specificity and total accuracy for 48 test samples. (author)

Associations of racial discrimination and parental discrimination coping messages with African American adolescent racial identity.

Science.gov (United States)

Richardson, Bridget L; Macon, Tamarie A; Mustafaa, Faheemah N; Bogan, Erin D; Cole-Lewis, Yasmin; Chavous, Tabbye M

2015-06-01

Research links racial identity to important developmental outcomes among African American adolescents, but less is known about the contextual experiences that shape youths' racial identity. In a sample of 491 African American adolescents (48% female), associations of youth-reported experiences of racial discrimination and parental messages about preparation for racial bias with adolescents' later racial identity were examined. Cluster analysis resulted in four profiles of adolescents varying in reported frequency of racial discrimination from teachers and peers at school and frequency of parental racial discrimination coping messages during adolescents' 8th grade year. Boys were disproportionately over-represented in the cluster of youth experiencing more frequent discrimination but receiving fewer parental discrimination coping messages, relative to the overall sample. Also examined were clusters of adolescents' 11th grade racial identity attitudes about the importance of race (centrality), personal group affect (private regard), and perceptions of societal beliefs about African Americans (public regard). Girls and boys did not differ in their representation in racial identity clusters, but 8th grade discrimination/parent messages clusters were associated with 11th grade racial identity cluster membership, and these associations varied across gender groups. Boys experiencing more frequent discrimination but fewer parental coping messages were over-represented in the racial identity cluster characterized by low centrality, low private regard, and average public regard. The findings suggest that adolescents who experience racial discrimination but receive fewer parental supports for negotiating and coping with discrimination may be at heightened risk for internalizing stigmatizing experiences. Also, the findings suggest the need to consider the context of gender in adolescents' racial discrimination and parental racial socialization.

Contributions to sensitivity analysis and generalized discriminant analysis; Contributions a l'analyse de sensibilite et a l'analyse discriminante generalisee

Energy Technology Data Exchange (ETDEWEB)

Jacques, J

2005-12-15

Two topics are studied in this thesis: sensitivity analysis and generalized discriminant analysis. Global sensitivity analysis of a mathematical model studies how the output variables of this last react to variations of its inputs. The methods based on the study of the variance quantify the part of variance of the response of the model due to each input variable and each subset of input variables. The first subject of this thesis is the impact of a model uncertainty on results of a sensitivity analysis. Two particular forms of uncertainty are studied: that due to a change of the model of reference, and that due to the use of a simplified model with the place of the model of reference. A second problem was studied during this thesis, that of models with correlated inputs. Indeed, classical sensitivity indices not having significance (from an interpretation point of view) in the presence of correlation of the inputs, we propose a multidimensional approach consisting in expressing the sensitivity of the output of the model to groups of correlated variables. Applications in the field of nuclear engineering illustrate this work. Generalized discriminant analysis consists in classifying the individuals of a test sample in groups, by using information contained in a training sample, when these two samples do not come from the same population. This work extends existing methods in a Gaussian context to the case of binary data. An application in public health illustrates the utility of generalized discrimination models thus defined. (author)

Online LDA BASED brain-computer interface system to aid disabled people

Directory of Open Access Journals (Sweden)

Apdullah Yayık

2017-06-01

Full Text Available This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, which can be elicited by oddball paradigm. Developed application has a basic interaction tool that enables disabled people to convey their needs to other people selecting related objects. These objects pseudo-randomly flash in a visual interface on computer screen. The user must focus on related object to convey desired needs. The system can convey desired needs correctly by detecting P300 wave in acquired 14-channel EEG signal and classifying using linear discriminant analysis classifier just in 15 seconds. Experiments have been carried out on 19 volunteers to validate developed BCI system. As a result, accuracy rate of 90.83% is achieved in online performance.

Enlarge the training set based on inter-class relationship for face recognition from one image per person.

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Qin Li

Full Text Available In some large-scale face recognition task, such as driver license identification and law enforcement, the training set only contains one image per person. This situation is referred to as one sample problem. Because many face recognition techniques implicitly assume that several (at least two images per person are available for training, they cannot deal with the one sample problem. This paper investigates principal component analysis (PCA, Fisher linear discriminant analysis (LDA, and locality preserving projections (LPP and shows why they cannot perform well in one sample problem. After that, this paper presents four reasons that make one sample problem itself difficult: the small sample size problem; the lack of representative samples; the underestimated intra-class variation; and the overestimated inter-class variation. Based on the analysis, this paper proposes to enlarge the training set based on the inter-class relationship. This paper also extends LDA and LPP to extract features from the enlarged training set. The experimental results show the effectiveness of the proposed method.

Close-To-Practice Assessment Of Meat Freshness With Metal Oxide Sensor Microarray Electronic Nose

International Nuclear Information System (INIS)

Musatov, V. Yu.; Sysoev, V. V.; Sommer, M.; Kiselev, I.

2009-01-01

In this report we estimate the ability of KAMINA e-nose, based on a metal oxide sensor (MOS) microarray and Linear Discriminant Analysis (LDA) pattern recognition, to evaluate meat freshness. The received results show that, 1) one or two exposures of standard meat samples to the e-nose are enough for the instrument to recognize the fresh meat prepared by the same supplier with 100% probability; 2) the meat samples of two kinds, stored at 4 deg. C and 25 deg. C, are mutually recognized at early stages of decay with the help of the LDA model built independently under the e-nose training to each kind of meat; 3) the 3-4 training cycles of exposure to meat from different suppliers are necessary for the e-nose to build a reliable LDA model accounting for the supplier factor. This study approves that the MOS e-nose is ready to be currently utilised in food industry for evaluation of product freshness. The e-nose performance is characterized by low training cost, a confident recognition power of various product decay conditions and easy adjustment to changing conditions.

Evaluation of errors for mass-spectrometric analysis with surface-ionization type mass-spectrometer (statistical evaluation of mass-discrimination effect)

International Nuclear Information System (INIS)

Wada, Y.

1981-01-01

The surface-ionization type mass-spectrometer is widely used as an apparatus for quality assurance, accountability and safeguarding of nuclear materials, and for this analysis it has become an important factor to statistically evaluate an analytical error which consists of a random error and a systematic error. The major factor of this systematic error was the mass-discrimination effect. In this paper, various assays for evaluating the factor of variation on the mass-discrimination effect were studied and the data obtained were statistically evaluated. As a result of these analyses, it was proved that the factor of variation on the mass-discrimination effect was not attributed to the acid concentration of sample, sample size on the filament and supplied voltage for a multiplier, but mainly to the filament temperature during the mass-spectrometric analysis. The mass-discrimination effect values β which were usually calculated from the measured data of uranium, plutonium or boron isotopic standard sample were not so significant dependently of the difference of U-235, Pu-239 or B-10 isotopic abundance. Furthermore, in the case of U and Pu, measurement conditions and the mass range of these isotopes were almost similar, and these values β were not statistically significant between U and Pu. On the other hand, the value β for boron was about a third of the value β for U or Pu, but compared with the coefficient of the correction on the mass-discrimination effect for the difference of mass-number, ΔM, these coefficient values were almost the same among U, Pu, and B.As for the isotopic analysis error of U, Pu, Nd and B, it was proved that the isotopic abundance of these elements and the isotopic analysis error were in a relationship of quadratic curves on a logarithmic-logarithmic scale

Rapid differentiation of Listeria monocytogenes epidemic clones III and IV and their intact compared with heat-killed populations using Fourier transform infrared spectroscopy and chemometrics.

Science.gov (United States)

Nyarko, Esmond B; Puzey, Kenneth A; Donnelly, Catherine W

2014-06-01

The objectives of this study were to determine if Fourier transform infrared (FT-IR) spectroscopy and multivariate statistical analysis (chemometrics) could be used to rapidly differentiate epidemic clones (ECs) of Listeria monocytogenes, as well as their intact compared with heat-killed populations. FT-IR spectra were collected from dried thin smears on infrared slides prepared from aliquots of 10 μL of each L. monocytogenes ECs (ECIII: J1-101 and R2-499; ECIV: J1-129 and J1-220), and also from intact and heat-killed cell populations of each EC strain using 250 scans at a resolution of 4 cm(-1) in the mid-infrared region in a reflectance mode. Chemometric analysis of spectra involved the application of the multivariate discriminant method for canonical variate analysis (CVA) and linear discriminant analysis (LDA). CVA of the spectra in the wavelength region 4000 to 600 cm(-1) separated the EC strains while LDA resulted in a 100% accurate classification of all spectra in the data set. Further, CVA separated intact and heat-killed cells of each EC strain and there was 100% accuracy in the classification of all spectra when LDA was applied. FT-IR spectral wavenumbers 1650 to 1390 cm(-1) were used to separate heat-killed and intact populations of L. monocytogenes. The FT-IR spectroscopy method allowed discrimination between strains that belong to the same EC. FT-IR is a highly discriminatory and reproducible method that can be used for the rapid subtyping of L. monocytogenes, as well as for the detection of live compared with dead populations of the organism. Fourier transform infrared (FT-IR) spectroscopy and multivariate statistical analysis can be used for L. monocytogenes source tracking and for clinical case isolate comparison during epidemiological investigations since the method is capable of differentiating epidemic clones and it uses a library of well-characterized strains. The FT-IR method is potentially less expensive and more rapid compared to genetic

Combined approach based on principal component analysis and canonical discriminant analysis for investigating hyperspectral plant response

Directory of Open Access Journals (Sweden)

Anna Maria Stellacci

2012-07-01

Full Text Available Hyperspectral (HS data represents an extremely powerful means for rapidly detecting crop stress and then aiding in the rational management of natural resources in agriculture. However, large volume of data poses a challenge for data processing and extracting crucial information. Multivariate statistical techniques can play a key role in the analysis of HS data, as they may allow to both eliminate redundant information and identify synthetic indices which maximize differences among levels of stress. In this paper we propose an integrated approach, based on the combined use of Principal Component Analysis (PCA and Canonical Discriminant Analysis (CDA, to investigate HS plant response and discriminate plant status. The approach was preliminary evaluated on a data set collected on durum wheat plants grown under different nitrogen (N stress levels. Hyperspectral measurements were performed at anthesis through a high resolution field spectroradiometer, ASD FieldSpec HandHeld, covering the 325-1075 nm region. Reflectance data were first restricted to the interval 510-1000 nm and then divided into five bands of the electromagnetic spectrum [green: 510-580 nm; yellow: 581-630 nm; red: 631-690 nm; red-edge: 705-770 nm; near-infrared (NIR: 771-1000 nm]. PCA was applied to each spectral interval. CDA was performed on the extracted components to identify the factors maximizing the differences among plants fertilised with increasing N rates. Within the intervals of green, yellow and red only the first principal component (PC had an eigenvalue greater than 1 and explained more than 95% of total variance; within the ranges of red-edge and NIR, the first two PCs had an eigenvalue higher than 1. Two canonical variables explained cumulatively more than 81% of total variance and the first was able to discriminate wheat plants differently fertilised, as confirmed also by the significant correlation with aboveground biomass and grain yield parameters. The combined

Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

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Noman Naseer

2016-01-01

Full Text Available We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest using functional near-infrared spectroscopy (fNIRS signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA, quadratic discriminant analysis (QDA, k-nearest neighbour (kNN, the Naïve Bayes approach, support vector machine (SVM, and artificial neural networks (ANN, were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the p values were statistically significant relative to all of the other classifiers (p < 0.005 using HbO signals.

Discrimination of Temperature and Strain in Brillouin Optical Time Domain Analysis Using a Multicore Optical Fiber.

Science.gov (United States)

Zaghloul, Mohamed A S; Wang, Mohan; Milione, Giovanni; Li, Ming-Jun; Li, Shenping; Huang, Yue-Kai; Wang, Ting; Chen, Kevin P

2018-04-12

Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores' temperature and strain coefficients are such that temperature (strain) changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μ ϵ /MHz), which is 2.63 (3.67) times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain) changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%).

Discrimination of Temperature and Strain in Brillouin Optical Time Domain Analysis Using a Multicore Optical Fiber

Directory of Open Access Journals (Sweden)

Mohamed A. S. Zaghloul

2018-04-01

Full Text Available Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores’ temperature and strain coefficients are such that temperature (strain changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μ ϵ /MHz, which is 2.63 (3.67 times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%.

Otolith shape analysis for stock discrimination of two Collichthys genus croaker (Pieces: Sciaenidae,) from the northern Chinese coast

Science.gov (United States)

Zhao, Bo; Liu, Jinhu; Song, Junjie; Cao, Liang; Dou, Shuozeng

2017-08-01

The otolith morphology of two croaker species (Collichthys lucidus and Collichthys niveatus) from three areas (Liaodong Bay, LD; Huanghe (Yellow) River estuary, HRE; Jiaozhou Bay, JZ) along the northern Chinese coast were investigated for species identification and stock discrimination. The otolith contour shape described by elliptic Fourier coefficients (EFC) were analysed using principal components analysis (PCA) and stepwise canonical discriminant analysis (CDA) to identify species and stocks. The two species were well differentiated, with an overall classification success rate of 97.8%. And variations in the otolith shapes were significant enough to discriminate among the three geographical samples of C. lucidus (67.7%) or C. niveatus (65.2%). Relatively high mis-assignment occurred between the geographically adjacent LD and HRE samples, which implied that individual mixing may exist between the two samples. This study yielded information complementary to that derived from genetic studies and provided information for assessing the stock structure of C. lucidus and C. niveatus in the Bohai Sea and the Yellow Sea.

The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

Science.gov (United States)

Leka, K. D.; Barnes, Graham; Wagner, Eric

2018-04-01

A classification infrastructure built upon Discriminant Analysis (DA) has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional DA, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms

Science.gov (United States)

Tom, C. H.; Miller, L. D.

1984-01-01

The Bayesian maximum likelihood parametric classifier has been tested against the data-based formulation designated 'linear discrimination analysis', using the 'GLIKE' decision and "CLASSIFY' classification algorithms in the Landsat Mapping System. Identical supervised training sets, USGS land use/land cover classes, and various combinations of Landsat image and ancilliary geodata variables, were used to compare the algorithms' thematic mapping accuracy on a single-date summer subscene, with a cellularized USGS land use map of the same time frame furnishing the ground truth reference. CLASSIFY, which accepts a priori class probabilities, is found to be more accurate than GLIKE, which assumes equal class occurrences, for all three mapping variable sets and both levels of detail. These results may be generalized to direct accuracy, time, cost, and flexibility advantages of linear discriminant analysis over Bayesian methods.

Discrimination of surface wear on obsidian tools using LSCM and RelA: pilot study results (area-scale analysis of obsidian tool surfaces).

Science.gov (United States)

Stemp, W James; Chung, Steven

2011-01-01

This pilot study tests the reliability of laser scanning confocal microscopy (LSCM) to quantitatively measure wear on experimental obsidian tools. To our knowledge, this is the first use of confocal microscopy to study wear on stone flakes made from an amorphous silicate like obsidian. Three-dimensional surface roughness or texture area scans on three obsidian flakes used on different contact materials (hide, shell, wood) were documented using the LSCM to determine whether the worn surfaces could be discriminated using area-scale analysis, specifically relative area (RelA). When coupled with the F-test, this scale-sensitive fractal analysis could not only discriminate the used from unused surfaces on individual tools, but was also capable of discriminating the wear histories of tools used on different contact materials. Results indicate that such discriminations occur at different scales. Confidence levels for the discriminations at different scales were established using the F-test (mean square ratios or MSRs). In instances where discrimination of surface roughness or texture was not possible above the established confidence level based on MSRs, photomicrographs and RelA assisted in hypothesizing why this was so. Copyright © 2011 Wiley Periodicals, Inc.

The 95% confidence intervals of error rates and discriminant coefficients

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Shuichi Shinmura

2015-02-01

Full Text Available Fisher proposed a linear discriminant function (Fisher’s LDF. From 1971, we analysed electrocardiogram (ECG data in order to develop the diagnostic logic between normal and abnormal symptoms by Fisher’s LDF and a quadratic discriminant function (QDF. Our four years research was inferior to the decision tree logic developed by the medical doctor. After this experience, we discriminated many data and found four problems of the discriminant analysis. A revised Optimal LDF by Integer Programming (Revised IP-OLDF based on the minimum number of misclassification (minimum NM criterion resolves three problems entirely [13, 18]. In this research, we discuss fourth problem of the discriminant analysis. There are no standard errors (SEs of the error rate and discriminant coefficient. We propose a k-fold crossvalidation method. This method offers a model selection technique and a 95% confidence intervals (C.I. of error rates and discriminant coefficients.

Discrimination in relation to parenthood reported by community psychiatric service users in the UK: a framework analysis.

Science.gov (United States)

Jeffery, Debra; Clement, Sarah; Corker, Elizabeth; Howard, Louise M; Murray, Joanna; Thornicroft, Graham

2013-04-20

Experienced discrimination refers to an individual's perception that they have been treated unfairly due to an attribute and is an important recent focus within stigma research. A significant proportion of mental health service users report experiencing mental illness-based discrimination in relation to parenthood. Existing studies in this area have not gone beyond prevalence, therefore little is known about the nature of experienced discrimination in relation to parenthood, and how is it constituted. This study aims to generate a typology of community psychiatric service users' reports of mental illness-based discrimination in relation to becoming or being a parent. A secondary aim is to assess the prevalence of these types of experienced discrimination. In a telephone survey 2026 community psychiatric service users in ten UK Mental Health service provider organisations (Trusts) were asked about discrimination experienced in the previous 12 months using the Discrimination and Stigma Scale (DISC). The sample were asked if, due to their mental health problem, they had been treated unfairly in starting a family, or in their role as a parent, and gave examples of this. Prevalence is reported and the examples of experienced discrimination in relation to parenthood were analysed using the framework method of qualitative analysis. Three hundred and four participants (73% female) reported experienced discrimination, with prevalences of 22.5% and 28.3% for starting a family and for the parenting role respectively. Participants gave 89 examples of discrimination about starting a family and 228 about parenting, and these occurred in social and professional contexts. Ten themes were identified. These related to being seen as an unfit parent; people not being understanding; being stopped from having children; not being allowed to see their children; not getting the support needed; children being affected; children avoiding their parents; children's difficulties being blamed

A framework about flow measurements by LDA-PDA as a spatio-temporal average: application to data post-processing

Science.gov (United States)

Calvo, Esteban; García, Juan A.; Santolaya, José Luis; García, Ignacio; Aísa, Luis

2012-05-01

Phase Doppler anemometry (PDA) is a well-established technique to study two-phase flows and its principles are also used in laser Doppler anemometry (LDA) for measurements of fluid velocity. Raw measurements of individual particle data require post-processing to obtain useful and consistent information (moments of velocity, particle concentration and flux, velocity autocorrelation, etc). This is called in this paper the reconstruction of statistical information. In the 1970s, several basic algorithms to perform the statistical reconstruction were developed for LDA measurements (such as the transit time method, the inverse velocity method, etc). With the advent of PDA, the scientific community developed reconstruction algorithms to obtain mean variables of the dispersed phase. All these basic algorithms were expounded as unconnected methods, following independent threads not integrated into a general framework. Assuming that the PDA works under ideal conditions (all particles that cross the probe volume are validated), this paper provides a general formulation and fully systematizes a large set of previous statistical reconstruction methods. In this paper, the statistical reconstruction of both the dispersed and the continuous phase is unified: the continuous phase post-processing emerges as the same reconstruction method of the dispersed phase. The general framework proposed offers many advantages. First, some previous calculation methods of particle concentration turn out to be particular cases of this general formulation. Second, it provides an easy way to deduce unbiased estimators of any statistical parameter of the flow. Third, a wide set of new post-processing methods are proposed to be tested by any member of the scientific community. In the fourth place, the generalized integral method to compute the particle concentration also gives information about the probe volume geometry and two new auto-calibration algorithms are proposed: the integral calibration

Differential discriminator

International Nuclear Information System (INIS)

Dukhanov, V.I.; Mazurov, I.B.

1981-01-01

A principal flowsheet of a differential discriminator intended for operation in a spectrometric circuit with statistical time distribution of pulses is described. The differential discriminator includes four integrated discriminators and a channel of piled-up signal rejection. The presence of the rejection channel enables the discriminator to operate effectively at loads of 14x10 3 pulse/s. The temperature instability of the discrimination thresholds equals 250 μV/ 0 C. The discrimination level changes within 0.1-5 V, the level shift constitutes 0.5% for the filling ratio of 1:10. The rejection coefficient is not less than 90%. Alpha spectrum of the 228 Th source is presented to evaluate the discriminator operation with the rejector. The rejector provides 50 ns time resolution

Structural Discrimination

DEFF Research Database (Denmark)

Thorsen, Mira Skadegård

discrimination as two ways of articulating particular, opaque forms of racial discrimination that occur in everyday Danish (and other) contexts, and have therefore become normalized. I present and discuss discrimination as it surfaces in data from my empirical studies of discrimination in Danish contexts...

Discrimination of Xihulongjing tea grade using an electronic tongue ...

African Journals Online (AJOL)

Five grades of Xihulongjing tea (grade: AAA, AA, A, B and C, from the same region and processed with the same processing method) were discriminated using -Astree II electronic tongue (e-tongue) coupled with pattern recognition methods including principal component analysis (PCA), canonical discriminant analysis ...

Surface-enhanced Raman spectroscopy for differentiation between benign and malignant thyroid tissues

Science.gov (United States)

Li, Zuanfang; Li, Chao; Lin, Duo; Huang, Zufang; Pan, Jianji; Chen, Guannan; Lin, Juqiang; Liu, Nenrong; Yu, Yun; Feng, Shangyuan; Chen, Rong

2014-04-01

The aim of this study was to evaluate the potential of applying silver nano-particle based surface-enhanced Raman scattering (SERS) to discriminate different types of human thyroid tissues. SERS measurements were performed on three groups of tissue samples including thyroid cancers (n = 32), nodular goiters (n = 20) and normal thyroid tissues (n = 25). Tentative assignments of the measured tissue SERS spectra suggest interesting cancer specific biomolecular differences. The principal component analysis (PCA) and linear discriminate analysis (LDA) together with the leave-one-out, cross-validated technique yielded diagnostic sensitivities of 92%, 75% and 87.5%; and specificities of 82.6%, 89.4% and 84.4%, respectively, for differentiation among normal, nodular and malignant thyroid tissue samples. This work demonstrates that tissue SERS spectroscopy associated with multivariate analysis diagnostic algorithms has great potential for detection of thyroid cancer at the molecular level.

Using near infrared spectroscopy to classify soybean oil according to expiration date.

Science.gov (United States)

da Costa, Gean Bezerra; Fernandes, David Douglas Sousa; Gomes, Adriano A; de Almeida, Valber Elias; Veras, Germano

2016-04-01

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard-Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA-LDA and PLS-DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

Combined use of correlation dimension and entropy as discriminating measures for time series analysis

Science.gov (United States)

Harikrishnan, K. P.; Misra, R.; Ambika, G.

2009-09-01

We show that the combined use of correlation dimension (D2) and correlation entropy (K2) as discriminating measures can extract a more accurate information regarding the different types of noise present in a time series data. For this, we make use of an algorithmic approach for computing D2 and K2 proposed by us recently [Harikrishnan KP, Misra R, Ambika G, Kembhavi AK. Physica D 2006;215:137; Harikrishnan KP, Ambika G, Misra R. Mod Phys Lett B 2007;21:129; Harikrishnan KP, Misra R, Ambika G. Pramana - J Phys, in press], which is a modification of the standard Grassberger-Proccacia scheme. While the presence of white noise can be easily identified by computing D2 of data and surrogates, K2 is a better discriminating measure to detect colored noise in the data. Analysis of time series from a real world system involving both white and colored noise is presented as evidence. To our knowledge, this is the first time that such a combined analysis is undertaken on a real world data.

[Discrimination of Rice Syrup Adulterant of Acacia Honey Based Using Near-Infrared Spectroscopy].

Science.gov (United States)

Zhang, Yan-nan; Chen, Lan-zhen; Xue, Xiao-feng; Wu, Li-ming; Li, Yi; Yang, Juan

2015-09-01

At present, the rice syrup as a low price of the sweeteners was often adulterated into acacia honey and the adulterated honeys were sold in honey markets, while there is no suitable and fast method to identify honey adulterated with rice syrup. In this study, Near infrared spectroscopy (NIR) combined with chemometric methods were used to discriminate authenticity of honey. 20 unprocessed acacia honey samples from the different honey producing areas, mixed? with different proportion of rice syrup, were prepared of seven different concentration gradient? including 121 samples. The near infrared spectrum (NIR) instrument and spectrum processing software have been applied in the? spectrum? scanning and data conversion on adulterant samples, respectively. Then it was analyzed by Principal component analysis (PCA) and canonical discriminant analysis methods in order to discriminating adulterated honey. The results showed that after principal components analysis, the first two principal components accounted for 97.23% of total variation, but the regionalism of the score plot of the first two PCs was not obvious, so the canonical discriminant analysis was used to make the further discrimination, all samples had been discriminated correctly, the first two discriminant functions accounted for 91.6% among the six canonical discriminant functions, Then the different concentration of adulterant samples can be discriminated correctly, it illustrate that canonical discriminant analysis method combined with NIR spectroscopy is not only feasible but also practical for rapid and effective discriminate of the rice syrup adulterant of acacia honey.

Multi-spectral Image Analysis for Astaxanthin Coating Classification

DEFF Research Database (Denmark)

Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht

2011-01-01

Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. In this study multi-spectral image analysis of pellets was performed using LDA, QDA, SNV and PCA on pixel level and mean value of pixels...

Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs.

Science.gov (United States)

Ganga, G M D; Esposto, K F; Braatz, D

2012-01-01

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Diagnosing basal cell carcinoma in vivo by near-infrared Raman spectroscopy: a Principal Components Analysis discrimination algorithm

Science.gov (United States)

Silveira, Landulfo, Jr.; Silveira, Fabrício L.; Bodanese, Benito; Pacheco, Marcos Tadeu T.; Zângaro, Renato A.

2012-02-01

This work demonstrated the discrimination among basal cell carcinoma (BCC) and normal human skin in vivo using near-infrared Raman spectroscopy. Spectra were obtained in the suspected lesion prior resectional surgery. After tissue withdrawn, biopsy fragments were submitted to histopathology. Spectra were also obtained in the adjacent, clinically normal skin. Raman spectra were measured using a Raman spectrometer (830 nm) with a fiber Raman probe. By comparing the mean spectra of BCC with the normal skin, it has been found important differences in the 800-1000 cm-1 and 1250-1350 cm-1 (vibrations of C-C and amide III, respectively, from lipids and proteins). A discrimination algorithm based on Principal Components Analysis and Mahalanobis distance (PCA/MD) could discriminate the spectra of both tissues with high sensitivity and specificity.

Quantitative analysis of the clinical data on leukemia, 5. Specificity of clinical features in acute myelocytic leukemia with 8; 21 translocation by multiple logistic discriminant analysis

Energy Technology Data Exchange (ETDEWEB)

Ueoka, Hiroshi; Kamada, Nanao; Yamamoto, Hisashi; Ohtaki, Megu; Takimoto, Yasuo; Kuramoto, Atsushi; Munaka, Masaki

1984-11-01

In order to determine the necessity of chromosome analysis required for the evaluation of 8;21 translocation, multiple logistic discriminant analysis was made on 124 patients with acute non-lymphocytic leukemia experienced in the authors' institution. Variables which showed positive correlation with the presence of 8;21 translocation were the presence of Auer body and granular abnormality of the cells, numbers of peripheral promyelocytes, myelocytes and metamyelocytes, and bone marrow promyelocytes, myelocytes, and the sum of rods and segments. Those which showed negative correlation with 8;21 translocation were peripheral platelet count, neutrocytealkaline phosphatase (N-AP) score, numbers of eosinocytes, monocytes and erythroblasts, and erythroblasts on myelogram. Auer body, four peripheral hematological features (platelet count, N-AP score, metamyelocytes and monocytes), and three myelogram features (myelocytes, reticular cells and granulocytes/eosionocytes) were used for the multiple logistic discriminant analysis. By the analysis, 2 of the 22 patients (9.1%) with translocation were judged not to have 8;21 translocation and 3 of the 102 patients (2.9%) without translocation were judged to have it. Therefore, this multiple logistic discriminant method has proved to be simple and useful in clinically evaluating acute non-lymphocytic leukemia. (Namekawa, K.).

Detection of Taste Change of Bovine and Goat Milk in Room Ambient Using Electronic Tongue

Directory of Open Access Journals (Sweden)

Imam Tazi

2017-11-01

Full Text Available An electronic tongue (e-tongue based on an array of lipid/polymer membranes has been successfully developed for measuring the taste evolution of natural milk. The e-tongue consisted of 16 different lipid/polymer membranes combined with or without a pH sensor. The natural milk of bovine and goat were purchased from the local farming store in Malang-Indonesia. The taste measurement was carried out, from fresh (0 h to stale (12 h, every two hours under room ambient without any treatment. The responses of the e-tongue were evaluated using a Principal Component Analysis (PCA and a Linear Discriminant Analysis (LDA. From PCA results, the taste of both milk samples tends to change by time although some groups show a partial overlapping. LDA results show the high precision of the e-tongue in clustering taste evolution. The correctly classified groups after the cross-validation procedure were achieved 95.7 and 87.1% for bovine and goat milk, respectively. The improvement of the classification using LDA was obtained by adding data from a pH sensor of each measurement as 100 and 98.6% for bovine and goat milk, respectively. This work indicates that the lab-made e-tongue may be useful to predict the quality of natural milk for the food industry.

Genetic Discrimination: A Legal Or Biological Issue?

Directory of Open Access Journals (Sweden)

Bárbara Augusta de Paula Araujo Myssior

2016-12-01

Full Text Available This essay debates the technological evolution that, from the decoding of the human genome has opened up many scientific benefits, and yet brings up a new kind of segregation: genetic discrimination. Based on the right to privacy, as well as the concept of genetic identity, as well as data protection and information, worked up the genetic discrimination. Therefore, documentary research and critical analysis of scientific papers were taken, using up of the inductive reasoning method. As a result, elucidate how such discrimination affects individuals, it is possible to conclude that regardless of the type of discrimination, all should be restrained by law.

Quartz tuning fork based sensor for detection of volatile organic compounds: towards breath analysis

Science.gov (United States)

Sampson, Abraham; Panchal, Suresh; Phadke, Apoorva; Kashyap, A.; Suman, Jilma; Unnikrishnan, G.; Datar, Suwarna

2018-04-01

Several volatile organic compounds (VOCs) are present in the exhaled human breath whose concentration can vary depending on the physiological changes occurring within a human being. These changes in the concentration or the occurrence of a particular VOC can be used as signature of a particular disease in a person. In the present work, a sensor has been developed to detect VOCs such as 1,4-dimethoxy-2,3-butanediol (BD), and cyclohexanone (CH), acetone, methanol and ethanol. Except for BD and CH, the rest of the VOCs are present in a healthy person in ppm levels. CH and BD have been reported to be present in the exhaled human breath of breast cancer patients in ppm levels and can be used to distinguish between a healthy person and a person with breast cancer. The selectivity of the sensor towards these two compounds in the presence of other VOCs commonly present in human breath like acetone, ethanol and methanol has been studied. The sensor has been developed using modified Quartz Tuning Forks (QTFs) with the intent of developing an array of such sensors identifying different VOCs present in a healthy human’s breath. Two differently modified QTFs have been used to detect 1 ppm of 1,4-dimethoxy-2,3-butanediol and 20 ppm of cyclohexanone. Linear Discriminants Analysis (LDA) has been used to classify seven different VOCs. For this purpose, features extracted from sensor responses -shift in resonant frequency, response time and recovery time of the sensors- have been used as features in the model. Differently modified array of QTFs along with the use of LDA can be a useful pathway towards development of a QTF based sensor array for human breath analysis.

Tunnel Diode Discriminator with Fixed Dead Time

DEFF Research Database (Denmark)

Diamond, J. M.

1965-01-01

A solid state discriminator for the range 0.4 to 10 V is described. Tunnel diodes are used for the discriminator element and in a special fixed dead time circuit. An analysis of temperature stability is presented. The regulated power supplies are described, including a special negative resistance...

Use of discriminant analysis to determine black shales of the Lesser Carpathian crystal field

Energy Technology Data Exchange (ETDEWEB)

Khun, M.

1980-01-01

Discriminant analysis of results from geochemical testing was used to separate black shales of the ore level from the nonproductive deposits. Based on a large number of experiments, the accuracy of isolating the black shales according to content of vandium, copper and nickel reached 78%. These elements have basic importance for separation of productive shales from nonproductive.

Numerical experiment on different validation cases of water coolant flow in supercritical pressure test sections assisted by discriminated dimensional analysis part I: the dimensional analysis

International Nuclear Information System (INIS)

Kiss, A.; Aszodi, A.

2011-01-01

As recent studies prove in contrast to 'classical' dimensional analysis, whose application is widely described in heat transfer textbooks despite its poor results, the less well known and used discriminated dimensional analysis approach can provide a deeper insight into the physical problems involved and much better results in all cases where it is applied. As a first step of this ongoing research discriminated dimensional analysis has been performed on supercritical pressure water pipe flow heated through the pipe solid wall to identify the independent dimensionless groups (which play an independent role in the above mentioned thermal hydraulic phenomena) in order to serve a theoretical base to comparison between well known supercritical pressure water pipe heat transfer experiments and results of their validated CFD simulations. (author)

Discrimination and chemical phylogenetic study of seven species of Dendrobium using infrared spectroscopy combined with cluster analysis

Science.gov (United States)

Luo, Congpei; He, Tao; Chun, Ze

2013-04-01

Dendrobium is a commonly used and precious herb in Traditional Chinese Medicine. The high biodiversity of Dendrobium and the therapeutic needs require tools for the correct and fast discrimination of different Dendrobium species. This study investigates Fourier transform infrared spectroscopy followed by cluster analysis for discrimination and chemical phylogenetic study of seven Dendrobium species. Despite the general pattern of the IR spectra, different intensities, shapes, peak positions were found in the IR spectra of these samples, especially in the range of 1800-800 cm-1. The second derivative transformation and alcoholic extracting procedure obviously enlarged the tiny spectral differences among these samples. The results indicated each Dendrobium species had a characteristic IR spectra profile, which could be used to discriminate them. The similarity coefficients among the samples were analyzed based on their second derivative IR spectra, which ranged from 0.7632 to 0.9700, among the seven Dendrobium species, and from 0.5163 to 0.9615, among the ethanol extracts. A dendrogram was constructed based on cluster analysis the IR spectra for studying the chemical phylogenetic relationships among the samples. The results indicated that D. denneanum and D. crepidatum could be the alternative resources to substitute D. chrysotoxum, D. officinale and D. nobile which were officially recorded in Chinese Pharmacopoeia. In conclusion, with the advantages of high resolution, speediness and convenience, the experimental approach can successfully discriminate and construct the chemical phylogenetic relationships of the seven Dendrobium species.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

Science.gov (United States)

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Discrimination Against State and Local Government LGBT Employees: An Analysis of Administrative Complaints

OpenAIRE