Sample records for discriminant analysis method
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Brenn, T; Arnesen, E
1985-01-01
For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.
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Heuristics to Facilitate Understanding of Discriminant Analysis.
ERIC Educational Resources Information Center
Van Epps, Pamela D.
This paper discusses the principles underlying discriminant analysis and constructs a simulated data set to illustrate its methods. Discriminant analysis is a multivariate technique for identifying the best combination of variables to maximally discriminate between groups. Discriminant functions are established on existing groups and used to…
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[Discrimination of Rice Syrup Adulterant of Acacia Honey Based Using Near-Infrared Spectroscopy].
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.
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USDA-ARS?s Scientific Manuscript database
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...
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Khansari, Maziyar M; O’Neill, William; Penn, Richard; Chau, Felix; Blair, Norman P; Shahidi, Mahnaz
2016-01-01
The conjunctiva is a densely vascularized mucus membrane covering the sclera of the eye with a unique advantage of accessibility for direct visualization and non-invasive imaging. The purpose of this study is to apply an automated quantitative method for discrimination of different stages of diabetic retinopathy (DR) using conjunctival microvasculature images. Fine structural analysis of conjunctival microvasculature images was performed by ordinary least square regression and Fisher linear discriminant analysis. Conjunctival images between groups of non-diabetic and diabetic subjects at different stages of DR were discriminated. The automated method’s discriminate rates were higher than those determined by human observers. The method allowed sensitive and rapid discrimination by assessment of conjunctival microvasculature images and can be potentially useful for DR screening and monitoring. PMID:27446692
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Zhang, Mengliang; Zhao, Yang; Harrington, Peter de B; Chen, Pei
2016-03-01
Two simple fingerprinting methods, flow-injection coupled to ultraviolet spectroscopy and proton nuclear magnetic resonance, were used for discriminating between Aurantii fructus immaturus and Fructus poniciri trifoliatae immaturus . Both methods were combined with partial least-squares discriminant analysis. In the flow-injection method, four data representations were evaluated: total ultraviolet absorbance chromatograms, averaged ultraviolet spectra, absorbance at 193, 205, 225, and 283 nm, and absorbance at 225 and 283 nm. Prediction rates of 100% were achieved for all data representations by partial least-squares discriminant analysis using leave-one-sample-out cross-validation. The prediction rate for the proton nuclear magnetic resonance data by partial least-squares discriminant analysis with leave-one-sample-out cross-validation was also 100%. A new validation set of data was collected by flow-injection with ultraviolet spectroscopic detection two weeks later and predicted by partial least-squares discriminant analysis models constructed by the initial data representations with no parameter changes. The classification rates were 95% with the total ultraviolet absorbance chromatograms datasets and 100% with the other three datasets. Flow-injection with ultraviolet detection and proton nuclear magnetic resonance are simple, high throughput, and low-cost methods for discrimination studies.
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Zheng, Wenming; Lin, Zhouchen; Wang, Haixian
2014-04-01
A novel discriminant analysis criterion is derived in this paper under the theoretical framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion, the major novelty of the proposed one is the use of L1 norm rather than L2 norm, which makes it less sensitive to the outliers. With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem. To solve the L1-LDA optimization problem, we propose an efficient iterative algorithm, in which a novel surrogate convex function is introduced such that the optimization problem in each iteration is to simply solve a convex programming problem and a close-form solution is guaranteed to this problem. Moreover, we also generalize the L1-LDA method to deal with the nonlinear robust feature extraction problems via the use of kernel trick, and hereafter proposed the L1-norm kernel discriminant analysis (L1-KDA) method. Extensive experiments on simulated and real data sets are conducted to evaluate the effectiveness of the proposed method in comparing with the state-of-the-art methods.
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NASA Astrophysics Data System (ADS)
Song, Biao; Lu, Dan; Peng, Ming; Li, Xia; Zou, Ye; Huang, Meizhen; Lu, Feng
2017-02-01
Raman spectroscopy is developed as a fast and non-destructive method for the discrimination and classification of hydroxypropyl methyl cellulose (HPMC) samples. 44 E series and 41 K series of HPMC samples are measured by a self-developed portable Raman spectrometer (Hx-Raman) which is excited by a 785 nm diode laser and the spectrum range is 200-2700 cm-1 with a resolution (FWHM) of 6 cm-1. Multivariate analysis is applied for discrimination of E series from K series. By methods of principal components analysis (PCA) and Fisher discriminant analysis (FDA), a discrimination result with sensitivity of 90.91% and specificity of 95.12% is achieved. The corresponding receiver operating characteristic (ROC) is 0.99, indicting the accuracy of the predictive model. This result demonstrates the prospect of portable Raman spectrometer for rapid, non-destructive classification and discrimination of E series and K series samples of HPMC.
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Monakhova, Yulia B; Godelmann, Rolf; Kuballa, Thomas; Mushtakova, Svetlana P; Rutledge, Douglas N
2015-08-15
Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements. Copyright © 2015 Elsevier B.V. All rights reserved.
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MATRIX DISCRIMINANT ANALYSIS WITH APPLICATION TO COLORIMETRIC SENSOR ARRAY DATA
Suslick, Kenneth S.
2014-01-01
With the rapid development of nano-technology, a “colorimetric sensor array” (CSA) which is referred to as an optical electronic nose has been developed for the identification of toxicants. Unlike traditional sensors which rely on a single chemical interaction, CSA can measure multiple chemical interactions by using chemo-responsive dyes. The color changes of the chemo-responsive dyes are recorded before and after exposure to toxicants and serve as a template for classification. The color changes are digitalized in the form of a matrix with rows representing dye effects and columns representing the spectrum of colors. Thus, matrix-classification methods are highly desirable. In this article, we develop a novel classification method, matrix discriminant analysis (MDA), which is a generalization of linear discriminant analysis (LDA) for the data in matrix form. By incorporating the intrinsic matrix-structure of the data in discriminant analysis, the proposed method can improve CSA’s sensitivity and more importantly, specificity. A penalized MDA method, PMDA, is also introduced to further incorporate sparsity structure in discriminant function. Numerical studies suggest that the proposed MDA and PMDA methods outperform LDA and other competing discriminant methods for matrix predictors. The asymptotic consistency of MDA is also established. R code and data are available online as supplementary material. PMID:26783371
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Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Oh, Kyoungmin; Yoo, Hyeonchae; Ham, Hyeonheui; Kim, Moon S.
2017-01-01
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. PMID:28974012
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Sabir, Aryani; Rafi, Mohamad; Darusman, Latifah K
2017-04-15
HPLC fingerprint analysis combined with chemometrics was developed to discriminate between the red and the white rice bran grown in Indonesia. The major component in rice bran is γ-oryzanol which consisted of 4 main compounds, namely cycloartenol ferulate, cyclobranol ferulate, campesterol ferulate and β-sitosterol ferulate. Separation of these four compounds along with other compounds was performed using C18 and methanol-acetonitrile with gradient elution system. By using these intensity variations, principal component and discriminant analysis were performed to discriminate the two samples. Discriminant analysis was successfully discriminated the red from the white rice bran with predictive ability of the model showed a satisfactory classification for the test samples. The results of this study indicated that the developed method was suitable as quality control method for rice bran in terms of identification and discrimination of the red and the white rice bran. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Sub-pattern based multi-manifold discriminant analysis for face recognition
NASA Astrophysics Data System (ADS)
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.
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Graphical methods for the sensitivity analysis in discriminant analysis
Kim, Youngil; Anderson-Cook, Christine M.; Dae-Heung, Jang
2015-09-30
Similar to regression, many measures to detect influential data points in discriminant analysis have been developed. Many follow similar principles as the diagnostic measures used in linear regression in the context of discriminant analysis. Here we focus on the impact on the predicted classification posterior probability when a data point is omitted. The new method is intuitive and easily interpretative compared to existing methods. We also propose a graphical display to show the individual movement of the posterior probability of other data points when a specific data point is omitted. This enables the summaries to capture the overall pattern ofmore » the change.« less
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Principal Component Analysis for pulse-shape discrimination of scintillation radiation detectors
NASA Astrophysics Data System (ADS)
Alharbi, T.
2016-01-01
In this paper, we report on the application of Principal Component analysis (PCA) for pulse-shape discrimination (PSD) of scintillation radiation detectors. The details of the method are described and the performance of the method is experimentally examined by discriminating between neutrons and gamma-rays with a liquid scintillation detector in a mixed radiation field. The performance of the method is also compared against that of the conventional charge-comparison method, demonstrating the superior performance of the method particularly at low light output range. PCA analysis has the important advantage of automatic extraction of the pulse-shape characteristics which makes the PSD method directly applicable to various scintillation detectors without the need for the adjustment of a PSD parameter.
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NASA Astrophysics Data System (ADS)
Trejos, Tatiana; Corzo, Ruthmara; Subedi, Kiran; Almirall, José
2014-02-01
Detection and sourcing of counterfeit currency, examination of counterfeit security documents and determination of authenticity of medical records are examples of common forensic document investigations. In these cases, the physical and chemical composition of the ink entries can provide important information for the assessment of the authenticity of the document or for making inferences about common source. Previous results reported by our group have demonstrated that elemental analysis, using either Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) or Laser Ablation Induced Breakdown Spectroscopy (LIBS), provides an effective, practical and robust technique for the discrimination of document substrates and writing inks with minimal damage to the document. In this study, laser-based methods and Scanning Electron Microscopy-Energy Dispersive X-Ray Spectroscopy (SEM-EDS) methods were developed, optimized and validated for the forensic analysis of more complex inks such as toners and inkjets, to determine if their elemental composition can differentiate documents printed from different sources and to associate documents that originated from the same printing source. Comparison of the performance of each of these methods is presented, including the analytical figures of merit, discrimination capability and error rates. Different calibration strategies resulting in semi-quantitative and qualitative analysis, comparison methods (match criteria) and data analysis and interpretation tools were also developed. A total of 27 black laser toners originating from different manufacturing sources and/or batches were examined to evaluate the discrimination capability of each method. The results suggest that SEM-EDS offers relatively poor discrimination capability for this set (~ 70.7% discrimination of all the possible comparison pairs or a 29.3% type II error rate). Nonetheless, SEM-EDS can still be used as a complementary method of analysis since it has the advantage of being non-destructive to the sample in addition to providing imaging capabilities to further characterize toner samples by their particle morphology. Laser sampling methods resulted in an improvement of the discrimination between different sources with LIBS producing 89% discrimination and LA-ICP-MS resulting in 100% discrimination. In addition, a set of 21 black inkjet samples was examined by each method. The results show that SEM-EDS is not appropriate for inkjet examinations since their elemental composition is typically below the detection capabilities with only sulfur detected in this set, providing only 47.4% discrimination between possible comparison pairs. Laser sampling methods were shown to provide discrimination greater than 94% for this same inkjet set with false exclusion and false inclusion rates lower than 4.1% and 5.7%, for LA-ICP-MS and LIBS respectively. Overall these results confirmed the utility of the examination of printed documents by laser-based micro-spectrochemical methods. SEM-EDS analysis of toners produced a limited utility for discrimination within sources but was not an effective tool for inkjet ink discrimination. Both LA-ICP-MS and LIBS can be used in forensic laboratories to chemically characterize inks on documents and to complement the information obtained by conventional methods and enhance their evidential value.
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NASA Astrophysics Data System (ADS)
Khansari, Maziyar M.; O'Neill, William; Penn, Richard; Blair, Norman P.; Chau, Felix; Shahidi, Mahnaz
2017-03-01
The conjunctiva is a densely vascularized tissue of the eye that provides an opportunity for imaging of human microcirculation. In the current study, automated fine structure analysis of conjunctival microvasculature images was performed to discriminate stages of diabetic retinopathy (DR). The study population consisted of one group of nondiabetic control subjects (NC) and 3 groups of diabetic subjects, with no clinical DR (NDR), non-proliferative DR (NPDR), or proliferative DR (PDR). Ordinary least square regression and Fisher linear discriminant analyses were performed to automatically discriminate images between group pairs of subjects. Human observers who were masked to the grouping of subjects performed image discrimination between group pairs. Over 80% and 70% of images of subjects with clinical and non-clinical DR were correctly discriminated by the automated method, respectively. The discrimination rates of the automated method were higher than human observers. The fine structure analysis of conjunctival microvasculature images provided discrimination of DR stages and can be potentially useful for DR screening and monitoring.
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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.
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NASA Astrophysics Data System (ADS)
Rohaeti, Eti; Rafi, Mohamad; Syafitri, Utami Dyah; Heryanto, Rudi
2015-02-01
Turmeric (Curcuma longa), java turmeric (Curcuma xanthorrhiza) and cassumunar ginger (Zingiber cassumunar) are widely used in traditional Indonesian medicines (jamu). They have similar color for their rhizome and possess some similar uses, so it is possible to substitute one for the other. The identification and discrimination of these closely-related plants is a crucial task to ensure the quality of the raw materials. Therefore, an analytical method which is rapid, simple and accurate for discriminating these species using Fourier transform infrared spectroscopy (FTIR) combined with some chemometrics methods was developed. FTIR spectra were acquired in the mid-IR region (4000-400 cm-1). Standard normal variate, first and second order derivative spectra were compared for the spectral data. Principal component analysis (PCA) and canonical variate analysis (CVA) were used for the classification of the three species. Samples could be discriminated by visual analysis of the FTIR spectra by using their marker bands. Discrimination of the three species was also possible through the combination of the pre-processed FTIR spectra with PCA and CVA, in which CVA gave clearer discrimination. Subsequently, the developed method could be used for the identification and discrimination of the three closely-related plant species.
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NASA Astrophysics Data System (ADS)
Ramos, M. Rosário; Carolino, E.; Viegas, Carla; Viegas, Sandra
2016-06-01
Health effects associated with occupational exposure to particulate matter have been studied by several authors. In this study were selected six industries of five different areas: Cork company 1, Cork company 2, poultry, slaughterhouse for cattle, riding arena and production of animal feed. The measurements tool was a portable device for direct reading. This tool provides information on the particle number concentration for six different diameters, namely 0.3 µm, 0.5 µm, 1 µm, 2.5 µm, 5 µm and 10 µm. The focus on these features is because they might be more closely related with adverse health effects. The aim is to identify the particles that better discriminate the industries, with the ultimate goal of classifying industries regarding potential negative effects on workers' health. Several methods of discriminant analysis were applied to data of occupational exposure to particulate matter and compared with respect to classification accuracy. The selected methods were linear discriminant analyses (LDA); linear quadratic discriminant analysis (QDA), robust linear discriminant analysis with selected estimators (MLE (Maximum Likelihood Estimators), MVE (Minimum Volume Elipsoid), "t", MCD (Minimum Covariance Determinant), MCD-A, MCD-B), multinomial logistic regression and artificial neural networks (ANN). The predictive accuracy of the methods was accessed through a simulation study. ANN yielded the highest rate of classification accuracy in the data set under study. Results indicate that the particle number concentration of diameter size 0.5 µm is the parameter that better discriminates industries.
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A Statistical Discrimination Experiment for Eurasian Events Using a Twenty-Seven-Station Network
1980-07-08
to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...the weight assigned to each variable whenever a new one is added. Jennrich, R. I. (1977). Stepwise discriminant analysis , in Statistical Methods for
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NASA Astrophysics Data System (ADS)
Kuntamalla, Srinivas; Lekkala, Ram Gopal Reddy
2014-10-01
Heart rate variability (HRV) is an important dynamic variable of the cardiovascular system, which operates on multiple time scales. In this study, Multiscale entropy (MSE) analysis is applied to HRV signals taken from Physiobank to discriminate Congestive Heart Failure (CHF) patients from healthy young and elderly subjects. The discrimination power of the MSE method is decreased as the amount of the data reduces and the lowest amount of the data at which there is a clear discrimination between CHF and normal subjects is found to be 4000 samples. Further, this method failed to discriminate CHF from healthy elderly subjects. In view of this, the Reduced Data Dualscale Entropy Analysis method is proposed to reduce the data size required (as low as 500 samples) for clearly discriminating the CHF patients from young and elderly subjects with only two scales. Further, an easy to interpret index is derived using this new approach for the diagnosis of CHF. This index shows 100 % accuracy and correlates well with the pathophysiology of heart failure.
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Comparison of cranial sex determination by discriminant analysis and logistic regression.
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).
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NASA Astrophysics Data System (ADS)
Zhang, Linna; Li, Gang; Sun, Meixiu; Li, Hongxiao; Wang, Zhennan; Li, Yingxin; Lin, Ling
2017-11-01
Identifying whole bloods to be either human or nonhuman is an important responsibility for import-export ports and inspection and quarantine departments. Analytical methods and DNA testing methods are usually destructive. Previous studies demonstrated that visible diffuse reflectance spectroscopy method can realize noncontact human and nonhuman blood discrimination. An appropriate method for calibration set selection was very important for a robust quantitative model. In this paper, Random Selection (RS) method and Kennard-Stone (KS) method was applied in selecting samples for calibration set. Moreover, proper stoichiometry method can be greatly beneficial for improving the performance of classification model or quantification model. Partial Least Square Discrimination Analysis (PLSDA) method was commonly used in identification of blood species with spectroscopy methods. Least Square Support Vector Machine (LSSVM) was proved to be perfect for discrimination analysis. In this research, PLSDA method and LSSVM method was used for human blood discrimination. Compared with the results of PLSDA method, this method could enhance the performance of identified models. The overall results convinced that LSSVM method was more feasible for identifying human and animal blood species, and sufficiently demonstrated LSSVM method was a reliable and robust method for human blood identification, and can be more effective and accurate.
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Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics
NASA Astrophysics Data System (ADS)
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.
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NASA Astrophysics Data System (ADS)
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.
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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.
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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 a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition-decision relationships not otherwise revealed in metabolomics data. Copyright © 2014 Elsevier Inc. All rights reserved.
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[Discrimination of varieties of brake fluid using visual-near infrared spectra].
Jiang, Lu-lu; Tan, Li-hong; Qiu, Zheng-jun; Lu, Jiang-feng; He, Yong
2008-06-01
A new method was developed to fast discriminate brands of brake fluid by means of visual-near infrared spectroscopy. Five different brands of brake fluid were analyzed using a handheld near infrared spectrograph, manufactured by ASD Company, and 60 samples were gotten from each brand of brake fluid. The samples data were pretreated using average smoothing and standard normal variable method, and then analyzed using principal component analysis (PCA). A 2-dimensional plot was drawn based on the first and the second principal components, and the plot indicated that the clustering characteristic of different brake fluid is distinct. The foregoing 6 principal components were taken as input variable, and the band of brake fluid as output variable to build the discriminate model by stepwise discriminant analysis method. Two hundred twenty five samples selected randomly were used to create the model, and the rest 75 samples to verify the model. The result showed that the distinguishing rate was 94.67%, indicating that the method proposed in this paper has good performance in classification and discrimination. It provides a new way to fast discriminate different brands of brake fluid.
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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
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Orthogonal sparse linear discriminant analysis
NASA Astrophysics Data System (ADS)
Liu, Zhonghua; Liu, Gang; Pu, Jiexin; Wang, Xiaohong; Wang, Haijun
2018-03-01
Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.
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Rohaeti, Eti; Rafi, Mohamad; Syafitri, Utami Dyah; Heryanto, Rudi
2015-02-25
Turmeric (Curcuma longa), java turmeric (Curcuma xanthorrhiza) and cassumunar ginger (Zingiber cassumunar) are widely used in traditional Indonesian medicines (jamu). They have similar color for their rhizome and possess some similar uses, so it is possible to substitute one for the other. The identification and discrimination of these closely-related plants is a crucial task to ensure the quality of the raw materials. Therefore, an analytical method which is rapid, simple and accurate for discriminating these species using Fourier transform infrared spectroscopy (FTIR) combined with some chemometrics methods was developed. FTIR spectra were acquired in the mid-IR region (4000-400 cm(-1)). Standard normal variate, first and second order derivative spectra were compared for the spectral data. Principal component analysis (PCA) and canonical variate analysis (CVA) were used for the classification of the three species. Samples could be discriminated by visual analysis of the FTIR spectra by using their marker bands. Discrimination of the three species was also possible through the combination of the pre-processed FTIR spectra with PCA and CVA, in which CVA gave clearer discrimination. Subsequently, the developed method could be used for the identification and discrimination of the three closely-related plant species. Copyright © 2014 Elsevier B.V. All rights reserved.
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2012-02-09
different sources [12,13], but the analytical techniques needed for such analysis (XRD, INAA , & ICP-MS) are time consuming and require expensive...partial least-squares discriminant analysis (PLSDA) that used the SIMPLS solving method [33]. In the experi- ment design, a leave-one-sample-out (LOSO) para...REPORT Advanced signal processing analysis of laser-induced breakdown spectroscopy data for the discrimination of obsidian sources 14. ABSTRACT 16
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Varietal discrimination of hop pellets by near and mid infrared spectroscopy.
Machado, Julio C; Faria, Miguel A; Ferreira, Isabel M P L V O; Páscoa, Ricardo N M J; Lopes, João A
2018-04-01
Hop is one of the most important ingredients of beer production and several varieties are commercialized. Therefore, it is important to find an eco-real-time-friendly-low-cost technique to distinguish and discriminate hop varieties. This paper describes the development of a method based on vibrational spectroscopy techniques, namely near- and mid-infrared spectroscopy, for the discrimination of 33 commercial hop varieties. A total of 165 samples (five for each hop variety) were analysed by both techniques. Principal component analysis, hierarchical cluster analysis and partial least squares discrimination analysis were the chemometric tools used to discriminate positively the hop varieties. After optimizing the spectral regions and pre-processing methods a total of 94.2% and 96.6% correct hop varieties discrimination were obtained for near- and mid-infrared spectroscopy, respectively. The results obtained demonstrate the suitability of these vibrational spectroscopy techniques to discriminate different hop varieties and consequently their potential to be used as an authenticity tool. Compared with the reference procedures normally used for hops variety discrimination these techniques are quicker, cost-effective, non-destructive and eco-friendly. Copyright © 2017 Elsevier B.V. All rights reserved.
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Tian, Huaixiang; Li, Fenghua; Qin, Lan; Yu, Haiyan; Ma, Xia
2014-11-01
This study examines the feasibility of electronic nose as a method to discriminate chicken and beef seasonings and to predict sensory attributes. Sensory evaluation showed that 8 chicken seasonings and 4 beef seasonings could be well discriminated and classified based on 8 sensory attributes. The sensory attributes including chicken/beef, gamey, garlic, spicy, onion, soy sauce, retention, and overall aroma intensity were generated by a trained evaluation panel. Principal component analysis (PCA), discriminant factor analysis (DFA), and cluster analysis (CA) combined with electronic nose were used to discriminate seasoning samples based on the difference of the sensor response signals of chicken and beef seasonings. The correlation between sensory attributes and electronic nose sensors signal was established using partial least squares regression (PLSR) method. The results showed that the seasoning samples were all correctly classified by the electronic nose combined with PCA, DFA, and CA. The electronic nose gave good prediction results for all the sensory attributes with correlation coefficient (r) higher than 0.8. The work indicated that electronic nose is an effective method for discriminating different seasonings and predicting sensory attributes. © 2014 Institute of Food Technologists®
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NASA Astrophysics Data System (ADS)
Li, Cuiling; Jiang, Kai; Zhao, Xueguan; Fan, Pengfei; Wang, Xiu; Liu, Chuan
2017-10-01
Impurity of melon seeds variety will cause reductions of melon production and economic benefits of farmers, this research aimed to adopt spectral technology combined with chemometrics methods to identify melon seeds variety. Melon seeds whose varieties were "Yi Te Bai", "Yi Te Jin", "Jing Mi NO.7", "Jing Mi NO.11" and " Yi Li Sha Bai "were used as research samples. A simple spectral system was developed to collect reflective spectral data of melon seeds, including a light source unit, a spectral data acquisition unit and a data processing unit, the detection wavelength range of this system was 200-1100nm with spectral resolution of 0.14 7.7nm. The original reflective spectral data was pre-treated with de-trend (DT), multiple scattering correction (MSC), first derivative (FD), normalization (NOR) and Savitzky-Golay (SG) convolution smoothing methods. Principal Component Analysis (PCA) method was adopted to reduce the dimensions of reflective spectral data and extract principal components. K-nearest neighbour (KNN) and Fisher discriminant analysis (FDA) methods were used to develop discriminant models of melon seeds variety based on PCA. Spectral data pretreatments improved the discriminant effects of KNN and FDA, FDA generated better discriminant results than KNN, both KNN and FDA methods produced discriminant accuracies reaching to 90.0% for validation set. Research results showed that using spectral technology in combination with KNN and FDA modelling methods to identify melon seeds variety was feasible.
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USDA-ARS?s Scientific Manuscript database
Mixing models have been used to predict sediment source contributions. The inherent problem of the mixing models limited the number of sediment sources. The objective of this study is to develop and evaluate a new method using Discriminant Function Analysis (DFA) to fingerprint sediment source contr...
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Study on bayes discriminant analysis of EEG data.
Shi, Yuan; He, DanDan; Qin, Fang
2014-01-01
In this paper, we have done Bayes Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. In accordance with the strength of α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Bayes Discriminant analysis to EEG data of six objects. Results In use of part of EEG data of 63 people, we have done Bayes Discriminant analysis, the electrode classification accuracy rates is 64.4%. Bayes Discriminant has higher prediction accuracy, EEG features (mainly αwave) extract more accurate. Bayes Discriminant would be better applied to the feature extraction and classification decisions of EEG data.
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Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms
NASA Astrophysics Data System (ADS)
Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan
2010-12-01
This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.
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Yang, Heejung; Lee, Dong Young; Jeon, Minji; Suh, Youngbae; Sung, Sang Hyun
2014-05-01
Five active compounds, chlorogenic acid, 3,5-di-O-caffeoylquinic acid, 4,5-di-O-caffeoylquinic acid, jaceosidin, and eupatilin, in Artemisia princeps (Compositae) were simultaneously determined by ultra-performance liquid chromatography connected to diode array detector. The morphological resemblance between A. princeps and A. capillaris makes it difficult to properly identify species properly. It occasionally leads to misuse or misapplication in Korean traditional medicine. In the study, the discrimination between A. princeps and A. capillaris was optimally performed by the developed validation method, which resulted in definitely a difference between two species. Also, it was developed the most reliable markers contributing to the discrimination of two species by the multivariate analysis methods, such as a principal component analysis and a partial least squares discrimination analysis.
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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.
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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.
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Credit scoring analysis using kernel discriminant
NASA Astrophysics Data System (ADS)
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.
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NASA Astrophysics Data System (ADS)
Wei, ZHANG; Tongyu, WU; Bowen, ZHENG; Shiping, LI; Yipo, ZHANG; Zejie, YIN
2018-04-01
A new neutron-gamma discriminator based on the support vector machine (SVM) method is proposed to improve the performance of the time-of-flight neutron spectrometer. The neutron detector is an EJ-299-33 plastic scintillator with pulse-shape discrimination (PSD) property. The SVM algorithm is implemented in field programmable gate array (FPGA) to carry out the real-time sifting of neutrons in neutron-gamma mixed radiation fields. This study compares the ability of the pulse gradient analysis method and the SVM method. The results show that this SVM discriminator can provide a better discrimination accuracy of 99.1%. The accuracy and performance of the SVM discriminator based on FPGA have been evaluated in the experiments. It can get a figure of merit of 1.30.
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Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS
NASA Astrophysics Data System (ADS)
Lu, Q.; Fan, Y.; Peng, Z.; Ding, H.; Gao, H.
2012-07-01
A new approach for the nondestructive discrimination between genuine wild ginsengs and the counterfeit ones by near infrared spectroscopy (NIRS) was developed. Both discriminant analysis and back propagation artificial neural network (BP-ANN) were applied to the model establishment for discrimination. Optimal modeling wavelengths were determined based on the anomalous spectral information of counterfeit samples. Through principal component analysis (PCA) of various wild ginseng samples, genuine and counterfeit, the cumulative percentages of variance of the principal components were obtained, serving as a reference for principal component (PC) factor determination. Discriminant analysis achieved an identification ratio of 88.46%. With sample' truth values as its outputs, a three-layer BP-ANN model was built, which yielded a higher discrimination accuracy of 100%. The overall results sufficiently demonstrate that NIRS combined with BP-ANN classification algorithm performs better on ginseng discrimination than discriminant analysis, and can be used as a rapid and nondestructive method for the detection of counterfeit wild ginsengs in food and pharmaceutical industry.
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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.
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Apel, William A.; Thompson, Vicki S; Lacey, Jeffrey A.; Gentillon, Cynthia A.
2016-08-09
A method for determining a plurality of proteins for discriminating and positively identifying an individual based from a biological sample. The method may include profiling a biological sample from a plurality of individuals against a protein array including a plurality of proteins. The protein array may include proteins attached to a support in a preselected pattern such that locations of the proteins are known. The biological sample may be contacted with the protein array such that a portion of antibodies in the biological sample reacts with and binds to the proteins forming immune complexes. A statistical analysis method, such as discriminant analysis, may be performed to determine discriminating proteins for distinguishing individuals. Proteins of interest may be used to form a protein array. Such a protein array may be used, for example, to compare a forensic sample from an unknown source with a sample from a known source.
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Thompson, Vicki S; Lacey, Jeffrey A; Gentillon, Cynthia A; Apel, William A
2015-03-03
A method for determining a plurality of proteins for discriminating and positively identifying an individual based from a biological sample. The method may include profiling a biological sample from a plurality of individuals against a protein array including a plurality of proteins. The protein array may include proteins attached to a support in a preselected pattern such that locations of the proteins are known. The biological sample may be contacted with the protein array such that a portion of antibodies in the biological sample reacts with and binds to the proteins forming immune complexes. A statistical analysis method, such as discriminant analysis, may be performed to determine discriminating proteins for distinguishing individuals. Proteins of interest may be used to form a protein array. Such a protein array may be used, for example, to compare a forensic sample from an unknown source with a sample from a known source.
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NASA Astrophysics Data System (ADS)
Chen, Xue; Li, Xiaohui; Yu, Xin; Chen, Deying; Liu, Aichun
2018-01-01
Diagnosis of malignancies is a challenging clinical issue. In this work, we present quick and robust diagnosis and discrimination of lymphoma and multiple myeloma (MM) using laser-induced breakdown spectroscopy (LIBS) conducted on human serum samples, in combination with chemometric methods. The serum samples collected from lymphoma and MM cancer patients and healthy controls were deposited on filter papers and ablated with a pulsed 1064 nm Nd:YAG laser. 24 atomic lines of Ca, Na, K, H, O, and N were selected for malignancy diagnosis. Principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k nearest neighbors (kNN) classification were applied to build the malignancy diagnosis and discrimination models. The performances of the models were evaluated using 10-fold cross validation. The discrimination accuracy, confusion matrix and receiver operating characteristic (ROC) curves were obtained. The values of area under the ROC curve (AUC), sensitivity and specificity at the cut-points were determined. The kNN model exhibits the best performances with overall discrimination accuracy of 96.0%. Distinct discrimination between malignancies and healthy controls has been achieved with AUC, sensitivity and specificity for healthy controls all approaching 1. For lymphoma, the best discrimination performance values are AUC = 0.990, sensitivity = 0.970 and specificity = 0.956. For MM, the corresponding values are AUC = 0.986, sensitivity = 0.892 and specificity = 0.994. The results show that the serum-LIBS technique can serve as a quick, less invasive and robust method for diagnosis and discrimination of human malignancies.
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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.
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Determination of Ignitable Liquids in Fire Debris: Direct Analysis by Electronic Nose
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
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NASA Astrophysics Data System (ADS)
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.
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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.
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NASA Astrophysics Data System (ADS)
Zabolotna, Natalia I.; Radchenko, Kostiantyn O.; Karas, Oleksandr V.
2018-01-01
A fibroadenoma diagnosing of breast using statistical analysis (determination and analysis of statistical moments of the 1st-4th order) of the obtained polarization images of Jones matrix imaginary elements of the optically thin (attenuation coefficient τ <= 0,1 ) blood plasma films with further intellectual differentiation based on the method of "fuzzy" logic and discriminant analysis were proposed. The accuracy of the intellectual differentiation of blood plasma samples to the "norm" and "fibroadenoma" of breast was 82.7% by the method of linear discriminant analysis, and by the "fuzzy" logic method is 95.3%. The obtained results allow to confirm the potentially high level of reliability of the method of differentiation by "fuzzy" analysis.
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NASA Astrophysics Data System (ADS)
Subedi, Kiran; Trejos, Tatiana; Almirall, José
2015-01-01
Elemental analysis, using either LA-ICP-MS or LIBS, can be used for the chemical characterization of materials of forensic interest to discriminate between source materials originating from different sources and also for the association of materials known to originate from the same source. In this study, a tandem LIBS/LA-ICP-MS system that combines the benefits of both LIBS and LA-ICP-MS was evaluated for the characterization of samples of printing inks (toners, inkjets, intaglio and offset.). The performance of both laser sampling methods is presented. A subset of 9 black laser toners, 10 colored (CMYK) inkjet samples, 12 colored (CMYK) offset samples and 12 intaglio inks originating from different manufacturing sources were analyzed to evaluate the discrimination capability of the tandem method. These samples were selected because they presented a very similar elemental profile by LA-ICP-MS. Although typical discrimination between different ink sources is found to be > 99% for a variety of inks when only LA-ICP-MS was used for the analysis, additional discrimination was achieved by combining the elemental results from the LIBS analysis to the LA-ICP-MS analysis in the tandem technique, enhancing the overall discrimination capability of the individual laser ablation methods. The LIBS measurements of the Ca, Fe, K and Si signals, in particular, improved the discrimination for this specific set of different ink samples previously shown to exhibit very similar LA-ICP-MS elemental profiles. The combination of these two techniques in a single setup resulted in better discrimination of the printing inks with two distinct fingerprint spectra, providing information from atomic/ionic emissions and isotopic composition (m/z) for each ink sample.
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Discriminant analysis of resting-state functional connectivity patterns on the Grassmann manifold
NASA Astrophysics Data System (ADS)
Fan, Yong; Liu, Yong; Jiang, Tianzi; Liu, Zhening; Hao, Yihui; Liu, Haihong
2010-03-01
The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional networks that are informative for schizophrenia diagnosis.
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Shawky, Eman; Abou El Kheir, Rasha M
2018-02-11
Species of Apiaceae are used in folk medicine as spices and in officinal medicinal preparations of drugs. They are an excellent source of phenolics exhibiting antioxidant activity, which are of great benefit to human health. Discrimination among Apiaceae medicinal herbs remains an intricate challenge due to their morphological similarity. In this study, a combined "untargeted" and "targeted" approach to investigate different Apiaceae plants species was proposed by using the merging of high-performance thin layer chromatography (HPTLC)-image analysis and pattern recognition methods which were used for fingerprinting and classification of 42 different Apiaceae samples collected from Egypt. Software for image processing was applied for fingerprinting and data acquisition. HPTLC fingerprint assisted by principal component analysis (PCA) and hierarchical cluster analysis (HCA)-heat maps resulted in a reliable untargeted approach for discrimination and classification of different samples. The "targeted" approach was performed by developing and validating an HPTLC method allowing the quantification of eight flavonoids. The combination of quantitative data with PCA and HCA-heat-maps allowed the different samples to be discriminated from each other. The use of chemometrics tools for evaluation of fingerprints reduced expense and analysis time. The proposed method can be adopted for routine discrimination and evaluation of the phytochemical variability in different Apiaceae species extracts. Copyright © 2018 John Wiley & Sons, Ltd.
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Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability.
Krafty, Robert T
2016-07-01
Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same group. Although a number of frequency domain methods for the discriminant analysis of time series have been explored, there is a dearth of models and methods that account for within-group spectral variability. This article proposes a model for groups of time series in which transfer functions are modeled as stochastic variables that can account for both between-group and within-group differences in spectra that are identified from individual replicates. An ensuing discriminant analysis of stochastic cepstra under this model is developed to obtain parsimonious measures of relative power that optimally separate groups in the presence of within-group spectral variability. The approach possess favorable properties in classifying new observations and can be consistently estimated through a simple discriminant analysis of a finite number of estimated cepstral coefficients. Benefits in accounting for within-group spectral variability are empirically illustrated in a simulation study and through an analysis of gait variability.
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Chen, Xiaomei; Wang, Fangfei; Wang, Yunqiang; Li, Xuelan; Wang, Airong; Wang, Chunlan; Guo, Shunxing
2012-12-01
The aim of this study was to establish a method for discriminating Dendrobium officinale from four of its close relatives Dendrobium chrysanthum, Dendrobium crystallinum, Dendrobium aphyllum and Dendrobium devonianum based on chemical composition analysis. We analyzed 62 samples of 24 Dendrobium species. High performance liquid chromatography analysis confirmed that the four low molecular weight compounds 4',5,7-trihydroxyflavanone (naringenin), 3,4-dihydroxy-4',5-dime-thoxybibenzyl (DDB-2), 3',4-dihydroxy-3,5'-dimethoxybibenzyl (gigantol), and 4,4'-dihydroxy-3,3',5-trimethoxybibenzy (moscatilin), were common in the genus. The phenol-sulfuric acid method was used to quantify polysaccharides, and the monosaccharide composition of the polysaccharides was determined by gas chromatography. Stepwise discriminant analysis was used to differentiate among the five closely related species based on the chemical composition analysis. This proved to be a simple and accurate approach for discriminating among these species. The results also showed that the polysaccharide content, the amounts of the four low molecular weight compounds, and the mannose to glucose ratio, were important factors for species discriminant. Therefore, we propose that a chemical analysis based on quantification of naringenin, bibenzyl, and polysaccharides is effective for identifying D. officinale.
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Automated Discrimination Method of Muscular and Subcutaneous Fat Layers Based on Tissue Elasticity
NASA Astrophysics Data System (ADS)
Inoue, Masahiro; Fukuda, Osamu; Tsubai, Masayoshi; Muraki, Satoshi; Okumura, Hiroshi; Arai, Kohei
Balance between human body composition, e.g. bones, muscles, and fat, is a major and basic indicator of personal health. Body composition analysis using ultrasound has been developed rapidly. However, interpretation of echo signal is conducted manually, and accuracy and confidence in interpretation requires experience. This paper proposes an automated discrimination method of tissue boundaries for measuring the thickness of subcutaneous fat and muscular layers. A portable one-dimensional ultrasound device was used in this study. The proposed method discriminated tissue boundaries based on tissue elasticity. Validity of the proposed method was evaluated in twenty-one subjects (twelve women, nine men; aged 20-70 yr) at three anatomical sites. Experimental results show that the proposed method can achieve considerably high discrimination performance.
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Discrimination among Panax species using spectral fingerprinting
USDA-ARS?s Scientific Manuscript database
Spectral fingerprints of samples of three Panax species (P. quinquefolius L., P. ginseng, and P. notoginseng) were acquired using UV, NIR, and MS spectrometry. With principal components analysis (PCA), all three methods allowed visual discrimination between all three species. All three methods wer...
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NASA Astrophysics Data System (ADS)
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.
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Jantzi, Sarah C; Almirall, José R
2011-07-01
A method for the quantitative elemental analysis of surface soil samples using laser-induced breakdown spectroscopy (LIBS) was developed and applied to the analysis of bulk soil samples for discrimination between specimens. The use of a 266 nm laser for LIBS analysis is reported for the first time in forensic soil analysis. Optimization of the LIBS method is discussed, and the results compared favorably to a laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) method previously developed. Precision for both methods was <10% for most elements. LIBS limits of detection were <33 ppm and bias <40% for most elements. In a proof of principle study, the LIBS method successfully discriminated samples from two different sites in Dade County, FL. Analysis of variance, Tukey's post hoc test and Student's t test resulted in 100% discrimination with no type I or type II errors. Principal components analysis (PCA) resulted in clear groupings of the two sites. A correct classification rate of 99.4% was obtained with linear discriminant analysis using leave-one-out validation. Similar results were obtained when the same samples were analyzed by LA-ICP-MS, showing that LIBS can provide similar information to LA-ICP-MS. In a forensic sampling/spatial heterogeneity study, the variation between sites, between sub-plots, between samples and within samples was examined on three similar Dade sites. The closer the sampling locations, the closer the grouping on a PCA plot and the higher the misclassification rate. These results underscore the importance of careful sampling for geographic site characterization.
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Discrimination of genetically modified sugar beets based on terahertz spectroscopy
NASA Astrophysics Data System (ADS)
Chen, Tao; Li, Zhi; Yin, Xianhua; Hu, Fangrong; Hu, Cong
2016-01-01
The objective of this paper was to apply terahertz (THz) spectroscopy combined with chemometrics techniques for discrimination of genetically modified (GM) and non-GM sugar beets. In this paper, the THz spectra of 84 sugar beet samples (36 GM sugar beets and 48 non-GM ones) were obtained by using terahertz time-domain spectroscopy (THz-TDS) system in the frequency range from 0.2 to 1.2 THz. Three chemometrics methods, principal component analysis (PCA), discriminant analysis (DA) and discriminant partial least squares (DPLS), were employed to classify sugar beet samples into two groups: genetically modified organisms (GMOs) and non-GMOs. The DPLS method yielded the best classification result, and the percentages of successful classification for GM and non-GM sugar beets were both 100%. Results of the present study demonstrate the usefulness of THz spectroscopy together with chemometrics methods as a powerful tool to distinguish GM and non-GM sugar beets.
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A Wavelet Packet Transform Inspired Method of Neutron-Gamma Discrimination
NASA Astrophysics Data System (ADS)
Shippen, David I.; Joyce, Malcolm J.; Aspinall, Michael D.
2010-10-01
A Simplified Digital Charge Collection (SDCC) method of discrimination between neutron and gamma pulses in an organic scintillator is presented and compared to the Pulse Gradient Analysis (PGA) discrimination method. Data used in this research were gathered from events arising from the 7Li(p,n)7Be reaction detected by an EJ-301 organic liquid scintillator recorded with a fast digital oscilloscope. Time-of-Flight (TOF) data were also recorded and used as a second means of identification. The SDCC method is found to improve on the figure of merit (FOM) given by PGA method at the equivalent sampling rate.
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Cresswell, Scott L; Eklund, Robert C
2006-02-01
Athlete burnout research has been hampered by the lack of an adequate measurement tool. The Athlete Burnout Questionnaire (ABQ) and the Maslach Burnout Inventory General Survey (MBI-GS) are two recently developed self-report instruments designed to assess burnout. The convergent and discriminant validity of the ABQ and MBI-GS were assessed through multi-trait/multi-method analysis with a sporting population. Overall, the ABQ and the MBI-GS displayed acceptable convergent validity with matching subscales highly correlated, and satisfactory internal discriminant validity with lower correlations between non-matching subscales. Both scales also indicated an adequate discrimination between the concepts of burnout and depression. These findings add support to previous findings in non-sporting populations that depression and burnout are separate constructs. Based on the psychometric results, construct validity analysis and practical considerations, the results support the use of the ABQ to assess athlete burnout.
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Score-moment combined linear discrimination analysis (SMC-LDA) as an improved discrimination method.
Han, Jintae; Chung, Hoeil; Han, Sung-Hwan; Yoon, Moon-Young
2007-01-01
A new discrimination method called the score-moment combined linear discrimination analysis (SMC-LDA) has been developed and its performance has been evaluated using three practical spectroscopic datasets. The key concept of SMC-LDA was to use not only the score from principal component analysis (PCA), but also the moment of the spectrum, as inputs for LDA to improve discrimination. Along with conventional score, moment is used in spectroscopic fields as an effective alternative for spectral feature representation. Three different approaches were considered. Initially, the score generated from PCA was projected onto a two-dimensional feature space by maximizing Fisher's criterion function (conventional PCA-LDA). Next, the same procedure was performed using only moment. Finally, both score and moment were utilized simultaneously for LDA. To evaluate discrimination performances, three different spectroscopic datasets were employed: (1) infrared (IR) spectra of normal and malignant stomach tissue, (2) near-infrared (NIR) spectra of diesel and light gas oil (LGO) and (3) Raman spectra of Chinese and Korean ginseng. For each case, the best discrimination results were achieved when both score and moment were used for LDA (SMC-LDA). Since the spectral representation character of moment was different from that of score, inclusion of both score and moment for LDA provided more diversified and descriptive information.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Uchida, Y., E-mail: h1312101@mailg.nc-toyama.ac.jp; Takada, E.; Fujisaki, A.
Neutron and γ-ray (n-γ) discrimination with a digital signal processing system has been used to measure the neutron emission profile in magnetic confinement fusion devices. However, a sampling rate must be set low to extend the measurement time because the memory storage is limited. Time jitter decreases a discrimination quality due to a low sampling rate. As described in this paper, a new charge comparison method was developed. Furthermore, automatic n-γ discrimination method was examined using a probabilistic approach. Analysis results were investigated using the figure of merit. Results show that the discrimination quality was improved. Automatic discrimination was appliedmore » using the EM algorithm and k-means algorithm.« less
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Spectral Regression Discriminant Analysis for Hyperspectral Image Classification
NASA Astrophysics Data System (ADS)
Pan, Y.; Wu, J.; Huang, H.; Liu, J.
2012-08-01
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.
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Discriminant forest classification method and system
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.
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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.
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Uchida, Y.; Takada, E.; Fujisaki, A.; Isobe, M.; Shinohara, K.; Tomita, H.; Kawarabayashi, J.; Iguchi, T.
2014-01-01
Neutron and γ-ray (n-γ) discrimination with a digital signal processing system has been used to measure the neutron emission profile in magnetic confinement fusion devices. However, a sampling rate must be set low to extend the measurement time because the memory storage is limited. Time jitter decreases a discrimination quality due to a low sampling rate. As described in this paper, a new charge comparison method was developed. Furthermore, automatic n-γ discrimination method was examined using a probabilistic approach. Analysis results were investigated using the figure of merit. Results show that the discrimination quality was improved. Automatic discrimination was applied using the EM algorithm and k-means algorithm. PMID:25430297
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NASA Astrophysics Data System (ADS)
He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei
2015-02-01
A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.
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Real-time image annotation by manifold-based biased Fisher discriminant analysis
NASA Astrophysics Data System (ADS)
Ji, Rongrong; Yao, Hongxun; Wang, Jicheng; Sun, Xiaoshuai; Liu, Xianming
2008-01-01
Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval. However, two crucial issues are not well addressed in state-of-art annotation algorithms: 1. The Small Sample Size (3S) problem in keyword classifier/model learning; 2. Most of annotation algorithms can not extend to real-time online usage due to their low computational efficiencies. This paper presents a novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address these two issues by transductive semantic learning and keyword filtering. To address the 3S problem, Co-Training based Manifold learning is adopted for keyword model construction. To achieve real-time annotation, a Bias Fisher Discriminant Analysis (BFDA) based semantic feature reduction algorithm is presented for keyword confidence discrimination and semantic feature reduction. Different from all existing annotation methods, MBFDA views image annotation from a novel Eigen semantic feature (which corresponds to keywords) selection aspect. As demonstrated in experiments, our manifold-based biased Fisher discriminant analysis annotation algorithm outperforms classical and state-of-art annotation methods (1.K-NN Expansion; 2.One-to-All SVM; 3.PWC-SVM) in both computational time and annotation accuracy with a large margin.
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Spatial-temporal discriminant analysis for ERP-based brain-computer interface.
Zhang, Yu; Zhou, Guoxu; Zhao, Qibin; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej
2013-03-01
Linear discriminant analysis (LDA) has been widely adopted to classify event-related potential (ERP) in brain-computer interface (BCI). Good classification performance of the ERP-based BCI usually requires sufficient data recordings for effective training of the LDA classifier, and hence a long system calibration time which however may depress the system practicability and cause the users resistance to the BCI system. In this study, we introduce a spatial-temporal discriminant analysis (STDA) to ERP classification. As a multiway extension of the LDA, the STDA method tries to maximize the discriminant information between target and nontarget classes through finding two projection matrices from spatial and temporal dimensions collaboratively, which reduces effectively the feature dimensionality in the discriminant analysis, and hence decreases significantly the number of required training samples. The proposed STDA method was validated with dataset II of the BCI Competition III and dataset recorded from our own experiments, and compared to the state-of-the-art algorithms for ERP classification. Online experiments were additionally implemented for the validation. The superior classification performance in using few training samples shows that the STDA is effective to reduce the system calibration time and improve the classification accuracy, thereby enhancing the practicability of ERP-based BCI.
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Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion
NASA Astrophysics Data System (ADS)
Hamid, M. R. Ab; Sami, W.; Mohmad Sidek, M. H.
2017-09-01
Assessment of discriminant validity is a must in any research that involves latent variables for the prevention of multicollinearity issues. Fornell and Larcker criterion is the most widely used method for this purpose. However, a new method has emerged for establishing the discriminant validity assessment through heterotrait-monotrait (HTMT) ratio of correlations method. Therefore, this article presents the results of discriminant validity assessment using these methods. Data from previous study was used that involved 429 respondents for empirical validation of value-based excellence model in higher education institutions (HEI) in Malaysia. From the analysis, the convergent, divergent and discriminant validity were established and admissible using Fornell and Larcker criterion. However, the discriminant validity is an issue when employing the HTMT criterion. This shows that the latent variables under study faced the issue of multicollinearity and should be looked into for further details. This also implied that the HTMT criterion is a stringent measure that could detect the possible indiscriminant among the latent variables. In conclusion, the instrument which consisted of six latent variables was still lacking in terms of discriminant validity and should be explored further.
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[Discrimination of Red Tide algae by fluorescence spectra and principle component analysis].
Su, Rong-guo; Hu, Xu-peng; Zhang, Chuan-song; Wang, Xiu-lin
2007-07-01
Fluorescence discrimination technology for 11 species of the Red Tide algae at genus level was constructed by principle component analysis and non-negative least squares. Rayleigh and Raman scattering peaks of 3D fluorescence spectra were eliminated by Delaunay triangulation method. According to the results of Fisher linear discrimination, the first principle component score and the second component score of 3D fluorescence spectra were chosen as discriminant feature and the feature base was established. The 11 algae species were tested, and more than 85% samples were accurately determinated, especially for Prorocentrum donghaiense, Skeletonema costatum, Gymnodinium sp., which have frequently brought Red tide in the East China Sea. More than 95% samples were right discriminated. The results showed that the genus discriminant feature of 3D fluorescence spectra of Red Tide algae given by principle component analysis could work well.
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A multiple maximum scatter difference discriminant criterion for facial feature extraction.
Song, Fengxi; Zhang, David; Mei, Dayong; Guo, Zhongwei
2007-12-01
Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart--multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD out-performs state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.
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NASA Astrophysics Data System (ADS)
Zhao, Bo; Liu, Jinhu; Song, Junjie; Cao, Liang; Dou, Shuozeng
2017-11-01
Removal of the length effect in otolith shape analysis for stock identification using length scaling is an important issue; however, few studies have attempted to investigate the effectiveness or weakness of this methodology in application. The aim of this study was to evaluate whether commonly used size scaling methods and normalized elliptic Fourier descriptors (NEFDs) could effectively remove the size effect of fish in stock discrimination. To achieve this goal, length groups from two known geographical stocks of yellow croaker, Larimichthys polyactis, along the Chinese coast (five groups from the Changjiang River estuary of the East China Sea and three groups from the Bohai Sea) were subjected to otolith shape analysis. The results indicated that the variation of otolith shape caused by intra-stock fish length might exceed that due to inter-stock geographical separation, even when otolith shape variables are standardized with length scaling methods. This variation could easily result in misleading stock discrimination through otolith shape analysis. Therefore, conclusions about fish stock structure should be carefully drawn from otolith shape analysis because the observed discrimination may primarily be due to length effects, rather than differences among stocks. The application of multiple methods, such as otoliths shape analysis combined with elemental fingering, tagging or genetic analysis, is recommended for sock identification.
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Kim, Keun Ho; Ku, Boncho; Kang, Namsik; Kim, Young-Su; Jang, Jun-Su; Kim, Jong Yeol
2012-01-01
The voice has been used to classify the four constitution types, and to recognize a subject's health condition by extracting meaningful physical quantities, in traditional Korean medicine. In this paper, we propose a method of selecting the reliable variables from various voice features, such as frequency derivative features, frequency band ratios, and intensity, from vowels and a sentence. Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis. Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed. Finally, the vocal features are applied to a discriminant analysis to classify each constitution type. This method of voice classification can be widely used in the u-Healthcare system of personalized medicine and for improving diagnostic accuracy. PMID:22529874
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McCulloch, G; Dawson, L A; Ross, J M; Morgan, R M
2018-07-01
There is a need to develop a wider empirical research base to expand the scope for utilising the organic fraction of soil in forensic geoscience, and to demonstrate the capability of the analytical techniques used in forensic geoscience to discriminate samples at close proximity locations. The determination of wax markers from soil samples by GC analysis has been used extensively in court and is known to be effective in discriminating samples from different land use types. A new HPLC method for the analysis of the organic fraction of forensic sediment samples has also been shown recently to add value in conjunction with existing inorganic techniques for the discrimination of samples derived from close proximity locations. This study compares the ability of these two organic techniques to discriminate samples derived from close proximity locations and finds the GC technique to provide good discrimination at this scale, providing quantification of known compounds, whilst the HPLC technique offered a shorter and simpler sample preparation method and provided very good discrimination between groups of samples of different provenance in most cases. The use of both data sets together gave further improved accuracy rates in some cases, suggesting that a combined organic approach can provide added benefits in certain case scenarios and crime reconstruction contexts. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Kolbasin, V. A.; Ivanov, A. I.; Pedash, V. Y.
The two pulse shape discrimination methods were implemented in real-time. The pulse gradient analysis method was implemented programmatically on PC. The method based on artificial neural network was programmatically implemented using CUDA platform. It is shown that both implementations can provide up to 10{sup 6} pulses per second processing performance. The results for pulse shape discrimination using polycrystalline stilbene and LiF detectors were shown. (authors)
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Li, X C; Li, J S; Meng, L; Bai, Y N; Yu, D S; Liu, X N; Liu, X F; Jiang, X J; Ren, X W; Yang, X T; Shen, X P; Zhang, J W
2017-08-10
Objective: To understand the dominant pathogens of febrile respiratory syndrome (FRS) patients in Gansu province and to establish the Bayes discriminant function in order to identify the patients infected with the dominant pathogens. Methods: FRS patients were collected in various sentinel hospitals of Gansu province from 2009 to 2015 and the dominant pathogens were determined by describing the composition of pathogenic profile. Significant clinical variables were selected by stepwise discriminant analysis to establish the Bayes discriminant function. Results: In the detection of pathogens for FRS, both influenza virus and rhinovirus showed higher positive rates than those caused by other viruses (13.79%, 8.63%), that accounting for 54.38%, 13.73% of total viral positive patients. Most frequently detected bacteria would include Streptococcus pneumoniae , and haemophilus influenza (44.41%, 18.07%) that accounting for 66.21% and 24.55% among the bacterial positive patients. The original-validated rate of discriminant function, established by 11 clinical variables, was 73.1%, with the cross-validated rate as 70.6%. Conclusion: Influenza virus, Rhinovirus, Streptococcus pneumoniae and Haemophilus influenzae were the dominant pathogens of FRS in Gansu province. Results from the Bayes discriminant analysis showed both higher accuracy in the classification of dominant pathogens, and applicative value for FRS.
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Zhang, Hong-Guang; Yang, Qin-Min; Lu, Jian-Gang
2014-04-01
In this paper, a novel discriminant methodology based on near infrared spectroscopic analysis technique and least square support vector machine was proposed for rapid and nondestructive discrimination of different types of Polyacrylamide. The diffuse reflectance spectra of samples of Non-ionic Polyacrylamide, Anionic Polyacrylamide and Cationic Polyacrylamide were measured. Then principal component analysis method was applied to reduce the dimension of the spectral data and extract of the principal compnents. The first three principal components were used for cluster analysis of the three different types of Polyacrylamide. Then those principal components were also used as inputs of least square support vector machine model. The optimization of the parameters and the number of principal components used as inputs of least square support vector machine model was performed through cross validation based on grid search. 60 samples of each type of Polyacrylamide were collected. Thus a total of 180 samples were obtained. 135 samples, 45 samples for each type of Polyacrylamide, were randomly split into a training set to build calibration model and the rest 45 samples were used as test set to evaluate the performance of the developed model. In addition, 5 Cationic Polyacrylamide samples and 5 Anionic Polyacrylamide samples adulterated with different proportion of Non-ionic Polyacrylamide were also prepared to show the feasibilty of the proposed method to discriminate the adulterated Polyacrylamide samples. The prediction error threshold for each type of Polyacrylamide was determined by F statistical significance test method based on the prediction error of the training set of corresponding type of Polyacrylamide in cross validation. The discrimination accuracy of the built model was 100% for prediction of the test set. The prediction of the model for the 10 mixing samples was also presented, and all mixing samples were accurately discriminated as adulterated samples. The overall results demonstrate that the discrimination method proposed in the present paper can rapidly and nondestructively discriminate the different types of Polyacrylamide and the adulterated Polyacrylamide samples, and offered a new approach to discriminate the types of Polyacrylamide.
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Unsupervised spike sorting based on discriminative subspace learning.
Keshtkaran, Mohammad Reza; Yang, Zhi
2014-01-01
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.
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Jiménez-Carvelo, Ana M; González-Casado, Antonio; Pérez-Castaño, Estefanía; Cuadros-Rodríguez, Luis
2017-03-01
A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.
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Discrimination of high-Z materials in concrete-filled containers using muon scattering tomography
NASA Astrophysics Data System (ADS)
Frazão, L.; Velthuis, J.; Thomay, C.; Steer, C.
2016-07-01
An analysis method of identifying materials using muon scattering tomography is presented, which uses previous knowledge of the position of high-Z objects inside a container and distinguishes them from similar materials. In particular, simulations were performed in order to distinguish a block of Uranium from blocks of Lead and Tungsten of the same size, inside a concrete-filled drum. The results show that, knowing the shape and position from previous analysis, it is possible to distinguish 5 × 5 × 5 cm3 blocks of these materials with about 4h of muon exposure, down to 2 × 2 × 2 cm3 blocks with 70h of data using multivariate analysis (MVA). MVA uses several variables, but it does not benefit the discrimination over a simpler method using only the scatter angles. This indicates that the majority of discrimination is provided by the angular information. Momentum information is shown to provide no benefits in material discrimination.
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Hondrogiannis, Ellen; Rotta, Kathryn; Zapf, Charles M
2013-03-01
Sixteen elements found in 37 vanilla samples from Madagascar, Uganda, India, Indonesia (all Vanilla planifolia species), and Papa New Guinea (Vanilla tahitensis species) were measured by wavelength dispersive X-ray fluorescence (WDXRF) spectroscopy for the purpose of determining the elemental concentrations to discriminate among the origins. Pellets were prepared of the samples and elemental concentrations were calculated based on calibration curves created using 4 Natl. Inst. of Standards and Technology (NIST) standards. Discriminant analysis was used to successfully classify the vanilla samples by their species and their geographical region. Our method allows for higher throughput in the rapid screening of vanilla samples in less time than analytical methods currently available. Wavelength dispersive X-ray fluorescence spectroscopy and discriminant function analysis were used to classify vanilla from different origins resulting in a model that could potentially serve to rapidly validate these samples before purchasing from a producer. © 2013 Institute of Food Technologists®
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Alvin H. Yu; Garry Chick
2010-01-01
This study compared the utility of two different post-hoc tests after detecting significant differences within factors on multiple dependent variables using multivariate analysis of variance (MANOVA). We compared the univariate F test (the Scheffé method) to descriptive discriminant analysis (DDA) using an educational-tour survey of university study-...
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ERIC Educational Resources Information Center
Ng, Kwong Bor; Rieh, Soo Young; Kantor, Paul
2000-01-01
Discussion of natural language processing focuses on experiments using linear discriminant analysis to distinguish "Wall Street Journal" texts from "Federal Register" tests using information about the frequency of occurrence of word boundaries, sentence boundaries, and punctuation marks. Displays and interprets results in terms…
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Zhang, Hanyuan; Tian, Xuemin; Deng, Xiaogang; Cao, Yuping
2018-05-16
As an attractive nonlinear dynamic data analysis tool, global preserving kernel slow feature analysis (GKSFA) has achieved great success in extracting the high nonlinearity and inherently time-varying dynamics of batch process. However, GKSFA is an unsupervised feature extraction method and lacks the ability to utilize batch process class label information, which may not offer the most effective means for dealing with batch process monitoring. To overcome this problem, we propose a novel batch process monitoring method based on the modified GKSFA, referred to as discriminant global preserving kernel slow feature analysis (DGKSFA), by closely integrating discriminant analysis and GKSFA. The proposed DGKSFA method can extract discriminant feature of batch process as well as preserve global and local geometrical structure information of observed data. For the purpose of fault detection, a monitoring statistic is constructed based on the distance between the optimal kernel feature vectors of test data and normal data. To tackle the challenging issue of nonlinear fault variable identification, a new nonlinear contribution plot method is also developed to help identifying the fault variable after a fault is detected, which is derived from the idea of variable pseudo-sample trajectory projection in DGKSFA nonlinear biplot. Simulation results conducted on a numerical nonlinear dynamic system and the benchmark fed-batch penicillin fermentation process demonstrate that the proposed process monitoring and fault diagnosis approach can effectively detect fault and distinguish fault variables from normal variables. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
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He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei
2015-02-25
A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety. Copyright © 2014 Elsevier B.V. All rights reserved.
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Parametric Time-Frequency Analysis and Its Applications in Music Classification
NASA Astrophysics Data System (ADS)
Shen, Ying; Li, Xiaoli; Ma, Ngok-Wah; Krishnan, Sridhar
2010-12-01
Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Shumway, R.H.; McQuarrie, A.D.
Robust statistical approaches to the problem of discriminating between regional earthquakes and explosions are developed. We compare linear discriminant analysis using descriptive features like amplitude and spectral ratios with signal discrimination techniques using the original signal waveforms and spectral approximations to the log likelihood function. Robust information theoretic techniques are proposed and all methods are applied to 8 earthquakes and 8 mining explosions in Scandinavia and to an event from Novaya Zemlya of unknown origin. It is noted that signal discrimination approaches based on discrimination information and Renyi entropy perform better in the test sample than conventional methods based onmore » spectral ratios involving the P and S phases. Two techniques for identifying the ripple-firing pattern for typical mining explosions are proposed and shown to work well on simulated data and on several Scandinavian earthquakes and explosions. We use both cepstral analysis in the frequency domain and a time domain method based on the autocorrelation and partial autocorrelation functions. The proposed approach strips off underlying smooth spectral and seasonal spectral components corresponding to the echo pattern induced by two simple ripple-fired models. For two mining explosions, a pattern is identified whereas for two earthquakes, no pattern is evident.« less
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Pan, Rui; Wang, Hansheng; Li, Runze
2016-01-01
This paper is concerned with the problem of feature screening for multi-class linear discriminant analysis under ultrahigh dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh dimensional predictor. The proposed procedure is directly applicable to the situation with many classes. We further prove that the proposed method is screening consistent. Simulation studies are conducted to assess the finite sample performance of the new procedure. We also demonstrate the proposed methodology via an empirical analysis of a real life example on handwritten Chinese character recognition. PMID:28127109
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Detection of Genetically Modified Sugarcane by Using Terahertz Spectroscopy and Chemometrics
NASA Astrophysics Data System (ADS)
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.
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Liu, Fei; Wang, Yuan-zhong; Yang, Chun-yan; Jin, Hang
2015-01-01
The genuineness and producing area of Panax notoginseng were studied based on infrared spectroscopy combined with discriminant analysis. The infrared spectra of 136 taproots of P. notoginseng from 13 planting point in 11 counties were collected and the second derivate spectra were calculated by Omnic 8. 0 software. The infrared spectra and their second derivate spectra in the range 1 800 - 700 cm-1 were used to build model by stepwise discriminant analysis, which was in order to distinguish study on the genuineness of P. notoginseng. The model built based on the second derivate spectra showed the better recognition effect for the genuineness of P. notoginseng. The correct rate of returned classification reached to 100%, and the prediction accuracy was 93. 4%. The stability of model was tested by cross validation and the method was performed extrapolation validation. The second derivate spectra combined with the same discriminant analysis method were used to distinguish the producing area of P. notoginseng. The recognition effect of models built based on different range of spectrum and different numbers of samples were compared and found that when the model was built by collecting 8 samples from each planting point as training sample and the spectrum in the range 1 500 - 1 200 cm-1 , the recognition effect was better, with the correct rate of returned classification reached to 99. 0%, and the prediction accuracy was 76. 5%. The results indicated that infrared spectroscopy combined with discriminant analysis showed good recognition effect for the genuineness of P. notoginseng. The method might be a hopeful new method for identification of genuineness of P. notoginseng in practice. The method could recognize the producing area of P. notoginseng to some extent and could be a new thought for identification of the producing area of P. natoginseng.
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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.
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Machine vision methods for use in grain variety discrimination and quality analysis
NASA Astrophysics Data System (ADS)
Winter, Philip W.; Sokhansanj, Shahab; Wood, Hugh C.
1996-12-01
Decreasing cost of computer technology has made it feasible to incorporate machine vision technology into the agriculture industry. The biggest attraction to using a machine vision system is the computer's ability to be completely consistent and objective. One use is in the variety discrimination and quality inspection of grains. Algorithms have been developed using Fourier descriptors and neural networks for use in variety discrimination of barley seeds. RGB and morphology features have been used in the quality analysis of lentils, and probability distribution functions and L,a,b color values for borage dockage testing. These methods have been shown to be very accurate and have a high potential for agriculture. This paper presents the techniques used and results obtained from projects including: a lentil quality discriminator, a barley variety classifier, a borage dockage tester, a popcorn quality analyzer, and a pistachio nut grading system.
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Guo, Jing; Yue, Tianli; Yuan, Yahong
2012-10-01
Apple juice is a complex mixture of volatile and nonvolatile components. To develop discrimination models on the basis of the volatile composition for an efficient classification of apple juices according to apple variety and geographical origin, chromatography volatile profiles of 50 apple juice samples belonging to 6 varieties and from 5 counties of Shaanxi (China) were obtained by headspace solid-phase microextraction coupled with gas chromatography. The volatile profiles were processed as continuous and nonspecific signals through multivariate analysis techniques. Different preprocessing methods were applied to raw chromatographic data. The blind chemometric analysis of the preprocessed chromatographic profiles was carried out. Stepwise linear discriminant analysis (SLDA) revealed satisfactory discriminations of apple juices according to variety and geographical origin, provided respectively 100% and 89.8% success rate in terms of prediction ability. Finally, the discriminant volatile compounds selected by SLDA were identified by gas chromatography-mass spectrometry. The proposed strategy was able to verify the variety and geographical origin of apple juices involving only a reduced number of discriminate retention times selected by the stepwise procedure. This result encourages the similar procedures to be considered in quality control of apple juices. This work presented a method for an efficient discrimination of apple juices according to apple variety and geographical origin using HS-SPME-GC-MS together with chemometric tools. Discrimination models developed could help to achieve greater control over the quality of the juice and to detect possible adulteration of the product. © 2012 Institute of Food Technologists®
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Factors that Affect Poverty Areas in North Sumatera Using Discriminant Analysis
NASA Astrophysics Data System (ADS)
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.
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A Novel Method to Handle the Effect of Uneven Sampling Effort in Biodiversity Databases
Pardo, Iker; Pata, María P.; Gómez, Daniel; García, María B.
2013-01-01
How reliable are results on spatial distribution of biodiversity based on databases? Many studies have evidenced the uncertainty related to this kind of analysis due to sampling effort bias and the need for its quantification. Despite that a number of methods are available for that, little is known about their statistical limitations and discrimination capability, which could seriously constrain their use. We assess for the first time the discrimination capacity of two widely used methods and a proposed new one (FIDEGAM), all based on species accumulation curves, under different scenarios of sampling exhaustiveness using Receiver Operating Characteristic (ROC) analyses. Additionally, we examine to what extent the output of each method represents the sampling completeness in a simulated scenario where the true species richness is known. Finally, we apply FIDEGAM to a real situation and explore the spatial patterns of plant diversity in a National Park. FIDEGAM showed an excellent discrimination capability to distinguish between well and poorly sampled areas regardless of sampling exhaustiveness, whereas the other methods failed. Accordingly, FIDEGAM values were strongly correlated with the true percentage of species detected in a simulated scenario, whereas sampling completeness estimated with other methods showed no relationship due to null discrimination capability. Quantifying sampling effort is necessary to account for the uncertainty in biodiversity analyses, however, not all proposed methods are equally reliable. Our comparative analysis demonstrated that FIDEGAM was the most accurate discriminator method in all scenarios of sampling exhaustiveness, and therefore, it can be efficiently applied to most databases in order to enhance the reliability of biodiversity analyses. PMID:23326357
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A novel method to handle the effect of uneven sampling effort in biodiversity databases.
Pardo, Iker; Pata, María P; Gómez, Daniel; García, María B
2013-01-01
How reliable are results on spatial distribution of biodiversity based on databases? Many studies have evidenced the uncertainty related to this kind of analysis due to sampling effort bias and the need for its quantification. Despite that a number of methods are available for that, little is known about their statistical limitations and discrimination capability, which could seriously constrain their use. We assess for the first time the discrimination capacity of two widely used methods and a proposed new one (FIDEGAM), all based on species accumulation curves, under different scenarios of sampling exhaustiveness using Receiver Operating Characteristic (ROC) analyses. Additionally, we examine to what extent the output of each method represents the sampling completeness in a simulated scenario where the true species richness is known. Finally, we apply FIDEGAM to a real situation and explore the spatial patterns of plant diversity in a National Park. FIDEGAM showed an excellent discrimination capability to distinguish between well and poorly sampled areas regardless of sampling exhaustiveness, whereas the other methods failed. Accordingly, FIDEGAM values were strongly correlated with the true percentage of species detected in a simulated scenario, whereas sampling completeness estimated with other methods showed no relationship due to null discrimination capability. Quantifying sampling effort is necessary to account for the uncertainty in biodiversity analyses, however, not all proposed methods are equally reliable. Our comparative analysis demonstrated that FIDEGAM was the most accurate discriminator method in all scenarios of sampling exhaustiveness, and therefore, it can be efficiently applied to most databases in order to enhance the reliability of biodiversity analyses.
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Longobardi, Francesco; Casiello, Grazia; Centonze, Valentina; Catucci, Lucia; Agostiano, Angela
2017-08-01
Although table grape is one of the most cultivated and consumed fruits worldwide, no study has been reported on its geographical origin or agronomic practice based on stable isotope ratios. This study aimed to evaluate the usefulness of isotopic ratios (i.e. 2 H/ 1 H, 13 C/ 12 C, 15 N/ 14 N and 18 O/ 16 O) as possible markers to discriminate the agronomic practice (conventional versus organic farming) and provenance of table grape. In order to quantitatively evaluate which of the isotopic variables were more discriminating, a t test was carried out, in light of which only δ 13 C and δ 18 O provided statistically significant differences (P ≤ 0.05) for the discrimination of geographical origin and farming method. Principal component analysis (PCA) showed no good separation of samples differing in geographical area and agronomic practice; thus, for classification purposes, supervised approaches were carried out. In particular, general discriminant analysis (GDA) was used, resulting in prediction abilities of 75.0 and 92.2% for the discrimination of farming method and origin respectively. The present findings suggest that stable isotopes (i.e. δ 18 O, δ 2 H and δ 13 C) combined with chemometrics can be successfully applied to discriminate the provenance of table grape. However, the use of bulk nitrogen isotopes was not effective for farming method discrimination. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.
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NASA Astrophysics Data System (ADS)
Kurniawan, Dian; Suparti; Sugito
2018-05-01
Population growth in Indonesia has increased every year. According to the population census conducted by the Central Bureau of Statistics (BPS) in 2010, the population of Indonesia has reached 237.6 million people. Therefore, to control the population growth rate, the government hold Family Planning or Keluarga Berencana (KB) program for couples of childbearing age. The purpose of this program is to improve the health of mothers and children in order to manifest prosperous society by controlling births while ensuring control of population growth. The data used in this study is the updated family data of Semarang city in 2016 that conducted by National Family Planning Coordinating Board (BKKBN). From these data, classifiers with kernel discriminant analysis will be obtained, and also classification accuracy will be obtained from that method. The result of the analysis showed that normal kernel discriminant analysis gives 71.05 % classification accuracy with 28.95 % classification error. Whereas triweight kernel discriminant analysis gives 73.68 % classification accuracy with 26.32 % classification error. Using triweight kernel discriminant for data preprocessing of family planning participation of childbearing age couples in Semarang City of 2016 can be stated better than with normal kernel discriminant.
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Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures
NASA Astrophysics Data System (ADS)
Li, Quanbao; Wei, Fajie; Zhou, Shenghan
2017-05-01
The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.
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Lee, Sang Min; Kim, Hye-Jin; Jang, Young Pyo
2012-01-01
It needs many years of special training to gain expertise on the organoleptic classification of botanical raw materials and, even for those experts, discrimination among Umbelliferae medicinal herbs remains an intricate challenge due to their morphological similarity. To develop a new chemometric classification method using a direct analysis in real time-time of flight-mass spectrometry (DART-TOF-MS) fingerprinting for Umbelliferae medicinal herbs and to provide a platform for its application to the discrimination of other herbal medicines. Angelica tenuissima, Angelica gigas, Angelica dahurica and Cnidium officinale were chosen for this study and ten samples of each species were purchased from various Korean markets. DART-TOF-MS was employed on powdered raw materials to obtain a chemical fingerprint of each sample and the orthogonal partial-least squares method in discriminant analysis (OPLS-DA) was used for multivariate analysis. All samples of collected species were successfully discriminated from each other according to their characteristic DART-TOF-MS fingerprint. Decursin (or decursinol angelate) and byakangelicol were identified as marker molecules for Angelica gigas and A. dahurica, respectively. Using the OPLS method for discriminant analysis, Angelica tenuissima and Cnidium officinale were clearly separated into two groups. Angelica tenuissima was characterised by the presence of ligustilide and unidentified molecular ions of m/z 239 and 283, while senkyunolide A together with signals with m/z 387 and 389 were the marker compounds for Cnidium officinale. Elaborating with chemoinformatics, DART-TOF-MS fingerprinting with chemoinformatic tools results in a powerful method for the classification of morphologically similar Umbelliferae medicinal herbs and quality control of medicinal herbal products, including the extracts of these crude drugs. Copyright © 2012 John Wiley & Sons, Ltd.
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Cerruela García, G; García-Pedrajas, N; Luque Ruiz, I; Gómez-Nieto, M Á
2018-03-01
This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.
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Sims, Mario; Wyatt, Sharon B.; Gutierrez, Mary Lou; Taylor, Herman A.; Williams, David R.
2009-01-01
Objective Assessing the discrimination-health disparities hypothesis requires psychometrically sound, multidimensional measures of discrimination. Among the available discrimination measures, few are multidimensional and none have adequate psychometric testing in a large, African American sample. We report the development and psychometric testing of the multidimensional Jackson Heart Study Discrimination (JHSDIS) Instrument. Methods A multidimensional measure assessing the occurrence, frequency, attribution, and coping responses to perceived everyday and lifetime discrimination; lifetime burden of discrimination; and effect of skin color was developed and tested in the 5302-member cohort of the Jackson Heart Study. Internal consistency was calculated by using Cronbach α. coefficient. Confirmatory factor analysis established the dimensions, and intercorrelation coefficients assessed the discriminant validity of the instrument. Setting Tri-county area of the Jackson, MS metropolitan statistical area. Results The JHSDIS was psychometrically sound (overall α=.78, .84 and .77, respectively, for the everyday and lifetime subscales). Confirmatory factor analysis yielded 11 factors, which confirmed the a priori dimensions represented. Conclusions The JHSDIS combined three scales into a single multidimensional instrument with good psychometric properties in a large sample of African Americans. This analysis lays the foundation for using this instrument in research that will examine the association between perceived discrimination and CVD among African Americans. PMID:19341164
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Lim, Dong Kyu; Mo, Changyeun; Lee, Dong-Kyu; Long, Nguyen Phuoc; Lim, Jongguk; Kwon, Sung Won
2018-01-01
The authenticity determination of white rice is crucial to prevent deceptive origin labeling and dishonest trading. However, a non-destructive and comprehensive method for rapidly discriminating the geographical origins of white rice between countries is still lacking. In the current study, we developed a volatile organic compound based geographical discrimination method using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS) to discriminate rice samples from Korea and China. A partial least squares discriminant analysis (PLS-DA) model exhibited a good classification of white rice between Korea and China (accuracy = 0.958, goodness of fit = 0.937, goodness of prediction = 0.831, and permutation test p-value = 0.043). Combining the PLS-DA based feature selection with the differentially expressed features from the unpaired t-test and significance analysis of microarrays, 12 discriminatory biomarkers were found. Among them, hexanal and 1-hexanol have been previously known to be associated with the cultivation environment and storage conditions. Other hydrocarbon biomarkers are novel, and their impact on rice production and storage remains to be elucidated. In conclusion, our findings highlight the ability to rapidly discriminate white rice from Korea and China. The developed method maybe useful for the authenticity and quality control of white rice. Copyright © 2017. Published by Elsevier B.V.
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Spectral discrimination of serum from liver cancer and liver cirrhosis using Raman spectroscopy
NASA Astrophysics Data System (ADS)
Yang, Tianyue; Li, Xiaozhou; Yu, Ting; Sun, Ruomin; Li, Siqi
2011-07-01
In this paper, Raman spectra of human serum were measured using Raman spectroscopy, then the spectra was analyzed by multivariate statistical methods of principal component analysis (PCA). Then linear discriminant analysis (LDA) was utilized to differentiate the loading score of different diseases as the diagnosing algorithm. Artificial neural network (ANN) was used for cross-validation. The diagnosis sensitivity and specificity by PCA-LDA are 88% and 79%, while that of the PCA-ANN are 89% and 95%. It can be seen that modern analyzing method is a useful tool for the analysis of serum spectra for diagnosing diseases.
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NASA Astrophysics Data System (ADS)
Crawford, I.; Ruske, S.; Topping, D. O.; Gallagher, M. W.
2015-07-01
In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP) by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-light induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study can be applied to data sets in excess of 1×106 points on a desktop computer, allowing for each fluorescent particle in a dataset to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient dataset. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4) where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best performing methods were applied to the BEACHON-RoMBAS ambient dataset where it was found that the z-score and range normalisation methods yield similar results with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution due to poor centroid definition and failure to assign particles to a cluster as a result of the subsampling and comparative attribution method employed by WASP. The methods used here allow for the entire fluorescent population of particles to be analysed yielding an explict cluster attribution for each particle, improving cluster centroid definition and our capacity to discriminate and quantify PBAP meta-classes compared to previous approaches.
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Jackman, Patrick; Sun, Da-Wen; Allen, Paul; Valous, Nektarios A; Mendoza, Fernando; Ward, Paddy
2010-04-01
A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. 2009 Elsevier Ltd. All rights reserved.
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Kernel PLS-SVC for Linear and Nonlinear Discrimination
NASA Technical Reports Server (NTRS)
Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan
2003-01-01
A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.
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Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing
Wen, Tailai; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi
2018-01-01
The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods. PMID:29382146
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Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing.
Wen, Tailai; Yan, Jia; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi
2018-01-29
The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors' responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose's classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.
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Discriminative factor analysis of juvenile delinquency in South Korea.
Kim, Hyun Sil; Kim, Hun Soo
2006-12-01
The present study was intended to compare difference in research variables between delinquent adolescents and student adolescents, and to analyze discriminative factors of delinquent behaviors among Korean adolescents. The research design of this study was a questionnaire survey. Questionnaires were administered to 2,167 adolescents (1,196 students and 971 delinquents), sampled from 8 middle and high school and 6 juvenile corrective institutions, using the proportional stratified random sampling method. Statistical methods employed were Chi-square, t-test, and logistic regression analysis. The discriminative factors of delinquent behaviors were smoking, alcohol use, other drug use, being sexually abused, viewing time of media violence and pornography. Among these discriminative factors, the factor most strongly associated with delinquency was smoking (odds ratio: 32.32). That is, smoking adolescent has a 32-fold higher possibility of becoming a delinquent adolescent than a non-smoking adolescent. Our findings, that smoking was the strongest discriminative factor of delinquent behavior, suggest that educational strategies to prevent adolescent smoking may reduce the rate of juvenile delinquency. Antismoking educational efforts are therefore urgently needed in South Korea.
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Insausti, Matías; Gomes, Adriano A; Cruz, Fernanda V; Pistonesi, Marcelo F; Araujo, Mario C U; Galvão, Roberto K H; Pereira, Claudete F; Band, Beatriz S F
2012-08-15
This paper investigates the use of UV-vis, near infrared (NIR) and synchronous fluorescence (SF) spectrometries coupled with multivariate classification methods to discriminate biodiesel samples with respect to the base oil employed in their production. More specifically, the present work extends previous studies by investigating the discrimination of corn-based biodiesel from two other biodiesel types (sunflower and soybean). Two classification methods are compared, namely full-spectrum SIMCA (soft independent modelling of class analogies) and SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm). Regardless of the spectrometric technique employed, full-spectrum SIMCA did not provide an appropriate discrimination of the three biodiesel types. In contrast, all samples were correctly classified on the basis of a reduced number of wavelengths selected by SPA-LDA. It can be concluded that UV-vis, NIR and SF spectrometries can be successfully employed to discriminate corn-based biodiesel from the two other biodiesel types, but wavelength selection by SPA-LDA is key to the proper separation of the classes. Copyright © 2012 Elsevier B.V. All rights reserved.
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Linear Discriminant Analysis on a Spreadsheet.
ERIC Educational Resources Information Center
Busbey, Arthur Bresnahan III
1989-01-01
Described is a software package, "Trapeze," within which a routine called LinDis can be used. Discussed are teaching methods, the linear discriminant model and equations, the LinDis worksheet, and an example. The set up for this routine is included. (CW)
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Estimating erosion risk on forest lands using improved methods of discriminant analysis
J. Lewis; R. M. Rice
1990-01-01
A population of 638 timber harvest areas in northwestern California was sampled for data related to the occurrence of critical amounts of erosion (>153 m3 within 0.81 ha). Separate analyses were done for forest roads and logged areas. Linear discriminant functions were computed in each analysis to contrast site conditions at critical plots with randomly selected...
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Chance-corrected classification for use in discriminant analysis: Ecological applications
Titus, K.; Mosher, J.A.; Williams, B.K.
1984-01-01
A method for evaluating the classification table from a discriminant analysis is described. The statistic, kappa, is useful to ecologists in that it removes the effects of chance. It is useful even with equal group sample sizes although the need for a chance-corrected measure of prediction becomes greater with more dissimilar group sample sizes. Examples are presented.
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Dantas, Hebertty V; Barbosa, Mayara F; Nascimento, Elaine C L; Moreira, Pablo N T; Galvão, Roberto K H; Araújo, Mário C U
2013-03-15
This paper proposes a NIR spectrometric method for screening analysis of liquefied petroleum gas (LPG) samples. The proposed method is aimed at discriminating samples with low and high propane content, which can be useful for the adjustment of burn settings in industrial applications. A gas flow system was developed to introduce the LPG sample into a NIR flow cell at constant pressure. In addition, a gas chromatographer was employed to determine the propane content of the sample for reference purposes. The results of a principal component analysis, as well as a classification study using SIMCA (soft independent modeling of class analogies), revealed that the samples can be successfully discriminated with respect to propane content by using the NIR spectrum in the range 8100-8800 cm(-1). In addition, by using SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm), it was found that perfect discrimination can also be achieved by using only two wavenumbers (8215 and 8324 cm(-1)). This finding may be of value for the design of a dedicated, low-cost instrument for routine analyses. Copyright © 2012 Elsevier B.V. All rights reserved.
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Someda, Hidetoshi; Gakuhari, Takashi; Akai, Junko; Araki, Yoshiyuki; Kodera, Tsutomu; Tsumatori, Gentaro; Kobayashi, Yasushi; Matsunaga, Satoru; Abe, Shinichi; Hashimoto, Masatsugu; Saito, Megumi; Yoneda, Minoru; Ishida, Hajime
2016-04-01
Stable isotope analysis has undergone rapid development in recent years and yielded significant results in the field of forensic sciences. In particular, carbon and oxygen isotopic ratios in tooth enamel obtained from human remains can provide useful information for the crosschecking of morphological and DNA analyses and facilitate rapid on-site prescreening for the identification of remains. This study analyzes carbon and oxygen isotopic ratios in the tooth enamel of Japanese people born between 1878 and 1930, in order to obtain data for methodological differentiation of Japanese and American remains from the Second World War. The carbon and oxygen isotopic ratios in the tooth enamel of the examined Japanese individuals are compared to previously reported data for American individuals (born post WWII), and statistical analysis is conducted using a discrimination method based on a logistic regression analysis. The discrimination between the Japanese and US populations, including Alaska and Hawaii, is found to be highly accurate. Thus, the present method has potential as a discrimination technique for both populations for use in the examination of mixed remains comprising Japanese and American fallen soldiers. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Mao, Zhi-Hua; Yin, Jian-Hua; Zhang, Xue-Xi; Wang, Xiao; Xia, Yang
2016-01-01
Fourier transform infrared spectroscopic imaging (FTIRI) technique can be used to obtain the quantitative information of content and spatial distribution of principal components in cartilage by combining with chemometrics methods. In this study, FTIRI combining with principal component analysis (PCA) and Fisher’s discriminant analysis (FDA) was applied to identify the healthy and osteoarthritic (OA) articular cartilage samples. Ten 10-μm thick sections of canine cartilages were imaged at 6.25μm/pixel in FTIRI. The infrared spectra extracted from the FTIR images were imported into SPSS software for PCA and FDA. Based on the PCA result of 2 principal components, the healthy and OA cartilage samples were effectively discriminated by the FDA with high accuracy of 94% for the initial samples (training set) and cross validation, as well as 86.67% for the prediction group. The study showed that cartilage degeneration became gradually weak with the increase of the depth. FTIRI combined with chemometrics may become an effective method for distinguishing healthy and OA cartilages in future. PMID:26977354
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Assessment of forward head posture in females: observational and photogrammetry methods.
Salahzadeh, Zahra; Maroufi, Nader; Ahmadi, Amir; Behtash, Hamid; Razmjoo, Arash; Gohari, Mahmoud; Parnianpour, Mohamad
2014-01-01
There are different methods to assess forward head posture (FHP) but the accuracy and discrimination ability of these methods are not clear. Here, we want to compare three postural angles for FHP assessment and also study the discrimination accuracy of three photogrammetric methods to differentiate groups categorized based on observational method. All Seventy-eight healthy female participants (23 ± 2.63 years), were classified into three groups: moderate-severe FHP, slight FHP and non FHP based on observational postural assessment rules. Applying three photogrammetric methods - craniovertebral angle, head title angle and head position angle - to measure FHP objectively. One - way ANOVA test showed a significant difference in three categorized group's craniovertebral angle (P< 0.05, F=83.07). There was no dramatic difference in head tilt angle and head position angle methods in three groups. According to Linear Discriminate Analysis (LDA) results, the canonical discriminant function (Wilks'Lambda) was 0.311 for craniovertebral angle with 79.5% of cross-validated grouped cases correctly classified. Our results showed that, craniovertebral angle method may discriminate the females with moderate-severe and non FHP more accurate than head position angle and head tilt angle. The photogrammetric method had excellent inter and intra rater reliability to assess the head and cervical posture.
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Meta-analysis of field experiments shows no change in racial discrimination in hiring over time.
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.
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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.
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Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu
2013-01-01
DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.
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Fischedick, Justin Thomas; Hazekamp, Arno; Erkelens, Tjalling; Choi, Young Hae; Verpoorte, Rob
2010-12-01
Cannabis sativa L. is an important medicinal plant. In order to develop cannabis plant material as a medicinal product quality control and clear chemotaxonomic discrimination between varieties is a necessity. Therefore in this study 11 cannabis varieties were grown under the same environmental conditions. Chemical analysis of cannabis plant material used a gas chromatography flame ionization detection method that was validated for quantitative analysis of cannabis monoterpenoids, sesquiterpenoids, and cannabinoids. Quantitative data was analyzed using principal component analysis to determine which compounds are most important in discriminating cannabis varieties. In total 36 compounds were identified and quantified in the 11 varieties. Using principal component analysis each cannabis variety could be chemically discriminated. This methodology is useful for both chemotaxonomic discrimination of cannabis varieties and quality control of plant material. Copyright © 2010 Elsevier Ltd. All rights reserved.
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Cousins, Matthew M.; Swan, David; Magaret, Craig A.; Hoover, Donald R.; Eshleman, Susan H.
2012-01-01
Background HIV diversity may be a useful biomarker for discriminating between recent and non-recent HIV infection. The high resolution melting (HRM) diversity assay was developed to quantify HIV diversity in viral populations without sequencing. In this assay, HIV diversity is expressed as a single numeric HRM score that represents the width of a melting peak. HRM scores are highly associated with diversity measures obtained with next generation sequencing. In this report, a software package, the HRM Diversity Assay Analysis Tool (DivMelt), was developed to automate calculation of HRM scores from melting curve data. Methods DivMelt uses computational algorithms to calculate HRM scores by identifying the start (T1) and end (T2) melting temperatures for a DNA sample and subtracting them (T2–T1 = HRM score). DivMelt contains many user-supplied analysis parameters to allow analyses to be tailored to different contexts. DivMelt analysis options were optimized to discriminate between recent and non-recent HIV infection and to maximize HRM score reproducibility. HRM scores calculated using DivMelt were compared to HRM scores obtained using a manual method that is based on visual inspection of DNA melting curves. Results HRM scores generated with DivMelt agreed with manually generated HRM scores obtained from the same DNA melting data. Optimal parameters for discriminating between recent and non-recent HIV infection were identified. DivMelt provided greater discrimination between recent and non-recent HIV infection than the manual method. Conclusion DivMelt provides a rapid, accurate method of determining HRM scores from melting curve data, facilitating use of the HRM diversity assay for large-scale studies. PMID:23240016
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NASA Astrophysics Data System (ADS)
Phinyomark, A.; Hu, H.; Phukpattaranont, P.; Limsakul, C.
2012-01-01
The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.
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NASA Astrophysics Data System (ADS)
Ding, Hao; Cao, Ming; DuPont, Andrew W.; Scott, Larry D.; Guha, Sushovan; Singhal, Shashideep; Younes, Mamoun; Pence, Isaac; Herline, Alan; Schwartz, David; Xu, Hua; Mahadevan-Jansen, Anita; Bi, Xiaohong
2016-03-01
Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.
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NASA Astrophysics Data System (ADS)
Tamura, Hiroto; Hotta, Yudai; Sato, Hiroaki
2013-08-01
Matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is one of the most widely used mass-based approaches for bacterial identification and classification because of the simple sample preparation and extremely rapid analysis within a few minutes. To establish the accurate MALDI-TOF MS bacterial discrimination method at strain level, the ribosomal subunit proteins coded in the S 10-spc-alpha operon, which encodes half of the ribosomal subunit protein and is highly conserved in eubacterial genomes, were selected as reliable biomarkers. This method, named the S10-GERMS method, revealed that the strains of genus Pseudomonas were successfully identified and discriminated at species and strain levels, respectively; therefore, the S10-GERMS method was further applied to discriminate the pathovar of P. syringae. The eight selected biomarkers (L24, L30, S10, S12, S14, S16, S17, and S19) suggested the rapid discrimination of P. syringae at the strain (pathovar) level. The S10-GERMS method appears to be a powerful tool for rapid and reliable bacterial discrimination and successful phylogenetic characterization. In this article, an overview of the utilization of results from the S10-GERMS method is presented, highlighting the characterization of the Lactobacillus casei group and discrimination of the bacteria of genera Bacillus and Sphingopyxis despite only two and one base difference in the 16S rRNA gene sequence, respectively.
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Discrimination of Cynanchum wilfordii and Cynanchum auriculatum by terahertz spectroscopic analysis.
Ham, Woo Sik; Kim, Jinju; Park, Dae Joon; Ryu, Han-Cheol; Jang, Young Pyo
2018-02-12
Precise identification of botanical origin of plant species is crucial for the quality control of herbal medicine. In Korea, the root part of Cynanchum auriculatum has been misused for C. wilfordii in the herbal drug market due to their morphological similarities. Currently, DNA analysis using the polymerase chain reaction (PCR) method is employed to discriminate between these species. In order to develop a new analytical tool for the rapid discrimination of C. wilfordii and C. auriculatum, terahertz (THz) spectroscopy was employed. Authentic samples of C. wilfordii and C. auriculatum were provided from the National Institute and standardized pellets for each species were prepared to get optimum results with terahertz time-domain spectroscopy (THz-TDS) in frequency range 0.2-1.20 THz. The C. wilfordii pellet showed longer time delay compare to the sample of C. auriculatum and this was due to the difference in permittivity. The pellet samples of C. wilfordii and C. auriculatum showed a permittivity difference of about 0.08 at 0.2-1.20 THz. The experimental results indicated that THz-TDS analysis can be an effective and rapid method for the discrimination of C. wilfordii and C. auriculatum, and this application can be expanded for the discrimination of other similar herbal medicines. Copyright © 2018 John Wiley & Sons, Ltd.
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Local connected fractal dimension analysis in gill of fish experimentally exposed to toxicants.
Manera, Maurizio; Giari, Luisa; De Pasquale, Joseph A; Sayyaf Dezfuli, Bahram
2016-06-01
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 48h. 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 connected to human personnel; and of CVA/LDA, could be an objective, sensitive and specific approach to study fish gill lamellar pathology. Furthermore this approach enabled discrimination with sufficient confidence between exposure classes or pathological states and avoided misdiagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.
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ASTM clustering for improving coal analysis by near-infrared spectroscopy.
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.
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Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.
Onishi, Akinari; Natsume, Kiyohisa
2014-01-01
Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.
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Guisande, Cástor; Vari, Richard P; Heine, Jürgen; García-Roselló, Emilio; González-Dacosta, Jacinto; Perez-Schofield, Baltasar J García; González-Vilas, Luis; Pelayo-Villamil, Patricia
2016-09-12
We present and discuss VARSEDIG, an algorithm which identifies the morphometric features that significantly discriminate two taxa and validates the morphological distinctness between them via a Monte-Carlo test. VARSEDIG is freely available as a function of the RWizard application PlotsR (http://www.ipez.es/RWizard) and as R package on CRAN. The variables selected by VARSEDIG with the overlap method were very similar to those selected by logistic regression and discriminant analysis, but overcomes some shortcomings of these methods. VARSEDIG is, therefore, a good alternative by comparison to current classical classification methods for identifying morphometric features that significantly discriminate a taxon and for validating its morphological distinctness from other taxa. As a demonstration of the potential of VARSEDIG for this purpose, we analyze morphological discrimination among some species of the Neotropical freshwater family Characidae.
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Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin
2015-01-01
The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.
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Characterization and Differentiation of Petroleum-Derived Products by E-Nose Fingerprints
Ferreiro-González, Marta; Palma, Miguel; Ayuso, Jesús; Álvarez, José A.; Barroso, Carmelo G.
2017-01-01
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. PMID:29113069
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ERIC Educational Resources Information Center
Finch, Holmes
2010-01-01
Discriminant Analysis (DA) is a tool commonly used for differentiating among 2 or more groups based on 2 or more predictor variables. DA works by finding 1 or more linear combinations of the predictors that yield maximal difference among the groups. One common goal of researchers using DA is to characterize the nature of group difference by…
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Combining 1D and 2D linear discriminant analysis for palmprint recognition
NASA Astrophysics Data System (ADS)
Zhang, Jian; Ji, Hongbing; Wang, Lei; Lin, Lin
2011-11-01
In this paper, a novel feature extraction method for palmprint recognition termed as Two-dimensional Combined Discriminant Analysis (2DCDA) is proposed. By connecting the adjacent rows of a image sequentially, the obtained new covariance matrices contain the useful information among local geometry structures in the image, which is eliminated by 2DLDA. In this way, 2DCDA combines LDA and 2DLDA for a promising recognition accuracy, but the number of coefficients of its projection matrix is lower than that of other two-dimensional methods. Experimental results on the CASIA palmprint database demonstrate the effectiveness of the proposed method.
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Lee, Byeong-Ju; Zhou, Yaoyao; Lee, Jae Soung; Shin, Byeung Kon; Seo, Jeong-Ah; Lee, Doyup; Kim, Young-Suk
2018-01-01
The ability to determine the origin of soybeans is an important issue following the inclusion of this information in the labeling of agricultural food products becoming mandatory in South Korea in 2017. This study was carried out to construct a prediction model for discriminating Chinese and Korean soybeans using Fourier-transform infrared (FT-IR) spectroscopy and multivariate statistical analysis. The optimal prediction models for discriminating soybean samples were obtained by selecting appropriate scaling methods, normalization methods, variable influence on projection (VIP) cutoff values, and wave-number regions. The factors for constructing the optimal partial-least-squares regression (PLSR) prediction model were using second derivatives, vector normalization, unit variance scaling, and the 4000–400 cm–1 region (excluding water vapor and carbon dioxide). The PLSR model for discriminating Chinese and Korean soybean samples had the best predictability when a VIP cutoff value was not applied. When Chinese soybean samples were identified, a PLSR model that has the lowest root-mean-square error of the prediction value was obtained using a VIP cutoff value of 1.5. The optimal PLSR prediction model for discriminating Korean soybean samples was also obtained using a VIP cutoff value of 1.5. This is the first study that has combined FT-IR spectroscopy with normalization methods, VIP cutoff values, and selected wave-number regions for discriminating Chinese and Korean soybeans. PMID:29689113
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NASA Astrophysics Data System (ADS)
Huang, Jian; Yuen, Pong C.; Chen, Wen-Sheng; Lai, J. H.
2005-05-01
Many face recognition algorithms/systems have been developed in the last decade and excellent performances have also been reported when there is a sufficient number of representative training samples. In many real-life applications such as passport identification, only one well-controlled frontal sample image is available for training. Under this situation, the performance of existing algorithms will degrade dramatically or may not even be implemented. We propose a component-based linear discriminant analysis (LDA) method to solve the one training sample problem. The basic idea of the proposed method is to construct local facial feature component bunches by moving each local feature region in four directions. In this way, we not only generate more samples with lower dimension than the original image, but also consider the face detection localization error while training. After that, we propose a subspace LDA method, which is tailor-made for a small number of training samples, for the local feature projection to maximize the discrimination power. Theoretical analysis and experiment results show that our proposed subspace LDA is efficient and overcomes the limitations in existing LDA methods. Finally, we combine the contributions of each local component bunch with a weighted combination scheme to draw the recognition decision. A FERET database is used for evaluating the proposed method and results are encouraging.
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Garway-Heath, David F; Quartilho, Ana; Prah, Philip; Crabb, David P; Cheng, Qian; Zhu, Haogang
2017-08-01
To evaluate the ability of various visual field (VF) analysis methods to discriminate treatment groups in glaucoma clinical trials and establish the value of time-domain optical coherence tomography (TD OCT) imaging as an additional outcome. VFs and retinal nerve fibre layer thickness (RNFLT) measurements (acquired by TD OCT) from 373 glaucoma patients in the UK Glaucoma Treatment Study (UKGTS) at up to 11 scheduled visits over a 2 year interval formed the cohort to assess the sensitivity of progression analysis methods. Specificity was assessed in 78 glaucoma patients with up to 11 repeated VF and OCT RNFLT measurements over a 3 month interval. Growth curve models assessed the difference in VF and RNFLT rate of change between treatment groups. Incident progression was identified by 3 VF-based methods: Guided Progression Analysis (GPA), 'ANSWERS' and 'PoPLR', and one based on VFs and RNFLT: 'sANSWERS'. Sensitivity, specificity and discrimination between treatment groups were evaluated. The rate of VF change was significantly faster in the placebo, compared to active treatment, group (-0.29 vs +0.03 dB/year, P <.001); the rate of RNFLT change was not different (-1.7 vs -1.1 dB/year, P =.14). After 18 months and at 95% specificity, the sensitivity of ANSWERS and PoPLR was similar (35%); sANSWERS achieved a sensitivity of 70%. GPA, ANSWERS and PoPLR discriminated treatment groups with similar statistical significance; sANSWERS did not discriminate treatment groups. Although the VF progression-detection method including VF and RNFLT measurements is more sensitive, it does not improve discrimination between treatment arms.
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Libert, Xavier; Packeu, Ann; Bureau, Fabrice; Roosens, Nancy H; De Keersmaecker, Sigrid C J
2017-04-04
Indoor air pollution caused by fungal contamination is suspected to have a public health impact. Monitoring of the composition of the indoor airborne fungal contaminants is therefore important. To avoid problems linked to culture-dependent protocols, molecular methods are increasingly being proposed as an alternative. Among these molecular methods, the polymerase chain reaction (PCR) and the real-time PCR are the most frequently used tools for indoor fungal detection. However, even if these tools have demonstrated their appropriate performance, some of them are not able to discriminate between species which are genetically close. A solution to this could be the use of a post-qPCR high resolution melting (HRM) analysis, which would allow the discrimination of these species based on the highly accurate determination of the difference in melting temperature of the obtained amplicon. In this study, we provide a proof-of-concept for this approach, using a dye adapted version of our previously developed qPCR SYBR®Green method to detect Aspergillus versicolor in indoor air, an important airborne fungus in terms of occurrence and cause of health problems. Despite the good performance observed for that qPCR method, no discrimination could previously be made between A. versicolor, Aspergillus creber and Aspergillus sydowii. In this study, we developed and evaluated an HRM assay for the discrimination between A. versicolor, Aspergillus creber and Aspergillus sydowii. Using HRM analysis, the discrimination of the 3 Aspergillus species could be made. No false positive, nor false negatives were observed during the performance assessment including 20 strains of Aspergillus. The limit of detection was determined for each species i.e., 0.5 pg of gDNA for A. creber and A. sydowii, and 0.1 pg of gDNA for A. versicolor. The HRM analysis was also successfully tested on environmental samples. We reported the development of HRM tools for the discrimination of A. versicolor, A. creber and A. sydowii. However, this study could be considered as a study case demonstrating that HRM based on existing qPCR assays, allows a more accurate identification of indoor air contaminants. This contributes to an improved insight in the diversity of indoor airborne fungi and hence, eventually in the causal link with health problems.
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A Fractal Analysis of CT Liver Images for the Discrimination of Hepatic Lesions: A Comparative Study
2001-10-25
liver images in order to estimate their fractal dimension and to differentiate normal liver parenchyma from hepatocellular carcinoma . Four fractal...methods; thus discriminating up to 93% of the normal parenchyma and up to 82% of the hepatocellular carcinoma , correctly.
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Fu, Haiyan; Fan, Yao; Zhang, Xu; Lan, Hanyue; Yang, Tianming; Shao, Mei; Li, Sihan
2015-01-01
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. PMID:26345990
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Zerger, Suzanne; Bacon, Sarah; Corneau, Simon; Skosireva, Anna; McKenzie, Kwame; Gapka, Susan; O'Campo, Patricia; Sarang, Aseefa; Stergiopoulos, Vicky
2014-12-14
This mixed methods study explored the characteristics of and experiences with perceived discrimination in an ethnically diverse urban sample of adults experiencing homelessness and mental illness. Data were collected in Toronto, Ontario, as part of a 4-year national randomized field trial of the Housing First treatment model. Rates of perceived discrimination were captured from survey questions regarding perceived discrimination among 231 ethnoracially diverse participants with moderate mental health needs. The qualitative component included thirty six in-depth interviews which explored how individuals who bear these multiple identities of oppression navigate stigma and discrimination, and what affects their capacity to do so. Quantitative analysis revealed very high rates of perceived discrimination related to: homelessness/poverty (61.5%), race/ethnicity/skin colour (50.6%) and mental illness/substance use (43.7%). Immigrants and those who had been homeless three or more years reported higher perceived discrimination on all three domains. Analysis of qualitative interviews revealed three common themes related to navigating these experiences of discrimination among participants: 1) social distancing; 2) old and new labels/identities; and, 3) 'homeland' cultures. These study findings underscore poverty and homelessness as major sources of perceived discrimination, and expose underlying complexities in the navigation of multiple identities in responding to stigma and discrimination. Current Controlled Trials ISRCTN42520374 . Registered 18 August 2009.
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Complexity-reduced implementations of complete and null-space-based linear discriminant analysis.
Lu, Gui-Fu; Zheng, Wenming
2013-10-01
Dimensionality reduction has become an important data preprocessing step in a lot of applications. Linear discriminant analysis (LDA) is one of the most well-known dimensionality reduction methods. However, the classical LDA cannot be used directly in the small sample size (SSS) problem where the within-class scatter matrix is singular. In the past, many generalized LDA methods has been reported to address the SSS problem. Among these methods, complete linear discriminant analysis (CLDA) and null-space-based LDA (NLDA) provide good performances. The existing implementations of CLDA are computationally expensive. In this paper, we propose a new and fast implementation of CLDA. Our proposed implementation of CLDA, which is the most efficient one, is equivalent to the existing implementations of CLDA in theory. Since CLDA is an extension of null-space-based LDA (NLDA), our implementation of CLDA also provides a fast implementation of NLDA. Experiments on some real-world data sets demonstrate the effectiveness of our proposed new CLDA and NLDA algorithms. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Micro-Raman spectroscopy of natural and synthetic indigo samples.
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.
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Energy-Discriminative Performance of a Spectral Micro-CT System
He, Peng; Yu, Hengyong; Bennett, James; Ronaldson, Paul; Zainon, Rafidah; Butler, Anthony; Butler, Phil; Wei, Biao; Wang, Ge
2013-01-01
Experiments were performed to evaluate the energy-discriminative performance of a spectral (multi-energy) micro-CT system. The system, designed by MARS (Medipix All Resolution System) Bio-Imaging Ltd. (Christchurch, New Zealand), employs a photon-counting energy-discriminative detector technology developed by CERN (European Organization for Nuclear Research). We used the K-edge attenuation characteristic of some known materials to calibrate the detector’s photon energy discrimination. For tomographic analysis, we used the compressed sensing (CS) based ordered-subset simultaneous algebraic reconstruction techniques (OS-SART) to reconstruct sample images, which is effective to reduce noise and suppress artifacts. Unlike conventional CT, the principal component analysis (PCA) method can be applied to extract and quantify additional attenuation information from a spectral CT dataset. Our results show that the spectral CT has a good energy-discriminative performance and provides more attenuation information than the conventional CT. PMID:24004864
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Jo, J A; Fang, Q; Papaioannou, T; Qiao, J H; Fishbein, M C; Dorafshar, A; Reil, T; Baker, D; Freischlag, J; Marcu, L
2004-01-01
This study investigates the ability of new analytical methods of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data to characterize tissue in-vivo, such as the composition of atherosclerotic vulnerable plaques. A total of 73 TR-LIFS measurements were taken in-vivo from the aorta of 8 rabbits, and subsequently analyzed using the Laguerre deconvolution technique. The investigated spots were classified as normal aorta, thin or thick lesions, and lesions rich in either collagen or macrophages/foam-cells. Different linear and nonlinear classification algorithms (linear discriminant analysis, stepwise linear discriminant analysis, principal component analysis, and feedforward neural networks) were developed using spectral and TR features (ratios of intensity values and Laguerre expansion coefficients, respectively). Normal intima and thin lesions were discriminated from thick lesions (sensitivity >90%, specificity 100%) using only spectral features. However, both spectral and time-resolved features were necessary to discriminate thick lesions rich in collagen from thick lesions rich in foam cells (sensitivity >85%, specificity >93%), and thin lesions rich in foam cells from normal aorta and thin lesions rich in collagen (sensitivity >85%, specificity >94%). Based on these findings, we believe that TR-LIFS information derived from the Laguerre expansion coefficients can provide a valuable additional dimension for in-vivo tissue characterization.
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Bavykin, Sergei G.; Mirzabekov, Andrei D.
2007-10-30
The present invention is directed to a novel method of discriminating a highly infectious bacterium Bacillus anthracis from a group of closely related microorganisms. Sequence variations in the 16S and 23S rRNA of the B. cereus subgroup including B. anthracis are utilized to construct an array that can detect these sequence variations through selective hybridizations. The identification and analysis of these sequence variations enables positive discrimination of isolates of the B. cereus group that includes B. anthracis. Discrimination of single base differences in rRNA was achieved with a microchip during analysis of B. cereus group isolates from both single and in mixed probes, as well as identification of polymorphic sites. Successful use of a microchip to determine the appropriate subgroup classification using eight reference microorganisms from the B. cereus group as a study set, was demonstrated.
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Taxonomic discrimination of higher plants by pyrolysis mass spectrometry.
Kim, S W; Ban, S H; Chung, H J; Choi, D W; Choi, P S; Yoo, O J; Liu, J R
2004-02-01
Pyrolysis mass spectrometry (PyMS) is a rapid, simple, high-resolution analytical method based on thermal degradation of complex material in a vacuum and has been widely applied to the discrimination of closely related microbial strains. Leaf samples of six species and one variety of higher plants (Rosa multiflora, R. multiflora var. platyphylla, Sedum kamtschaticum, S. takesimense, S. sarmentosum, Hepatica insularis, and H. asiatica) were subjected to PyMS for spectral fingerprinting. Principal component analysis of PyMS data was not able to discriminate these plants in discrete clusters. However, canonical variate analysis of PyMS data separated these plants from one another. A hierarchical dendrogram based on canonical variate analysis was in agreement with the known taxonomy of the plants at the variety level. These results indicate that PyMS is able to discriminate higher plants based on taxonomic classification at the family, genus, species, and variety level.
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NASA Astrophysics Data System (ADS)
Crawford, I.; Ruske, S.; Topping, D. O.; Gallagher, M. W.
2015-11-01
In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs) by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4) where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen-Rocky Mountain Biogenic Aerosol Study) ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misattribution due to poor centroid definition and failure to assign particles to a cluster as a result of the subsampling and comparative attribution method employed by WASP. The methods used here allow for the entire fluorescent population of particles to be analysed, yielding an explicit cluster attribution for each particle and improving cluster centroid definition and our capacity to discriminate and quantify PBAP meta-classes compared to previous approaches.
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NASA Astrophysics Data System (ADS)
Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad
2015-01-01
Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.
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A manual and an automatic TERS based virus discrimination
NASA Astrophysics Data System (ADS)
Olschewski, Konstanze; Kämmer, Evelyn; Stöckel, Stephan; Bocklitz, Thomas; Deckert-Gaudig, Tanja; Zell, Roland; Cialla-May, Dana; Weber, Karina; Deckert, Volker; Popp, Jürgen
2015-02-01
Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%.Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr07033j
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NASA Astrophysics Data System (ADS)
Che, Il-Young; Jeon, Jeong-Soo
2010-05-01
Korea Institute of Geoscience and Mineral Resources (KIGAM) operates an infrasound network consisting of seven seismo-acoustic arrays in South Korea. Development of the arrays began in 1999, partially in collaboration with Southern Methodist University, with the goal of detecting distant infrasound signals from natural and anthropogenic phenomena in and around the Korean Peninsula. The main operational purpose of this network is to discriminate man-made seismic events from seismicity including thousands of seismic events per year in the region. The man-made seismic events are major cause of error in estimating the natural seismicity, especially where the seismic activity is weak or moderate such as in the Korean Peninsula. In order to discriminate the man-made explosions from earthquakes, we have applied the seismo-acoustic analysis associating seismic and infrasonic signals generated from surface explosion. The observations of infrasound at multiple arrays made it possible to discriminate surface explosion, because small or moderate size earthquake is not sufficient to generate infrasound. Till now we have annually discriminated hundreds of seismic events in seismological catalog as surface explosions by the seismo-acoustic analysis. Besides of the surface explosions, the network also detected infrasound signals from other sources, such as bolide, typhoons, rocket launches, and underground nuclear test occurred in and around the Korean Peninsula. In this study, ten years of seismo-acoustic data are reviewed with recent infrasonic detection algorithm and association method that finally linked to the seismic monitoring system of the KIGAM to increase the detection rate of surface explosions. We present the long-term results of seismo-acoustic analysis, the detection capability of the multiple arrays, and implications for seismic source location. Since the seismo-acoustic analysis is proved as a definite method to discriminate surface explosion, the analysis will be continuously used for estimating natural seismicity and understanding infrasonic sources.
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NASA Astrophysics Data System (ADS)
Åberg Lindell, M.; Andersson, P.; Grape, S.; Hellesen, C.; Håkansson, A.; Thulin, M.
2018-03-01
This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost. A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items. The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope 134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.
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Hettick, Justin M; Green, Brett J; Buskirk, Amanda D; Kashon, Michael L; Slaven, James E; Janotka, Erika; Blachere, Francoise M; Schmechel, Detlef; Beezhold, Donald H
2008-09-15
Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used to generate highly reproducible mass spectral fingerprints for 12 species of fungi of the genus Aspergillus and 5 different strains of Aspergillus flavus. Prior to MALDI-TOF MS analysis, the fungi were subjected to three 1-min bead beating cycles in an acetonitrile/trifluoroacetic acid solvent. The mass spectra contain abundant peaks in the range of 5 to 20kDa and may be used to discriminate between species unambiguously. A discriminant analysis using all peaks from the MALDI-TOF MS data yielded error rates for classification of 0 and 18.75% for resubstitution and cross-validation methods, respectively. If a subset of 28 significant peaks is chosen, resubstitution and cross-validation error rates are 0%. Discriminant analysis of the MALDI-TOF MS data for 5 strains of A. flavus using all peaks yielded error rates for classification of 0 and 5% for resubstitution and cross-validation methods, respectively. These data indicate that MALDI-TOF MS data may be used for unambiguous identification of members of the genus Aspergillus at both the species and strain levels.
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Buzzini, Patrick; Massonnet, Genevieve
2015-05-01
In the second part of this survey, the ability of micro-Raman spectroscopy to discriminate 180 fiber samples of blue, black, and red cottons, wools, and acrylics was compared to that gathered with the traditional methods for the examination of textile fibers in a forensic context (including light microscopy methods, UV-vis microspectrophotometry and thin-layer chromatography). This study shows that the Raman technique plays a complementary and useful role to obtain further discriminations after the application of light microscopy methods and UV-vis microspectrophotometry and assure the nondestructive nature of the analytical sequence. These additional discriminations were observed despite the lower discriminating powers of Raman data considered individually, compared to those of light microscopy and UV-vis MSP. This study also confirms that an instrument equipped with several laser lines is necessary for an efficient use as applied to the examination of textile fibers in a forensic setting. © 2015 American Academy of Forensic Sciences.
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Wang, Mei; Wang, Yan-Hong; Avula, Bharathi; Radwan, Mohamed M; Wanas, Amira S; Mehmedic, Zlatko; van Antwerp, John; ElSohly, Mahmoud A; Khan, Ikhlas A
2017-05-01
Ultra-high-performance supercritical fluid chromatography (UHPSFC) is an efficient analytical technique and has not been fully employed for the analysis of cannabis. Here, a novel method was developed for the analysis of 30 cannabis plant extracts and preparations using UHPSFC/PDA-MS. Nine of the most abundant cannabinoids, viz. CBD, ∆ 8 -THC, THCV, ∆ 9 -THC, CBN, CBG, THCA-A, CBDA, and CBGA, were quantitatively determined (RSDs < 6.9%). Unlike GC methods, no derivatization or decarboxylation was required prior to UHPSFC analysis. The UHPSFC chromatographic separation of cannabinoids displayed an inverse elution order compared to UHPLC. Combining with PDA-MS, this orthogonality is valuable for discrimination of cannabinoids in complex matrices. The developed method was validated, and the quantification results were compared with a standard UHPLC method. The RSDs of these two methods were within ±13.0%. Finally, chemometric analysis including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to differentiate between cannabis samples. © 2016 American Academy of Forensic Sciences.
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Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza
2013-03-01
Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
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Lee, Ga-Young; Kim, Jeonghun; Kim, Ju Han; Kim, Kiwoong; Seong, Joon-Kyung
2014-01-01
Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.
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Methods for presentation and display of multivariate data
NASA Technical Reports Server (NTRS)
Myers, R. H.
1981-01-01
Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.
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Lee, Byeong-Ju; Kim, Hye-Youn; Lim, Sa Rang; Huang, Linfang; Choi, Hyung-Kyoon
2017-01-01
Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values.
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Lim, Sa Rang; Huang, Linfang
2017-01-01
Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values. PMID:29049369
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2011-01-01
Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043
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Bowers, Andrew; Saltuklaroglu, Tim; Harkrider, Ashley; Cuellar, Megan
2013-01-01
Background Constructivist theories propose that articulatory hypotheses about incoming phonetic targets may function to enhance perception by limiting the possibilities for sensory analysis. To provide evidence for this proposal, it is necessary to map ongoing, high-temporal resolution changes in sensorimotor activity (i.e., the sensorimotor μ rhythm) to accurate speech and non-speech discrimination performance (i.e., correct trials.) Methods Sixteen participants (15 female and 1 male) were asked to passively listen to or actively identify speech and tone-sweeps in a two-force choice discrimination task while the electroencephalograph (EEG) was recorded from 32 channels. The stimuli were presented at signal-to-noise ratios (SNRs) in which discrimination accuracy was high (i.e., 80–100%) and low SNRs producing discrimination performance at chance. EEG data were decomposed using independent component analysis and clustered across participants using principle component methods in EEGLAB. Results ICA revealed left and right sensorimotor µ components for 14/16 and 13/16 participants respectively that were identified on the basis of scalp topography, spectral peaks, and localization to the precentral and postcentral gyri. Time-frequency analysis of left and right lateralized µ component clusters revealed significant (pFDR<.05) suppression in the traditional beta frequency range (13–30 Hz) prior to, during, and following syllable discrimination trials. No significant differences from baseline were found for passive tasks. Tone conditions produced right µ beta suppression following stimulus onset only. For the left µ, significant differences in the magnitude of beta suppression were found for correct speech discrimination trials relative to chance trials following stimulus offset. Conclusions Findings are consistent with constructivist, internal model theories proposing that early forward motor models generate predictions about likely phonemic units that are then synthesized with incoming sensory cues during active as opposed to passive processing. Future directions and possible translational value for clinical populations in which sensorimotor integration may play a functional role are discussed. PMID:23991030
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Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun
2016-01-01
As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.
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Fuji apple storage time rapid determination method using Vis/NIR spectroscopy.
Liu, Fuqi; Tang, Xuxiang
2015-01-01
Fuji apple storage time rapid determination method using visible/near-infrared (Vis/NIR) spectroscopy was studied in this paper. Vis/NIR diffuse reflection spectroscopy responses to samples were measured for 6 days. Spectroscopy data were processed by stochastic resonance (SR). Principal component analysis (PCA) was utilized to analyze original spectroscopy data and SNR eigen value. Results demonstrated that PCA could not totally discriminate Fuji apples using original spectroscopy data. Signal-to-noise ratio (SNR) spectrum clearly classified all apple samples. PCA using SNR spectrum successfully discriminated apple samples. Therefore, Vis/NIR spectroscopy was effective for Fuji apple storage time rapid discrimination. The proposed method is also promising in condition safety control and management for food and environmental laboratories.
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Fuji apple storage time rapid determination method using Vis/NIR spectroscopy
Liu, Fuqi; Tang, Xuxiang
2015-01-01
Fuji apple storage time rapid determination method using visible/near-infrared (Vis/NIR) spectroscopy was studied in this paper. Vis/NIR diffuse reflection spectroscopy responses to samples were measured for 6 days. Spectroscopy data were processed by stochastic resonance (SR). Principal component analysis (PCA) was utilized to analyze original spectroscopy data and SNR eigen value. Results demonstrated that PCA could not totally discriminate Fuji apples using original spectroscopy data. Signal-to-noise ratio (SNR) spectrum clearly classified all apple samples. PCA using SNR spectrum successfully discriminated apple samples. Therefore, Vis/NIR spectroscopy was effective for Fuji apple storage time rapid discrimination. The proposed method is also promising in condition safety control and management for food and environmental laboratories. PMID:25874818
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NASA Astrophysics Data System (ADS)
Giana, Fabián Eduardo; Bonetto, Fabián José; Bellotti, Mariela Inés
2018-03-01
In this work we present an assay to discriminate between normal and cancerous cells. The method is based on the measurement of electrical impedance spectra of in vitro cell cultures. We developed a protocol consisting on four consecutive measurement phases, each of them designed to obtain different information about the cell cultures. Through the analysis of the measured data, 26 characteristic features were obtained for both cell types. From the complete set of features, we selected the most relevant in terms of their discriminant capacity by means of conventional statistical tests. A linear discriminant analysis was then carried out on the selected features, allowing the classification of the samples in normal or cancerous with 4.5% of false positives and no false negatives.
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Giana, Fabián Eduardo; Bonetto, Fabián José; Bellotti, Mariela Inés
2018-03-01
In this work we present an assay to discriminate between normal and cancerous cells. The method is based on the measurement of electrical impedance spectra of in vitro cell cultures. We developed a protocol consisting on four consecutive measurement phases, each of them designed to obtain different information about the cell cultures. Through the analysis of the measured data, 26 characteristic features were obtained for both cell types. From the complete set of features, we selected the most relevant in terms of their discriminant capacity by means of conventional statistical tests. A linear discriminant analysis was then carried out on the selected features, allowing the classification of the samples in normal or cancerous with 4.5% of false positives and no false negatives.
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Cousins, Matthew M; Swan, David; Magaret, Craig A; Hoover, Donald R; Eshleman, Susan H
2012-01-01
HIV diversity may be a useful biomarker for discriminating between recent and non-recent HIV infection. The high resolution melting (HRM) diversity assay was developed to quantify HIV diversity in viral populations without sequencing. In this assay, HIV diversity is expressed as a single numeric HRM score that represents the width of a melting peak. HRM scores are highly associated with diversity measures obtained with next generation sequencing. In this report, a software package, the HRM Diversity Assay Analysis Tool (DivMelt), was developed to automate calculation of HRM scores from melting curve data. DivMelt uses computational algorithms to calculate HRM scores by identifying the start (T1) and end (T2) melting temperatures for a DNA sample and subtracting them (T2 - T1 = HRM score). DivMelt contains many user-supplied analysis parameters to allow analyses to be tailored to different contexts. DivMelt analysis options were optimized to discriminate between recent and non-recent HIV infection and to maximize HRM score reproducibility. HRM scores calculated using DivMelt were compared to HRM scores obtained using a manual method that is based on visual inspection of DNA melting curves. HRM scores generated with DivMelt agreed with manually generated HRM scores obtained from the same DNA melting data. Optimal parameters for discriminating between recent and non-recent HIV infection were identified. DivMelt provided greater discrimination between recent and non-recent HIV infection than the manual method. DivMelt provides a rapid, accurate method of determining HRM scores from melting curve data, facilitating use of the HRM diversity assay for large-scale studies.
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NASA Astrophysics Data System (ADS)
Izdihar, R. P.; Maryono, M.; Widjonarko, W.; Rahayu, S.
2018-02-01
This research aims to assess street lighting infrastructure in rural-urban of Mount Merapi Evacuation road. Three evacuation road/corridor; Mriyan-Boyolali, Wonodoyo-Boyolali and Samiran-Boyolali are selected as case study. By using discriminant this study examine 6 variables namely type of lamp, physical component, height, time, power and cons consumption. In addition this study also using GIS method to assessing geographical feature as of previous result. According to the discriminant analysis, the characteristic of street lighting could be distinguished as two characteristic, while from the GIS assessment, the study found three characteristic of geographical street lighting feature.
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Zhang, Zhen; Wang, Bao-Jie; Guan, Hong-Yu; Pang, Hao; Xuan, Jin-Feng
2009-11-01
Reducing amplicon sizes has become a major strategy for analyzing degraded DNA typical of forensic samples. However, amplicon sizes in current mini-short tandem repeat-polymerase chain reaction (PCR) and mini-sequencing assays are still not suitable for analysis of severely degraded DNA. In this study, we present a multiplex typing method that couples ligase detection reaction with PCR that can be used to identify single nucleotide polymorphisms and small-scale insertion/deletions in a sample of severely fragmented DNA. This method adopts thermostable ligation for allele discrimination and subsequent PCR for signal enhancement. In this study, four polymorphic loci were used to assess the ability of this technique to discriminate alleles in an artificially degraded sample of DNA with fragment sizes <100 bp. Our results showed clear allelic discrimination of single or multiple loci, suggesting that this method might aid in the analysis of extremely degraded samples in which allelic drop out of larger fragments is observed.
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Hirayama, Junichi; Tazumi, Akihiro; Hayashi, Kyohei; Tasaki, Erina; Kuribayashi, Takashi; Moore, John E; Millar, Beverley C; Matsuda, Motoo
2011-06-01
In the present study, the reliability of full-length gene sequence information for several genes including 16S rRNA was examined, for the discrimination of the two representative Campylobacter lari taxa, namely urease-negative (UN) C. lari and urease-positive thermophilic Campylobacter (UPTC). As previously described, 16S rRNA gene sequence are not reliable for the molecular discrimination of UN C. lari from UPTC organisms employing both the unweighted pair group method using arithmetic means analysis (UPGMA) and neighbor joining (NJ) methods. In addition, three composite full-length gene sequences (ciaB, flaC and vacJ) out of seven gene loci examined were reliable for discrimination employing dendrograms constructed by the UPGMA method. In addition, all the dendrograms of the NJ phylogenetic trees constructed based on the nine gene information were not reliable for the discrimination. Three composite full-length gene sequences (ciaB, flaC and vacJ) were reliable for the molecular discrimination between UN C. lari and UPTC organisms employing the UPGMA method, as well as among four thermophilic Campylobacter species. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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NASA Astrophysics Data System (ADS)
Liu, Wen; Zhang, Yuying; Yang, Si; Han, Donghai
2018-05-01
A new technique to identify the floral resources of honeys is demanded. Terahertz time-domain attenuated total reflection spectroscopy combined with chemometrics methods was applied to discriminate different categorizes (Medlar honey, Vitex honey, and Acacia honey). Principal component analysis (PCA), cluster analysis (CA) and partial least squares-discriminant analysis (PLS-DA) have been used to find information of the botanical origins of honeys. Spectral range also was discussed to increase the precision of PLS-DA model. The accuracy of 88.46% for validation set was obtained, using PLS-DA model in 0.5-1.5 THz. This work indicated terahertz time-domain attenuated total reflection spectroscopy was an available approach to evaluate the quality of honey rapidly.
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NASA Astrophysics Data System (ADS)
Luna, Aderval S.; da Silva, Arnaldo P.; Pinho, Jéssica S. A.; Ferré, Joan; Boqué, Ricard
Near infrared (NIR) spectroscopy and multivariate classification were applied to discriminate soybean oil samples into non-transgenic and transgenic. Principal Component Analysis (PCA) was applied to extract relevant features from the spectral data and to remove the anomalous samples. The best results were obtained when with Support Vectors Machine-Discriminant Analysis (SVM-DA) and Partial Least Squares-Discriminant Analysis (PLS-DA) after mean centering plus multiplicative scatter correction. For SVM-DA the percentage of successful classification was 100% for the training group and 100% and 90% in validation group for non transgenic and transgenic soybean oil samples respectively. For PLS-DA the percentage of successful classification was 95% and 100% in training group for non transgenic and transgenic soybean oil samples respectively and 100% and 80% in validation group for non transgenic and transgenic respectively. The results demonstrate that NIR spectroscopy can provide a rapid, nondestructive and reliable method to distinguish non-transgenic and transgenic soybean oils.
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Maione, Camila; Barbosa, Rommel Melgaço
2018-01-24
Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.
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Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning.
Wang, Jinhua; Yang, Xi; Cai, Hongmin; Tan, Wanchang; Jin, Cangzheng; Li, Li
2016-06-07
Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.
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Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning
Wang, Jinhua; Yang, Xi; Cai, Hongmin; Tan, Wanchang; Jin, Cangzheng; Li, Li
2016-01-01
Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer. PMID:27273294
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NASA Astrophysics Data System (ADS)
Kumar, Raj; Sharma, Vishal
2017-03-01
The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%).
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[The homogeneity of a population of yeasts from Camembert cheeses].
Schmidt, J L; Daudin, J J
1983-01-01
Yeasts are found to a large extent in cheeses, more particularly in soft cheeses such as Camembert. The proximity between two species previously identified by standard methods was studied using a factorial discriminant analysis on 326 strains. Twenty-three fermentation and assimilation tests (discriminant variables) gave a fairly good discrimination between species. This treatment has allowed us to confirm the present tendencies noticed in yeast classification and has also enabled us to group some of the species.
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A novel method for qualitative analysis of edible oil oxidation using an electronic nose.
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.
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Early Numeracy Intervention: Does Quantity Discrimination Really Work?
ERIC Educational Resources Information Center
Hansmann, Paul
2013-01-01
Scope and Method of Study: The current study demonstrates that a taped problem intervention is an effective tool for increasing the early numeracy skill of QD. A taped problems intervention was used with two variations of the quantity discrimination measure (triangle and traditional). A 3x2 doubly multivariate multivariate analysis of variance was…
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Discriminant analysis of functional optical topography for schizophrenia diagnosis
NASA Astrophysics Data System (ADS)
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.
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Yang, Jun-Ho; Yoh, Jack J
2018-01-01
A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.
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NASA Astrophysics Data System (ADS)
Shahrajabian, Maryam; Hormozi-Nezhad, M. Reza
2016-08-01
Array-based sensor is an interesting approach that suggests an alternative to expensive analytical methods. In this work, we introduce a novel, simple, and sensitive nanoparticle-based chemiluminescence (CL) sensor array for discrimination of biothiols (e.g., cysteine, glutathione and glutathione disulfide). The proposed CL sensor array is based on the CL efficiencies of four types of enhanced nanoparticle-based CL systems. The intensity of CL was altered to varying degrees upon interaction with biothiols, producing unique CL response patterns. These distinct CL response patterns were collected as “fingerprints” and were then identified through chemometric methods, including linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA). The developed array was able to successfully differentiate between cysteine, glutathione and glutathione disulfide in a wide concentration range. Moreover, it was applied to distinguish among the above analytes in human plasma.
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Ryder, Alan G
2002-03-01
Eighty-five solid samples consisting of illegal narcotics diluted with several different materials were analyzed by near-infrared (785 nm excitation) Raman spectroscopy. Principal Component Analysis (PCA) was employed to classify the samples according to narcotic type. The best sample discrimination was obtained by using the first derivative of the Raman spectra. Furthermore, restricting the spectral variables for PCA to 2 or 3% of the original spectral data according to the most intense peaks in the Raman spectrum of the pure narcotic resulted in a rapid discrimination method for classifying samples according to narcotic type. This method allows for the easy discrimination between cocaine, heroin, and MDMA mixtures even when the Raman spectra are complex or very similar. This approach of restricting the spectral variables also decreases the computational time by a factor of 30 (compared to the complete spectrum), making the methodology attractive for rapid automatic classification and identification of suspect materials.
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Yoshida, T; Kondo, N; Hanifah, Y A; Hiramatsu, K
1997-01-01
We have previously reported the phenotypic characterization of methicillin-resistant Staphylococcus aureus (MRSA) clinical strains isolated in Malaya University Hospital in the period 1987 to 1989 using antibiogram, coagulase typing, plasmid profiles, and phage typing. Here, we report the analysis of the same strains with three genotyping methods; ribotyping, pulsed-field gel electrophoresis (PFGE) typing, and IS431 typing (a restriction enzyme fragment length polymorphism analysis using an IS431 probe). Ribotyping could discriminate 46 clinical MRSA strains into 5 ribotypes, PFGE typing into 22 types, and IS431 typing into 15 types. Since the differences of the three genotyping patterns from strain to strain were quite independent from one another, the combined use of the three genotyping methods could discriminate 46 strains into 39 genotypes. Thus, the powerful discriminatory ability of the combination was demonstrated.
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Sex estimation from sternal measurements using multidetector computed tomography.
Ekizoglu, Oguzhan; Hocaoglu, Elif; Inci, Ercan; Bilgili, Mustafa Gokhan; Solmaz, Dilek; Erdil, Irem; Can, Ismail Ozgur
2014-12-01
We aimed to show the utility and reliability of sternal morphometric analysis for sex estimation.Sex estimation is a very important step in forensic identification. Skeletal surveys are main methods for sex estimation studies. Morphometric analysis of sternum may provide high accuracy rated data in sex discrimination. In this study, morphometric analysis of sternum was evaluated in 1 mm chest computed tomography scans for sex estimation. Four hundred forty 3 subjects (202 female, 241 male, mean age: 44 ± 8.1 [distribution: 30-60 year old]) were included the study. Manubrium length (ML), mesosternum length (2L), Sternebra 1 (S1W), and Sternebra 3 (S3W) width were measured and also sternal index (SI) was calculated. Differences between genders were evaluated by student t-test. Predictive factors of sex were determined by discrimination analysis and receiver operating characteristic (ROC) analysis. Male sternal measurement values are significantly higher than females (P < 0.001) while SI is significantly low in males (P < 0.001). In discrimination analysis, MSL has high accuracy rate with 80.2% in females and 80.9% in males. MSL also has the best sensitivity (75.9%) and specificity (87.6%) values. Accuracy rates were above 80% in 3 stepwise discrimination analysis for both sexes. Stepwise 1 (ML, MSL, S1W, S3W) has the highest accuracy rate in stepwise discrimination analysis with 86.1% in females and 83.8% in males. Our study showed that morphometric computed tomography analysis of sternum might provide important information for sex estimation.
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Testing alternative ground water models using cross-validation and other methods
Foglia, L.; Mehl, S.W.; Hill, M.C.; Perona, P.; Burlando, P.
2007-01-01
Many methods can be used to test alternative ground water models. Of concern in this work are methods able to (1) rank alternative models (also called model discrimination) and (2) identify observations important to parameter estimates and predictions (equivalent to the purpose served by some types of sensitivity analysis). Some of the measures investigated are computationally efficient; others are computationally demanding. The latter are generally needed to account for model nonlinearity. The efficient model discrimination methods investigated include the information criteria: the corrected Akaike information criterion, Bayesian information criterion, and generalized cross-validation. The efficient sensitivity analysis measures used are dimensionless scaled sensitivity (DSS), composite scaled sensitivity, and parameter correlation coefficient (PCC); the other statistics are DFBETAS, Cook's D, and observation-prediction statistic. Acronyms are explained in the introduction. Cross-validation (CV) is a computationally intensive nonlinear method that is used for both model discrimination and sensitivity analysis. The methods are tested using up to five alternative parsimoniously constructed models of the ground water system of the Maggia Valley in southern Switzerland. The alternative models differ in their representation of hydraulic conductivity. A new method for graphically representing CV and sensitivity analysis results for complex models is presented and used to evaluate the utility of the efficient statistics. The results indicate that for model selection, the information criteria produce similar results at much smaller computational cost than CV. For identifying important observations, the only obviously inferior linear measure is DSS; the poor performance was expected because DSS does not include the effects of parameter correlation and PCC reveals large parameter correlations. ?? 2007 National Ground Water Association.
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A Review of Classical Methods of Item Analysis.
ERIC Educational Resources Information Center
French, Christine L.
Item analysis is a very important consideration in the test development process. It is a statistical procedure to analyze test items that combines methods used to evaluate the important characteristics of test items, such as difficulty, discrimination, and distractibility of the items in a test. This paper reviews some of the classical methods for…
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Lee, Chin Mei; Sieo, Chin Chin; Cheah, Yoke-Kqueen; Abdullah, Norhani; Ho, Yin Wan
2012-02-01
Four repetitive element sequence-based polymerase chain reaction (rep-PCR) methods, namely repetitive extragenic palindromic PCR (REP-PCR), enterobacterial repetitive intergenic consensus PCR (ERIC-PCR), polytrinucleotide (GTG)₅ -PCR and BOX-PCR, were evaluated for the molecular differentiation of 12 probiotic Lactobacillus strains previously isolated from the gastrointestinal tract of chickens and used as a multistrain probiotic. This study represents the first analysis of the comparative efficacy of these four rep-PCR methods and their combination (composite rep-PCR) in the molecular typing of Lactobacillus strains based on a discriminatory index (D). Species-specific and strain-specific profiles were observed from rep-PCR. From the numerical analysis of composite rep-PCR, BOX-PCR, (GTG)₅ -PCR, REP-PCR and ERIC-PCR, D values of 0.9118, 0.9044, 0.8897, 0.8750 and 0.8529 respectively were obtained. Composite rep-PCR analysis was the most discriminative method, with eight Lactobacillus strains, namely L. brevis ATCC 14869(T) , L. reuteri C 10, L. reuteri ATCC 23272(T) , L. gallinarum ATCC 33199(T) , L. salivarius ATCC 11741(T) , L. salivarius I 24, L. panis JCM 11053(T) and L. panis C 17, being differentiated at the strain level. Composite rep-PCR analysis is potentially a useful fingerprinting method to discriminate probiotic Lactobacillus strains isolated from the gastrointestinal tract of chickens. Copyright © 2011 Society of Chemical Industry.
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Radar signal analysis of ballistic missile with micro-motion based on time-frequency distribution
NASA Astrophysics Data System (ADS)
Wang, Jianming; Liu, Lihua; Yu, Hua
2015-12-01
The micro-motion of ballistic missile targets induces micro-Doppler modulation on the radar return signal, which is a unique feature for the warhead discrimination during flight. In order to extract the micro-Doppler feature of ballistic missile targets, time-frequency analysis is employed to process the micro-Doppler modulated time-varying radar signal. The images of time-frequency distribution (TFD) reveal the micro-Doppler modulation characteristic very well. However, there are many existing time-frequency analysis methods to generate the time-frequency distribution images, including the short-time Fourier transform (STFT), Wigner distribution (WD) and Cohen class distribution, etc. Under the background of ballistic missile defence, the paper aims at working out an effective time-frequency analysis method for ballistic missile warhead discrimination from the decoys.
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The role of explicit and implicit standards in visual speed discrimination.
Norman, J Farley; Pattison, Kristina F; Norman, Hideko F; Craft, Amy E; Wiesemann, Elizabeth Y; Taylor, M Jett
2008-01-01
Five experiments were designed to investigate visual speed discrimination. Variations of the method of constant stimuli were used to obtain speed discrimination thresholds in experiments 1, 2, 4, and 5, while the method of single stimuli was used in experiment 3. The observers' thresholds were significantly influenced by the choice of psychophysical method and by changes in the standard speed. The observers' judgments were unaffected, however, by changes in the magnitude of random variations in stimulus duration, reinforcing the conclusions of Lappin et al (1975 Journal of Experimental Psychology: Human Perception and Performance 1 383 394). When an implicit standard was used, the observers produced relatively low discrimination thresholds (7.0% of the standard speed), verifying the results of McKee (1981 Vision Research 21 491-500). When an explicit standard was used in a 2AFC variant of the method of constant stimuli, however, the observers' discrimination thresholds increased by 74% (to 12.2%), resembling the high thresholds obtained by Mandriota et al (1962 Science 138 437-438). A subsequent signal-detection analysis revealed that the observers' actual sensitivities to differences in speed were in fact equivalent for both psychophysical methods. The formation of an implicit standard in the method of single stimuli allows human observers to make judgments of speed that are as precise as those obtained when explicit standards are available.
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Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN.
Guo, Hao; Wu, Danni; An, Jubai
2017-08-09
Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.
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Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN
An, Jubai
2017-01-01
Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features. PMID:28792477
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Li, Yan; Hughes, Jan N.; Kwok, Oi-man; Hsu, Hsien-Yuan
2012-01-01
This study investigated the construct validity of measures of teacher-student support in a sample of 709 ethnically diverse second and third grade academically at-risk students. Confirmatory factor analysis investigated the convergent and discriminant validities of teacher, child, and peer reports of teacher-student support and child conduct problems. Results supported the convergent and discriminant validity of scores on the measures. Peer reports accounted for the largest proportion of trait variance and non-significant method variance. Child reports accounted for the smallest proportion of trait variance and the largest method variance. A model with two latent factors provided a better fit to the data than a model with one factor, providing further evidence of the discriminant validity of measures of teacher-student support. Implications for research, policy, and practice are discussed. PMID:21767024
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Histogram contrast analysis and the visual segregation of IID textures.
Chubb, C; Econopouly, J; Landy, M S
1994-09-01
A new psychophysical methodology is introduced, histogram contrast analysis, that allows one to measure stimulus transformations, f, used by the visual system to draw distinctions between different image regions. The method involves the discrimination of images constructed by selecting texture micropatterns randomly and independently (across locations) on the basis of a given micropattern histogram. Different components of f are measured by use of different component functions to modulate the micropattern histogram until the resulting textures are discriminable. When no discrimination threshold can be obtained for a given modulating component function, a second titration technique may be used to measure the contribution of that component to f. The method includes several strong tests of its own assumptions. An example is given of the method applied to visual textures composed of small, uniform squares with randomly chosen gray levels. In particular, for a fixed mean gray level mu and a fixed gray-level variance sigma 2, histogram contrast analysis is used to establish that the class S of all textures composed of small squares with jointly independent, identically distributed gray levels with mean mu and variance sigma 2 is perceptually elementary in the following sense: there exists a single, real-valued function f S of gray level, such that two textures I and J in S are discriminable only if the average value of f S applied to the gray levels in I is significantly different from the average value of f S applied to the gray levels in J. Finally, histogram contrast analysis is used to obtain a seventh-order polynomial approximation of f S.
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Edmands, William M B; Ferrari, Pietro; Scalbert, Augustin
2014-11-04
Extraction of meaningful biological information from urinary metabolomic profiles obtained by liquid-chromatography coupled to mass spectrometry (MS) necessitates the control of unwanted sources of variability associated with large differences in urine sample concentrations. Different methods of normalization either before analysis (preacquisition normalization) through dilution of urine samples to the lowest specific gravity measured by refractometry, or after analysis (postacquisition normalization) to urine volume, specific gravity and median fold change are compared for their capacity to recover lead metabolites for a potential future use as dietary biomarkers. Twenty-four urine samples of 19 subjects from the European Prospective Investigation into Cancer and nutrition (EPIC) cohort were selected based on their high and low/nonconsumption of six polyphenol-rich foods as assessed with a 24 h dietary recall. MS features selected on the basis of minimum discriminant selection criteria were related to each dietary item by means of orthogonal partial least-squares discriminant analysis models. Normalization methods ranked in the following decreasing order when comparing the number of total discriminant MS features recovered to that obtained in the absence of normalization: preacquisition normalization to specific gravity (4.2-fold), postacquisition normalization to specific gravity (2.3-fold), postacquisition median fold change normalization (1.8-fold increase), postacquisition normalization to urinary volume (0.79-fold). A preventative preacquisition normalization based on urine specific gravity was found to be superior to all curative postacquisition normalization methods tested for discovery of MS features discriminant of dietary intake in these urinary metabolomic datasets.
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Partial Least Squares for Discrimination in fMRI Data
Andersen, Anders H.; Rayens, William S.; Liu, Yushu; Smith, Charles D.
2011-01-01
Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using either principal component analysis (PCA), partial least squares (PLS), or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contains more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using fMRI as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk. PMID:22227352
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Automatic telangiectasia analysis in dermoscopy images using adaptive critic design.
Cheng, B; Stanley, R J; Stoecker, W V; Hinton, K
2012-11-01
Telangiectasia, tiny skin vessels, are important dermoscopy structures used to discriminate basal cell carcinoma (BCC) from benign skin lesions. This research builds off of previously developed image analysis techniques to identify vessels automatically to discriminate benign lesions from BCCs. A biologically inspired reinforcement learning approach is investigated in an adaptive critic design framework to apply action-dependent heuristic dynamic programming (ADHDP) for discrimination based on computed features using different skin lesion contrast variations to promote the discrimination process. Lesion discrimination results for ADHDP are compared with multilayer perception backpropagation artificial neural networks. This study uses a data set of 498 dermoscopy skin lesion images of 263 BCCs and 226 competitive benign images as the input sets. This data set is extended from previous research [Cheng et al., Skin Research and Technology, 2011, 17: 278]. Experimental results yielded a diagnostic accuracy as high as 84.6% using the ADHDP approach, providing an 8.03% improvement over a standard multilayer perception method. We have chosen BCC detection rather than vessel detection as the endpoint. Although vessel detection is inherently easier, BCC detection has potential direct clinical applications. Small BCCs are detectable early by dermoscopy and potentially detectable by the automated methods described in this research. © 2011 John Wiley & Sons A/S.
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Liang, Wenyi; Chen, Wenjing; Wu, Lingfang; Li, Shi; Qi, Qi; Cui, Yaping; Liang, Linjin; Ye, Ting; Zhang, Lanzhen
2017-03-17
Danshen, the dried root of Salvia miltiorrhiza Bge., is a widely used commercially available herbal drug, and unstable quality of different samples is a current issue. This study focused on a comprehensive and systematic method combining fingerprints and chemical identification with chemometrics for discrimination and quality assessment of Danshen samples. Twenty-five samples were analyzed by HPLC-PAD and HPLC-MS n . Forty-nine components were identified and characteristic fragmentation regularities were summarized for further interpretation of bioactive components. Chemometric analysis was employed to differentiate samples and clarify the quality differences of Danshen including hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis. Consistent results were that the samples were divided into three categories which reflected the difference in quality of Danshen samples. By analyzing the reasons for sample classification, it was revealed that the processing method had a more obvious impact on sample classification than the geographical origin, it induced the different content of bioactive compounds and finally lead to different qualities. Cryptotanshinone, trijuganone B, and 15,16-dihydrotanshinone I were screened out as markers to distinguish samples by different processing methods. The developed strategy could provide a reference for evaluation and discrimination of other traditional herbal medicines.
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NASA Astrophysics Data System (ADS)
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.
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Vigilance in the discrimination-stress model for Black Americans
Himmelstein, Mary S.; Young, Danielle M.; Sanchez, Diana T.; Jackson, James S.
2015-01-01
Objective Daily events of discrimination are important factors in understanding health disparities. Vigilant coping, or protecting against anticipated discrimination by monitoring and modifying behaviour, is an understudied mechanism that may link discrimination and health outcomes. This study investigates how responding to everyday discrimination with anticipatory vigilance relates to the health of Black men and women. Methods Black adults (N = 221) from the Detroit area completed measures of discrimination, adverse life events, vigilance coping, stress, depressive symptoms and self-reported health. Results Vigilance coping strategies mediated the relationship between discrimination and stress. Multi-group path analysis revealed that stress in turn was associated with increased depression in men and women. Self-reported health consequences of stress differed between men and women. Conclusions Vigilance coping mediates the link between discrimination and stress, and stress has consequences for health outcomes resulting from discrimination. More research is needed to understand other underlying contributors to discrimination, stress and poor health outcomes as well as to create potential interventions to ameliorate health outcomes in the face of discrimination-related stress. PMID:25247925
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NASA Astrophysics Data System (ADS)
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.
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Optical Fourier diffractometry applied to degraded bone structure recognition
NASA Astrophysics Data System (ADS)
Galas, Jacek; Godwod, Krzysztof; Szawdyn, Jacek; Sawicki, Andrzej
1993-09-01
Image processing and recognition methods are useful in many fields. This paper presents the hybrid optical and digital method applied to recognition of pathological changes in bones involved by metabolic bone diseases. The trabecular bone structure, registered by x ray on the photographic film, is analyzed in the new type of computer controlled diffractometer. The set of image parameters, extracted from diffractogram, is evaluated by statistical analysis. The synthetic image descriptors in discriminant space, constructed on the base of 3 training groups of images (control, osteoporosis, and osteomalacia groups) by discriminant analysis, allow us to recognize bone samples with degraded bone structure and to recognize the disease. About 89% of the images were classified correctly. This method after optimization process will be verified in medical investigations.
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Polarimetric Decomposition Analysis of the Deepwater Horizon Oil Slick Using L-Band UAVSAR Data
NASA Technical Reports Server (NTRS)
Jones, Cathleen; Minchew, Brent; Holt, Benjamin
2011-01-01
We report here an analysis of the polarization dependence of L-band radar backscatter from the main slick of the Deepwater Horizon oil spill, with specific attention to the utility of polarimetric decomposition analysis for discrimination of oil from clean water and identification of variations in the oil characteristics. For this study we used data collected with the UAVSAR instrument from opposing look directions directly over the main oil slick. We find that both the Cloude-Pottier and Shannon entropy polarimetric decomposition methods offer promise for oil discrimination, with the Shannon entropy method yielding the same information as contained in the Cloude-Pottier entropy and averaged in tensity parameters, but with significantly less computational complexity
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A simple solubility tests for the discrimination of acrylic and modacrylic fibers.
Suga, Keisuke; Narita, Yuji; Suzuki, Shinichi
2014-05-01
In a crime scene investigation, single fibers play an important role as significant trace physical evidence. Acrylic fibers are frequently encountered in forensic analysis. Currently, acrylic and modacrylic are not discriminated clearly in Japan. Only results of FT-IR, some of acrylics were difficult to separate clearly to acrylic and modacrylic fibers. Solubility test is primitive but convenient useful method, and Japan Industrial Standards (JIS) recommends FT-IR and solubility test to distinguish acrylic and modacrylic fibers. But recommended JIS dissolving test using 100% N,N-dimethylformamide (DMF) as a solvent, some acrylics could not be discriminated. In this report, we used DMF and ethanol (90:10, v/v) solvent. The JIS method could not discriminate 6 acrylics in 60 acrylics; hence, DMF and ethanol (90:10, v/v) solvent discriminated 59 of the 60 fibers (43 acrylic and 16 modacrylic fibers) clearly, but only one modacrylic fiber incorrectly identified as acrylic. © 2014 American Academy of Forensic Sciences.
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Lou, Yun-xiao; Fu, Xian-shu; Yu, Xiao-ping; Zhang, Ya-fen
2017-01-01
This paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n = 99) collected from nine producing areas and non-Wuyi-Rock tea (n = 33) from eleven nonproducing areas were analysed for SIR and MEP by established methods. The SVM model based on coupled data produced the best prediction accuracy (0.9773). This prediction shows that instrumental methods combined with a classification model can provide an effective and stable tool for provenance discrimination. Moreover, every feature variable in stable isotope and metallic element data was ranked by its contribution to the model. The results show that δ2H, δ18O, Cs, Cu, Ca, and Rb contents are significant indications for provenance discrimination and not all of the metallic elements improve the prediction accuracy of the SVM model. PMID:28473941
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Akiyama, Yoshihiro B; Iseri, Erina; Kataoka, Tomoya; Tanaka, Makiko; Katsukoshi, Kiyonori; Moki, Hirotada; Naito, Ryoji; Hem, Ramrav; Okada, Tomonari
2017-02-15
In the present study, we determined the common morphological characteristics of the feces of Mytilus galloprovincialis to develop a method for visually discriminating the feces of this mussel in deposited materials. This method can be used to assess the effect of mussel feces on benthic environments. The accuracy of visual morphology-based discrimination of mussel feces in deposited materials was confirmed by DNA analysis. Eighty-nine percent of mussel feces shared five common morphological characteristics. Of the 372 animal species investigated, only four species shared all five of these characteristics. More than 96% of the samples were visually identified as M. galloprovincialis feces on the basis of morphology of the particles containing the appropriate mitochondrial DNA. These results suggest that mussel feces can be discriminated with high accuracy on the basis of their morphological characteristics. Thus, our method can be used to quantitatively assess the effect of mussel feces on local benthic environments. Copyright © 2016 Elsevier Ltd. All rights reserved.
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2017-01-01
The objective of this study was to standardize the high-resolution melting method for identification and discrimination of Toxoplasma gondii, Sarcocystis spp., Neospora spp., and Cryptosporidium spp. by amplification of 18S ribosomal DNA (rDNA) using a single primer pair. The analyses were performed on individual reactions (containing DNA from a single species of a protozoan), on duplex reactions (containing DNA from two species of protozoa in each reaction), and on a multiplex reaction (containing DNA of four parasites in a single reaction). The proposed method allowed us to identify and discriminate the four species by analyzing the derivative, normalized, and difference melting curves, with high reproducibility among and within the experiments, as demonstrated by low coefficients of variation (less than 2.2% and 2.0%, respectively). This is the first study where this method is used for discrimination of these four species of protozoa in a single reaction. PMID:28346485
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Yasui, Miwa; Dishion, Thomas J.; Stormshak, Elizabeth; Ball, Alison
2016-01-01
Objective The current study examines the interrelations between observed parental cultural socialization and socialization of coping with discrimination, and youth outcomes among a sample of 92 American Indian adolescents and their parents in a rural reservation. Method Path analysis is used to examine the relationships among observed parental socialization (cultural socialization and socialization of coping with discrimination), and youth-reported perceived discrimination, ethnic identity and depression. Results Findings reveal that higher levels of observed parental cultural socialization and socialization of coping with discrimination predict lower levels of depression as reported by youth 1 year later. Path analyses also show that observed parental cultural socialization and socialization of coping with discrimination are positively associated with youth ethnic identity. Conclusions These findings point to the importance of integrating familial socialization of culture and coping with discrimination in fostering resilience among American Indian youth. PMID:28503256
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Multiplexed microsatellite recovery using massively parallel sequencing
T.N. Jennings; B.J. Knaus; T.D. Mullins; S.M. Haig; R.C. Cronn
2011-01-01
Conservation and management of natural populations requires accurate and inexpensive genotyping methods. Traditional microsatellite, or simple sequence repeat (SSR), marker analysis remains a popular genotyping method because of the comparatively low cost of marker development, ease of analysis and high power of genotype discrimination. With the availability of...
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Errorless discrimination and picture fading as techniques for teaching sight words to TMR students.
Walsh, B F; Lamberts, F
1979-03-01
The effectiveness of two approaches for teaching beginning sight words to 30 TMR students was compared. In Dorry and Zeaman's picture-fading technique, words are taught through association with pictures that are faded out over a series of trials, while in the Edmark program errorless-discrimination technique, words are taught through shaped sequences of visual and auditory--visual matching-to-sample, with the target word first appearing alone and eventually appearing with orthographically similar words. Students were instructed on two lists of 10 words each, one list in the picture-fading and one in the discrimination method, in a double counter-balanced, repeated-measures design. Covariance analysis on three measures (word identification, word recognition, and picture--word matching) showed highly significant differences between the two methods. Students' performance was better after instruction with the errorless-discrimination method than after instruction with the picture-fading method. The findings on picture fading were interpreted as indicating a possible failure of the shifting of control from picture to printed word that earlier researchers have hypothesized as occurring.
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Sex estimation from measurements of the first rib in a contemporary Polish population.
Kubicka, Anna Maria; Piontek, Janusz
2016-01-01
The aim of this study was to evaluate the accuracy of sex assessment using measurements of the first rib from computed tomography (CT) to develop a discriminant formula. Four discriminant formulae were derived based on CT imaging of the right first rib of 85 female and 91 male Polish patients of known age and sex. In direct discriminant analysis, the first equation consisted of all first rib variables; the second included measurements of the rib body; the third comprised only two measurements of the sternal end of the first rib. The stepwise method selected the four best variables from all measurements. The discriminant function equation was then tested on a cross-validated group consisting of 23 females and 24 males. The direct discriminant analysis showed that sex assessment was possible in 81.5% of cases in the first group and in 91.5% in the cross-validated group when all variables for the first rib were included. The average accuracy for the original group for rib body and sternal end was 80.9 and 67.9%, respectively. The percentages of correctly assigned individuals for the functions based on the rib body and sternal end in the cross-validated group were 76.6 and 85.0%, respectively. Higher average accuracies were obtained for stepwise discriminant analysis: 83.1% for the original group and 91.2% for the cross-validated group. The exterior edge, anterior-posterior of the sternal end, and depth of the arc were the most reliable parameters. Our results suggest that the first rib is dimorphic and that the described method can be used for sex assessment.
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Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization.
Jia, Zhilong; Zhang, Xiang; Guan, Naiyang; Bo, Xiaochen; Barnes, Michael R; Luo, Zhigang
2015-01-01
RNA-sequencing is rapidly becoming the method of choice for studying the full complexity of transcriptomes, however with increasing dimensionality, accurate gene ranking is becoming increasingly challenging. This paper proposes an accurate and sensitive gene ranking method that implements discriminant non-negative matrix factorization (DNMF) for RNA-seq data. To the best of our knowledge, this is the first work to explore the utility of DNMF for gene ranking. When incorporating Fisher's discriminant criteria and setting the reduced dimension as two, DNMF learns two factors to approximate the original gene expression data, abstracting the up-regulated or down-regulated metagene by using the sample label information. The first factor denotes all the genes' weights of two metagenes as the additive combination of all genes, while the second learned factor represents the expression values of two metagenes. In the gene ranking stage, all the genes are ranked as a descending sequence according to the differential values of the metagene weights. Leveraging the nature of NMF and Fisher's criterion, DNMF can robustly boost the gene ranking performance. The Area Under the Curve analysis of differential expression analysis on two benchmarking tests of four RNA-seq data sets with similar phenotypes showed that our proposed DNMF-based gene ranking method outperforms other widely used methods. Moreover, the Gene Set Enrichment Analysis also showed DNMF outweighs others. DNMF is also computationally efficient, substantially outperforming all other benchmarked methods. Consequently, we suggest DNMF is an effective method for the analysis of differential gene expression and gene ranking for RNA-seq data.
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Shin, Jung-Sub; Park, Hee-Won; In, Gyo; Seo, Hyun Kyu; Won, Tae Hyung; Jang, Kyoung Hwa; Cho, Byung-Goo; Han, Chang Kyun; Shin, Jongheon
2016-09-01
Panax ginseng C.A. MEYER is one of the most popular medicinal herbs in Asia and the chemical constituents are changed by processing methods such as steaming or sun drying. Metabolomic analysis was performed to distinguish age discrimination of four- and six-year-old red ginseng using ultra-performance liquid chromatography quadruple time of flight mass spectrometry (UPLC-QToF-MS) with multivariate statistical analysis. Principal component analysis (PCA) showed clear discrimination between extracts of red ginseng of different ages and suggest totally six discrimination markers (two for four-year-old and four for six-year-old red ginseng). Among these, one marker was isolated and the structure determined by NMR spectroscopic analysis was 13-cis-docosenamide (marker 6-1) from six-year-old red ginseng. This is the first report of a metabolomic study regarding the age differentiation of red ginseng using UPLC-QToF-MS and determination of the structure of the marker. These results will contribute to the quality control and standardization as well as provide a scientific basis for pharmacological research on red ginseng.
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Srůtková, Dagmar; Spanova, Alena; Spano, Miroslav; Dráb, Vladimír; Schwarzer, Martin; Kozaková, Hana; Rittich, Bohuslav
2011-10-01
Bifidobacterium longum is considered to play an important role in health maintenance of the human gastrointestinal tract. Probiotic properties of bifidobacterial isolates are strictly strain-dependent and reliable methods for the identification and discrimination of this species at both subspecies and strain levels are thus required. Differentiation between B. longum ssp. longum and B. longum ssp. infantis is difficult due to high genomic similarities. In this study, four molecular-biological methods (species- and subspecies-specific PCRs, random amplified polymorphic DNA (RAPD) method using 5 primers, repetitive sequence-based (rep)-PCR with BOXA1R and (GTG)(5) primers and amplified ribosomal DNA restriction analysis (ARDRA)) and biochemical analysis, were compared for the classification of 30 B. longum strains (28 isolates and 2 collection strains) on subspecies level. Strains originally isolated from the faeces of breast-fed healthy infants (25) and healthy adults (3) showed a high degree of genetic homogeneity by PCR with subspecies-specific primers and rep-PCR. When analysed by RAPD, the strains formed many separate clusters without any potential for subspecies discrimination. These methods together with arabionose/melezitose fermentation analysis clearly differentiated only the collection strains into B. longum ssp. longum and B. longum ssp. infantis at the subspecies level. On the other hand, ARDRA analysis differentiated the strains into the B. longum/infantis subspecies using the cleavage analysis of genus-specific amplicon with just one enzyme, Sau3AI. According to our results the majority of the strains belong to the B. longum ssp. infantis (75%). Therefore we suggest ARDRA using Sau3AI restriction enzyme as the first method of choice for distinguishing between B. longum ssp. longum and B. longum ssp. infantis. Copyright © 2011 Elsevier B.V. All rights reserved.
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Multi-level discriminative dictionary learning with application to large scale image classification.
Shen, Li; Sun, Gang; Huang, Qingming; Wang, Shuhui; Lin, Zhouchen; Wu, Enhua
2015-10-01
The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.
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Bootstrap Methods: A Very Leisurely Look.
ERIC Educational Resources Information Center
Hinkle, Dennis E.; Winstead, Wayland H.
The Bootstrap method, a computer-intensive statistical method of estimation, is illustrated using a simple and efficient Statistical Analysis System (SAS) routine. The utility of the method for generating unknown parameters, including standard errors for simple statistics, regression coefficients, discriminant function coefficients, and factor…
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Grimbergen, M C M; van Swol, C F P; Kendall, C; Verdaasdonk, R M; Stone, N; Bosch, J L H R
2010-01-01
The overall quality of Raman spectra in the near-infrared region, where biological samples are often studied, has benefited from various improvements to optical instrumentation over the past decade. However, obtaining ample spectral quality for analysis is still challenging due to device requirements and short integration times required for (in vivo) clinical applications of Raman spectroscopy. Multivariate analytical methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), are routinely applied to Raman spectral datasets to develop classification models. Data compression is necessary prior to discriminant analysis to prevent or decrease the degree of over-fitting. The logical threshold for the selection of principal components (PCs) to be used in discriminant analysis is likely to be at a point before the PCs begin to introduce equivalent signal and noise and, hence, include no additional value. Assessment of the signal-to-noise ratio (SNR) at a certain peak or over a specific spectral region will depend on the sample measured. Therefore, the mean SNR over the whole spectral region (SNR(msr)) is determined in the original spectrum as well as for spectra reconstructed from an increasing number of principal components. This paper introduces a method of assessing the influence of signal and noise from individual PC loads and indicates a method of selection of PCs for LDA. To evaluate this method, two data sets with different SNRs were used. The sets were obtained with the same Raman system and the same measurement parameters on bladder tissue collected during white light cystoscopy (set A) and fluorescence-guided cystoscopy (set B). This method shows that the mean SNR over the spectral range in the original Raman spectra of these two data sets is related to the signal and noise contribution of principal component loads. The difference in mean SNR over the spectral range can also be appreciated since fewer principal components can reliably be used in the low SNR data set (set B) compared to the high SNR data set (set A). Despite the fact that no definitive threshold could be found, this method may help to determine the cutoff for the number of principal components used in discriminant analysis. Future analysis of a selection of spectral databases using this technique will allow optimum thresholds to be selected for different applications and spectral data quality levels.
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Zhao, Mingbo; Zhang, Zhao; Chow, Tommy W S; Li, Bing
2014-07-01
Dealing with high-dimensional data has always been a major problem in research of pattern recognition and machine learning, and Linear Discriminant Analysis (LDA) is one of the most popular methods for dimension reduction. However, it only uses labeled samples while neglecting unlabeled samples, which are abundant and can be easily obtained in the real world. In this paper, we propose a new dimension reduction method, called "SL-LDA", by using unlabeled samples to enhance the performance of LDA. The new method first propagates label information from the labeled set to the unlabeled set via a label propagation process, where the predicted labels of unlabeled samples, called "soft labels", can be obtained. It then incorporates the soft labels into the construction of scatter matrixes to find a transformed matrix for dimension reduction. In this way, the proposed method can preserve more discriminative information, which is preferable when solving the classification problem. We further propose an efficient approach for solving SL-LDA under a least squares framework, and a flexible method of SL-LDA (FSL-LDA) to better cope with datasets sampled from a nonlinear manifold. Extensive simulations are carried out on several datasets, and the results show the effectiveness of the proposed method. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Workplace Discrimination and Health Among Filipinos in the United States
de Castro, Arnold B.; Gee, Gilbert C.; Takeuchi, David T.
2008-01-01
Objectives. We examined the association between work discrimination and morbidity among Filipinos in the United States, independent of more-global measures of discrimination. Methods. Data were collected from the Filipino American Community Epidemiological Survey. Our analysis focused on 1652 participants who were employed at the time of data collection, and we used negative binomial regression to determine the association between work discrimination and health conditions. Results. The report of workplace discrimination specific to being Filipino was associated with an increased number of health conditions. This association persisted even after we controlled for everyday discrimination, a general assessment of discrimination; job concerns, a general assessment of unpleasant work circumstances; having immigrated for employment reasons; job category; income; education; gender; and other sociodemographic factors. Conclusions. Racial discrimination in the workplace was positively associated with poor health among Filipino Americans after we controlled for reports of everyday discrimination and general concerns about one’s job. This finding shows the importance of considering the work setting as a source of discrimination and its effect on morbidity among racial minorities. PMID:18235069
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A simple randomisation procedure for validating discriminant analysis: a methodological note.
Wastell, D G
1987-04-01
Because the goal of discriminant analysis (DA) is to optimise classification, it designedly exaggerates between-group differences. This bias complicates validation of DA. Jack-knifing has been used for validation but is inappropriate when stepwise selection (SWDA) is employed. A simple randomisation test is presented which is shown to give correct decisions for SWDA. The general superiority of randomisation tests over orthodox significance tests is discussed. Current work on non-parametric methods of estimating the error rates of prediction rules is briefly reviewed.
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Item analysis of examinations in the Faculty of Medicine of Tunis.
Hermi, Amene; Achour, Wafa
2016-04-01
Introduction Item analysis is the process of collecting, summarizing and using information from students' responses to assess test items' quality. This study used this approach to evaluate the quality of items and examinations given in the Faculty of Medicine of Tunis (FMT). Methods This study concerned the examinations of 2012-2013 (principal session). It analyzed 3138 items from 66 examinations, of which, 46 were multidisciplinary (187 disciplines). A total of 2515 students took the examinations. "AnItem.xls" file was used for the analysis that focused on difficulty, discrimination and internal consistency. Results Mean difficulty for all examinations was optimum (mean difficulty index: 0.59). Majority of items (89.17%) were either easy or of acceptable difficulty. Mean discrimination for all examinations was moderate (mean item discrimination coefficient: 0.28) with poor discrimination in 23.62% of items. Maximal discrimination occurred with disciplines of difficulty index between 0.4-0.6. « Ideal » items represented 27.02%. Mean internal consistency for all examinations was acceptable (Cronbach's alpha: 0.79). Disciplines with nonacceptable internal consistency (68.45%) contained a maximum of 33 items (each one) and a positive correlation between their alpha and the number of their questions. Distributions were mostly (72.73%) platykurtic and negatively asymmetric (89.39%). First year of studies had the best parameters. Conclusion Our examinations had an acceptable internal consistency, and a good level of difficulty and discrimination. They tended to facility and discriminated basically students of medium level. Item analysis is useful as a guide to item writers to improve the overall quality of questions in the future.
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Kumar, Raj; Sharma, Vishal
2017-03-15
The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%). Copyright © 2016 Elsevier B.V. All rights reserved.
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Chang, Xiangwei; Zhang, Juanjuan; Li, Dekun; Zhou, Dazheng; Zhang, Yuling; Wang, Jincheng; Hu, Bing; Ju, Aichun; Ye, Zhengliang
2017-07-15
The adulteration or falsification of the cultivation age of mountain cultivated ginseng (MCG) has been a serious problem in the commercial MCG market. To develop an efficient discrimination tool for the cultivation age and to explore potential age-dependent markers, an optimized ultra high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS)-based metabolomics approach was applied in the global metabolite profiling of 156 MCG leaf (MGL) samples aged from 6 to 18 years. Multivariate statistical methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to compare the derived patterns between MGL samples of different cultivation ages. The present study demonstrated that 6-18-year-old MGL samples can be successfully discriminated using two simple successive steps, together with four PLS-DA discrimination models. Furthermore, 39 robust age-dependent markers enabling differentiation among the 6-18-year-old MGL samples were discovered. The results were validated by a permutation test and an external test set to verify the predictability and reliability of the established discrimination models. More importantly, without destroying the MCG roots, the proposed approach could also be applied to discriminate MCG root ages indirectly, using a minimum amount of homophyletic MGL samples combined with the established four PLS-DA models and identified markers. Additionally, to the best of our knowledge, this is the first study in which 6-18-year-old MCG root ages have been nondestructively differentiated by analyzing homophyletic MGL samples using UHPLC/QTOF-MS analysis and two simple successive steps together with four PLS-DA models. The method developed in this study can be used as a standard protocol for discriminating and predicting MGL ages directly and homophyletic MCG root ages indirectly. Copyright © 2017 Elsevier B.V. All rights reserved.
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Sex Estimation From Sternal Measurements Using Multidetector Computed Tomography
Ekizoglu, Oguzhan; Hocaoglu, Elif; Inci, Ercan; Bilgili, Mustafa Gokhan; Solmaz, Dilek; Erdil, Irem; Can, Ismail Ozgur
2014-01-01
Abstract We aimed to show the utility and reliability of sternal morphometric analysis for sex estimation. Sex estimation is a very important step in forensic identification. Skeletal surveys are main methods for sex estimation studies. Morphometric analysis of sternum may provide high accuracy rated data in sex discrimination. In this study, morphometric analysis of sternum was evaluated in 1 mm chest computed tomography scans for sex estimation. Four hundred forty 3 subjects (202 female, 241 male, mean age: 44 ± 8.1 [distribution: 30–60 year old]) were included the study. Manubrium length (ML), mesosternum length (2L), Sternebra 1 (S1W), and Sternebra 3 (S3W) width were measured and also sternal index (SI) was calculated. Differences between genders were evaluated by student t-test. Predictive factors of sex were determined by discrimination analysis and receiver operating characteristic (ROC) analysis. Male sternal measurement values are significantly higher than females (P < 0.001) while SI is significantly low in males (P < 0.001). In discrimination analysis, MSL has high accuracy rate with 80.2% in females and 80.9% in males. MSL also has the best sensitivity (75.9%) and specificity (87.6%) values. Accuracy rates were above 80% in 3 stepwise discrimination analysis for both sexes. Stepwise 1 (ML, MSL, S1W, S3W) has the highest accuracy rate in stepwise discrimination analysis with 86.1% in females and 83.8% in males. Our study showed that morphometric computed tomography analysis of sternum might provide important information for sex estimation. PMID:25501090
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Liu, Wei; Wang, Dongmei; Liu, Jianjun; Li, Dengwu; Yin, Dongxue
2016-01-01
The present study was performed to assess the quality of Potentilla fruticosa L. sampled from distinct regions of China using high performance liquid chromatography (HPLC) fingerprinting coupled with a suite of chemometric methods. For this quantitative analysis, the main active phytochemical compositions and the antioxidant activity in P. fruticosa were also investigated. Considering the high percentages and antioxidant activities of phytochemicals, P. fruticosa samples from Kangding, Sichuan were selected as the most valuable raw materials. Similarity analysis (SA) of HPLC fingerprints, hierarchical cluster analysis (HCA), principle component analysis (PCA), and discriminant analysis (DA) were further employed to provide accurate classification and quality estimates of P. fruticosa. Two principal components (PCs) were collected by PCA. PC1 separated samples from Kangding, Sichuan, capturing 57.64% of the variance, whereas PC2 contributed to further separation, capturing 18.97% of the variance. Two kinds of discriminant functions with a 100% discrimination ratio were constructed. The results strongly supported the conclusion that the eight samples from different regions were clustered into three major groups, corresponding with their morphological classification, for which HPLC analysis confirmed the considerable variation in phytochemical compositions and that P. fruticosa samples from Kangding, Sichuan were of high quality. The results of SA, HCA, PCA, and DA were in agreement and performed well for the quality assessment of P. fruticosa. Consequently, HPLC fingerprinting coupled with chemometric techniques provides a highly flexible and reliable method for the quality evaluation of traditional Chinese medicines.
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Liu, Wei; Wang, Dongmei; Liu, Jianjun; Li, Dengwu; Yin, Dongxue
2016-01-01
The present study was performed to assess the quality of Potentilla fruticosa L. sampled from distinct regions of China using high performance liquid chromatography (HPLC) fingerprinting coupled with a suite of chemometric methods. For this quantitative analysis, the main active phytochemical compositions and the antioxidant activity in P. fruticosa were also investigated. Considering the high percentages and antioxidant activities of phytochemicals, P. fruticosa samples from Kangding, Sichuan were selected as the most valuable raw materials. Similarity analysis (SA) of HPLC fingerprints, hierarchical cluster analysis (HCA), principle component analysis (PCA), and discriminant analysis (DA) were further employed to provide accurate classification and quality estimates of P. fruticosa. Two principal components (PCs) were collected by PCA. PC1 separated samples from Kangding, Sichuan, capturing 57.64% of the variance, whereas PC2 contributed to further separation, capturing 18.97% of the variance. Two kinds of discriminant functions with a 100% discrimination ratio were constructed. The results strongly supported the conclusion that the eight samples from different regions were clustered into three major groups, corresponding with their morphological classification, for which HPLC analysis confirmed the considerable variation in phytochemical compositions and that P. fruticosa samples from Kangding, Sichuan were of high quality. The results of SA, HCA, PCA, and DA were in agreement and performed well for the quality assessment of P. fruticosa. Consequently, HPLC fingerprinting coupled with chemometric techniques provides a highly flexible and reliable method for the quality evaluation of traditional Chinese medicines. PMID:26890416
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Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods
Wang, C. L.; Funk, L. L.; Riedel, R. A.; ...
2017-02-10
3He gas based neutron linear-position-sensitive detectors (LPSDs) have been applied for many neutron scattering instruments. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio on the orders of 10 5-10 6. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher linear discriminant analysis (FLDA)more » and three multivariate analyses (MVAs) of the features were performed. The NGD ratios are improved by about 10 2-10 3 times compared with the traditional PHA method. Finally, our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.« less
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Tang, Jin-Fa; Li, Wei-Xia; Zhang, Fan; Li, Yu-Hui; Cao, Ying-Jie; Zhao, Ya; Li, Xue-Lin; Ma, Zhi-Jie
2017-01-01
Nowadays, Radix Polygoni Multiflori (RPM, Heshouwu in Chinese) from different geographical origins were used in clinic. In order to characterize the chemical profiles of different geographical origins of RPM samples, ultra-high performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-QTOF/MS) combined with chemometrics (partial least squared discriminant analysis, PLS‑DA) method was applied in the present study. The chromatography, chemical composition and MS information of RPM samples from 18 geographical origins were acquired and profiled by UPLC-QTOF/MS. The chemical markers contributing the differentiation of RPM samples were observed and characterized by supervised PLS‑DA method of chemometrics. The chemical composition differences of RPM samples derived from 18 different geographical origins were observed. Nine chemical markers were tentatively identified which could be used as specific chemical markers for the differentiation of geographical RPM samples. UPLC-QTOF/MS method coupled with chemometrics analysis has potential to be used for discriminating different geographical TCMs. Results will help to develop strategies for conservation and utilization of RPM samples.
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Study of the method of water-injected meat identifying based on low-field nuclear magnetic resonance
NASA Astrophysics Data System (ADS)
Xu, Jianmei; Lin, Qing; Yang, Fang; Zheng, Zheng; Ai, Zhujun
2018-01-01
The aim of this study to apply low-field nuclear magnetic resonance technique was to study regular variation of the transverse relaxation spectral parameters of water-injected meat with the proportion of water injection. Based on this, the method of one-way ANOVA and discriminant analysis was used to analyse the differences between these parameters in the capacity of distinguishing water-injected proportion, and established a model for identifying water-injected meat. The results show that, except for T 21b, T 22e and T 23b, the other parameters of the T 2 relaxation spectrum changed regularly with the change of water-injected proportion. The ability of different parameters to distinguish water-injected proportion was different. Based on S, P 22 and T 23m as the prediction variable, the Fisher model and the Bayes model were established by discriminant analysis method, qualitative and quantitative classification of water-injected meat can be realized. The rate of correct discrimination of distinguished validation and cross validation were 88%, the model was stable.
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Tang, Jun; Wang, Qing; Tong, Hong; Liao, Xiang; Zhang, Zheng-fang
2016-03-01
This work aimed to use attenuated total reflectance Fourier transform infrared spectroscopy to identify the lavender essential oil by establishing a Lavender variety and quality analysis model. So, 96 samples were tested. For all samples, the raw spectra were pretreated as second derivative, and to determine the 1 750-900 cm(-1) wavelengths for pattern recognition analysis on the basis of the variance calculation. The results showed that principal component analysis (PCA) can basically discriminate lavender oil cultivar and the first three principal components mainly represent the ester, alcohol and terpenoid substances. When the orthogonal partial least-squares discriminant analysis (OPLS-DA) model was established, the 68 samples were used for the calibration set. Determination coefficients of OPLS-DA regression curve were 0.959 2, 0.976 4, and 0.958 8 respectively for three varieties of lavender essential oil. Three varieties of essential oil's the root mean square error of prediction (RMSEP) in validation set were 0.142 9, 0.127 3, and 0.124 9, respectively. The discriminant rate of calibration set and the prediction rate of validation set had reached 100%. The model has the very good recognition capability to detect the variety and quality of lavender essential oil. The result indicated that a model which provides a quick, intuitive and feasible method had been built to discriminate lavender oils.
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NASA Astrophysics Data System (ADS)
Li, Xiaohui; Yang, Sibo; Fan, Rongwei; Yu, Xin; Chen, Deying
2018-06-01
In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.
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NASA Astrophysics Data System (ADS)
Ogruc Ildiz, G.; Arslan, M.; Unsalan, O.; Araujo-Andrade, C.; Kurt, E.; Karatepe, H. T.; Yilmaz, A.; Yalcinkaya, O. B.; Herken, H.
2016-01-01
In this study, a methodology based on Fourier-transform infrared spectroscopy and principal component analysis and partial least square methods is proposed for the analysis of blood plasma samples in order to identify spectral changes correlated with some biomarkers associated with schizophrenia and bipolarity. Our main goal was to use the spectral information for the calibration of statistical models to discriminate and classify blood plasma samples belonging to bipolar and schizophrenic patients. IR spectra of 30 samples of blood plasma obtained from each, bipolar and schizophrenic patients and healthy control group were collected. The results obtained from principal component analysis (PCA) show a clear discrimination between the bipolar (BP), schizophrenic (SZ) and control group' (CG) blood samples that also give possibility to identify three main regions that show the major differences correlated with both mental disorders (biomarkers). Furthermore, a model for the classification of the blood samples was calibrated using partial least square discriminant analysis (PLS-DA), allowing the correct classification of BP, SZ and CG samples. The results obtained applying this methodology suggest that it can be used as a complimentary diagnostic tool for the detection and discrimination of these mental diseases.
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Park, Hee-Won; In, Gyo; Kim, Jeong-Han; Cho, Byung-Goo; Han, Gyeong-Ho; Chang, Il-Moo
2013-01-01
Discriminating between two herbal medicines (Panax ginseng and Panax quinquefolius), with similar chemical and physical properties but different therapeutic effects, is a very serious and difficult problem. Differentiation between two processed ginseng genera is even more difficult because the characteristics of their appearance are very similar. An ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS)-based metabolomic technique was applied for the metabolite profiling of 40 processed P. ginseng and processed P. quinquefolius. Currently known biomarkers such as ginsenoside Rf and F11 have been used for the analysis using the UPLC-photodiode array detector. However, this method was not able to fully discriminate between the two processed ginseng genera. Thus, an optimized UPLC-QTOF-based metabolic profiling method was adapted for the analysis and evaluation of two processed ginseng genera. As a result, all known biomarkers were identified by the proposed metabolomics, and additional potential biomarkers were extracted from the huge amounts of global analysis data. Therefore, it is expected that such metabolomics techniques would be widely applied to the ginseng research field. PMID:24558312
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Bavykin, Sergei G.; Mirzabekova, legal representative, Natalia V.; Mirzabekov, deceased, Andrei D.
2007-12-04
The present invention relates to methods and compositions for using nucleotide sequence variations of 16S and 23S rRNA within the B. cereus group to discriminate a highly infectious bacterium B. anthracis from closely related microorganisms. Sequence variations in the 16S and 23S rRNA of the B. cereus subgroup including B. anthracis are utilized to construct an array that can detect these sequence variations through selective hybridizations and discriminate B. cereus group that includes B. anthracis. Discrimination of single base differences in rRNA was achieved with a microchip during analysis of B. cereus group isolates from both single and in mixed samples, as well as identification of polymorphic sites. Successful use of a microchip to determine the appropriate subgroup classification using eight reference microorganisms from the B. cereus group as a study set, was demonstrated.
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Rolland, Y; Bézy-Wendling, J; Duvauferrier, R; Coatrieux, J L
1999-03-01
To demonstrate the usefulness of a model of the parenchymous vascularization to evaluate texture analysis methods. Slices with thickness varying from 1 to 4 mm were reformatted from a 3D vascular model corresponding to either normal tissue perfusion or local hypervascularization. Parameters of statistical methods were measured on 16128x128 regions of interest, and mean values and standard deviation were calculated. For each parameter, the performances (discrimination power and stability) were evaluated. Among 11 calculated statistical parameters, three (homogeneity, entropy, mean of gradients) were found to have a good discriminating power to differentiate normal perfusion from hypervascularization, but only the gradient mean was found to have a good stability with respect to the thickness. Five parameters (run percentage, run length distribution, long run emphasis, contrast, and gray level distribution) were found to have intermediate results. In the remaining three, curtosis and correlation was found to have little discrimination power, skewness none. This 3D vascular model, which allows the generation of various examples of vascular textures, is a powerful tool to assess the performance of texture analysis methods. This improves our knowledge of the methods and should contribute to their a priori choice when designing clinical studies.
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Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini
2013-01-01
Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.
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Discrimination of complex mixtures by a colorimetric sensor array: coffee aromas.
Suslick, Benjamin A; Feng, Liang; Suslick, Kenneth S
2010-03-01
The analysis of complex mixtures presents a difficult challenge even for modern analytical techniques, and the ability to discriminate among closely similar such mixtures often remains problematic. Coffee provides a readily available archetype of such highly multicomponent systems. The use of a low-cost, sensitive colorimetric sensor array for the detection and identification of coffee aromas is reported. The color changes of the sensor array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). PCA revealed that the sensor array has exceptionally high dimensionality with 18 dimensions required to define 90% of the total variance. In quintuplicate runs of 10 commercial coffees and controls, no confusions or errors in classification by HCA were observed in 55 trials. In addition, the effects of temperature and time in the roasting of green coffee beans were readily observed and distinguishable with a resolution better than 10 degrees C and 5 min, respectively. Colorimetric sensor arrays demonstrate excellent potential for complex systems analysis in real-world applications and provide a novel method for discrimination among closely similar complex mixtures.
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Discrimination of Complex Mixtures by a Colorimetric Sensor Array: Coffee Aromas
Suslick, Benjamin A.; Feng, Liang; Suslick, Kenneth S.
2010-01-01
The analysis of complex mixtures presents a difficult challenge even for modern analytical techniques, and the ability to discriminate among closely similar such mixtures often remains problematic. Coffee provides a readily available archetype of such highly multicomponent systems. The use of a low-cost, sensitive colorimetric sensor array for the detection and identification of coffee aromas is reported. The color changes of the sensor array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). PCA revealed that the sensor array has exceptionally high dimensionality with 18 dimensions required to define 90% of the total variance. In quintuplicate runs of 10 commercial coffees and controls, no confusions or errors in classification by HCA were observed in 55 trials. In addition, the effects of temperature and time in the roasting of green coffee beans were readily observed and distinguishable with a resolution better than 10 °C and 5 min, respectively. Colorimetric sensor arrays demonstrate excellent potential for complex systems analysis in real-world applications and provide a novel method for discrimination among closely similar complex mixtures. PMID:20143838
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Rinaldi, Maurizio; Gindro, Roberto; Barbeni, Massimo; Allegrone, Gianna
2009-01-01
Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product. To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches. Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample. Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds. SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices.
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Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath
Machado, Roberto F.; Laskowski, Daniel; Deffenderfer, Olivia; Burch, Timothy; Zheng, Shuo; Mazzone, Peter J.; Mekhail, Tarek; Jennings, Constance; Stoller, James K.; Pyle, Jacqueline; Duncan, Jennifer; Dweik, Raed A.; Erzurum, Serpil C.
2005-01-01
Rationale: Electronic noses are successfully used in commercial applications, including detection and analysis of volatile organic compounds in the food industry. Objectives: We hypothesized that the electronic nose could identify and discriminate between lung diseases, especially bronchogenic carcinoma. Methods: In a discovery and training phase, exhaled breath of 14 individuals with bronchogenic carcinoma and 45 healthy control subjects or control subjects without cancer was analyzed. Principal components and canonic discriminant analysis of the sensor data was used to determine whether exhaled gases could discriminate between cancer and noncancer. Discrimination between classes was performed using Mahalanobis distance. Support vector machine analysis was used to create and apply a cancer prediction model prospectively in a separate group of 76 individuals, 14 with and 62 without cancer. Main Results: Principal components and canonic discriminant analysis demonstrated discrimination between samples from patients with lung cancer and those from other groups. In the validation study, the electronic nose had 71.4% sensitivity and 91.9% specificity for detecting lung cancer; positive and negative predictive values were 66.6 and 93.4%, respectively. In this population with a lung cancer prevalence of 18%, positive and negative predictive values were 66.6 and 94.5%, respectively. Conclusion: The exhaled breath of patients with lung cancer has distinct characteristics that can be identified with an electronic nose. The results provide feasibility to the concept of using the electronic nose for managing and detecting lung cancer. PMID:15750044
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Using radar imagery for crop discrimination: a statistical and conditional probability study
Haralick, R.M.; Caspall, F.; Simonett, D.S.
1970-01-01
A number of the constraints with which remote sensing must contend in crop studies are outlined. They include sensor, identification accuracy, and congruencing constraints; the nature of the answers demanded of the sensor system; and the complex temporal variances of crops in large areas. Attention is then focused on several methods which may be used in the statistical analysis of multidimensional remote sensing data.Crop discrimination for radar K-band imagery is investigated by three methods. The first one uses a Bayes decision rule, the second a nearest-neighbor spatial conditional probability approach, and the third the standard statistical techniques of cluster analysis and principal axes representation.Results indicate that crop type and percent of cover significantly affect the strength of the radar return signal. Sugar beets, corn, and very bare ground are easily distinguishable, sorghum, alfalfa, and young wheat are harder to distinguish. Distinguishability will be improved if the imagery is examined in time sequence so that changes between times of planning, maturation, and harvest provide additional discriminant tools. A comparison between radar and photography indicates that radar performed surprisingly well in crop discrimination in western Kansas and warrants further study.
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[Determination of somatotype of man in cranio-facial personality identification].
2004-01-01
On the basis of their independent research and through the analysis of published data the authors suggested quantitative criteria for the diagnosis of a somatotype of man by the dimensional features of the face and skull. M. A. Negasheva method, based on the discriminative analysis of 7 measurement features, was used in the individual diagnosis of a somatotype by V. V. Bunaka scheme (somatotypes-pectoral, muscular, abdominal and indefinite). The authors suggest 2 diagnostic models based on the linear and discriminative analysis of 11 and 7 measurement features for the skull. The diagnostic accuracy in case of main male som-atotypes makes 87 and 64.4%, respectively, with the canonic correlations of 0.574 and 0.292. The designed methods can be used in forensic medicine for the cranio-facial and portrait expertise.
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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.
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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.
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NASA Astrophysics Data System (ADS)
Chen, Zhe; Qiu, Zurong; Huo, Xinming; Fan, Yuming; Li, Xinghua
2017-03-01
A fiber-capacitive drop analyzer is an instrument which monitors a growing droplet to produce a capacitive opto-tensiotrace (COT). Each COT is an integration of fiber light intensity signals and capacitance signals and can reflect the unique physicochemical property of a liquid. In this study, we propose a solution analytical and concentration quantitative method based on multivariate statistical methods. Eight characteristic values are extracted from each COT. A series of COT characteristic values of training solutions at different concentrations compose a data library of this kind of solution. A two-stage linear discriminant analysis is applied to analyze different solution libraries and establish discriminant functions. Test solutions can be discriminated by these functions. After determining the variety of test solutions, Spearman correlation test and principal components analysis are used to filter and reduce dimensions of eight characteristic values, producing a new representative parameter. A cubic spline interpolation function is built between the parameters and concentrations, based on which we can calculate the concentration of the test solution. Methanol, ethanol, n-propanol, and saline solutions are taken as experimental subjects in this paper. For each solution, nine or ten different concentrations are chosen to be the standard library, and the other two concentrations compose the test group. By using the methods mentioned above, all eight test solutions are correctly identified and the average relative error of quantitative analysis is 1.11%. The method proposed is feasible which enlarges the applicable scope of recognizing liquids based on the COT and improves the concentration quantitative precision, as well.
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Noh, Jin-Won; Park, Hyunchun; Kim, Minji; Kwon, Young Dae; Kim, Jin-Seok
2018-01-01
Objective This study investigated the mediation effect of stress between the experience of discrimination and life satisfaction among North Korean refugees who resettled in South Korea. The findings of the current study provide empirical evidence for the need of social interventions to mitigate adverse effects of stress on North Korean refugees who are subject to social discrimination on a daily basis. Methods In this study, we included 500 subjects among 2,138 North Korean refugees who took refuge in South Korea in 2007. The interview started from April 6th 2009 and finished on May 25th 2009. We conducted moderator effect analysis with Path analysis was conducted because we confirm the experience of discrimination was affected by life satisfaction and stress can affected life satisfaction as a moderator. Results The experience of discrimination significantly affects stress and stress significantly affects life satisfaction. However, the experience of discrimination was not directly related to life satisfaction. The more stress the study respondents experienced, the lower the life satisfaction they reported. Conclusion The present finding suggests that the effects of discriminating experiences on the life satisfaction of North Korean refugees in South Korea were mediated by their own perceived stress. PMID:29422925
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Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie
2014-01-01
Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928
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Racial discrimination and cortisol output: A meta-analysis.
Korous, Kevin M; Causadias, José M; Casper, Deborah M
2017-11-01
Although the relation between stress and physiology is well documented, attempts at understanding the link between racial discrimination and cortisol output, specifically, have produced mixed results, likely due to study characteristics such as racial/ethnic composition of the samples (e.g., African American, Latino), measures of discrimination, and research design (e.g., cross-sectional, experimental). To estimate the overall association between racial discrimination and cortisol output among racial/ethnic minority individuals and to determine if the association between racial discrimination and cortisol output is moderated by age, race/ethnicity, type of discrimination measure, sex, and research design. Using a random effects model, the overall effect size based on k = 16 studies (19% unpublished) and N = 1506 participants was r¯ = 0.040, 95% CI = -0.038 to 0.117. Studies were conducted predominantly in the U.S. (81%). Notably, experimental studies (r¯ = 0.267) exhibited larger effect sizes compared to non-experimental studies (r¯ = -0.007). Age, race/ethnicity, type of discrimination measure, and sex did not moderate the effect sizes. This meta-analysis provides evidence that the measurement of the association between racial discrimination and cortisol is complex, and it offers valuable insight regarding methods and designs that can inform future research on this topic. Limitations and future directions are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Kao, E.-Fong; Lin, Wei-Chen; Hsu, Jui-Sheng; Chou, Ming-Chung; Jaw, Twei-Shiun; Liu, Gin-Chung
2011-12-01
A computerized scheme was developed for automated identification of erect posteroanterior (PA) and supine anteroposterior (AP) chest radiographs. The method was based on three features, the tilt angle of the scapula superior border, the tilt angle of the clavicle and the extent of radiolucence in lung fields, to identify the view of a chest radiograph. The three indices Ascapula, Aclavicle and Clung were determined from a chest image for the three features. Linear discriminant analysis was used to classify PA and AP chest images based on the three indices. The performance of the method was evaluated by receiver operating characteristic analysis. The proposed method was evaluated using a database of 600 PA and 600 AP chest radiographs. The discriminant performances Az of Ascapula, Aclavicle and Clung were 0.878 ± 0.010, 0.683 ± 0.015 and 0.962 ± 0.006, respectively. The combination of the three indices obtained an Az value of 0.979 ± 0.004. The results indicate that the combination of the three indices could yield high discriminant performance. The proposed method could provide radiologists with information about the view of chest radiographs for interpretation or could be used as a preprocessing step for analyzing chest images.
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Isolation of Circulating Tumor Cells by Dielectrophoresis
Gascoyne, Peter R. C.; Shim, Sangjo
2014-01-01
Dielectrophoresis (DEP) is an electrokinetic method that allows intrinsic dielectric properties of suspended cells to be exploited for discrimination and separation. It has emerged as a promising method for isolating circulation tumor cells (CTCs) from blood. DEP-isolation of CTCs is independent of cell surface markers. Furthermore, isolated CTCs are viable and can be maintained in culture, suggesting that DEP methods should be more generally applicable than antibody-based approaches. The aim of this article is to review and synthesize for both oncologists and biomedical engineers interested in CTC isolation the pertinent characteristics of DEP and CTCs. The aim is to promote an understanding of the factors involved in realizing DEP-based instruments having both sufficient discrimination and throughput to allow routine analysis of CTCs in clinical practice. The article brings together: (a) the principles of DEP; (b) the biological basis for the dielectric differences between CTCs and blood cells; (c) why such differences are expected to be present for all types of tumors; and (d) instrumentation requirements to process 10 mL blood specimens in less than 1 h to enable routine clinical analysis. The force equilibrium method of dielectrophoretic field-flow fractionation (DEP-FFF) is shown to offer higher discrimination and throughput than earlier DEP trapping methods and to be applicable to clinical studies. PMID:24662940
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Sase, Shigeru; Yamamoto, Homaro; Kawashima, Ena; Tan, Xin; Sawa, Yutaka
The aim of this study was to develop a method for discriminating between patients with Alzheimer disease (AD) and healthy subjects using layer analysis of cerebral blood flow (CBF) and xenon solubility coefficient (λ) in xenon-enhanced computed tomography (CT). Xenon-enhanced CT was performed on 27 patients with AD (81.7 [3.3] years old) and 15 healthy volunteers (78.6 [4.0] years old) using a wide volume CT. For each subject, we created the first- (surface) to sixth-layer images of CBF and λ for the 6 viewing directions (layer thickness, 5 mm). For the discriminant views, receiver operating characteristic curves for the ratio of CBF to λ were created to identify patients with AD. For the third- and fourth-layer left lateral views, which were designated as the discriminant views, areas under the receiver operating characteristic curve were 96.8% and 97.4%, respectively. With the use of the discriminant views obtained by xenon-enhanced CT, we could effectively discriminate between patients with AD and healthy subjects using both CBF and λ.
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Nemmi, Federico; Saint-Aubert, Laure; Adel, Djilali; Salabert, Anne-Sophie; Pariente, Jérémie; Barbeau, Emmanuel; Payoux, Pierre; Péran, Patrice
2014-01-01
Purpose AV-45 amyloid biomarker is known to show uptake in white matter in patients with Alzheimer’s disease (AD) but also in healthy population. This binding; thought to be of a non-specific lipophilic nature has not yet been investigated. The aim of this study was to determine the differential pattern of AV-45 binding in healthy and pathological populations in white matter. Methods We recruited 24 patients presenting with AD at early stage and 17 matched, healthy subjects. We used an optimized PET-MRI registration method and an approach based on intensity histogram using several indexes. We compared the results of the intensity histogram analyses with a more canonical approach based on target-to-cerebellum Standard Uptake Value (SUVr) in white and grey matters using MANOVA and discriminant analyses. A cluster analysis on white and grey matter histograms was also performed. Results White matter histogram analysis revealed significant differences between AD and healthy subjects, which were not revealed by SUVr analysis. However, white matter histograms was not decisive to discriminate groups, and indexes based on grey matter only showed better discriminative power than SUVr. The cluster analysis divided our sample in two clusters, showing different uptakes in grey but also in white matter. Conclusion These results demonstrate that AV-45 binding in white matter conveys subtle information not detectable using SUVr approach. Although it is not better than standard SUVr to discriminate AD patients from healthy subjects, this information could reveal white matter modifications. PMID:24573658
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NASA Astrophysics Data System (ADS)
Liu, Yue; Zhang, Ying; Zhang, Jing; Fan, Gang; Tu, Ya; Sun, Suqin; Shen, Xudong; Li, Qingzhu; Zhang, Yi
2018-03-01
As an important ethnic medicine, sea buckthorn was widely used to prevent and treat various diseases due to its nutritional and medicinal properties. According to the Chinese Pharmacopoeia, sea buckthorn was originated from H. rhamnoides, which includes five subspecies distributed in China. Confusion and misidentification usually occurred due to their similar morphology, especially in dried and powdered forms. Additionally, these five subspecies have vital differences in quality and physiological efficacy. This paper focused on the quick classification and identification method of sea buckthorn berry powders from five H. rhamnoides subspecies using multi-step IR spectroscopy coupled with multivariate data analysis. The holistic chemical compositions revealed by the FT-IR spectra demonstrated that flavonoids, fatty acids and sugars were the main chemical components. Further, the differences in FT-IR spectra regarding their peaks, positions and intensities were used to identify H. rhamnoides subspecies samples. The discrimination was achieved using principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). The results showed that the combination of multi-step IR spectroscopy and chemometric analysis offered a simple, fast and reliable method for the classification and identification of the sea buckthorn berry powders from different H. rhamnoides subspecies.
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Atkins, Rahshida
2014-01-01
Several compendiums of instruments that measure perceived racism and/or discrimination are present in the literature. Other works have reviewed the psychometric properties of these instruments in terms of validity and reliability and have indicated if the instrument was factor analyzed. However, little attention has been given to the quality of the factor analysis performed. The aim of this study was to evaluate the exploratory factor analyses done on instruments measuring perceived racism/racial discrimination using guidelines from experts in psychometric theory. The techniques used for factor analysis were reviewed and critiqued and the adequacy of reporting was evaluated. Internet search engines and four electronic abstract databases were used to identify 16 relevant instruments that met the inclusion/exclusion criteria. Principal component analysis was the most frequent method of extraction (81%). Sample sizes were adequate for factor analysis in 81 percent of studies. The majority of studies reported appropriate criteria for the acceptance of un-rotated factors (81%) and justified the rotation method (75%). Exactly 94 percent of studies reported partially acceptable criteria for the acceptance of rotated factors. The majority of articles (69%) reported adequate coefficient alphas for the resultant subscales. In 81 percent of the studies, the conceptualized dimensions were supported by factor analysis.
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Atkins, Rahshida
2015-01-01
Several compendiums of instruments that measure perceived racism and/or discrimination are present in the literature. Other works have reviewed the psychometric properties of these instruments in terms of validity and reliability and have indicated if the instrument was factor analyzed. However, little attention has been given to the quality of the factor analysis performed. The aim of this study was to evaluate the exploratory factor analyses done on instruments measuring perceived racism/racial discrimination using guidelines from experts in psychometric theory. The techniques used for factor analysis were reviewed and critiqued and the adequacy of reporting was evaluated. Internet search engines and four electronic abstract databases were used to identify 16 relevant instruments that met the inclusion/exclusion criteria. Principal component analysis was the most frequent method of extraction (81%). Sample sizes were adequate for factor analysis in 81 percent of studies. The majority of studies reported appropriate criteria for the acceptance of un-rotated factors (81%) and justified the rotation method (75%). Exactly 94 percent of studies reported partially acceptable criteria for the acceptance of rotated factors. The majority of articles (69%) reported adequate coefficient alphas for the resultant subscales. In 81 percent of the studies, the conceptualized dimensions were supported by factor analysis. PMID:25626225
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Perception of Patients With HIV/AIDS From Stigma and Discrimination
Saki, Mandana; Mohammad Khan Kermanshahi, Sima; Mohammadi, Eesa; Mohraz, Minoo
2015-01-01
Background: Stigma and discrimination among patients with HIV/AIDS cause various problems for the patients and their health systems. Objectives: The purpose of this study was to explain the perceived experiences of the patients from stigma and discrimination and their roles on health-seeking services among patients. Patients and Methods: This was a qualitative research using content analysis approach and semi-structured interviews, conducted on patients living with HIV/ADS, during 2013 - 2014 in Iran. Sampling started purposefully and continued in a snowball. Results: The experiences of patients with HIV/AIDS from stigma and discrimination led to exploring three main themes and nine subthemes. The main themes were multidimensional stigma, rejection, and insult and discrimination in receiving health services. Conclusions: Stigma and discrimination play an important role in patients' lives and hinder them from accessing the treatment. The patients' responses to this event by secrecy strategy can be an important factor in the disease prevalence. PMID:26290751
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Monakhova, Yulia B; Diehl, Bernd W K; Fareed, Jawed
2018-02-05
High resolution (600MHz) nuclear magnetic resonance (NMR) spectroscopy is used to distinguish heparin and low-molecular weight heparins (LMWHs) produced from porcine, bovine and ovine mucosal tissues as well as their blends. For multivariate analysis several statistical methods such as principal component analysis (PCA), factor discriminant analysis (FDA), partial least squares - discriminant analysis (PLS-DA), linear discriminant analysis (LDA) were utilized for the modeling of NMR data of more than 100 authentic samples. Heparin and LMWH samples from the independent test set (n=15) were 100% correctly classified according to its animal origin. Moreover, by using 1 H NMR coupled with chemometrics and several batches of bovine heparins from two producers were differentiated. Thus, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of animal origin and process based manufacturing difference in heparin products. Copyright © 2017 Elsevier B.V. All rights reserved.
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Identification of offal adulteration in beef by laser induced breakdown spectroscopy (LIBS).
Velioglu, Hasan Murat; Sezer, Banu; Bilge, Gonca; Baytur, Süleyman Efe; Boyaci, Ismail Hakki
2018-04-01
Minced meat is the major ingredient in sausages, beef burgers, and similar products; and thus it is the main product subjected to adulteration with meat offal. Determination of this kind of meat adulteration is crucial due to religious, economic and ethical concerns. The aim of the present study is to discriminate the beef meat and offal samples by using laser induced breakdown spectroscopy (LIBS). To this end, LIBS and multivariate data analysis were used to discriminate pure beef and offal samples qualitatively and to determine the offal mixture adulteration quantitatively. In this analysis, meat samples were frozen and LIBS analysis were performed. The results indicate that by using principal component analysis (PCA), discrimination of pure offal and offal mixture adulterated beef samples can be achieved successfully. Besides, adulteration ratio can be determined using partial least square analysis method (PLS) with 0.947 coefficient of determination (R 2 ) and 3.8% of limit of detection (LOD) values for offal mixture adulterated beef samples. Copyright © 2017 Elsevier Ltd. All rights reserved.
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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.
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Teodoro, Janaína Aparecida Reis; Pereira, Hebert Vinicius; Sena, Marcelo Martins; Piccin, Evandro; Zacca, Jorge Jardim; Augusti, Rodinei
2017-12-15
A direct method based on the application of paper spray mass spectrometry (PS-MS) combined with a chemometric supervised method (partial least square discriminant analysis, PLS-DA) was developed and applied to the discrimination of authentic and counterfeit samples of blended Scottish whiskies. The developed methodology employed the negative ion mode MS, included 44 authentic whiskies from diverse brands and batches and 44 counterfeit samples of the same brands seized during operations of the Brazilian Federal Police, totalizing 88 samples. An exploratory principal component analysis (PCA) model showed a reasonable discrimination of the counterfeit whiskies in PC2. In spite of the samples heterogeneity, a robust, reliable and accurate PLS-DA model was generated and validated, which was able to correctly classify the samples with nearly 100% success rate. The use of PS-MS also allowed the identification of the main marker compounds associated with each type of sample analyzed: authentic or counterfeit. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).
Bevilacqua, Marta; Marini, Federico
2014-08-01
The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks). Copyright © 2014 Elsevier B.V. All rights reserved.
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Benign-malignant mass classification in mammogram using edge weighted local texture features
NASA Astrophysics Data System (ADS)
Rabidas, Rinku; Midya, Abhishek; Sadhu, Anup; Chakraborty, Jayasree
2016-03-01
This paper introduces novel Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for the classification of mammographic masses as benign or malignant. Mass is one of the common, however, challenging evidence of breast cancer in mammography and diagnosis of masses is a difficult task. Since DRLBP and DRLTP overcome the drawbacks of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) by discriminating a brighter object against the dark background and vice-versa, in addition to the preservation of the edge information along with the texture information, several edge-preserving texture features are extracted, in this study, from DRLBP and DRLTP. Finally, a Fisher Linear Discriminant Analysis method is incorporated with discriminating features, selected by stepwise logistic regression method, for the classification of benign and malignant masses. The performance characteristics of DRLBP and DRLTP features are evaluated using a ten-fold cross-validation technique with 58 masses from the mini-MIAS database, and the best result is observed with DRLBP having an area under the receiver operating characteristic curve of 0.982.
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General tensor discriminant analysis and gabor features for gait recognition.
Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J
2007-10-01
The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.
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Zhang, Jian; Li, Li; Gao, Nianfa; Wang, Depei; Gao, Qiang; Jiang, Shengping
2010-03-10
This work was undertaken to evaluate whether it is possible to determine the variety of a Chinese wine on the basis of its volatile compounds, and to investigate if discrimination models could be developed with the experimental wines that could be used for the commercial ones. A headspace solid-phase microextraction gas chromatographic (HS-SPME-GC) procedure was used to determine the volatile compounds and a blind analysis based on Ac/Ais (peak area of volatile compound/peak area of internal standard) was carried out for statistical purposes. One way analysis of variance (ANOVA), principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to process data and to develop discriminant models. Only 11 peaks enabled to differentiate and classify the experimental wines. SLDA allowed 100% recognition ability for three grape varieties, 100% prediction ability for Cabernet Sauvignon and Cabernet Gernischt wines, but only 92.31% for Merlot wines. A more valid and robust way was to use the PCA scores to do the discriminant analysis. When we performed SLDA this way, 100% recognition ability and 100% prediction ability were obtained. At last, 11 peaks which selected by SLDA from raw analysis set had been identified. When we demonstrated the models using commercial wines, the models showed 100% recognition ability for the wines collected directly from winery and without ageing, but only 65% for the others. Therefore, the varietal factor was currently discredited as a differentiating parameter for commercial wines in China. Nevertheless, this method could be applied as a screening tool and as a complement to other methods for grape base liquors which do not need ageing and blending procedures. 2010 Elsevier B.V. All rights reserved.
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Analysis of Optimal Sequential State Discrimination for Linearly Independent Pure Quantum States.
Namkung, Min; Kwon, Younghun
2018-04-25
Recently, J. A. Bergou et al. proposed sequential state discrimination as a new quantum state discrimination scheme. In the scheme, by the successful sequential discrimination of a qubit state, receivers Bob and Charlie can share the information of the qubit prepared by a sender Alice. A merit of the scheme is that a quantum channel is established between Bob and Charlie, but a classical communication is not allowed. In this report, we present a method for extending the original sequential state discrimination of two qubit states to a scheme of N linearly independent pure quantum states. Specifically, we obtain the conditions for the sequential state discrimination of N = 3 pure quantum states. We can analytically provide conditions when there is a special symmetry among N = 3 linearly independent pure quantum states. Additionally, we show that the scenario proposed in this study can be applied to quantum key distribution. Furthermore, we show that the sequential state discrimination of three qutrit states performs better than the strategy of probabilistic quantum cloning.
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Yu, Chunhao; Wang, Chong-Zhi; Zhou, Chun-Jie; Wang, Bin; Han, Lide; Zhang, Chun-Feng; Wu, Xiao-Hui; Yuan, Chun-Su
2014-01-01
American ginseng (Panax quinquefolius) is originally grown in North America. Due to price difference and supply shortage, American ginseng recently has been cultivated in northern China. Further, in the market, some Asian ginsengs are labeled as American ginseng. In this study, forty-three American ginseng samples cultivated in the USA, Canada or China were collected and 14 ginseng saponins were determined using HPLC. HPLC coupled with hierarchical cluster analysis and principal component analysis was developed to identify the species. Subsequently, an HPLC-linear discriminant analysis was established to discriminate cultivation regions of American ginseng. This method was successfully applied to identify the sources of 6 commercial American ginseng samples. Two of them were identified as Asian ginseng, while 4 others were identified as American ginseng, which were cultivated in the USA (3) and China (1). Our newly developed method can be used to identify American ginseng with different cultivation regions. PMID:25044150
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NASA Astrophysics Data System (ADS)
Tan, Shanjuan; Feng, Feifei; Wu, Yongjun; Wu, Yiming
To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) combined with tumor markers for diagnosis of hepatic carcinoma (HCC) as a clinical assistant method. 140 serum samples (50 malignant, 40 benign and 50 normal) were analyzed for α-fetoprotein (AFP), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), sialic acid (SA) and calcium (Ca). The five tumor marker values were then used as ANN inputs data. The result of ANN was compared with that of discriminant analysis by receiver operating characteristic (ROC) curve (AUC) analysis. The diagnostic accuracy of ANN and discriminant analysis among all samples of the test group was 95.5% and 79.3%, respectively. Analysis of multiple tumor markers based on ANN may be a better choice than the traditional statistical methods for differentiating HCC from benign or normal.
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Comparison between cachaça and rum using pattern recognition methods.
Cardoso, Daniel R; Andrade-Sobrinho, Luiz G; Leite-Neto, Alexandre F; Reche, Roni V; Isique, William D; Ferreira, Marcia M C; Lima-Neto, Benedito S; Franco, Douglas W
2004-06-02
The differentiation between cachaça and rum using analytical data referred to alcohols (methanol, propanol, isobutanol, and isopentanol), acetaldehyde, ethyl acetate, organic acids (octanoic acid, decanoic acid, and dodecanoic acid), metals (Al, Ca, Co, Cu, Cr, Fe, Mg, Mn, Ni, Na, and Zn), and polyphenols (protocatechuic acid, sinapaldehyde, syringaldehyde, ellagic acid, syringic acid, gallic acid, (-)-epicatechin, vanillic acid, vanillin, p-coumaric acid, coniferaldehyde, coniferyl alcohol, kaempferol, and quercetin) is described. The organic and metal analyte contents were determined in 18 cachaça and 21 rum samples using chromatographic methods (GC-MS, GC-FID, and HPLC-UV-vis) and inductively coupled plasma atomic emission spectrometry, respectively. The analytical data of the above compounds, when treated by principal component analysis, hierarchical cluster analysis, discriminant analysis, and K-nearest neighbor analysis, provide a very good discrimination between the two classes of beverages.
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Distefano, Gaetano; Caruso, Marco; La Malfa, Stefano; Gentile, Alessandra; Wu, Shu-Biao
2012-01-01
High resolution melting curve analysis (HRM) has been used as an efficient, accurate and cost-effective tool to detect single nucleotide polymorphisms (SNPs) or insertions or deletions (INDELs). However, its efficiency, accuracy and applicability to discriminate microsatellite polymorphism have not been extensively assessed. The traditional protocols used for SSR genotyping include PCR amplification of the DNA fragment and the separation of the fragments on electrophoresis-based platform. However, post-PCR handling processes are laborious and costly. Furthermore, SNPs present in the sequences flanking repeat motif cannot be detected by polyacrylamide-gel-electrophoresis based methods. In the present study, we compared the discriminating power of HRM with the traditional electrophoresis-based methods and provided a panel of primers for HRM genotyping in Citrus. The results showed that sixteen SSR markers produced distinct polymorphic melting curves among the Citrus spp investigated through HRM analysis. Among those, 10 showed more genotypes by HRM analysis than capillary electrophoresis owing to the presence of SNPs in the amplicons. For the SSR markers without SNPs present in the flanking region, HRM also gave distinct melting curves which detected same genotypes as were shown in capillary electrophoresis (CE) analysis. Moreover, HRM analysis allowed the discrimination of most of the 15 citrus genotypes and the resulting genetic distance analysis clustered them into three main branches. In conclusion, it has been approved that HRM is not only an efficient and cost-effective alternative of electrophoresis-based method for SSR markers, but also a method to uncover more polymorphisms contributed by SNPs present in SSRs. It was therefore suggested that the panel of SSR markers could be used in a variety of applications in the citrus biodiversity and breeding programs using HRM analysis. Furthermore, we speculate that the HRM analysis can be employed to analyse SSR markers in a wide range of applications in all other species.
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NASA Astrophysics Data System (ADS)
Padilla-Jiménez, Amira C.; Ortiz-Rivera, William; Rios-Velazquez, Carlos; Vazquez-Ayala, Iris; Hernández-Rivera, Samuel P.
2014-06-01
Investigations focusing on devising rapid and accurate methods for developing signatures for microorganisms that could be used as biological warfare agents' detection, identification, and discrimination have recently increased significantly. Quantum cascade laser (QCL)-based spectroscopic systems have revolutionized many areas of defense and security including this area of research. In this contribution, infrared spectroscopy detection based on QCL was used to obtain the mid-infrared (MIR) spectral signatures of Bacillus thuringiensis, Escherichia coli, and Staphylococcus epidermidis. These bacteria were used as microorganisms that simulate biothreats (biosimulants) very truthfully. The experiments were conducted in reflection mode with biosimulants deposited on various substrates including cardboard, glass, travel bags, wood, and stainless steel. Chemometrics multivariate statistical routines, such as principal component analysis regression and partial least squares coupled to discriminant analysis, were used to analyze the MIR spectra. Overall, the investigated infrared vibrational techniques were useful for detecting target microorganisms on the studied substrates, and the multivariate data analysis techniques proved to be very efficient for classifying the bacteria and discriminating them in the presence of highly IR-interfering media.
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Intractable Ménière's disease. Modelling of the treatment by means of statistical analysis.
Sanchez-Ferrandiz, Noelia; Fernandez-Gonzalez, Secundino; Guillen-Grima, Francisco; Perez-Fernandez, Nicolas
2010-08-01
To evaluate the value of different variables of the clinical history, auditory and vestibular tests and handicap measurements to define intractable or disabling Ménière's disease. This is a prospective study with 212 patients of which 155 were treated with intratympanic gentamicin and considered to be suffering a medically intractable Ménière's disease. Age and sex adjustments were performed with the 11 variables selected. Discriminant analysis was performed either using the aforementioned variables or following the stepwise method. Different variables needed to be sex and/or age adjusted and both data were included in the discriminant function. Two different mathematical formulas were obtained and four models were analyzed. With the model selected, diagnostic accuracy is 77.7%, sensitivity is 94.9% and specificity is 52.8%. After discriminant analysis we found that the most informative variables were the number of vertigo spells, the speech discrimination score, the time constant of the VOR and a measure of handicap, the "dizziness index". Copyright 2009 Elsevier Ireland Ltd. All rights reserved.
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Longobardi, Francesco; Innamorato, Valentina; Di Gioia, Annalisa; Ventrella, Andrea; Lippolis, Vincenzo; Logrieco, Antonio F; Catucci, Lucia; Agostiano, Angela
2017-12-15
Lentil samples coming from two different countries, i.e. Italy and Canada, were analysed using untargeted 1 H NMR fingerprinting in combination with chemometrics in order to build models able to classify them according to their geographical origin. For such aim, Soft Independent Modelling of Class Analogy (SIMCA), k-Nearest Neighbor (k-NN), Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR data and the results were compared. The best combination of average recognition (100%) and cross-validation prediction abilities (96.7%) was obtained for the PCA-LDA. All the statistical models were validated both by using a test set and by carrying out a Monte Carlo Cross Validation: the obtained performances were found to be satisfying for all the models, with prediction abilities higher than 95% demonstrating the suitability of the developed methods. Finally, the metabolites that mostly contributed to the lentil discrimination were indicated. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Sugden, Nicole A; Marquis, Alexandra R
2017-11-01
Infants show facility for discriminating between individual faces within hours of birth. Over the first year of life, infants' face discrimination shows continued improvement with familiar face types, such as own-race faces, but not with unfamiliar face types, like other-race faces. The goal of this meta-analytic review is to provide an effect size for infants' face discrimination ability overall, with own-race faces, and with other-race faces within the first year of life, how this differs with age, and how it is influenced by task methodology. Inclusion criteria were (a) infant participants aged 0 to 12 months, (b) completing a human own- or other-race face discrimination task, (c) with discrimination being determined by infant looking. Our analysis included 30 works (165 samples, 1,926 participants participated in 2,623 tasks). The effect size for infants' face discrimination was small, 6.53% greater than chance (i.e., equal looking to the novel and familiar). There was a significant difference in discrimination by race, overall (own-race, 8.18%; other-race, 3.18%) and between ages (own-race: 0- to 4.5-month-olds, 7.32%; 5- to 7.5-month-olds, 9.17%; and 8- to 12-month-olds, 7.68%; other-race: 0- to 4.5-month-olds, 6.12%; 5- to 7.5-month-olds, 3.70%; and 8- to 12-month-olds, 2.79%). Multilevel linear (mixed-effects) models were used to predict face discrimination; infants' capacity to discriminate faces is sensitive to face characteristics including race, gender, and emotion as well as the methods used, including task timing, coding method, and visual angle. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
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ERIC Educational Resources Information Center
Garnett, Bernice R.; Masyn, Katherine E.; Austin, S. Bryn; Williams, David R.; Viswanath, Kasisomayajula
2015-01-01
Background: We used a latent class analysis (LCA) to characterize coping styles of urban youth and examined if coping styles moderated the association between experiencing discrimination and bullying and depressive symptoms. Methods: The data come from the 2006 Boston Youth Survey, where students were asked to select 2 behaviors they do most often…
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ERIC Educational Resources Information Center
Feinstein, Brian A.; Goldfried, Marvin R.; Davila, Joanne
2012-01-01
Objective: The current study used path analysis to examine potential mechanisms through which experiences of discrimination influence depressive and social anxiety symptoms. Method: The sample included 218 lesbians and 249 gay men (total N = 467) who participated in an online survey about minority stress and mental health. The proposed model…
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2013-01-01
Background 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. Methods 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. Results 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 on the parent’s mental health problem; not being listened to; and being undermined as a parent. Conclusions This research highlights the need for: greater support for parents with mental illness, those wishing to have children, and those who lose access or custody; services to better meet the needs of children with a mentally ill parent; training about discrimination for professionals; and parenting issues to be included in anti-stigma programmes. PMID:23601350
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Feng, Yong-E
2016-06-01
Malaria parasite secretes various proteins in infected red blood cell for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine or drug against malaria. In this study, the modified method of quadratic discriminant analysis is presented for predicting the secretory proteins. Firstly, 20 amino acids are divided into five types according to the physical and chemical characteristics of amino acids. Then, we used five types of amino acids compositions as inputs of the modified quadratic discriminant algorithm. Finally, the best prediction performance is obtained by using 20 amino acid compositions, the sensitivity of 96 %, the specificity of 92 % with 0.88 of Mathew's correlation coefficient in fivefold cross-validation test. The results are also compared with those of existing prediction methods. The compared results shown our method are prominent in the prediction of secretory proteins.
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NASA Astrophysics Data System (ADS)
Rocha-Osornio, L. N.; Pichardo-Molina, J. L.; Barbosa-Garcia, O.; Frausto-Reyes, C.; Araujo-Andrade, C.; Huerta-Franco, R.; Gutiérrez-Juárez, G.
2008-02-01
Raman spectroscopy and Multivariate methods were used to study serum blood samples of control and breast cancer patients. Blood samples were obtained from 11 patients and 12 controls from the central region of Mexico. Our results show that principal component analysis is able to discriminate serum sample of breast cancer patients from those of control group, also the loading vectors of PCA plotted as a function of Raman shift shown which bands permitted to make the maximum discrimination between both groups of samples.
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Zhu, Yong; Wen, Wen; Zhang, Fengmin; Hardie, Jim W.
2015-01-01
Background and Aims Proton nuclear magnetic resonance spectroscopy coupled multivariate analysis (1H NMR-PCA/PLS-DA) is an important tool for the discrimination of wine products. Although 1H NMR has been shown to discriminate wines of different cultivars, a grape genetic component of the discrimination has been inferred only from discrimination of cultivars of undefined genetic homology and in the presence of many confounding environmental factors. We aimed to confirm the influence of grape genotypes in the absence of those factors. Methods and Results We applied 1H NMR-PCA/PLS-DA and hierarchical cluster analysis (HCA) to wines from five, variously genetically-related grapevine (V. vinifera) cultivars; all grown similarly on the same site and vinified similarly. We also compared the semi-quantitative profiles of the discriminant metabolites of each cultivar with previously reported chemical analyses. The cultivars were clearly distinguishable and there was a general correlation between their grouping and their genetic homology as revealed by recent genomic studies. Between cultivars, the relative amounts of several of the cultivar-related discriminant metabolites conformed closely with reported chemical analyses. Conclusions Differences in grape-derived metabolites associated with genetic differences alone are a major source of 1H NMR-based discrimination of wines and 1H NMR has the capacity to discriminate between very closely related cultivars. Significance of the Study The study confirms that genetic variation among grape cultivars alone can account for the discrimination of wine by 1H NMR-PCA/PLS and indicates that 1H NMR spectra of wine of single grape cultivars may in future be used in tandem with hierarchical cluster analysis to elucidate genetic lineages and metabolomic relations of grapevine cultivars. In the absence of genetic information, for example, where predecessor varieties are no longer extant, this may be a particularly useful approach. PMID:26658757
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[Quality evaluation of American ginseng using UPLC coupled with multivariate analysis].
Tang, Yan; Yan, Shu-Mo; Wang, Jing-Jing; Yuan, Yuan; Yang, Bin
2016-05-01
An ultra performance liquid chromatography (UPLC)method combined with multivariate data analysis was developed to evaluate the quality of American ginseng by simultaneously determining the concentrations of six ginsenosides (Rg₁, Re, Rb₁, Rc, Ro and Rd)in the samples. For UPLC, acetonitrile with 0.01% formic acid and water with 0.01% formic acid were used as the mobile phase with gradient elution. Under the established chromatographic conditions, the six ginsenosides could be well separated and the results of linearity, stability, precision, repeatability, and recovery rate all reached the requirement of quantification analysis, respectively. The total contents of Rg₁, Re, and Rb₁ in 57 samples all reached the requirement of the 2015 edition of Chinese Pharmacopoeia. At the same time, the experimental data were analyzed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The crude drugs and the decoction pieces can be discriminated by a PCA method and the samples with different age can be distinguished by a PLS-DA method. Copyright© by the Chinese Pharmaceutical Association.
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NASA Astrophysics Data System (ADS)
Toma, Eiji
2018-06-01
In recent years, as the weight of IT equipment has been reduced, the demand for motor fans for cooling the interior of electronic equipment is on the rise. Sensory test technique by inspectors is the mainstream for quality inspection of motor fans in the field. This sensory test requires a lot of experience to accurately diagnose differences in subtle sounds (sound pressures) of the fans, and the judgment varies depending on the condition of the inspector and the environment. In order to solve these quality problems, development of an analysis method capable of quantitatively and automatically diagnosing the sound/vibration level of a fan is required. In this study, it was clarified that the analysis method applying the MT system based on the waveform information of noise and vibration is more effective than the conventional frequency analysis method for the discrimination diagnosis technology of normal and abnormal items. Furthermore, it was found that due to the automation of the vibration waveform analysis system, there was a factor influencing the discrimination accuracy in relation between the fan installation posture and the vibration waveform.
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Hondrogiannis, Ellen M; Ehrlinger, Erin; Poplaski, Alyssa; Lisle, Meredith
2013-11-27
A total of 11 elements found in 25 vanilla samples from Uganda, Madagascar, Indonesia, and Papua New Guinea were measured by laser ablation-inductively coupled plasma-time-of-flight-mass spectrometry (LA-ICP-TOF-MS) for the purpose of collecting data that could be used to discriminate among the origins. Pellets were prepared of the samples, and elemental concentrations were obtained on the basis of external calibration curves created using five National Institute of Standards and Technology (NIST) standards and one Chinese standard with (13)C internal standardization. These curves were validated using NIST 1573a (tomato leaves) as a check standard. Discriminant analysis was used to successfully classify the vanilla samples by their origin. Our method illustrates the feasibility of using LA-ICP-TOF-MS with an external calibration curve for high-throughput screening of spice screening analysis.
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NASA Astrophysics Data System (ADS)
Kumar, Raj; Kumar, Vinay; Sharma, Vishal
2017-01-01
The aim of the present work is to explore the non-destructive application of ATR-FTIR technique for characterization and discrimination of paper samples which could be helpful to give forensic aid in resolving legal cases. Twenty-four types of paper brands were purchased from local market in and around Chandigarh, India. All the paper samples were subjected to ATR-FTIR analysis from 400 to 4000 cm- 1 wavenumber range. The qualitative feature and Chemometrics of the obtained spectral data are used for characterization and discrimination. Characterization is achieved by matching the peaks with standards of cellulose and inorganic fillers, a usual constituents of paper. Three different regions of IR, i.e. 400-2000 cm- 1, 2000-4000 cm- 1 and 400-4000 cm- 1 were selected for differentiation by Chemometrics analysis. The discrimination is achieved on the basis of three principal components, i.e. PC 1, PC 2 and PC 3. It is observed that maximum discrimination was procured in the wave number range of i.e. 2000-4000 cm- 1. Discriminating power was calculated on the basis of qualitative features as well, and it is found that the discrimination of paper samples was better achieved by Chemometrics analysis rather than qualitative features. The discriminating power by Chemometrics is 99.64% and which is larger as ever achieved by any group for present number of samples. The present result confirms that this study will be highly useful in forensic document examination work in the legal cases, where the authenticity of the document is challenged. The results are completely analytical and, therefore, overcome the problem encounter in traditional routine light/radiation scanning methods which are still in practice by various questioned document laboratories.
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Shen, Shi; Wang, Jingbo; Zhuo, Qin; Chen, Xi; Liu, Tingting; Zhang, Shuang-Qing
2018-05-08
Phenolics and flavonoids in honey are considered as the main phytonutrients which not only act as natural antioxidants, but can also be used as floral markers for honey identification. In this study, the chemical profiles of phenolics and flavonoids, antioxidant competences including total phenolic content, DPPH and ABTS assays and discrimination using chemometric analysis of various Chinese monofloral honeys from six botanical origins (acacia, Vitex , linden, rapeseed, Astragalus and Codonopsis ) were examined. A reproducible and sensitive ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was optimized and validated for the simultaneous determination of 38 phenolics, flavonoids and abscisic acid in honey. Formononetin, ononin, calycosin and calycosin-7- O -β-d-glucoside were identified and quantified in honeys for the first time. Principal component analysis (PCA) showed obvious differences among the honey samples in three-dimensional space accounting for 72.63% of the total variance. Hierarchical cluster analysis (HCA) also revealed that the botanical origins of honey samples correlated with their phenolic and flavonoid contents. Partial least squares-discriminant analysis (PLS-DA) classification was performed to derive a model with high prediction ability. Orthogonal partial least squares-discriminant analysis (OPLS-DA) model was employed to identify markers specific to a particular honey type. The results indicated that Chinese honeys contained various and discriminative phenolics and flavonoids, as well as antioxidant competence from different botanical origins, which was an alternative approach to honey identification and nutritional evaluation.
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The use of multicomponent statistical analysis in hydrogeological environmental research.
Lambrakis, Nicolaos; Antonakos, Andreas; Panagopoulos, George
2004-04-01
The present article examines the possibilities of investigating NO(3)(-) spread in aquifers by applying multicomponent statistical methods (factor, cluster and discriminant analysis) on hydrogeological, hydrochemical, and environmental parameters. A 4-R-Mode factor model determined from the analysis showed its useful role in investigating hydrogeological parameters affecting NO(3)(-) concentration, such as its dilution by upcoming groundwater of the recharge areas. The relationship between NO(3)(-) concentration and agricultural activities can be determined sufficiently by the first factor which relies on NO(3)(-) and SO(4)(2-) of the same origin-that of agricultural fertilizers. The other three factors of R-Mode analysis are not connected directly to the NO(3)(-) problem. They do however, by extracting the role of the unsaturated zone, show an interesting relationship between organic matter content, thickness and saturated hydraulic conductivity. The application of Hirerarchical Cluster Analysis, based on all possible combinations of classification method, showed two main groups of samples. The first group comprises samples from the edges and the second from the central part of the study area. By the application of Discriminant Analysis it was shown that NO(3)(-) and SO(4)(2-) ions are the most significant variables in the discriminant function. Therefore, the first group is considered to comprise all samples from areas not influenced by fertilizers lying on the edges of contaminating activities such as crop cultivation, while the second comprises all the other samples.
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Manges, Amee R; Tellis, Patricia A; Vincent, Caroline; Lifeso, Kimberley; Geneau, Geneviève; Reid-Smith, Richard J; Boerlin, Patrick
2009-11-01
Discriminatory genotyping methods for the analysis of Escherichia coli other than O157:H7 are necessary for public health-related activities. A new multi-locus variable number tandem repeat analysis protocol is presented; this method achieves an index of discrimination of 99.5% and is reproducible and valid when tested on a collection of 836 diverse E. coli.
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Optimisation of nasal swab analysis by liquid scintillation counting.
Dai, Xiongxin; Liblong, Aaron; Kramer-Tremblay, Sheila; Priest, Nicholas; Li, Chunsheng
2012-06-01
When responding to an emergency radiological incident, rapid methods are needed to provide the physicians and radiation protection personnel with an early estimation of possible internal dose resulting from the inhalation of radionuclides. This information is needed so that appropriate medical treatment and radiological protection control procedures can be implemented. Nasal swab analysis, which employs swabs swiped inside a nostril followed by liquid scintillation counting of alpha and beta activity on the swab, could provide valuable information to quickly identify contamination of the affected population. In this study, various parameters (such as alpha/beta discrimination, swab materials, counting time and volume of scintillation cocktail etc) were evaluated in order to optimise the effectiveness of the nasal swab analysis method. An improved nasal swab procedure was developed by replacing cotton swabs with polyurethane-tipped swabs. Liquid scintillation counting was performed using a Hidex 300SL counter with alpha/beta pulse shape discrimination capability. Results show that the new method is more reliable than existing methods using cotton swabs and effectively meets the analysis requirements for screening personnel in an emergency situation. This swab analysis procedure is also applicable to wipe tests of surface contamination to minimise the source self-absorption effect on liquid scintillation counting.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Wang, C. L., E-mail: wangc@ornl.gov; Riedel, R. A.
2016-01-15
A {sup 6}Li-glass scintillator (GS20) based neutron Anger camera was developed for time-of-flight single-crystal diffraction instruments at Spallation Neutron Source. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio (defined as NGD ratio) on the order of 10{sup 4}. The NGD ratios of Anger cameras need to be improved for broader applications including neutron reflectometers. For this purpose, six digital signal analysis methods of individual waveforms acquired from photomultiplier tubes were proposed using (i) charge integration, (ii) pulse-amplitude histograms, (iii) power spectrum analysis combined with the maximum pulse-amplitude, (iv) two event parameters (a{sub 1}, b{submore » 0}) obtained from a Wiener filter, (v) an effective amplitude (m) obtained from an adaptive least-mean-square filter, and (vi) a cross-correlation coefficient between individual and reference waveforms. The NGD ratios are about 70 times those from the traditional PHA method. Our results indicate the NGD capabilities of neutron Anger cameras based on GS20 scintillators can be significantly improved with digital signal analysis methods.« less
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Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics
Belo, David; Gamboa, Hugo
2017-01-01
The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components. PMID:28831239
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Reports of Insurance-Based Discrimination in Health Care and Its Association With Access to Care
Call, Kathleen Thiede; Pintor, Jessie Kemmick; Alarcon-Espinoza, Giovann; Simon, Alisha Baines
2015-01-01
Objectives. We examined reports of insurance-based discrimination and its association with insurance type and access to care in the early years of the Patient Protection and Affordable Care Act. Methods. We used data from the 2013 Minnesota Health Access Survey to identify 4123 Minnesota adults aged 18 to 64 years who reported about their experiences of insurance-based discrimination. We modeled the association between discrimination and insurance type and predicted odds of having reduced access to care among those reporting discrimination, controlling for sociodemographic factors. Data were weighted to represent the state’s population. Results. Reports of insurance-based discrimination were higher among uninsured (25%) and publicly insured (21%) adults than among privately insured adults (3%), which held in the regression analysis. Those reporting discrimination had higher odds of lacking a usual source of care, lacking confidence in getting care, forgoing care because of cost, and experiencing provider-level barriers than those who did not. Conclusions. Further research and policy interventions are needed to address insurance-based discrimination in health care settings. PMID:25905821
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Scagliusi, F B; Ferriolli, E; Pfrimer, K; Laureano, C; Cunha, C S F; Gualano, B; Lourenço, B H; Lancha, A H
2009-10-01
We applied three dietary assessment methods and aimed at obtaining a set of physical, social and psychological variables that can discriminate those individuals who did not underreport ('never under-reporters'), those who underreported in one dietary assessment method ('occasional under-reporters') and those who underreported in two or three dietary assessment methods ('frequent under-reporters'). Sixty-five women aged 18-57 years were recruited for this study. Total energy expenditure was determined by doubly labelled water, and energy intake was estimated by three 24-h diet recalls, 3-day food records and a food frequency questionnaire. A multiple discriminant analysis was used to identify which of those variables better discriminated the three groups: body mass index (BMI), income, education, social desirability, nutritional knowledge, dietary restraint, physical activity practice, body dissatisfaction and binge-eating symptoms. Twenty-three participants were 'never under-reporters'. Twenty-four participants were 'occasional under-reporters' and 18 were 'frequent under-reporters'. Four variables entered the discriminant model: income, BMI, social desirability and body dissatisfaction. According to potency indices, income contributed the most to the total discriminant power, followed in decreasing order by social desirability score, BMI and body dissatisfaction. Income, social desirability and BMI were the characteristics that mainly separated the 'never under-reporters' from the under-reporters (occasional or frequent). Body dissatisfaction better discriminated the 'occasional under-reporters' from the 'frequent under-reporters'. 'Frequent under-reporters' have a greater BMI, social desirability score, body dissatisfaction score and lower income. These four variables seemed to be able to discriminate individuals who are more prone to systematic under reporting.
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USDA-ARS?s Scientific Manuscript database
A rapid, simple, and reliable flow-injection mass spectrometric (FIMS) method was developed to discriminate two major Echinacea species (E. purpurea and E. angustifolia) samples. Fifty-eight Echinacea samples collected from United States were analyzed using FIMS. Principle component analysis (PCA) a...
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Gamble, Gary R; Park, Bosoon; Yoon, Seung-Chul; Lawrence, Kurt C
2016-03-01
Laser-induced breakdown spectroscopy (LIBS) is used as the basis for discrimination between two genera of gram-negative bacteria and two genera of gram-positive bacteria representing pathogenic threats commonly found in poultry processing rinse waters. Because LIBS-based discrimination relies primarily upon the relative proportions of inorganic cell components including Na, K, Mg, and Ca, this study aims to determine the effects of trace mineral content and pH found in the water source used to isolate the bacteria upon the reliability of the resulting discriminant analysis. All four genera were cultured using tryptic soy agar (TSA) as the nutrient medium, and were grown under identical environmental conditions. The only variable introduced is the source water used to isolate the cultured bacteria. Cultures of each bacterium were produced using deionized (DI) water under two atmosphere conditions, reverse osmosis (RO) water, tap water, phosphate buffered saline (PBS) water, and TRIS buffered water. After 3 days of culture growth, the bacteria were centrifuged and washed three times in the same water source. Bacteria were then freeze dried, mixed with microcrystalline cellulose, and a pellet was made for LIBS analysis. Principal component analysis (PCA) was used to extract related variations in LIBS spectral data among the four bacteria genera and six water types used to isolate the bacteria, and Mahalanobis discriminant analysis (MDA) was used for classification. Results indicate not only that the four genera can be discriminated from each other in each water type, but that each genus can be discriminated by water type used for isolation. It is concluded that in order for LIBS to be a reliable and repeatable method for discrimination of bacteria grown in liquid nutrient media, care must be taken to insure that the water source used in purification of the culture be precisely controlled regarding pH, ionic strength, and proportionate amounts of mineral cations present. © The Author(s) 2016.
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Grow, Laura L; Kodak, Tiffany; Carr, James E
2014-01-01
Previous research has demonstrated that the conditional-only method (starting with a multiple-stimulus array) is more efficient than the simple-conditional method (progressive incorporation of more stimuli into the array) for teaching receptive labeling to children with autism spectrum disorders (Grow, Carr, Kodak, Jostad, & Kisamore,). The current study systematically replicated the earlier study by comparing the 2 approaches using progressive prompting with 2 boys with autism. The results showed that the conditional-only method was a more efficient and reliable teaching procedure than the simple-conditional method. The results further call into question the practice of teaching simple discriminations to facilitate acquisition of conditional discriminations. © Society for the Experimental Analysis of Behavior.
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A new discriminative kernel from probabilistic models.
Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert
2002-10-01
Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.
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Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.
Echinaka, Yuki; Ozeki, Yukiyasu
2016-10-01
The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.
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Zianni, Michael R; Nikbakhtzadeh, Mahmood R; Jackson, Bryan T; Panescu, Jenny; Foster, Woodbridge A
2013-04-01
There is a need for more cost-effective options to more accurately discriminate among members of the Anopheles gambiae complex, particularly An. gambiae and Anopheles arabiensis. These species are morphologically indistinguishable in the adult stage, have overlapping distributions, but are behaviorally and ecologically different, yet both are efficient vectors of malaria in equatorial Africa. The method described here, High-Resolution Melt (HRM) analysis, takes advantage of minute differences in DNA melting characteristics, depending on the number of incongruent single nucleotide polymorphisms in an intragenic spacer region of the X-chromosome-based ribosomal DNA. The two species in question differ by an average of 13 single-nucleotide polymorphisms giving widely divergent melting curves. A real-time PCR system, Bio-Rad CFX96, was used in combination with a dsDNA-specific dye, EvaGreen, to detect and measure the melting properties of the amplicon generated from leg-extracted DNA of selected mosquitoes. Results with seven individuals from pure colonies of known species, as well as 10 field-captured individuals unambiguously identified by DNA sequencing, demonstrated that the method provided a high level of accuracy. The method was used to identify 86 field mosquitoes through the assignment of each to the two common clusters with a high degree of certainty. Each cluster was defined by individuals from pure colonies. HRM analysis is simpler to use than most other methods and provides comparable or more accurate discrimination between the two sibling species but requires a specialized melt-analysis instrument and software.
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Zianni, Michael R.; Nikbakhtzadeh, Mahmood R.; Jackson, Bryan T.; Panescu, Jenny; Foster, Woodbridge A.
2013-01-01
There is a need for more cost-effective options to more accurately discriminate among members of the Anopheles gambiae complex, particularly An. gambiae and Anopheles arabiensis. These species are morphologically indistinguishable in the adult stage, have overlapping distributions, but are behaviorally and ecologically different, yet both are efficient vectors of malaria in equatorial Africa. The method described here, High-Resolution Melt (HRM) analysis, takes advantage of minute differences in DNA melting characteristics, depending on the number of incongruent single nucleotide polymorphisms in an intragenic spacer region of the X-chromosome-based ribosomal DNA. The two species in question differ by an average of 13 single-nucleotide polymorphisms giving widely divergent melting curves. A real-time PCR system, Bio-Rad CFX96, was used in combination with a dsDNA-specific dye, EvaGreen, to detect and measure the melting properties of the amplicon generated from leg-extracted DNA of selected mosquitoes. Results with seven individuals from pure colonies of known species, as well as 10 field-captured individuals unambiguously identified by DNA sequencing, demonstrated that the method provided a high level of accuracy. The method was used to identify 86 field mosquitoes through the assignment of each to the two common clusters with a high degree of certainty. Each cluster was defined by individuals from pure colonies. HRM analysis is simpler to use than most other methods and provides comparable or more accurate discrimination between the two sibling species but requires a specialized melt-analysis instrument and software. PMID:23543777
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Perceived age discrimination in older adults
Rippon, Isla; Kneale, Dylan; de Oliveira, Cesar; Demakakos, Panayotes; Steptoe, Andrew
2014-01-01
Objectives: to examine perceived age discrimination in a large representative sample of older adults in England. Methods: 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. Results: 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. Conclusion: 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. PMID:24077751
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Kuhnen, Shirley; Bernardi Ogliari, Juliana; Dias, Paulo Fernando; da Silva Santos, Maiara; Ferreira, Antônio Gilberto; Bonham, Connie C; Wood, Karl Vernon; Maraschin, Marcelo
2010-02-24
Aqueous extract from maize silks is used by traditional medicine for the treatment of several ailments, mainly related to the urinary system. This work focuses on the application of NMR spectroscopy and chemometric analysis for the determination of metabolic fingerprint and pattern recognition of silk extracts from seven maize landraces cultivated in southern Brazil. Principal component analysis (PCA) of the (1)H NMR data set showed clear discrimination among the maize varieties by PC1 and PC2, pointing out three distinct metabolic profiles. Target compounds analysis showed significant differences (p < 0.05) in the contents of protocatechuic acid, gallic acid, t-cinnamic acid, and anthocyanins, corroborating the discrimination of the genotypes in this study as revealed by PCA analysis. Thus the combination of (1)H NMR and PCA is a useful tool for the discrimination of maize silks in respect to their chemical composition, including rapid authentication of the raw material of current pharmacological interest.
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A new comprehensive index for discriminating adulteration in bovine raw milk.
Liu, Jing; Ren, Jing; Liu, Zhen-Min; Guo, Ben-Heng
2015-04-01
This paper proposes a new comprehensive index, called Q, which can effectively discriminate artificial adulterated milk from unadulterated milk. Both normal and adulterated samples of bovine raw milk were analysed by Fourier transform infrared spectroscopic instrument to measure the traditional indices of quality, including fat (FAT), protein (PRO), lactose (LAC), total solids (TS), non-fat solid (NFS), freezing point (FP) and somatic cell counts (SCC). From these traditional indices, this paper elaborates a method to build the index Q. First, correlated analysis and principle component analysis were used to select parameter pairs TS-FAT and FP-LAC as predominant variables. Second, linear-regression analysis and residual analysis are applied to determine the index Q and its discriminating ranges. The verification and two-blind trial results suggested that index Q could accurately detect milk adulteration with maltodextrin and water (as low as 1.0% of adulteration proportions), and with other nine kinds of synthetic adulterants (as low as 0.5% of adulteration proportions). Copyright © 2014 Elsevier Ltd. All rights reserved.
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Canizo, Brenda V; Escudero, Leticia B; Pérez, María B; Pellerano, Roberto G; Wuilloud, Rodolfo G
2018-03-01
The feasibility of the application of chemometric techniques associated with multi-element analysis for the classification of grape seeds according to their provenance vineyard soil was investigated. Grape seed samples from different localities of Mendoza province (Argentina) were evaluated. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-nine elements (Ag, As, Ce, Co, Cs, Cu, Eu, Fe, Ga, Gd, La, Lu, Mn, Mo, Nb, Nd, Ni, Pr, Rb, Sm, Te, Ti, Tl, Tm, U, V, Y, Zn and Zr). Once the analytical data were collected, supervised pattern recognition techniques such as linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), support vector machine (SVM) and Random Forest (RF) were applied to construct classification/discrimination rules. The results indicated that nonlinear methods, RF and SVM, perform best with up to 98% and 93% accuracy rate, respectively, and therefore are excellent tools for classification of grapes. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Optical system for tablet variety discrimination using visible/near-infrared spectroscopy
NASA Astrophysics Data System (ADS)
Shao, Yongni; He, Yong; Hu, Xingyue
2007-12-01
An optical system based on visible/near-infrared spectroscopy (Vis/NIRS) for variety discrimination of ginkgo (Ginkgo biloba L.) tablets was developed. This system consisted of a light source, beam splitter system, sample chamber, optical detector (diffuse reflection detector), and data collection. The tablet varieties used in the research include Da na kang, Xin bang, Tian bao ning, Yi kang, Hua na xing, Dou le, Lv yuan, Hai wang, and Ji yao. All samples (n=270) were scanned in the Vis/NIR region between 325 and 1075 nm using a spectrograph. The chemometrics method of principal component artificial neural network (PC-ANN) was used to establish discrimination models of them. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN, and the best discrimination rate of 91.1% was reached. Principal component analysis was also executed to select several optimal wavelengths based on loading values. Wavelengths at 481, 458, 466, 570, 1000, 662, and 400 nm were then used as the input data of stepwise multiple linear regression, the regression equation of ginkgo tablets was obtained, and the discrimination rate was researched 84.4%. The results indicated that this optical system could be applied to discriminating ginkgo (Ginkgo biloba L.) tablets, and it supplied a new method for fast ginkgo tablet variety discrimination.
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Warmack, Robert J. Bruce; Wolf, Dennis A; Frank, Steven Shane
2015-04-28
Methods and apparatus for smoke detection are disclosed. In one embodiment, a smoke detector uses linear discriminant analysis (LDA) to determine whether observed conditions indicate that an alarm is warranted.
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Improved pulse shape discrimination in EJ-301 liquid scintillators
NASA Astrophysics Data System (ADS)
Lang, R. F.; Masson, D.; Pienaar, J.; Röttger, S.
2017-06-01
Digital pulse shape discrimination has become readily available to distinguish nuclear recoil and electronic recoil events in scintillation detectors. We evaluate digital implementations of pulse shape discrimination algorithms discussed in the literature, namely the Charge Comparison Method, Pulse-Gradient Analysis, Fourier Series and Standard Event Fitting. In addition, we present a novel algorithm based on a Laplace Transform. Instead of comparing the performance of these algorithms based on a single Figure of Merit, we evaluate them as a function of recoil energy. Specifically, using commercial EJ-301 liquid scintillators, we examined both the resulting acceptance of nuclear recoils at a given rejection level of electronic recoils, as well as the purity of the selected nuclear recoil event samples. We find that both a Standard Event fit and a Laplace Transform can be used to significantly improve the discrimination capabilities over the whole considered energy range of 0 - 800keVee . Furthermore, we show that the Charge Comparison Method performs poorly in accurately identifying nuclear recoils.
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Kawamura, Yusuke; Ikeda, Kenji; Arase, Yasuji; Fujiyama, Shunichiro; Hosaka, Tetsuya; Kobayashi, Masahiro; Saitoh, Satoshi; Sezaki, Hitomi; Akuta, Norio; Suzuki, Fumitaka; Suzuki, Yoshiyuki; Kumada, Hiromitsu
2017-01-01
Objective To detect the aggressive phenotype (AP) of non-alcoholic fatty liver disease (NAFLD) based on the initial laboratory data and clinical characteristics. Methods We enrolled 144 patients with histologically proven NAFLD. For the first analysis, 24 NAFLD patients underwent repeat biopsy to establish a discriminant formula for predicting the AP of NAFLD (D-APN). The AP was defined by NAFLD that had been maintained or progressed to a fibrotic stage beyond stage 2. In the second analysis, we analyzed the distribution of the AP in each stage of disease and the incidence of the PNPLA3 rs738409 GG genotype in AP in 120 other patients. Results After the analysis, the following function was found to discriminate the disease phenotype: z=0.150×body mass index (kg/m 2 )+0.085×age (years)+1.112×ln (AST) (IU/L)+0.127×ln (m-AST)-12.96. A positive result indicates the AP of NAFLD. The discriminant functions had a positive predictive value of 94% and a negative predictive value of 71%. The distribution of the AP and the incidence of the PNPLA3 GG genotype in the AP in each stage of the disease among the 120 patients were as follows: non-alcoholic fatty liver, 30%/33%; non-alcoholic steatohepatitis (NASH) stage 1, 53%/26%; stage 2, 71%/70%; stage 3, 92%/57%; and stage 4, 93%/64%; there was a significant increase in the incidence of the AP as the disease progressed (p<0.001). Conclusion The new discriminant formula was useful for predicting disease progression potential in NAFLD patients and the incidence of the PNPLA3 GG genotype was elevated according to the distribution of AP.
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Elliott, Marc N.; Kanouse, David E.; Grunbaum, Jo Anne; Schwebel, David C.; Gilliland, M. Janice; Tortolero, Susan R.; Peskin, Melissa F.; Schuster, Mark A.
2009-01-01
Objectives. We sought to describe the prevalence, characteristics, and mental health problems of children who experience perceived racial/ethnic discrimination. Methods. We analyzed cross-sectional data from a study of 5147 fifth-grade students and their parents from public schools in 3 US metropolitan areas. We used multivariate logistic regression (overall and stratified by race/ethnicity) to examine the associations of sociodemographic factors and mental health problems with perceived racial/ethnic discrimination. Results. Fifteen percent of children reported perceived racial/ethnic discrimination, with 80% reporting that discrimination occurred at school. A greater percentage of Black (20%), Hispanic (15%), and other (16%) children reported perceived racial/ethnic discrimination compared with White (7%) children. Children who reported perceived racial/ethnic discrimination were more likely to have symptoms of each of the 4 mental health conditions included in the analysis: depression, attention deficit hyperactivity disorder, oppositional defiant disorder, and conduct disorder. An association between perceived racial/ethnic discrimination and depressive symptoms was found for Black, Hispanic, and other children but not for White children. Conclusions. Perceived racial/ethnic discrimination is not an uncommon experience among fifth-grade students and may be associated with a variety of mental health disorders. PMID:19299673
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Feature selection from hyperspectral imaging for guava fruit defects detection
NASA Astrophysics Data System (ADS)
Mat Jafri, Mohd. Zubir; Tan, Sou Ching
2017-06-01
Development of technology makes hyperspectral imaging commonly used for defect detection. In this research, a hyperspectral imaging system was setup in lab to target for guava fruits defect detection. Guava fruit was selected as the object as to our knowledge, there is fewer attempts were made for guava defect detection based on hyperspectral imaging. The common fluorescent light source was used to represent the uncontrolled lighting condition in lab and analysis was carried out in a specific wavelength range due to inefficiency of this particular light source. Based on the data, the reflectance intensity of this specific setup could be categorized in two groups. Sequential feature selection with linear discriminant (LD) and quadratic discriminant (QD) function were used to select features that could potentially be used in defects detection. Besides the ordinary training method, training dataset in discriminant was separated in two to cater for the uncontrolled lighting condition. These two parts were separated based on the brighter and dimmer area. Four evaluation matrixes were evaluated which are LD with common training method, QD with common training method, LD with two part training method and QD with two part training method. These evaluation matrixes were evaluated using F1-score with total 48 defected areas. Experiment shown that F1-score of linear discriminant with the compensated method hitting 0.8 score, which is the highest score among all.
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The UXO Classification Demonstration at the Former Camp Butner, NC
2011-07-01
Symposium and Workshop, Technical Session 2D: Classification Methods for Military Munitions Response. 1 December 2010. [49] Pasion , L. Personal...Communication. 15 June 2011. [50] Pasion , L. “Practical Strategies for UXO Discrimination: Camp Butner Analysis.” ESTCP Munitions Management In-Progress...Review. 9 February 2011. [51] Pasion , L., et al. “UXO Discrimination Using Full Coverage and Cued Interrogation Data Sets at Camp Butner, NC.” Partners
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Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis
Xu, Rui; Zhen, Zonglei; Liu, Jia
2010-01-01
Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081
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Ziółkowska, Angelika; Wąsowicz, Erwin; Jeleń, Henryk H
2016-12-15
Among methods to detect wine adulteration, profiling volatiles is one with a great potential regarding robustness, analysis time and abundance of information for subsequent data treatment. Volatile fraction fingerprinting by solid-phase microextraction with direct analysis by mass spectrometry without compounds separation (SPME-MS) was used for differentiation of white as well as red wines. The aim was to differentiate between varieties used for wine production and to also differentiate wines by country of origin. The results obtained were compared to SPME-GC/MS analysis in which compounds were resolved by gas chromatography. For both approaches the same type of statistical procedure was used to compare samples: principal component analysis (PCA) followed by linear discriminant analysis (LDA). White wines (38) and red wines (41) representing different grape varieties and various regions of origin were analysed. SPME-MS proved to be advantageous in use due to better discrimination and higher sample throughput. Copyright © 2016 Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Xu, Ye; Sonka, Milan; McLennan, Geoffrey; Guo, Junfeng; Hoffman, Eric
2005-04-01
Lung parenchyma evaluation via multidetector-row CT (MDCT), has significantly altered clinical practice in the early detection of lung disease. Our goal is to enhance our texture-based tissue classification ability to differentiate early pathologic processes by extending our 2-D Adaptive Multiple Feature Method (AMFM) to 3-D AMFM. We performed MDCT on 34 human volunteers in five categories: emphysema in severe Chronic Obstructive Pulmonary Disease (COPD) as EC, emphysema in mild COPD (MC), normal appearing lung in COPD (NC), non-smokers with normal lung function (NN), smokers with normal function (NS). We volumetrically excluded the airway and vessel regions, calculated 24 volumetric texture features for each Volume of Interest (VOI); and used Bayesian rules for discrimination. Leave-one-out and half-half methods were used for testing. Sensitivity, specificity and accuracy were calculated. The accuracy of the leave-one-out method for the four-class classification in the form of 3-D/2-D is: EC: 84.9%/70.7%, MC: 89.8%/82.7%; NC: 87.5.0%/49.6%; NN: 100.0%/60.0%. The accuracy of the leave-one-out method for the two-class classification in the form of 3-D/2-D is: NN: 99.3%/71.6%; NS: 99.7%/74.5%. We conclude that 3-D AMFM analysis of the lung parenchyma improves discrimination compared to 2-D analysis of the same images.
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Chen, Yun; Yang, Hui
2013-01-01
Heart rate variability (HRV) analysis has emerged as an important research topic to evaluate autonomic cardiac function. However, traditional time and frequency-domain analysis characterizes and quantify only linear and stationary phenomena. In the present investigation, we made a comparative analysis of three alternative approaches (i.e., wavelet multifractal analysis, Lyapunov exponents and multiscale entropy analysis) for quantifying nonlinear dynamics in heart rate time series. Note that these extracted nonlinear features provide information about nonlinear scaling behaviors and the complexity of cardiac systems. To evaluate the performance, we used 24-hour HRV recordings from 54 healthy subjects and 29 heart failure patients, available in PhysioNet. Three nonlinear methods are evaluated not only individually but also in combination using three classification algorithms, i.e., linear discriminate analysis, quadratic discriminate analysis and k-nearest neighbors. Experimental results show that three nonlinear methods capture nonlinear dynamics from different perspectives and the combined feature set achieves the best performance, i.e., sensitivity 97.7% and specificity 91.5%. Collectively, nonlinear HRV features are shown to have the promise to identify the disorders in autonomic cardiovascular function.
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A Comparison of Two-Group Classification Methods
ERIC Educational Resources Information Center
Holden, Jocelyn E.; Finch, W. Holmes; Kelley, Ken
2011-01-01
The statistical classification of "N" individuals into "G" mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis,…
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Song, Weiran; Wang, Hui; Maguire, Paul; Nibouche, Omar
2018-06-07
Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most effective multivariate analysis methods for spectral data analysis, which extracts latent variables and uses them to predict responses. In particular, it is an effective method for handling high-dimensional and collinear spectral data. However, PLS-DA does not explicitly address data multimodality, i.e., within-class multimodal distribution of data. In this paper, we present a novel method termed nearest clusters based PLS-DA (NCPLS-DA) for addressing the multimodality and nonlinearity issues explicitly and improving the performance of PLS-DA on spectral data classification. The new method applies hierarchical clustering to divide samples into clusters and calculates the corresponding centre of every cluster. For a given query point, only clusters whose centres are nearest to such a query point are used for PLS-DA. Such a method can provide a simple and effective tool for separating multimodal and nonlinear classes into clusters which are locally linear and unimodal. Experimental results on 17 datasets, including 12 UCI and 5 spectral datasets, show that NCPLS-DA can outperform 4 baseline methods, namely, PLS-DA, kernel PLS-DA, local PLS-DA and k-NN, achieving the highest classification accuracy most of the time. Copyright © 2018 Elsevier B.V. All rights reserved.
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A Critical Analysis of Anti-Discrimination Law and Microaggressions in Academia
ERIC Educational Resources Information Center
Lukes, Robin; Bangs, Joann
2014-01-01
This article provides a critical analysis of microaggressions and anti-discrimination law in academia. There are many challenges for faculty claiming discrimination under current civil rights laws. Examples of microaggressions that fall outside of anti-discrimination law will be provided. Traditional legal analysis of discrimination will not end…
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29 CFR 1608.4 - Establishing affirmative action plans.
Code of Federal Regulations, 2014 CFR
2014-07-01
... three elements: a reasonable self analysis; a reasonable basis for concluding action is appropriate; and reasonable action. (a) Reasonable self analysis. The objective of a self analysis is to determine whether... discrimination, and if so, to attempt to determine why. There is no mandatory method of conducting a self...
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29 CFR 1608.4 - Establishing affirmative action plans.
Code of Federal Regulations, 2012 CFR
2012-07-01
... three elements: a reasonable self analysis; a reasonable basis for concluding action is appropriate; and reasonable action. (a) Reasonable self analysis. The objective of a self analysis is to determine whether... discrimination, and if so, to attempt to determine why. There is no mandatory method of conducting a self...
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29 CFR 1608.4 - Establishing affirmative action plans.
Code of Federal Regulations, 2011 CFR
2011-07-01
... three elements: a reasonable self analysis; a reasonable basis for concluding action is appropriate; and reasonable action. (a) Reasonable self analysis. The objective of a self analysis is to determine whether... discrimination, and if so, to attempt to determine why. There is no mandatory method of conducting a self...
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29 CFR 1608.4 - Establishing affirmative action plans.
Code of Federal Regulations, 2013 CFR
2013-07-01
... three elements: a reasonable self analysis; a reasonable basis for concluding action is appropriate; and reasonable action. (a) Reasonable self analysis. The objective of a self analysis is to determine whether... discrimination, and if so, to attempt to determine why. There is no mandatory method of conducting a self...
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29 CFR 1608.4 - Establishing affirmative action plans.
Code of Federal Regulations, 2010 CFR
2010-07-01
... three elements: a reasonable self analysis; a reasonable basis for concluding action is appropriate; and reasonable action. (a) Reasonable self analysis. The objective of a self analysis is to determine whether... discrimination, and if so, to attempt to determine why. There is no mandatory method of conducting a self...
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Detecting Outliers in Factor Analysis Using the Forward Search Algorithm
ERIC Educational Resources Information Center
Mavridis, Dimitris; Moustaki, Irini
2008-01-01
In this article we extend and implement the forward search algorithm for identifying atypical subjects/observations in factor analysis models. The forward search has been mainly developed for detecting aberrant observations in regression models (Atkinson, 1994) and in multivariate methods such as cluster and discriminant analysis (Atkinson, Riani,…
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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 dependent variable. The total positive predictive value was 64.7%(77/119), with 66.7% for bacterial infection, 78.0% for viral infection and 33.3% for the mixed infection. Conclusions: The mostly detected pathogens were Pseudomonas aeruginosa, atypitcal pathogens, Klebsiella pneumoniae, influenza A virus and human cytomegalovirus in hospitalized patients with LRTI in this hospital. The discriminant diagnostic model established by clinical features may contribute to predict the pathogens of LRTI.
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Lu, Xiaobing; Yang, Yongzhe; Wu, Fengchun; Gao, Minjian; Xu, Yong; Zhang, Yue; Yao, Yongcheng; Du, Xin; Li, Chengwei; Wu, Lei; Zhong, Xiaomei; Zhou, Yanling; Fan, Ni; Zheng, Yingjun; Xiong, Dongsheng; Peng, Hongjun; Escudero, Javier; Huang, Biao; Li, Xiaobo; Ning, Yuping; Wu, Kai
2016-07-01
Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.
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Diao, Jiayin; Xu, Can; Zheng, Huiting; He, Siyi; Wang, Shumei
2018-06-21
Viticis Fructus is a traditional Chinese herbal drug processed by various methods to achieve different clinical purposes. Thermal treatment potentially alters chemical composition, which may impact on effectiveness and toxicity. In order to interpret the constituent discrepancies of raw versus processed (stir-fried) Viticis Fructus, a multivariate detection method (NIR, HPLC, and UPLC-MS) based on metabonomics and chemometrics was developed. Firstly, synergy interval partial least squares and partial least squares-discriminant analysis were employed to screen the distinctive wavebands (4319 - 5459 cm -1 ) based on preprocessed near-infrared spectra. Then, HPLC with principal component analysis was performed to characterize the distinction. Subsequently, a total of 49 compounds were identified by UPLC-MS, among which 42 compounds were eventually characterized as having a significant change during processing via the semiquantitative volcano plot analysis. Moreover, based on the partial least squares-discriminant analysis, 16 compounds were chosen as characteristic markers that could be in close correlation with the discriminatory near-infrared wavebands. Together, all of these characterization techniques effectively discriminated raw and processed products of Viticis Fructus. In general, our work provides an integrated way of classifying Viticis Fructus, and a strategy to explore discriminatory chemical markers for other traditional Chinese herbs, thus ensuring safety and efficacy for consumers. Georg Thieme Verlag KG Stuttgart · New York.
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Brežná, Barbara; Šmíd, Jiří; Costa, Joana; Radvanszky, Jan; Mafra, Isabel; Kuchta, Tomáš
2015-04-01
Ten published DNA-based analytical methods aiming at detecting material of almond (Prunus dulcis) were in silico evaluated for potential cross-reactivity with other stone fruits (Prunus spp.), including peach, apricot, plum, cherry, sour cherry and Sargent cherry. For most assays, the analysis of nucleotide databases suggested none or insufficient discrimination of at least some stone fruits. On the other hand, the assay targeting non-specific lipid transfer protein (Röder et al., 2011, Anal Chim Acta 685:74-83) was sufficiently discriminative, judging from nucleotide alignments. Empirical evaluation was performed for three of the published methods, one modification of a commercial kit (SureFood allergen almond) and one attempted novel method targeting thaumatin-like protein gene. Samples of leaves and kernels were used in the experiments. The empirical results were favourable for the method from Röder et al. (2011) and a modification of SureFood allergen almond kit, both showing cross-reactivity <10(-3) compared to the model almond. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Rešková, Z; Koreňová, J; Kuchta, T
2014-04-01
A total of 256 isolates of Staphylococcus aureus were isolated from 98 samples (34 swabs and 64 food samples) obtained from small or medium meat- and cheese-processing plants in Slovakia. The strains were genotypically characterized by multiple locus variable number of tandem repeats analysis (MLVA), involving multiplex polymerase chain reaction (PCR) with subsequent separation of the amplified DNA fragments by an automated flow-through gel electrophoresis. With the panel of isolates, MLVA produced 31 profile types, which was a sufficient discrimination to facilitate the description of spatial and temporal aspects of contamination. Further data on MLVA discrimination were obtained by typing a subpanel of strains by multiple locus sequence typing (MLST). MLVA coupled to automated electrophoresis proved to be an effective, comparatively fast and inexpensive method for tracing S. aureus contamination of food-processing factories. Subspecies genotyping of microbial contaminants in food-processing factories may facilitate identification of spatial and temporal aspects of the contamination. This may help to properly manage the process hygiene. With S. aureus, multiple locus variable number of tandem repeats analysis (MLVA) proved to be an effective method for the purpose, being sufficiently discriminative, yet comparatively fast and inexpensive. The application of automated flow-through gel electrophoresis to separation of DNA fragments produced by multiplex PCR helped to improve the accuracy and speed of the method. © 2013 The Society for Applied Microbiology.
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NASA Astrophysics Data System (ADS)
Manfredi, Marcello; Barberis, Elettra; Aceto, Maurizio; Marengo, Emilio
2017-06-01
During the last years the need for non-invasive and non-destructive analytical methods brought to the development and application of new instrumentation and analytical methods for the in-situ analysis of cultural heritage objects. In this work we present the application of a portable diffuse reflectance infrared Fourier transform (DRIFT) method for the non-invasive characterization of colorants prepared according to ancient recipes and using egg white and Gum Arabic as binders. Approximately 50 colorants were analyzed with the DRIFT spectroscopy: we were able to identify and discriminate the most used yellow (i.e. yellow ochres, Lead-tin Yellow, Orpiment, etc.), red (i.e. red ochres, Hematite) and blue (i.e. Lapis Lazuli, Azurite, indigo) colorants, creating a complete DRIFT spectral library. The Principal Component Analysis-Discriminant Analysis (PCA-DA) was then employed for the colorants classification according to the chemical/mineralogical composition. The DRIFT analysis was also performed on a gouache painting of the artist Sutherland; and the colorants used by the painter were identified directly in-situ and in a non-invasive manner.
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Discriminant analysis in wildlife research: Theory and applications
Williams, B.K.; Capen, D.E.
1981-01-01
Discriminant analysis, a method of analyzing grouped multivariate data, is often used in ecological investigations. It has both a predictive and an explanatory function, the former aiming at classification of individuals of unknown group membership. The goal of the latter function is to exhibit group separation by means of linear transforms, and the corresponding method is called canonical analysis. This discussion focuses on the application of canonical analysis in ecology. In order to clarify its meaning, a parametric approach is taken instead of the usual data-based formulation. For certain assumptions the data-based canonical variates are shown to result from maximum likelihood estimation, thus insuring consistency and asymptotic efficiency. The distorting effects of covariance heterogeneity are examined, as are certain difficulties which arise in interpreting the canonical functions. A 'distortion metric' is defined, by means of which distortions resulting from the canonical transformation can be assessed. Several sampling problems which arise in ecological applications are considered. It is concluded that the method may prove valuable for data exploration, but is of limited value as an inferential procedure.
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Earnshaw, Valerie A.; Rosenthal, Lisa; Lewis, Jessica B.; Stasko, Emily C.; Tobin, Jonathan N.; Lewis, Tené T.; Reid, Allecia E.; Ickovics, Jeannette R.
2012-01-01
Background Racial/ethnic disparities in birth weight persist within the United States. Purpose Examine the association between maternal everyday discrimination and infant birth weight among young, urban women of color; as well as mediators (depressive symptoms, pregnancy distress, pregnancy symptoms) and moderators (age, race/ethnicity, attributions of discrimination) of this association. Methods 420 women participated (14–21 years old; 62% Latina, 38% Black), completing measures of everyday discrimination and moderators during their second trimester of pregnancy and mediators during their third trimester. Birth weight was primarily recorded from medical record review. Results Path analysis demonstrated that everyday discrimination was associated with lower birth weight. Depressive symptoms mediated this relationship, and no tested factors moderated this relationship. Conclusions Given the association between birth weight and health across the lifespan, it is critical to reduce discrimination directed at young, urban women of color so that all children can begin life with greater promise for health. PMID:22927016
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Hara, Risa; Ishigaki, Mika; Kitahama, Yasutaka; Ozaki, Yukihiro; Genkawa, Takuma
2018-02-15
In this study, a lycopene-content-based discriminant analysis was performed using a portable near-infrared-excited Raman spectrometer. In the vegetable-juice Raman spectra, the peak intensity of the lycopene band increased with increasing lycopene concentration, but scattering decreased the repeatability of the peak intensity. Consequently, developing a lycopene-concentration regression model using peak intensity is not straightforward. Therefore, a new method known as the product of mean intensity ratio (PMIR) analysis was developed to rapidly identify lycopene-rich samples on-site. In the PMIR analysis, Raman spectra are measured with short exposure times, confirming only the peaks of carotenoids with high concentrations, and thus the lycopene concentrations of vegetable juice samples could be determined successfully. Exposure times of 20ms and 100ms could detect lycopene concentrations of ≥5mg/100g and ≥2mg/100g with 93.2% and 97.7% accuracy, respectively; thus, lycopene-content-based discriminant analysis using the PMIR and a portable Raman spectrometer is feasible. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Defeyt, C; Van Pevenage, J; Moens, L; Strivay, D; Vandenabeele, P
2013-11-01
In art analysis, copper phthalocyanine (CuPc) is often identified as an important pigment (PB15) in 20th century artworks. Raman spectroscopy is a very valuable technique for the detection of this pigment in paint systems. However, PB15 is used in different polymorphic forms and identification of the polymorph could retrieve information on the production process of the pigment at the moment. Raman spectroscopy, being a molecular spectroscopic method of analysis, is able to discriminate between polymorphs of crystals. However, in the case of PB15, spectral interpretation is not straightforward, and Raman data treatment requires some improvements concerning the PB15 polymorphic discrimination in paints. Here, Raman spectroscopy is combined with chemometrical analysis in order to develop a procedure allowing us to identify the PB15 crystalline structure in painted layers and in artworks. The results obtained by Linear Discriminant Analysis (LDA), using intensity ratios as variables, demonstrate the ability of this procedure to predict the crystalline structure of a PB15 pigment in unknown paint samples. Copyright © 2013 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Sugimoto, Asuka; Sumi, Takuya; Kang, Jiyoung; Tateno, Masaru
2017-07-01
Recognition in biological macromolecular systems, such as DNA-protein recognition, is one of the most crucial problems to solve toward understanding the fundamental mechanisms of various biological processes. Since specific base sequences of genome DNA are discriminated by proteins, such as transcription factors (TFs), finding TF binding motifs (TFBMs) in whole genome DNA sequences is currently a central issue in interdisciplinary biophysical and information sciences. In the present study, a novel strategy to create a discriminant function for discrimination of TFBMs by constituting mathematical neural networks (NNs) is proposed, together with a method to determine the boundary of signals (TFBMs) and noise in the NN-score (output) space. This analysis also leads to the mathematical limitation of discrimination in the recognition of features representing TFBMs, in an information geometrical manifold. Thus, the present strategy enables the identification of the whole space of TFBMs, right up to the noise boundary.
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Normalized distance aggregation of discriminative features for person reidentification
NASA Astrophysics Data System (ADS)
Hou, Li; Han, Kang; Wan, Wanggen; Hwang, Jenq-Neng; Yao, Haiyan
2018-03-01
We propose an effective person reidentification method based on normalized distance aggregation of discriminative features. Our framework is built on the integration of three high-performance discriminative feature extraction models, including local maximal occurrence (LOMO), feature fusion net (FFN), and a concatenation of LOMO and FFN called LOMO-FFN, through two fast and discriminant metric learning models, i.e., cross-view quadratic discriminant analysis (XQDA) and large-scale similarity learning (LSSL). More specifically, we first represent all the cross-view person images using LOMO, FFN, and LOMO-FFN, respectively, and then apply each extracted feature representation to train XQDA and LSSL, respectively, to obtain the optimized individual cross-view distance metric. Finally, the cross-view person matching is computed as the sum of the optimized individual cross-view distance metric through the min-max normalization. Experimental results have shown the effectiveness of the proposed algorithm on three challenging datasets (VIPeR, PRID450s, and CUHK01).
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A Comparison of Analytical and Data Preprocessing Methods for Spectral Fingerprinting
LUTHRIA, DEVANAND L.; MUKHOPADHYAY, SUDARSAN; LIN, LONG-ZE; HARNLY, JAMES M.
2013-01-01
Spectral fingerprinting, as a method of discriminating between plant cultivars and growing treatments for a common set of broccoli samples, was compared for six analytical instruments. Spectra were acquired for finely powdered solid samples using Fourier transform infrared (FT-IR) and Fourier transform near-infrared (NIR) spectrometry. Spectra were also acquired for unfractionated aqueous methanol extracts of the powders using molecular absorption in the ultraviolet (UV) and visible (VIS) regions and mass spectrometry with negative (MS−) and positive (MS+) ionization. The spectra were analyzed using nested one-way analysis of variance (ANOVA) and principal component analysis (PCA) to statistically evaluate the quality of discrimination. All six methods showed statistically significant differences between the cultivars and treatments. The significance of the statistical tests was improved by the judicious selection of spectral regions (IR and NIR), masses (MS+ and MS−), and derivatives (IR, NIR, UV, and VIS). PMID:21352644
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Detection and monitoring of anaerobic rumen fungi using an ARISA method.
Denman, S E; Nicholson, M J; Brookman, J L; Theodorou, M K; McSweeney, C S
2008-12-01
To develop an automated ribosomal intergenic spacer region analysis (ARISA) method for the detection of anaerobic rumen fungi and also to demonstrate utility of the technique to monitor colonization and persistence of fungi, and diet-induced changes in community structure. The method could discriminate between three genera of anaerobic rumen fungal isolates, representing Orpinomyces, Piromyces and Neocallimastix species. Changes in anaerobic fungal composition were observed between animals fed a high-fibre diet compared with a grain-based diet. ARISA analysis of rumen samples from animals on grain showed a decrease in fungal diversity with a dominance of Orpinomyces and Piromyces spp. Clustering analysis of ARISA profile patterns grouped animals based on diet. A single strain of Orpinomyces was dosed into a cow and was detectable within the rumen fungal population for several weeks afterwards. The ARISA technique was capable of discriminating between pure cultures at the genus level. Diet composition has a significant influence on the diversity of anaerobic fungi in the rumen and the method can be used to monitor introduced strains. Through the use of ARISA analysis, a better understanding of the effect of diets on rumen anaerobic fungi populations is provided.
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Shape classification of wear particles by image boundary analysis using machine learning algorithms
NASA Astrophysics Data System (ADS)
Yuan, Wei; Chin, K. S.; Hua, Meng; Dong, Guangneng; Wang, Chunhui
2016-05-01
The shape features of wear particles generated from wear track usually contain plenty of information about the wear states of a machinery operational condition. Techniques to quickly identify types of wear particles quickly to respond to the machine operation and prolong the machine's life appear to be lacking and are yet to be established. To bridge rapid off-line feature recognition with on-line wear mode identification, this paper presents a new radial concave deviation (RCD) method that mainly involves the use of the particle boundary signal to analyze wear particle features. Signal output from the RCDs subsequently facilitates the determination of several other feature parameters, typically relevant to the shape and size of the wear particle. Debris feature and type are identified through the use of various classification methods, such as linear discriminant analysis, quadratic discriminant analysis, naïve Bayesian method, and classification and regression tree method (CART). The average errors of the training and test via ten-fold cross validation suggest CART is a highly suitable approach for classifying and analyzing particle features. Furthermore, the results of the wear debris analysis enable the maintenance team to diagnose faults appropriately.
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Arvanitoyannis, Ioannis S; Vlachos, Antonios
2007-01-01
The authenticity of products labeled as olive oils, and in particular as virgin olive oils, stands for a very important issue both in terms of its health and commercial aspects. In view of the continuously increasing interest in virgin olive oil therapeutic properties, the traditional methods of characterization and physical and sensory analysis were further enriched with more advanced and sophisticated methods such as HPLC-MS, HPLC-GC/C/IRMS, RPLC-GC, DEPT, and CSIA among others. The results of both traditional and "novel" methods were treated both by means of classical multivariate analysis (cluster, principal component, correspondence, canonical, and discriminant) and artificial intelligence methods showing that nowadays the adulteration of virgin olive oil with seed oil is detectable at very low percentages, sometimes even at less than 1%. Furthermore, the detection of geographical origin of olive oil is equally feasible and much more accurate in countries like Italy and Spain where databases of physical/chemical properties exist. However, this geographical origin classification can also be accomplished in the absence of such databases provided that an adequate number of oil samples are used and the parameters studied have "discriminating power."
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Invariant approach to the character classification
NASA Astrophysics Data System (ADS)
Šariri, Kristina; Demoli, Nazif
2008-04-01
Image moments analysis is a very useful tool which allows image description invariant to translation and rotation, scale change and some types of image distortions. The aim of this work was development of simple method for fast and reliable classification of characters by using Hu's and affine moment invariants. Measure of Eucleidean distance was used as a discrimination feature with statistical parameters estimated. The method was tested in classification of Times New Roman font letters as well as sets of the handwritten characters. It is shown that using all Hu's and three affine invariants as discrimination set improves recognition rate by 30%.
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Identification of anisodamine tablets by Raman and near-infrared spectroscopy with chemometrics.
Li, Lian; Zang, Hengchang; Li, Jun; Chen, Dejun; Li, Tao; Wang, Fengshan
2014-06-05
Vibrational spectroscopy including Raman and near-infrared (NIR) spectroscopy has become an attractive tool for pharmaceutical analysis. In this study, effective calibration models for the identification of anisodamine tablet and its counterfeit and the distinguishment of manufacturing plants, based on Raman and NIR spectroscopy, were built, respectively. Anisodamine counterfeit tablets were identified by Raman spectroscopy with correlation coefficient method, and the results showed that the predictive accuracy was 100%. The genuine anisodamine tablets from 5 different manufacturing plants were distinguished by NIR spectroscopy using partial least squares discriminant analysis (PLS-DA) models based on interval principal component analysis (iPCA) method. And the results showed the recognition rate and rejection rate were 100% respectively. In conclusion, Raman spectroscopy and NIR spectroscopy combined with chemometrics are feasible and potential tools for rapid pharmaceutical tablet discrimination. Copyright © 2014 Elsevier B.V. All rights reserved.
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Moment Tensor Analysis of Shallow Sources
NASA Astrophysics Data System (ADS)
Chiang, A.; Dreger, D. S.; Ford, S. R.; Walter, W. R.; Yoo, S. H.
2015-12-01
A potential issue for moment tensor inversion of shallow seismic sources is that some moment tensor components have vanishing amplitudes at the free surface, which can result in bias in the moment tensor solution. The effects of the free-surface on the stability of the moment tensor method becomes important as we continue to investigate and improve the capabilities of regional full moment tensor inversion for source-type identification and discrimination. It is important to understand these free surface effects on discriminating shallow explosive sources for nuclear monitoring purposes. It may also be important in natural systems that have shallow seismicity such as volcanoes and geothermal systems. In this study, we apply the moment tensor based discrimination method to the HUMMING ALBATROSS quarry blasts. These shallow chemical explosions at approximately 10 m depth and recorded up to several kilometers distance represent rather severe source-station geometry in terms of vanishing traction issues. We show that the method is capable of recovering a predominantly explosive source mechanism, and the combined waveform and first motion method enables the unique discrimination of these events. Recovering the correct yield using seismic moment estimates from moment tensor inversion remains challenging but we can begin to put error bounds on our moment estimates using the NSS technique.
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Behavioural evidence of colour vision in free flying stingless bees.
Spaethe, J; Streinzer, M; Eckert, J; May, S; Dyer, A G
2014-06-01
Colour vision was first demonstrated with behavioural experiments in honeybees 100 years ago. Since that time a wealth of quality physiological data has shown a highly conserved set of trichromatic colour receptors in most bee species. Despite the subsequent wealth of behavioural research on honeybees and bumblebees, there currently is a relative dearth of data on stingless bees, which are the largest tribe of the eusocial bees comprising of more than 600 species. In our first experiment we tested Trigona cf. fuscipennis, a stingless bee species from Costa Rica in a field setting using the von Frisch method and show functional colour vision. In a second experiment with these bees, we use a simultaneous colour discrimination test designed for honeybees to enable a comparative analysis of relative colour discrimination. In a third experiment, we test in laboratory conditions Tetragonula carbonaria, an Australian stingless bee species using a similar simultaneous colour discrimination test. Both stingless bee species show relatively poorer colour discrimination compared to honeybees and bumblebees; and we discuss the value of being able to use these behavioural methods to efficiently extend our current knowledge of colour vision and discrimination in different bee species.
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Ding, Hang
2014-01-01
Structures in recurrence plots (RPs), preserving the rich information of nonlinear invariants and trajectory characteristics, have been increasingly analyzed in dynamic discrimination studies. The conventional analysis of RPs is mainly focused on quantifying the overall diagonal and vertical line structures through a method, called recurrence quantification analysis (RQA). This study extensively explores the information in RPs by quantifying local complex RP structures. To do this, an approach was developed to analyze the combination of three major RQA variables: determinism, laminarity, and recurrence rate (DLR) in a metawindow moving over a RP. It was then evaluated in two experiments discriminating (1) ideal nonlinear dynamic series emulated from the Lorenz system with different control parameters and (2) data sets of human heart rate regulations with normal sinus rhythms (n = 18) and congestive heart failure (n = 29). Finally, the DLR was compared with seven major RQA variables in terms of discriminatory power, measured by standardized mean difference (DSMD). In the two experiments, DLR resulted in the highest discriminatory power with DSMD = 2.53 and 0.98, respectively, which were 7.41 and 2.09 times the best performance from RQA. The study also revealed that the optimal RP structures for the discriminations were neither typical diagonal structures nor vertical structures. These findings indicate that local complex RP structures contain some rich information unexploited by RQA. Therefore, future research to extensively analyze complex RP structures would potentially improve the effectiveness of the RP analysis in dynamic discrimination studies.
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Workplace discrimination: experiences of practicing physicians.
Coombs, Alice A. Tolbert; King, Roderick K.
2005-01-01
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Physicians practicing in academic, research, and private practice sectors experience discrimination based on gender, ethnic/racial, and IMG status. Images Figure 3 Figure 4 Figure 5 PMID:15868767
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Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini
2013-01-01
Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6–7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification. PMID:24086666
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Qiu, Shanshan; Wang, Jun; Gao, Liping
2014-07-09
An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterize five types of strawberry juices based on processing approaches (i.e., microwave pasteurization, steam blanching, high temperature short time pasteurization, frozen-thawed, and freshly squeezed). Juice quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio) were detected by traditional measuring methods. Multivariate statistical methods (linear discriminant analysis (LDA) and partial least squares regression (PLSR)) and neural networks (Random Forest (RF) and Support Vector Machines) were employed to qualitative classification and quantitative regression. E-tongue system reached higher accuracy rates than E-nose did, and the simultaneous utilization did have an advantage in LDA classification and PLSR regression. According to cross-validation, RF has shown outstanding and indisputable performances in the qualitative and quantitative analysis. This work indicates that the simultaneous utilization of E-nose and E-tongue can discriminate processed fruit juices and predict quality parameters successfully for the beverage industry.
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NASA Astrophysics Data System (ADS)
Luo, Shuwen; Chen, Changshui; Mao, Hua; Jin, Shaoqin
2013-06-01
The feasibility of early detection of gastric cancer using near-infrared (NIR) Raman spectroscopy (RS) by distinguishing premalignant lesions (adenomatous polyp, n=27) and cancer tissues (adenocarcinoma, n=33) from normal gastric tissues (n=45) is evaluated. Significant differences in Raman spectra are observed among the normal, adenomatous polyp, and adenocarcinoma gastric tissues at 936, 1003, 1032, 1174, 1208, 1323, 1335, 1450, and 1655 cm-1. Diverse statistical methods are employed to develop effective diagnostic algorithms for classifying the Raman spectra of different types of ex vivo gastric tissues, including principal component analysis (PCA), linear discriminant analysis (LDA), and naive Bayesian classifier (NBC) techniques. Compared with PCA-LDA algorithms, PCA-NBC techniques together with leave-one-out, cross-validation method provide better discriminative results of normal, adenomatous polyp, and adenocarcinoma gastric tissues, resulting in superior sensitivities of 96.3%, 96.9%, and 96.9%, and specificities of 93%, 100%, and 95.2%, respectively. Therefore, NIR RS associated with multivariate statistical algorithms has the potential for early diagnosis of gastric premalignant lesions and cancer tissues in molecular level.
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Sex assessment using measurements of the first lumbar vertebra.
Zheng, Wen Xu; Cheng, Fu Bo; Cheng, Kai Liang; Tian, Yong; Lai, Ying; Zhang, Wen Song; Zheng, Ya Juan; Li, You Qiong
2012-06-10
Sex determination is a vital part of the medico-legal system but can be difficult in cases where the integrity of the body has been compromised. The purpose of this study was to develop a technique for sex assessment from measurements of the first lumber vertebrate. Twenty-nine linear measurements and five ratios were collected from 113 Chinese adult males and 97 Chinese adult females using digital three-dimensional anthropometry methods. By using discriminant analysis, we found that 23 linear measurements and two ratios identified sexual dimorphism (P<0.01), with predictive accuracy ranging from 57.1% to 86.6%. Using a stepwise method of discriminant function analysis, we found three dimensions predicted sex with 88.6% accuracy: (a) upper end-plate width (EPWu), (b) left pedicle height (PHl), and (c) middle end-plate depth (EPDm). This study shows that a single first lumber vertebra can be used for this purpose, and that the discriminant equation will help forensic determination of sex in the Chinese population. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
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Macaluso, P J
2011-02-01
Digital photogrammetric methods were used to collect diameter, area, and perimeter data of the acetabulum for a twentieth-century skeletal sample from France (Georges Olivier Collection, Musée de l'Homme, Paris) consisting of 46 males and 36 females. The measurements were then subjected to both discriminant function and logistic regression analyses in order to develop osteometric standards for sex assessment. Univariate discriminant functions and logistic regression equations yielded overall correct classification accuracy rates for both the left and the right acetabula ranging from 84.1% to 89.6%. The multivariate models developed in this study did not provide increased accuracy over those using only a single variable. Classification sex bias ratios ranged between 1.1% and 7.3% for the majority of models. The results of this study, therefore, demonstrate that metric analysis of acetabular size provides a highly accurate, and easily replicable, method of discriminating sex in this documented skeletal collection. The results further suggest that the addition of area and perimeter data derived from digital images may provide a more effective method of sex assessment than that offered by traditional linear measurements alone. Copyright © 2010 Elsevier GmbH. All rights reserved.
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Unique volatolomic signatures of TP53 and KRAS in lung cells
Davies, M P A; Barash, O; Jeries, R; Peled, N; Ilouze, M; Hyde, R; Marcus, M W; Field, J K; Haick, H
2014-01-01
Background: Volatile organic compounds (VOCs) are potential biomarkers for cancer detection in breath, but it is unclear if they reflect specific mutations. To test this, we have compared human bronchial epithelial cell (HBEC) cell lines carrying the KRASV12 mutation, knockdown of TP53 or both with parental HBEC cells. Methods: VOC from headspace above cultured cells were collected by passive sampling and analysed by thermal desorption gas chromatography mass spectrometry (TD-GC–MS) or sensor array with discriminant factor analysis (DFA). Results: In TD-GC–MS analysis, individual compounds had limited ability to discriminate between cell lines, but by applying DFA analysis combinations of 20 VOCs successfully discriminated between all cell types (accuracies 80–100%, with leave-one-out cross validation). Sensor array detection DFA demonstrated the ability to discriminate samples based on their cell type for all comparisons with accuracies varying between 77% and 93%. Conclusions: Our results demonstrate that minimal genetic changes in bronchial airway cells lead to detectable differences in levels of specific VOCs identified by TD-GC–MS or of patterns of VOCs identified by sensor array output. From the clinical aspect, these results suggest the possibility of breath analysis for detection of minimal genetic changes for earlier diagnosis or for genetic typing of lung cancers. PMID:25051409
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Tada, Atsuko; Ishizuki, Kyoko; Yamazaki, Takeshi; Sugimoto, Naoki; Akiyama, Hiroshi
2014-07-01
Natural ester-type gum bases, which are used worldwide as food additives, mainly consist of wax esters composed of long-chain fatty acids and long-chain fatty alcohols. There are many varieties of ester-type gum bases, and thus a useful method for their discrimination is needed in order to establish official specifications and manage their quality control. Herein is reported a rapid and simple method for the analysis of different ester-type gum bases used as food additives by high-temperature gas chromatography/mass spectrometry (GC/MS). With this method, the constituent wax esters in ester-type gum bases can be detected without hydrolysis and derivatization. The method was applied to the determination of 10 types of gum bases, including beeswax, carnauba wax, lanolin, and jojoba wax, and it was demonstrated that the gum bases derived from identical origins have specific and characteristic total ion chromatogram (TIC) patterns and ester compositions. Food additive gum bases were thus distinguished from one another based on their TIC patterns and then more clearly discriminated using simultaneous monitoring of the fragment ions corresponding to the fatty acid moieties of the individual molecular species of the wax esters. This direct high-temperature GC/MS method was shown to be very useful for the rapid and simple discrimination of varieties of ester-type gum bases used as food additives.
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Tada, Atsuko; Ishizuki, Kyoko; Yamazaki, Takeshi; Sugimoto, Naoki; Akiyama, Hiroshi
2014-01-01
Natural ester-type gum bases, which are used worldwide as food additives, mainly consist of wax esters composed of long-chain fatty acids and long-chain fatty alcohols. There are many varieties of ester-type gum bases, and thus a useful method for their discrimination is needed in order to establish official specifications and manage their quality control. Herein is reported a rapid and simple method for the analysis of different ester-type gum bases used as food additives by high-temperature gas chromatography/mass spectrometry (GC/MS). With this method, the constituent wax esters in ester-type gum bases can be detected without hydrolysis and derivatization. The method was applied to the determination of 10 types of gum bases, including beeswax, carnauba wax, lanolin, and jojoba wax, and it was demonstrated that the gum bases derived from identical origins have specific and characteristic total ion chromatogram (TIC) patterns and ester compositions. Food additive gum bases were thus distinguished from one another based on their TIC patterns and then more clearly discriminated using simultaneous monitoring of the fragment ions corresponding to the fatty acid moieties of the individual molecular species of the wax esters. This direct high-temperature GC/MS method was shown to be very useful for the rapid and simple discrimination of varieties of ester-type gum bases used as food additives. PMID:25473499
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Linear discriminant analysis based on L1-norm maximization.
Zhong, Fujin; Zhang, Jiashu
2013-08-01
Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique, which is widely used for many purposes. However, conventional LDA is sensitive to outliers because its objective function is based on the distance criterion using L2-norm. This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L1-norm-based between-class dispersion and the L1-norm-based within-class dispersion. The proposed method is theoretically proved to be feasible and robust to outliers while overcoming the singular problem of the within-class scatter matrix for conventional LDA. Experiments on artificial datasets, standard classification datasets and three popular image databases demonstrate the efficacy of the proposed method.
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NASA Astrophysics Data System (ADS)
Jelinek, Herbert F.; Cree, Michael J.; Leandro, Jorge J. G.; Soares, João V. B.; Cesar, Roberto M.; Luckie, A.
2007-05-01
Proliferative diabetic retinopathy can lead to blindness. However, early recognition allows appropriate, timely intervention. Fluorescein-labeled retinal blood vessels of 27 digital images were automatically segmented using the Gabor wavelet transform and classified using traditional features such as area, perimeter, and an additional five morphological features based on the derivatives-of-Gaussian wavelet-derived data. Discriminant analysis indicated that traditional features do not detect early proliferative retinopathy. The best single feature for discrimination was the wavelet curvature with an area under the curve (AUC) of 0.76. Linear discriminant analysis with a selection of six features achieved an AUC of 0.90 (0.73-0.97, 95% confidence interval). The wavelet method was able to segment retinal blood vessels and classify the images according to the presence or absence of proliferative retinopathy.
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Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun
2017-08-01
The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.
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de Lima Morais da Silva, Patricia; de Lima, Liliane Schier; Caetano, Ísis Kaminski; Torres, Yohandra Reyes
2017-12-01
The volatile composition of honeys produced by eight species of stingless bees collected in three municipalities in the state of Paraná (Brazil) was compared by combining static headspace GC-MS and chemometrics methods. Forty-four compounds were identified using NIST library and linear retention index relative to n-alkanes (C 8 -C 40 ). Linalool derivatives were the most abundant peaks in most honeys regardless geographical or entomological origin. However, Principal Component Analysis discriminated honeys from different geographical origins considering their distinctive minor volatile components. Honey samples from Guaraqueçaba were characterized by the presence of hotrienol while those from Cambará showed epoxylinalol, benzaldehyde and TDN as minor discriminating compounds. Punctual species such as Borá showed similar fingerprints regardless geographical origin, with ethyl octanoate and ethyl decanoate as characteristic intense chromatographic peaks, which may suggest a specialized behavior for nectar collection. Discriminant Analysis allowed correct geographic discrimination of most honeys produced in the three spots tested. We concluded that volatile profile of stingless bee honeys can be used to attest authenticity related to regional origin of honeys. Copyright © 2017. Published by Elsevier Ltd.
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Durán-Guerrero, Enrique; Chinnici, Fabio; Natali, Nadia; Riponi, Claudio
2015-09-01
Thirty-six high-quality vinegars with geographical indication belonging to Sherry and Modena areas (vinegars of Jerez, balsamic vinegars of Modena and traditional balsamic vinegars of Modena) with all possible aging periods were analyzed to determine the content of volatile aldehydes. A solid-phase extraction method with in-cartridge derivatization using O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine followed by gas chromatography-mass spectrometry was employed. Twenty-two volatile aldehydes were identified and determined in the samples. Analysis of variance provided significant differences among the samples as a function of the type of vinegar, aging time and raw material. Principal component analysis and linear discriminant analysis demonstrated the possibility of discriminating the samples in terms of aging time and raw material. Linear aldehydes and compounds such as furfural, methional, nonenal, hexenal, 2-methylbutanal and i-butyraldehyde were the most significant variables able to discriminate the samples. Aldehyde content of premium quality vinegars is a function of both ageing time and raw material. Their evaluation could be a useful tool with a view to ascertaining vinegar origin and genuineness. © 2014 Society of Chemical Industry.
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Jiang, Shun-Yuan; Sun, Hong-Bing; Sun, Hui; Ma, Yu-Ying; Chen, Hong-Yu; Zhu, Wen-Tao; Zhou, Yi
2016-03-01
This paper aims to explore a comprehensive assessment method combined traditional Chinese medicinal material specifications with quantitative quality indicators. Seventy-six samples of Notopterygii Rhizoma et Radix were collected on market and at producing areas. Traditional commercial specifications were described and assigned, and 10 chemical components and volatile oils were determined for each sample. Cluster analysis, Fisher discriminant analysis and correspondence analysis were used to establish the relationship between the traditional qualitative commercial specifications and quantitative chemical indices for comprehensive evaluating quality of medicinal materials, and quantitative classification of commercial grade and quality grade. A herb quality index (HQI) including traditional commercial specifications and chemical components for quantitative grade classification were established, and corresponding discriminant function were figured out for precise determination of quality grade and sub-grade of Notopterygii Rhizoma et Radix. The result showed that notopterol, isoimperatorin and volatile oil were the major components for determination of chemical quality, and their dividing values were specified for every grade and sub-grade of the commercial materials of Notopterygii Rhizoma et Radix. According to the result, essential relationship between traditional medicinal indicators, qualitative commercial specifications, and quantitative chemical composition indicators can be examined by K-mean cluster, Fisher discriminant analysis and correspondence analysis, which provide a new method for comprehensive quantitative evaluation of traditional Chinese medicine quality integrated traditional commodity specifications and quantitative modern chemical index. Copyright© by the Chinese Pharmaceutical Association.
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Han, Bangxing; Peng, Huasheng; Yan, Hui
2016-01-01
Mugua is a common Chinese herbal medicine. There are three main medicinal origin places in China, Xuancheng City Anhui Province, Qijiang District Chongqing City, Yichang City, Hubei Province, and suitable for food origin places Linyi City Shandong Province. To construct a qualitative analytical method to identify the origin of medicinal Mugua by near infrared spectroscopy (NIRS). Partial least squares discriminant analysis (PLSDA) model was established after the Mugua derived from five different origins were preprocessed by the original spectrum. Moreover, the hierarchical cluster analysis was performed. The result showed that PLSDA model was established. According to the relationship of the origins-related important score and wavenumber, and K-mean cluster analysis, the Muguas derived from different origins were effectively identified. NIRS technology can quickly and accurately identify the origin of Mugua, provide a new method and technology for the identification of Chinese medicinal materials. After preprocessed by D1+autoscale, more peaks were increased in the preprocessed Mugua in the near infrared spectrumFive latent variable scores could reflect the information related to the origin place of MuguaOrigins of Mugua were well-distinguished according to K. mean value clustering analysis. Abbreviations used: TCM: Traditional Chinese Medicine, NIRS: Near infrared spectroscopy, SG: Savitzky-Golay smoothness, D1: First derivative, D2: Second derivative, SNV: Standard normal variable transformation, MSC: Multiplicative scatter correction, PLSDA: Partial least squares discriminant analysis, LV: Latent variable, VIP scores: Important score.
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Zuo, Yamin; Deng, Xuehua; Wu, Qing
2018-05-04
Discrimination of Gastrodia elata ( G. elata ) geographical origin is of great importance to pharmaceutical companies and consumers in China. this paper focuses on the feasibility of near infrared spectrum (NIRS) combined multivariate analysis as a rapid and non-destructive method to prove its fit for this purpose. Firstly, 16 batches of G. elata samples from four main-cultivation regions in China were quantified by traditional HPLC method. It showed that samples from different origins could not be efficiently differentiated by the contents of four phenolic compounds in this study. Secondly, the raw near infrared (NIR) spectra of those samples were acquired and two different pattern recognition techniques were used to classify the geographical origins. The results showed that with spectral transformation optimized, discriminant analysis (DA) provided 97% and 99% correct classification for the calibration and validation sets of samples from discriminating of four different main-cultivation regions, and provided 98% and 99% correct classifications for the calibration and validation sets of samples from eight different cities, respectively, which all performed better than the principal component analysis (PCA) method. Thirdly, as phenolic compounds content (PCC) is highly related with the quality of G. elata , synergy interval partial least squares (Si-PLS) was applied to build the PCC prediction model. The coefficient of determination for prediction (R p ²) of the Si-PLS model was 0.9209, and root mean square error for prediction (RMSEP) was 0.338. The two regions (4800 cm −1 ⁻5200 cm −1 , and 5600 cm −1 ⁻6000 cm −1 ) selected by Si-PLS corresponded to the absorptions of aromatic ring in the basic phenolic structure. It can be concluded that NIR spectroscopy combined with PCA, DA and Si-PLS would be a potential tool to provide a reference for the quality control of G. elata.
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Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Oh, Kyoungmin; Kim, Geonseob; Ham, Hyeonheui; Kim, Seongmin; Kim, Moon S.
2018-01-01
Fusarium is a common fungal disease in grains that reduces the yield of barley and wheat. In this study, a near infrared reflectance spectroscopic technique was used with a statistical prediction model to rapidly and non-destructively discriminate grain samples contaminated with Fusarium. Reflectance spectra were acquired from hulled barley, naked barley, and wheat samples contaminated with Fusarium using near infrared reflectance (NIR) spectroscopy with a wavelength range of 1175–2170 nm. After measurement, the samples were cultured in a medium to discriminate contaminated samples. A partial least square discrimination analysis (PLS-DA) prediction model was developed using the acquired reflectance spectra and the culture results. The correct classification rate (CCR) of Fusarium for the hulled barley, naked barley, and wheat samples developed using raw spectra was 98% or higher. The accuracy of discrimination prediction improved when second and third-order derivative pretreatments were applied. The grains contaminated with Fusarium could be rapidly discriminated using spectroscopy technology and a PLS-DA discrimination model, and the potential of the non-destructive discrimination method could be verified. PMID:29301319
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Park, Je Sung; Lee, Byung Kook; Jeung, Kyung Woon; Choi, Sung Soo; Park, Sang Wook; Song, Kyung Hwan; Lee, Sung Min; Heo, Tag; Min, Yong Il
2015-04-01
We investigated the use of blood color brightness and blood gas variables for discriminating arterial from venous puncture during cardiopulmonary resuscitation (CPR). The study's aims were to determine if discrimination using Po2 is superior to using blood color brightness, and if blood color brightness, Po2, and acid-base variables derived from blood gas analysis accurately discriminate arterial from venous blood during CPR. Fifteen pigs underwent ventricular fibrillation followed by CPR. During CPR, paired femoral arterial and venous blood samples were obtained, and 2 blinded observers were asked to identify the blood's origin. Blood color brightness was measured using a blood brightness scale (BBS). The discriminatory performances of the BBS and blood gas variables were evaluated by calculating the area under receiver operating characteristic curves (AUC). The observers accurately discriminated arterial from venous blood with a sensitivity of 97.0% (84.7%-99.5%) and specificity of 84.9% (69.1%-93.4%). The BBS (AUC = 0.983) and Po2 (AUC = 0.981) methods both showed comparable and excellent discriminatory performances. pH, Pco2, and HCO3(-) all discriminated arterial from venous blood (AUC = 0.831, 0.971, and 0.652, respectively). The AUC for Pco2 was comparable to that for Po2 but significantly larger than that for pH (P = .002) or HCO3(-) (P < .001). The BBS and Po2 methods showed comparable and excellent discrimination performances. Using pH, Pco2, and HCO3(-) levels also discriminated arterial from venous blood during CPR with statistical significance. Copyright © 2015 Elsevier Inc. All rights reserved.
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Cui, Jiangyu; Zhou, Yumin; Tian, Jia; Wang, Xinwang; Zheng, Jingping; Zhong, Nanshan; Ran, Pixin
2012-12-01
COPD is often underdiagnosed in a primary care setting where the spirometry is unavailable. This study was aimed to develop a simple, economical and applicable model for COPD screening in those settings. First we established a discriminant function model based on Bayes' Rule by stepwise discriminant analysis, using the data from 243 COPD patients and 112 non-COPD subjects from our COPD survey in urban and rural communities and local primary care settings in Guangdong Province, China. We then used this model to discriminate COPD in additional 150 subjects (50 non-COPD and 100 COPD ones) who had been recruited by the same methods as used to have established the model. All participants completed pre- and post-bronchodilator spirometry and questionnaires. COPD was diagnosed according to the Global Initiative for Chronic Obstructive Lung Disease criteria. The sensitivity and specificity of the discriminant function model was assessed. THE ESTABLISHED DISCRIMINANT FUNCTION MODEL INCLUDED NINE VARIABLES: age, gender, smoking index, body mass index, occupational exposure, living environment, wheezing, cough and dyspnoea. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, accuracy and error rate of the function model to discriminate COPD were 89.00%, 82.00%, 4.94, 0.13, 86.66% and 13.34%, respectively. The accuracy and Kappa value of the function model to predict COPD stages were 70% and 0.61 (95% CI, 0.50 to 0.71). This discriminant function model may be used for COPD screening in primary care settings in China as an alternative option instead of spirometry.
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NASA Astrophysics Data System (ADS)
Javidnia, Katayoun; Parish, Maryam; Karimi, Sadegh; Hemmateenejad, Bahram
2013-03-01
By using FT-IR spectroscopy, many researchers from different disciplines enrich the experimental complexity of their research for obtaining more precise information. Moreover chemometrics techniques have boosted the use of IR instruments. In the present study we aimed to emphasize on the power of FT-IR spectroscopy for discrimination between different oil samples (especially fat from vegetable oils). Also our data were used to compare the performance of different classification methods. FT-IR transmittance spectra of oil samples (Corn, Colona, Sunflower, Soya, Olive, and Butter) were measured in the wave-number interval of 450-4000 cm-1. Classification analysis was performed utilizing PLS-DA, interval PLS-DA, extended canonical variate analysis (ECVA) and interval ECVA methods. The effect of data preprocessing by extended multiplicative signal correction was investigated. Whilst all employed method could distinguish butter from vegetable oils, iECVA resulted in the best performances for calibration and external test set with 100% sensitivity and specificity.
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Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan
2017-09-01
In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
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Wang, Cai -Lin; Riedel, Richard A.
2016-01-14
A 6Li-glass scintillator (GS20) based neutron Anger camera was developed for time-of-flight single-crystal diffraction instruments at SNS. Traditional pulse-height analysis (PHA) for neutron-gamma discrimination (NGD) resulted in the neutron-gamma efficiency ratio (defined as NGD ratio) on the order of 10 4. The NGD ratios of Anger cameras need to be improved for broader applications including neutron reflectometers. For this purpose, five digital signal analysis methods of individual waveforms from PMTs were proposed using: i). pulse-amplitude histogram; ii). power spectrum analysis combined with the maximum pulse amplitude; iii). two event parameters (a 1, b 0) obtained from Wiener filter; iv). anmore » effective amplitude (m) obtained from an adaptive least-mean-square (LMS) filter; and v). a cross-correlation (CC) coefficient between an individual waveform and a reference. The NGD ratios can be 1-102 times those from traditional PHA method. A brighter scintillator GS2 has better NGD ratio than GS20, but lower neutron detection efficiency. The ultimate NGD ratio is related to the ambient, high-energy background events. Moreover, our results indicate the NGD capability of neutron Anger cameras can be improved using digital signal analysis methods and brighter neutron scintillators.« less
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Using color histograms and SPA-LDA to classify bacteria.
de Almeida, Valber Elias; da Costa, Gean Bezerra; de Sousa Fernandes, David Douglas; Gonçalves Dias Diniz, Paulo Henrique; Brandão, Deysiane; de Medeiros, Ana Claudia Dantas; Véras, Germano
2014-09-01
In this work, a new approach is proposed to verify the differentiating characteristics of five bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) by using digital images obtained with a simple webcam and variable selection by the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). In this sense, color histograms in the red-green-blue (RGB), hue-saturation-value (HSV), and grayscale channels and their combinations were used as input data, and statistically evaluated by using different multivariate classifiers (Soft Independent Modeling by Class Analogy (SIMCA), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Partial Least Squares Discriminant Analysis (PLS-DA) and Successive Projections Algorithm-Linear Discriminant Analysis (SPA-LDA)). The bacteria strains were cultivated in a nutritive blood agar base layer for 24 h by following the Brazilian Pharmacopoeia, maintaining the status of cell growth and the nature of nutrient solutions under the same conditions. The best result in classification was obtained by using RGB and SPA-LDA, which reached 94 and 100 % of classification accuracy in the training and test sets, respectively. This result is extremely positive from the viewpoint of routine clinical analyses, because it avoids bacterial identification based on phenotypic identification of the causative organism using Gram staining, culture, and biochemical proofs. Therefore, the proposed method presents inherent advantages, promoting a simpler, faster, and low-cost alternative for bacterial identification.
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Development and Validation of a Racial Discrimination Measure for Cambodian American Adolescents
Sangalang, Cindy C.; Chen, Angela C. C.; Kulis, Stephen S.; Yabiku, Scott T.
2015-01-01
To date, the majority of studies examining experiences of racial discrimination among youth use measures initially developed for African American and Latino adults or college students. Few studies have attended to the ways in which discrimination experiences may be unique for Asian American youth, particularly subgroups such as Southeast Asians. The purpose of this study was twofold: (a) to describe the development of a racial discrimination measure using community-based participatory research with Cambodian American adolescents and (b) to psychometrically test the measure with respect to validity and reliability. This research used mixed-methods and comprised 3 phases. Phase 1 consisted of qualitative focus group research to assess community-identified needs. Phase 2 included quantitative survey development with community members and resulted in an 18-item measure assessing the frequency of ethnicity-based discrimination. Phase 3 involved psychometric testing of the measure’s validity and reliability (n = 423). Exploratory factor analysis procedures yielded a 3-factor structure describing peer, school, and police discrimination from all items, capturing 96% of the combined variance. Using confirmatory factor analysis, the data demonstrated good fit with the 3-factor structure (CFI = .98; RMSEA = .054), with factor loadings ranging from .59 to .96 and all estimates statistically significant at the p < .05 level. Correlational analyses of racial discrimination subfactors and depression supported concurrent validity. In sum, this measure can be used to examine the degree and sources of racial discrimination reported by Cambodian American adolescents and potentially other adolescents of Southeast Asian descent living in diverse urban communities. PMID:26388972
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Li, Chao-Ran; Li, Meng-Ning; Yang, Hua; Li, Ping; Gao, Wen
2018-06-01
Processing of herbal medicines is a characteristic pharmaceutical technique in Traditional Chinese Medicine, which can reduce toxicity and side effect, improve the flavor and efficacy, and even change the pharmacological action entirely. It is significant and crucial to perform a method to find chemical markers for differentiating herbal medicines in different processed degrees. The aim of this study was to perform a rapid and reasonable method to discriminate Moutan Cortex and its processed products, and to reveal the characteristics of chemical components depend on chemical markers. Thirty batches of Moutan Cortex and its processed products, including 11 batches of Raw Moutan Cortex (RMC), 9 batches of Moutan Cortex Tostus (MCT) and 10 batches of Moutan Cortex Carbonisatus (MCC), were directly injected in electrospray ionization quadrupole time-of-flight mass spectrometry (ESI-QTOF MS) for rapid analysis in positive and negative mode. Without chromatographic separation, each run was completed within 3 min. The raw MS data were automatically extracted by background deduction and molecular feature (MF) extraction algorithm. In negative mode, a total of 452 MFs were obtained and then pretreated by data filtration and differential analysis. After that, the filtered 85 MFs were treated by principal component analysis (PCA) to reduce the dimensions. Subsequently, a partial least squares discrimination analysis (PLS-DA) model was constructed for differentiation and chemical markers detection of Moutan Cortex in different processed degrees. The positive mode data were treated as same as those in negative mode. RMC, MCT and MCC were successfully classified. Moreover, 14 and 3 chemical markers from negative and positive mode respectively, were screened by the combination of their relative peak areas and the parameter variable importance in the projection (VIP) values in PLS-DA model. The content changes of these chemical markers were employed in order to illustrate chemical changes of Moutan Cortex after processed. These results showed that the proposed method which combined non-targeted metabolomics analysis with multivariate statistics analysis is reasonable and effective. It could not only be applied to discriminate herbal medicines and their processing products, but also to reveal the characteristics of chemical components during processing. Copyright © 2018. Published by Elsevier GmbH.
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MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.
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.
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Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.
Zhou, Pan; Lin, Zhouchen; Zhang, Chao
2016-05-01
Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.
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Bowers, Andrew; Saltuklaroglu, Tim; Harkrider, Ashley; Cuellar, Megan
2013-01-01
Constructivist theories propose that articulatory hypotheses about incoming phonetic targets may function to enhance perception by limiting the possibilities for sensory analysis. To provide evidence for this proposal, it is necessary to map ongoing, high-temporal resolution changes in sensorimotor activity (i.e., the sensorimotor μ rhythm) to accurate speech and non-speech discrimination performance (i.e., correct trials.). Sixteen participants (15 female and 1 male) were asked to passively listen to or actively identify speech and tone-sweeps in a two-force choice discrimination task while the electroencephalograph (EEG) was recorded from 32 channels. The stimuli were presented at signal-to-noise ratios (SNRs) in which discrimination accuracy was high (i.e., 80-100%) and low SNRs producing discrimination performance at chance. EEG data were decomposed using independent component analysis and clustered across participants using principle component methods in EEGLAB. ICA revealed left and right sensorimotor µ components for 14/16 and 13/16 participants respectively that were identified on the basis of scalp topography, spectral peaks, and localization to the precentral and postcentral gyri. Time-frequency analysis of left and right lateralized µ component clusters revealed significant (pFDR<.05) suppression in the traditional beta frequency range (13-30 Hz) prior to, during, and following syllable discrimination trials. No significant differences from baseline were found for passive tasks. Tone conditions produced right µ beta suppression following stimulus onset only. For the left µ, significant differences in the magnitude of beta suppression were found for correct speech discrimination trials relative to chance trials following stimulus offset. Findings are consistent with constructivist, internal model theories proposing that early forward motor models generate predictions about likely phonemic units that are then synthesized with incoming sensory cues during active as opposed to passive processing. Future directions and possible translational value for clinical populations in which sensorimotor integration may play a functional role are discussed.
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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.
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2013-01-01
Introduction: Risk for smoking initiation increases as Hispanic youth acculturate to U.S. society, and this association seems to be stronger for Hispanic girls than boys. To better understand the influence of culture, family, and everyday discrimination on cigarette smoking, we tested a process-oriented model of acculturation and cigarette smoking. Methods: Data came from Project RED (Reteniendo y Entendiendo Diversidad para Salud), which included 1,436 Hispanic students (54% girls) from Southern California. We used data from 9th to 11th grade (85% were 14 years old, and 86% were U.S. born) to test the influence of acculturation-related experiences on smoking over time. Results: Multigroup structural equation analysis suggested that acculturation was associated with increased familismo and lower traditional gender roles, and enculturation was linked more with familismo and respeto. Familismo, respeto, and traditional gender roles were linked with lower family conflict and increased family cohesion, and these links were stronger for girls. Familismo and respeto were further associated with lower discrimination. Conversely, fatalismo was linked with worse family functioning (especially for boys) and increased discrimination in both the groups. Discrimination was the only predictor of smoking for boys and girls. Conclusions: In all, the results of the current study indicate that reducing discrimination and helping youth cope with discrimination may prevent or reduce smoking in Hispanic boys and girls. This may be achieved by promoting familismo and respeto and by discouraging fatalistic beliefs. PMID:23109671
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NASA Astrophysics Data System (ADS)
Mohammadimanesh, F.; Salehi, B.; Mahdianpari, M.; Homayouni, S.
2016-06-01
Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.
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Timmons, Chris; Trees, Eija; Ribot, Efrain M; Gerner-Smidt, Peter; LaFon, Patti; Im, Sung; Ma, Li Maria
2016-06-01
Non-O157 Shiga toxin-producing Escherichia coli (STEC) are foodborne pathogens of growing concern worldwide that have been associated with several recent multistate and multinational outbreaks of foodborne illness. Rapid and sensitive molecular-based bacterial strain discrimination methods are critical for timely outbreak identification and contaminated food source traceback. One such method, multiple-locus variable-number tandem repeat analysis (MLVA), is being used with increasing frequency in foodborne illness outbreak investigations to augment the current gold standard bacterial subtyping technique, pulsed-field gel electrophoresis (PFGE). The objective of this study was to develop a MLVA assay for intra- and inter-serogroup discrimination of six major non-O157 STEC serogroups-O26, O111, O103, O121, O45, and O145-and perform a preliminary internal validation of the method on a limited number of clinical isolates. The resultant MLVA scheme consists of ten variable number tandem repeat (VNTR) loci amplified in three multiplex PCR reactions. Sixty-five unique MLVA types were obtained among 84 clinical non-O157 STEC strains comprised of geographically diverse sporadic and outbreak related isolates. Compared to PFGE, the developed MLVA scheme allowed similar discrimination among serogroups O26, O111, O103, and O121 but not among O145 and O45. To more fully compare the discriminatory power of this preliminary MLVA method to PFGE and to determine its epidemiological congruence, a thorough internal and external validation needs to be performed on a carefully selected large panel of strains, including multiple isolates from single outbreaks. Copyright © 2016. Published by Elsevier B.V.
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Socaci, Sonia A; Socaciu, Carmen; Tofană, Maria; Raţi, Ioan V; Pintea, Adela
2013-01-01
The health benefits of sea buckthorn (Hippophae rhamnoides L.) are well documented due to its rich content in bioactive phytochemicals (pigments, phenolics and vitamins) as well as volatiles responsible for specific flavours and bacteriostatic action. The volatile compounds are good biomarkers of berry freshness, quality and authenticity. To develop a fast and efficient GC-MS method including a minimal sample preparation technique (in-tube extraction, ITEX) for the discrimination of sea buckthorn varieties based on their chromatographic volatile fingerprint. Twelve sea buckthorn varieties (wild and cultivated) were collected from forestry departments and experimental fields, respectively. The extraction of volatile compounds was performed using the ITEX technique whereas separation and identification was performed using a GC-MS QP-2010. Principal component analysis (PCA) was applied to discriminate the differences among sample composition. Using GC-MS analysis, from the headspace of sea buckthorn samples, 46 volatile compounds were separated with 43 being identified. The most abundant derivatives were ethyl esters of 2-methylbutanoic acid, 3-methylbutanoic acid, hexanoic acid, octanoic acid and butanoic acid, as well as 3-methylbutyl 3-methylbutanoate, 3-methylbutyl 2-methylbutanoate and benzoic acid ethyl ester (over 80% of all volatile compounds). Principal component analysis showed that the first two components explain 79% of data variance, demonstrating a good discrimination between samples. A reliable, fast and eco-friendly ITEX/GC-MS method was applied to fingerprint the volatile profile and to discriminate between wild and cultivated sea buckthorn berries originating from the Carpathians, with relevance to food science and technology. Copyright © 2013 John Wiley & Sons, Ltd.
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Parasites as biological tags of fish stocks: a meta-analysis of their discriminatory power.
Poulin, Robert; Kamiya, Tsukushi
2015-01-01
The use of parasites as biological tags to discriminate among marine fish stocks has become a widely accepted method in fisheries management. Here, we first link this approach to its unstated ecological foundation, the decay in the similarity of the species composition of assemblages as a function of increasing distance between them, a phenomenon almost universal in nature. We explain how distance decay of similarity can influence the use of parasites as biological tags. Then, we perform a meta-analysis of 61 uses of parasites as tags of marine fish populations in multivariate discriminant analyses, obtained from 29 articles. Our main finding is that across all studies, the observed overall probability of correct classification of fish based on parasite data was about 71%. This corresponds to a two-fold improvement over the rate of correct classification expected by chance alone, and the average effect size (Zr = 0·463) computed from the original values was also indicative of a medium-to-large effect. However, none of the moderator variables included in the meta-analysis had a significant effect on the proportion of correct classification; these moderators included the total number of fish sampled, the number of parasite species used in the discriminant analysis, the number of localities from which fish were sampled, the minimum and maximum distance between any pair of sampling localities, etc. Therefore, there are no clear-cut situations in which the use of parasites as tags is more useful than others. Finally, we provide recommendations for the future usage of parasites as tags for stock discrimination, to ensure that future applications of the method achieve statistical rigour and a high discriminatory power.
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Zhang, Xufeng; Liu, Yu; Li, Ying; Zhao, Xinda
2017-03-01
Geographic traceability is an important issue for food quality and safety control of seafood. In this study,δ 13 C and δ 15 N values, as well as fatty acid (FA) content of 133 samples of A. japonicus from seven sampling points in northern China Sea were determined to evaluate their applicability in the origin traceability of A. japonicus. Principal component analysis (PCA) and discriminant analysis (DA) were applied to different data sets in order to evaluate their performance in terms of classification or predictive ability. δ 13 C and δ 15 N values could effectively discriminate between different origins of A. japonicus. Significant differences in the FA compositions showed the effectiveness of FA composition as a tool for distinguishing between different origins of A. japonicus. The two technologies, combined with multivariate statistical analysis, can be promising methods to discriminate A. japonicus from different geographical areas. Copyright © 2016. Published by Elsevier Ltd.
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NASA Astrophysics Data System (ADS)
Niu, Xiaoying; Ying, Yibin; Yu, Haiyan; Xie, Lijuan; Fu, Xiaping; Zhou, Ying; Jiang, Xuesong
2007-09-01
In this paper, 104 samples of Chinese rice wines of the same variety (Shaoxing rice wine), collected in three winery ("guyuelongshan", "pagoda" brand, "kuaijishan"), three brewed years (2002, 2004, 2004-2006) were analyzed by near-infrared transmission spectroscopy between 800 and 2500 nm. The spectral differences were studied by principal components analysis (PCA), and Classifications, according the brand, were carried out by discriminant analysis (DA) and partial least squares discriminant analysis (PLSDA). The DA model gained a total accuracy of 94.23% and when used to predict the brand of the validation set samples, a better result, correctly classified all of the three kinds of Chinese rice wine up to 100%, are obtained by PLSDA model. The work reported here is a feasibility study and requires further development with considerable samples of more different brands. Further studies are needed in order to improve the accuracy and robustness, and to extend the discrimination to other Chinese rice wine varieties or brands.
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Characterization of Microbiota in Children with Chronic Functional Constipation.
de Meij, Tim G J; de Groot, Evelien F J; Eck, Anat; Budding, Andries E; Kneepkens, C M Frank; Benninga, Marc A; van Bodegraven, Adriaan A; Savelkoul, Paul H M
2016-01-01
Disruption of the intestinal microbiota is considered an etiological factor in pediatric functional constipation. Scientifically based selection of potential beneficial probiotic strains in functional constipation therapy is not feasible due to insufficient knowledge of microbiota composition in affected subjects. The aim of this study was to describe microbial composition and diversity in children with functional constipation, compared to healthy controls. Fecal samples from 76 children diagnosed with functional constipation according to the Rome III criteria (median age 8.0 years; range 4.2-17.8) were analyzed by IS-pro, a PCR-based microbiota profiling method. Outcome was compared with intestinal microbiota profiles of 61 healthy children (median 8.6 years; range 4.1-17.9). Microbiota dissimilarity was depicted by principal coordinate analysis (PCoA), diversity was calculated by Shannon diversity index. To determine the most discriminative species, cross validated logistic ridge regression was performed. Applying total microbiota profiles (all phyla together) or per phylum analysis, no disease-specific separation was observed by PCoA and by calculation of diversity indices. By ridge regression, however, functional constipation and controls could be discriminated with 82% accuracy. Most discriminative species were Bacteroides fragilis, Bacteroides ovatus, Bifidobacterium longum, Parabacteroides species (increased in functional constipation) and Alistipes finegoldii (decreased in functional constipation). None of the commonly used unsupervised statistical methods allowed for microbiota-based discrimination of children with functional constipation and controls. By ridge regression, however, both groups could be discriminated with 82% accuracy. Optimization of microbiota-based interventions in constipated children warrants further characterization of microbial signatures linked to clinical subgroups of functional constipation.
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Signal peptide discrimination and cleavage site identification using SVM and NN.
Kazemian, H B; Yusuf, S A; White, K
2014-02-01
About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd.
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Buddhachat, Kittisak; Thitaram, Chatchote; Brown, Janine L.; Klinhom, Sarisa; Bansiddhi, Pakkanut; Penchart, Kitichaya; Ouitavon, Kanita; Sriaksorn, Khanittha; Pa-in, Chalermpol; Kanchanasaka, Budsabong; Somgird, Chaleamchat; Nganvongpanit, Korakot
2016-01-01
We describe the use of handheld X-ray fluorescence, for elephant tusk species identification. Asian (n = 72) and African (n = 85) elephant tusks were scanned and we utilized the species differences in elemental composition to develop a functional model differentiating between species with high precision. Spatially, the majority of measured elements (n = 26) exhibited a homogeneous distribution in cross-section, but a more heterologous pattern in the longitudinal direction. Twenty-one of twenty four elements differed between Asian and African samples. Data were subjected to hierarchical cluster analysis followed by a stepwise discriminant analysis, which identified elements for the functional equation. The best equation consisted of ratios of Si, S, Cl, Ti, Mn, Ag, Sb and W, with Zr as the denominator. Next, Bayesian binary regression model analysis was conducted to predict the probability that a tusk would be of African origin. A cut-off value was established to improve discrimination. This Bayesian hybrid classification model was then validated by scanning an additional 30 Asian and 41 African tusks, which showed high accuracy (94%) and precision (95%) rates. We conclude that handheld XRF is an accurate, non-invasive method to discriminate origin of elephant tusks provides rapid results applicable to use in the field. PMID:27097717
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NASA Astrophysics Data System (ADS)
Buddhachat, Kittisak; Thitaram, Chatchote; Brown, Janine L.; Klinhom, Sarisa; Bansiddhi, Pakkanut; Penchart, Kitichaya; Ouitavon, Kanita; Sriaksorn, Khanittha; Pa-in, Chalermpol; Kanchanasaka, Budsabong; Somgird, Chaleamchat; Nganvongpanit, Korakot
2016-04-01
We describe the use of handheld X-ray fluorescence, for elephant tusk species identification. Asian (n = 72) and African (n = 85) elephant tusks were scanned and we utilized the species differences in elemental composition to develop a functional model differentiating between species with high precision. Spatially, the majority of measured elements (n = 26) exhibited a homogeneous distribution in cross-section, but a more heterologous pattern in the longitudinal direction. Twenty-one of twenty four elements differed between Asian and African samples. Data were subjected to hierarchical cluster analysis followed by a stepwise discriminant analysis, which identified elements for the functional equation. The best equation consisted of ratios of Si, S, Cl, Ti, Mn, Ag, Sb and W, with Zr as the denominator. Next, Bayesian binary regression model analysis was conducted to predict the probability that a tusk would be of African origin. A cut-off value was established to improve discrimination. This Bayesian hybrid classification model was then validated by scanning an additional 30 Asian and 41 African tusks, which showed high accuracy (94%) and precision (95%) rates. We conclude that handheld XRF is an accurate, non-invasive method to discriminate origin of elephant tusks provides rapid results applicable to use in the field.
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Kumar, Raj; Kumar, Vinay; Sharma, Vishal
2017-01-05
The aim of the present work is to explore the non-destructive application of ATR-FTIR technique for characterization and discrimination of paper samples which could be helpful to give forensic aid in resolving legal cases. Twenty-four types of paper brands were purchased from local market in and around Chandigarh, India. All the paper samples were subjected to ATR-FTIR analysis from 400 to 4000cm(-1) wavenumber range. The qualitative feature and Chemometrics of the obtained spectral data are used for characterization and discrimination. Characterization is achieved by matching the peaks with standards of cellulose and inorganic fillers, a usual constituents of paper. Three different regions of IR, i.e. 400-2000cm(-1), 2000-4000cm(-1) and 400-4000cm(-1) were selected for differentiation by Chemometrics analysis. The discrimination is achieved on the basis of three principal components, i.e. PC 1, PC 2 and PC 3. It is observed that maximum discrimination was procured in the wave number range of i.e. 2000-4000cm(-1). Discriminating power was calculated on the basis of qualitative features as well, and it is found that the discrimination of paper samples was better achieved by Chemometrics analysis rather than qualitative features. The discriminating power by Chemometrics is 99.64% and which is larger as ever achieved by any group for present number of samples. The present result confirms that this study will be highly useful in forensic document examination work in the legal cases, where the authenticity of the document is challenged. The results are completely analytical and, therefore, overcome the problem encounter in traditional routine light/radiation scanning methods which are still in practice by various questioned document laboratories. Copyright © 2016 Elsevier B.V. All rights reserved.
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Bermudo, R; Abia, D; Mozos, A; García-Cruz, E; Alcaraz, A; Ortiz, Á R; Thomson, T M; Fernández, P L
2011-01-01
Introduction: Currently, final diagnosis of prostate cancer (PCa) is based on histopathological analysis of needle biopsies, but this process often bears uncertainties due to small sample size, tumour focality and pathologist's subjective assessment. Methods: Prostate cancer diagnostic signatures were generated by applying linear discriminant analysis to microarray and real-time RT–PCR (qRT–PCR) data from normal and tumoural prostate tissue samples. Additionally, after removal of biopsy tissues, material washed off from transrectal biopsy needles was used for molecular profiling and discriminant analysis. Results: Linear discriminant analysis applied to microarray data for a set of 318 genes differentially expressed between non-tumoural and tumoural prostate samples produced 26 gene signatures, which classified the 84 samples used with 100% accuracy. To identify signatures potentially useful for the diagnosis of prostate biopsies, surplus material washed off from routine biopsy needles from 53 patients was used to generate qRT–PCR data for a subset of 11 genes. This analysis identified a six-gene signature that correctly assigned the biopsies as benign or tumoural in 92.6% of the cases, with 88.8% sensitivity and 96.1% specificity. Conclusion: Surplus material from prostate needle biopsies can be used for minimal-size gene signature analysis for sensitive and accurate discrimination between non-tumoural and tumoural prostates, without interference with current diagnostic procedures. This approach could be a useful adjunct to current procedures in PCa diagnosis. PMID:22009027
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Li, Jie; Zhang, Ji; Zuo, Zhitian; Huang, Hengyu; Wang, Yuanzhong
2018-05-09
Background : Swertia nervosa (Wall. ex G. Don) C. B. Clarke, a promising traditional herbal medicine for the treatment of liver disorders, is endangered due to its extensive collection and unsustainable harvesting practices. Objective : The aim of this study is to discuss the diversity of metabolites (loganic acid, sweroside, swertiamarin, and gentiopicroside) at different growth stages and organs of Swertia nervosa using the ultra-high-performance LC (UPLC)/UV coupled with chemometric method. Methods : UPLC data, UV data, and data fusion were treated separately to find more useful information by partial least-squares discriminant analysis (PLS-DA). Hierarchical cluster analysis (HCA), an unsupervised method, was then employed for validating the results from PLS-DA. Results : Three strategies displayed different chemical information associated with the sample discrimination. UV information mainly contributed to the classification of different organs; UPLC information was prominently responsible for both organs and growth periods; the data fusion did not perform with apparent superiority compared with single data analysis, although it provided useful information to differentiate leaves that could not be recognized by UPLC. The quantification result showed that the content of swertiamarin was the highest compared with the other three metabolites, especially in leaves at the rooted stage (19.57 ± 5.34 mg/g). Therefore, we speculated that interactive transformations occurred among these four metabolites, facilitated by root formation. Conclusions : This work will contribute to exploitation of bioactive compounds of S. nervosa , as well as its large-scale propagation. Highlights : The roots formation may influence the distribution and accumulation of metabolites.
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NIR spectroscopy as a tool for discriminating between lichens exposed to air pollution.
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.
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Fujihara, J; Fujita, Y; Yamamoto, T; Nishimoto, N; Kimura-Kataoka, K; Kurata, S; Takinami, Y; Yasuda, T; Takeshita, H
2017-03-01
Raman spectroscopy is commonly used in chemistry to identify molecular structure. This technique is a nondestructive analysis and needs no sample preparation. Recently, Raman spectroscopy has been shown to be effective as a multipurpose analytical method for forensic applications. In the present study, blood identification and discrimination between human and nonhuman blood were performed by a portable Raman spectrometer, which can be used at a crime scene. To identify the blood and to discriminate between human and nonhuman blood, Raman spectra of bloodstains from 11 species (human, rat, mouse, cow, horse, sheep, pig, rabbit, cat, dog, and chicken) were taken using a portable Raman spectrometer. Raman peaks for blood (742, 1001, 1123, 1247, 1341, 1368, 1446, 1576, and 1619 cm -1 ) could be observed by the portable Raman spectrometer in all 11 species, and the human bloodstain could be distinguished from the nonhuman ones by using a principal component analysis. This analysis can be performed on a bloodstain sample of at least 3 months old. The portable Raman spectrometer can be used at a crime scene, and this analysis is useful for forensic examination.
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Jo, Javier A; Fang, Qiyin; Papaioannou, Thanassis; Baker, J Dennis; Dorafshar, Amir H; Reil, Todd; Qiao, Jian-Hua; Fishbein, Michael C; Freischlag, Julie A; Marcu, Laura
2006-01-01
We report the application of the Laguerre deconvolution technique (LDT) to the analysis of in-vivo time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data and the diagnosis of atherosclerotic plaques. TR-LIFS measurements were obtained in vivo from normal and atherosclerotic aortas (eight rabbits, 73 areas), and subsequently analyzed using LDT. Spectral and time-resolved features were used to develop four classification algorithms: linear discriminant analysis (LDA), stepwise LDA (SLDA), principal component analysis (PCA), and artificial neural network (ANN). Accurate deconvolution of TR-LIFS in-vivo measurements from normal and atherosclerotic arteries was provided by LDT. The derived Laguerre expansion coefficients reflected changes in the arterial biochemical composition, and provided a means to discriminate lesions rich in macrophages with high sensitivity (>85%) and specificity (>95%). Classification algorithms (SLDA and PCA) using a selected number of features with maximum discriminating power provided the best performance. This study demonstrates the potential of the LDT for in-vivo tissue diagnosis, and specifically for the detection of macrophages infiltration in atherosclerotic lesions, a key marker of plaque vulnerability.
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NASA Astrophysics Data System (ADS)
Jo, Javier A.; Fang, Qiyin; Papaioannou, Thanassis; Baker, J. Dennis; Dorafshar, Amir; Reil, Todd; Qiao, Jianhua; Fishbein, Michael C.; Freischlag, Julie A.; Marcu, Laura
2006-03-01
We report the application of the Laguerre deconvolution technique (LDT) to the analysis of in-vivo time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data and the diagnosis of atherosclerotic plaques. TR-LIFS measurements were obtained in vivo from normal and atherosclerotic aortas (eight rabbits, 73 areas), and subsequently analyzed using LDT. Spectral and time-resolved features were used to develop four classification algorithms: linear discriminant analysis (LDA), stepwise LDA (SLDA), principal component analysis (PCA), and artificial neural network (ANN). Accurate deconvolution of TR-LIFS in-vivo measurements from normal and atherosclerotic arteries was provided by LDT. The derived Laguerre expansion coefficients reflected changes in the arterial biochemical composition, and provided a means to discriminate lesions rich in macrophages with high sensitivity (>85%) and specificity (>95%). Classification algorithms (SLDA and PCA) using a selected number of features with maximum discriminating power provided the best performance. This study demonstrates the potential of the LDT for in-vivo tissue diagnosis, and specifically for the detection of macrophages infiltration in atherosclerotic lesions, a key marker of plaque vulnerability.
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Jo, Javier A.; Fang, Qiyin; Papaioannou, Thanassis; Baker, J. Dennis; Dorafshar, Amir H.; Reil, Todd; Qiao, Jian-Hua; Fishbein, Michael C.; Freischlag, Julie A.; Marcu, Laura
2007-01-01
We report the application of the Laguerre deconvolution technique (LDT) to the analysis of in-vivo time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data and the diagnosis of atherosclerotic plaques. TR-LIFS measurements were obtained in vivo from normal and atherosclerotic aortas (eight rabbits, 73 areas), and subsequently analyzed using LDT. Spectral and time-resolved features were used to develop four classification algorithms: linear discriminant analysis (LDA), stepwise LDA (SLDA), principal component analysis (PCA), and artificial neural network (ANN). Accurate deconvolution of TR-LIFS in-vivo measurements from normal and atherosclerotic arteries was provided by LDT. The derived Laguerre expansion coefficients reflected changes in the arterial biochemical composition, and provided a means to discriminate lesions rich in macrophages with high sensitivity (>85%) and specificity (>95%). Classification algorithms (SLDA and PCA) using a selected number of features with maximum discriminating power provided the best performance. This study demonstrates the potential of the LDT for in-vivo tissue diagnosis, and specifically for the detection of macrophages infiltration in atherosclerotic lesions, a key marker of plaque vulnerability. PMID:16674179
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NASA Astrophysics Data System (ADS)
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.
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Carricarte Naranjo, Claudia; Sanchez-Rodriguez, Lazaro M; Brown Martínez, Marta; Estévez Báez, Mario; Machado García, Andrés
2017-07-01
Heart rate variability (HRV) analysis is a relevant tool for the diagnosis of cardiovascular autonomic neuropathy (CAN). To our knowledge, no previous investigation on CAN has assessed the complexity of HRV from an ordinal perspective. Therefore, the aim of this work is to explore the potential of permutation entropy (PE) analysis of HRV complexity for the assessment of CAN. For this purpose, we performed a short-term PE analysis of HRV in healthy subjects and type 1 diabetes mellitus patients, including patients with CAN. Standard HRV indicators were also calculated in the control group. A discriminant analysis was used to select the variables combination with best discriminative power between control and CAN patients groups, as well as for classifying cases. We found that for some specific temporal scales, PE indicators were significantly lower in CAN patients than those calculated for controls. In such cases, there were ordinal patterns with high probabilities of occurrence, while others were hardly found. We posit this behavior occurs due to a decrease of HRV complexity in the diseased system. Discriminant functions based on PE measures or probabilities of occurrence of ordinal patterns provided an average of 75% and 96% classification accuracy. Correlations of PE and HRV measures showed to depend only on temporal scale, regardless of pattern length. PE analysis at some specific temporal scales, seem to provide additional information to that obtained with traditional HRV methods. We concluded that PE analysis of HRV is a promising method for the assessment of CAN. Copyright © 2017 Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Su, Rongguo; Chen, Xiaona; Wu, Zhenzhen; Yao, Peng; Shi, Xiaoyong
2015-07-01
The feasibility of using fluorescence excitation-emission matrix (EEM) along with parallel factor analysis (PARAFAC) and nonnegative least squares (NNLS) method for the differentiation of phytoplankton taxonomic groups was investigated. Forty-one phytoplankton species belonging to 28 genera of five divisions were studied. First, the PARAFAC model was applied to EEMs, and 15 fluorescence components were generated. Second, 15 fluorescence components were found to have a strong discriminating capability based on Bayesian discriminant analysis (BDA). Third, all spectra of the fluorescence component compositions for the 41 phytoplankton species were spectrographically sorted into 61 reference spectra using hierarchical cluster analysis (HCA), and then, the reference spectra were used to establish a database. Finally, the phytoplankton taxonomic groups was differentiated by the reference spectra database using the NNLS method. The five phytoplankton groups were differentiated with the correct discrimination ratios (CDRs) of 100% for single-species samples at the division level. The CDRs for the mixtures were above 91% for the dominant phytoplankton species and above 73% for the subdominant phytoplankton species. Sixteen of the 85 field samples collected from the Changjiang River estuary were analyzed by both HPLC-CHEMTAX and the fluorometric technique developed. The results of both methods reveal that Bacillariophyta was the dominant algal group in these 16 samples and that the subdominant algal groups comprised Dinophyta, Chlorophyta and Cryptophyta. The differentiation results by the fluorometric technique were in good agreement with those from HPLC-CHEMTAX. The results indicate that the fluorometric technique could differentiate algal taxonomic groups accurately at the division level.
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Potential of FTIR spectroscopy for analysis of tears for diagnosis purposes.
Travo, Adrian; Paya, Clément; Déléris, Gérard; Colin, Joseph; Mortemousque, Bruno; Forfar, Isabelle
2014-04-01
It has been widely reported that the tear film, which is crucially important as a protective barrier of the eye, undergoes biochemical changes as a result of a wide range of ocular pathology. This tends to suggest the possibility of early detection of ocular diseases on the basis of biochemical analysis of tears. However, studies of tears by conventional methods of biomolecular and biochemical analysis are often limited by methodological difficulties. Moreover, such analysis could not be applied in the clinic, where structural and morphological analyses by, mainly, slit-lamp biomicroscopy remains the recommended method. In this study, we assessed, for the first time, the potential of FTIR spectroscopy combined with advanced chemometric processing of spectral data for analysis of raw tears for diagnosis purposes. We first optimized sampling and spectral acquisition (tears collection method, tear sample volume, and preservation of the samples) for accurate spectral measurement. On the basis of the results, we focused our study on the possibility of discriminating tears from normal individuals from those of patients with different ocular pathologies, and showed that the most discriminating spectral range is that corresponding to variations of CH2 and CH3 of lipid aliphatic chains. We also report more subtle discrimination of tears from patients with keratoconus and those from patients with non-specific inflammatory ocular diseases, on the basis of variations in spectral ranges attributed notably to lipid and carbohydrate vibrations. Finally, we also succeeded in distinguishing tears from patients with early-stage and late-stage keratoconus on the basis of spectral features attributed to protein structure. Therefore, this study strongly suggests that FTIR spectral analysis of tears could be developed as a valuable and cost-saving tool for biochemical-based detection of ocular diseases, potentially before the appearance of the first morphological signs of diseases. Combined with supervised modelling methods and with use of a spectral data base acquired for representative patients, such a spectral approach could be a useful addition to current methods of clinical analysis for improvement of patient care.
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Thin-layer chromatographic identification of Chinese propolis using chemometric fingerprinting.
Tang, Tie-xin; Guo, Wei-yan; Xu, Ye; Zhang, Si-ming; Xu, Xin-jun; Wang, Dong-mei; Zhao, Zhi-min; Zhu, Long-ping; Yang, De-po
2014-01-01
Poplar tree gum has a similar chemical composition and appearance to Chinese propolis (bee glue) and has been widely used as a counterfeit propolis because Chinese propolis is typically the poplar-type propolis, the chemical composition of which is determined mainly by the resin of poplar trees. The discrimination of Chinese propolis from poplar tree gum is a challenging task. To develop a rapid thin-layer chromatographic (TLC) identification method using chemometric fingerprinting to discriminate Chinese propolis from poplar tree gum. A new TLC method using a combination of ammonia and hydrogen peroxide vapours as the visualisation reagent was developed to characterise the chemical profile of Chinese propolis. Three separate people performed TLC on eight Chinese propolis samples and three poplar tree gum samples of varying origins. Five chemometric methods, including similarity analysis, hierarchical clustering, k-means clustering, neural network and support vector machine, were compared for use in classifying the samples based on their densitograms obtained from the TLC chromatograms via image analysis. Hierarchical clustering, neural network and support vector machine analyses achieved a correct classification rate of 100% in classifying the samples. A strategy for TLC identification of Chinese propolis using chemometric fingerprinting was proposed and it provided accurate sample classification. The study has shown that the TLC identification method using chemometric fingerprinting is a rapid, low-cost method for the discrimination of Chinese propolis from poplar tree gum and may be used for the quality control of Chinese propolis. Copyright © 2014 John Wiley & Sons, Ltd.
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Zou, Shanmei; Fei, Cong; Song, Jiameng; Bao, Yachao; He, Meilin; Wang, Changhai
2016-01-01
Several different barcoding methods of distinguishing species have been advanced, but which method is the best is still controversial. Chlorella is becoming particularly promising in the development of second-generation biofuels. However, the taxonomy of Chlorella-like organisms is easily confused. Here we report a comprehensive barcoding analysis of Chlorella-like species from Chlorella, Chloroidium, Dictyosphaerium and Actinastrum based on rbcL, ITS, tufA and 16S sequences to test the efficiency of traditional barcoding, GMYC, ABGD, PTP, P ID and character-based barcoding methods. First of all, the barcoding results gave new insights into the taxonomic assessment of Chlorella-like organisms studied, including the clear species discrimination and resolution of potentially cryptic species complexes in C. sorokiniana, D. ehrenbergianum and C. Vulgaris. The tufA proved to be the most efficient barcoding locus, which thus could be as potential "specific barcode" for Chlorella-like species. The 16S failed in discriminating most closely related species. The resolution of GMYC, PTP, P ID, ABGD and character-based barcoding methods were variable among rbcL, ITS and tufA genes. The best resolution for species differentiation appeared in tufA analysis where GMYC, PTP, ABGD and character-based approaches produced consistent groups while the PTP method over-split the taxa. The character analysis of rbcL, ITS and tufA sequences could clearly distinguish all taxonomic groups respectively, including the potentially cryptic lineages, with many character attributes. Thus, the character-based barcoding provides an attractive complement to coalescent and distance-based barcoding. Our study represents the test that proves the efficiency of multiple DNA barcoding in species discrimination of microalgaes.
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Zou, Shanmei; Fei, Cong; Song, Jiameng; Bao, Yachao; He, Meilin; Wang, Changhai
2016-01-01
Several different barcoding methods of distinguishing species have been advanced, but which method is the best is still controversial. Chlorella is becoming particularly promising in the development of second-generation biofuels. However, the taxonomy of Chlorella–like organisms is easily confused. Here we report a comprehensive barcoding analysis of Chlorella-like species from Chlorella, Chloroidium, Dictyosphaerium and Actinastrum based on rbcL, ITS, tufA and 16S sequences to test the efficiency of traditional barcoding, GMYC, ABGD, PTP, P ID and character-based barcoding methods. First of all, the barcoding results gave new insights into the taxonomic assessment of Chlorella-like organisms studied, including the clear species discrimination and resolution of potentially cryptic species complexes in C. sorokiniana, D. ehrenbergianum and C. Vulgaris. The tufA proved to be the most efficient barcoding locus, which thus could be as potential “specific barcode” for Chlorella-like species. The 16S failed in discriminating most closely related species. The resolution of GMYC, PTP, P ID, ABGD and character-based barcoding methods were variable among rbcL, ITS and tufA genes. The best resolution for species differentiation appeared in tufA analysis where GMYC, PTP, ABGD and character-based approaches produced consistent groups while the PTP method over-split the taxa. The character analysis of rbcL, ITS and tufA sequences could clearly distinguish all taxonomic groups respectively, including the potentially cryptic lineages, with many character attributes. Thus, the character-based barcoding provides an attractive complement to coalescent and distance-based barcoding. Our study represents the test that proves the efficiency of multiple DNA barcoding in species discrimination of microalgaes. PMID:27092945
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NASA Astrophysics Data System (ADS)
Díaz-Ayil, Gilberto; Amouroux, Marine; Clanché, Fabien; Granjon, Yves; Blondel, Walter C. P. M.
2009-07-01
Spatially-resolved bimodal spectroscopy (multiple AutoFluorescence AF excitation and Diffuse Reflectance DR), was used in vivo to discriminate various healthy and precancerous skin stages in a pre-clinical model (UV-irradiated mouse): Compensatory Hyperplasia CH, Atypical Hyperplasia AH and Dysplasia D. A specific data preprocessing scheme was applied to intensity spectra (filtering, spectral correction and intensity normalization), and several sets of spectral characteristics were automatically extracted and selected based on their discrimination power, statistically tested for every pair-wise comparison of histological classes. Data reduction with Principal Components Analysis (PCA) was performed and 3 classification methods were implemented (k-NN, LDA and SVM), in order to compare diagnostic performance of each method. Diagnostic performance was studied and assessed in terms of Sensibility (Se) and Specificity (Sp) as a function of the selected features, of the combinations of 3 different inter-fibres distances and of the numbers of principal components, such that: Se and Sp ~ 100% when discriminating CH vs. others; Sp ~ 100% and Se > 95% when discriminating Healthy vs. AH or D; Sp ~ 74% and Se ~ 63% for AH vs. D.
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Whole brain white matter connectivity analysis using machine learning: An application to autism.
Zhang, Fan; Savadjiev, Peter; Cai, Weidong; Song, Yang; Rathi, Yogesh; Tunç, Birkan; Parker, Drew; Kapur, Tina; Schultz, Robert T; Makris, Nikos; Verma, Ragini; O'Donnell, Lauren J
2018-05-15
In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering. Copyright © 2017 Elsevier Inc. All rights reserved.
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Dutra, Lauren M; Williams, David R; Kawachi, Ichiro; Okechukwu, Cassandra A
2014-01-01
Background Despite a long history of discrimination and persisting racial disparities in smoking prevalence, little research exists on the relationship between discrimination and smoking in South Africa. Methods This analysis examined chronic (day to day) and acute (lifetime) experiences of racial and nonracial (e.g., age, gender, or physical appearance) discrimination and smoking status among respondents to the South Africa Stress and Health Study (SASH). Logistic regression models were constructed using SAS-Callable SUDAAN. Results Both chronic racial discrimination (RR=1.45, 95%CI: 1.14–1.85) and chronic nonracial discrimination (RR=1.69, 95%CI: 1.37–2.08) predicted a higher risk of smoking, but neither type of acute discrimination did. Total (sum of racial and nonracial) chronic discrimination (RR=1.46, 95%CI: 1.20–1.78) and total acute discrimination (RR=1.28, 95%CI: 1.01–1.60) predicted a higher risk of current smoking. Conclusions Racial and nonracial discrimination may be related to South African adults’ smoking behavior, but this relationship likely varies by the timing and frequency of these experiences. Future research should use longitudinal data to identify the temporal ordering of the relationships studied, include areas outside of South Africa to increase generalizability, and consider the implications of these findings for smoking cessation approaches in South Africa. PMID:24789604
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Thayer, Zaneta M.; Blair, Irene V.; Buchwald, Dedra S.; Manson, Spero M.
2017-01-01
Objectives Hypertension prevalence is high among American Indians (AIs). AIs experience a substantial burden of interpersonal racial discrimination, which in other populations has been associated with higher blood pressure. The purpose of this study is to understand whether racial discrimination experiences are associated with higher blood pressure in AIs. Materials and Methods We used the Everyday Discrimination Scale to evaluate the relationship between discrimination and measured blood pressure among 77 AIs from two reservation communities in the Northern Plains. We used multivariate linear regression to evaluate the association of racial discrimination with systolic and diastolic blood pressure, respectively. Racial discrimination, systolic blood pressure, and diastolic blood pressure were analyzed as continuous variables. All analyses adjusted for sex, waist circumference, age, posttraumatic stress disorder status, and education. Results We found that 61% of participants experienced discrimination that they attributed to their race or ancestry. Racial discrimination was associated with significantly higher diastolic blood pressure (β = 0.22, SE = 0.09, P = 0.02), and with a similar non-significant trend toward higher systolic blood pressure (β = 0.25, SE = 0.15, P = 0.09). Conclusion The results of this analysis suggest that racial discrimination may contribute to higher diastolic blood pressure within Native communities. These findings highlight one pathway through which the social environment can shape patterns of biology and health in AI and other socially and politically marginalized groups. PMID:28198537
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Xue, Gang; Song, Wen-qi; Li, Shu-chao
2015-01-01
In order to achieve the rapid identification of fire resistive coating for steel structure of different brands in circulating, a new method for the fast discrimination of varieties of fire resistive coating for steel structure by means of near infrared spectroscopy was proposed. The raster scanning near infrared spectroscopy instrument and near infrared diffuse reflectance spectroscopy were applied to collect the spectral curve of different brands of fire resistive coating for steel structure and the spectral data were preprocessed with standard normal variate transformation(standard normal variate transformation, SNV) and Norris second derivative. The principal component analysis (principal component analysis, PCA)was used to near infrared spectra for cluster analysis. The analysis results showed that the cumulate reliabilities of PC1 to PC5 were 99. 791%. The 3-dimentional plot was drawn with the scores of PC1, PC2 and PC3 X 10, which appeared to provide the best clustering of the varieties of fire resistive coating for steel structure. A total of 150 fire resistive coating samples were divided into calibration set and validation set randomly, the calibration set had 125 samples with 25 samples of each variety, and the validation set had 25 samples with 5 samples of each variety. According to the principal component scores of unknown samples, Mahalanobis distance values between each variety and unknown samples were calculated to realize the discrimination of different varieties. The qualitative analysis model for external verification of unknown samples is a 10% recognition ration. The results demonstrated that this identification method can be used as a rapid, accurate method to identify the classification of fire resistive coating for steel structure and provide technical reference for market regulation.
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Determination of glucose-6-phosphate dehydrogenase cut-off values in a Tunisian population.
Laouini, Naouel; Sahli, Chaima Abdelhafidh; Jouini, Latifa; Haloui, Sabrine; Fredj, Sondes Hadj; Daboubi, Rym; Siala, Hajer; Ouali, Faida; Becher, Meriam; Toumi, Nourelhouda; Bibi, Amina; Messsaoud, Taieb
2017-07-26
Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the commonest enzymopathy worldwide. The incidence depends essentially on the methods used for the assessment. In this respect, we attempted in this study to set cut-off values of G6PD activity to discriminate among normal, heterozygous, and deficient individuals using the World Health Organization (WHO) classification and the receiver operating characteristics (ROC) curve analysis. Blood samples from 250 female and 302 male subjects were enrolled in this study. The G6PD activity was determined using a quantitative assay. The common G6PD mutations in Tunisia were determined using the amplification refractory mutation system (ARMS-PCR) method. The ROC curve was used to choice the best cut-off. Normal G6PD values were 7.69±2.37, 7.86±2.39, and 7.51±2.35 U/g Hb for the entire, male, and female groups, respectively. Cut-off values for the total, male, and female were determined using the WHO classification and ROC curves analysis. In the male population, both cut-offs established using ROC curve analysis (4.00 U/g Hb) and the 60% level (3.82 U/g Hb), respectively are sensitive and specific resulting in a good efficiency of discrimination between deficient and normal males. For the female group the ROC cut-off (5.84 U/g Hb) seems better than the 60% level cut-off (3.88 U/g Hb) to discriminate between normal and heterozygote or homozygote women with higher Youden Index. The establishment of the normal values for a population is important for a better evaluation of the assay result. The ROC curve analysis is an alternative method to determine the status of patients since it correlates DNA analysis and G6PD activity.
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Research of facial feature extraction based on MMC
NASA Astrophysics Data System (ADS)
Xue, Donglin; Zhao, Jiufen; Tang, Qinhong; Shi, Shaokun
2017-07-01
Based on the maximum margin criterion (MMC), a new algorithm of statistically uncorrelated optimal discriminant vectors and a new algorithm of orthogonal optimal discriminant vectors for feature extraction were proposed. The purpose of the maximum margin criterion is to maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection. Compared with original MMC method and principal component analysis (PCA) method, the proposed methods are better in terms of reducing or eliminating the statistically correlation between features and improving recognition rate. The experiment results on Olivetti Research Laboratory (ORL) face database shows that the new feature extraction method of statistically uncorrelated maximum margin criterion (SUMMC) are better in terms of recognition rate and stability. Besides, the relations between maximum margin criterion and Fisher criterion for feature extraction were revealed.
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Olsen, Jaran S; Aarskaug, Tone; Skogan, Gunnar; Fykse, Else Marie; Ellingsen, Anette Bauer; Blatny, Janet M
2009-09-01
Vibrio cholerae is the etiological agent of cholera and may be used in bioterror actions due to the easiness of its dissemination, and the public fear for acquiring the cholera disease. A simple and highly discriminating method for connecting clinical and environmental isolates of V. cholerae is needed in microbial forensics. Twelve different loci containing variable numbers of tandem-repeats (VNTRs) were evaluated in which six loci were polymorphic. Two multiplex reactions containing PCR primers targeting these six VNTRs resulted in successful DNA amplification of 142 various environmental and clinical V. cholerae isolates. The genetic distribution inside the V. cholerae strain collection was used to evaluate the discriminating power (Simpsons Diversity Index=0.99) of this new MLVA analysis, showing that the assay have a potential to differentiate between various strains, but also to identify those isolates which are collected from a common V. cholerae outbreak. This work has established a rapid and highly discriminating MLVA assay useful for track back analyses and/or forensic studies of V. cholerae infections.
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Predictor of increase in caregiver burden for disabled elderly at home.
Okamoto, Kazushi; Harasawa, Yuko
2009-01-01
In order to classify the caregivers at high risk of increase in their burden early, linear discriminant analysis was performed to obtain an effective discriminant model for differentiation of the presence or absence of increase in caregiver burden. The data obtained by self-administered questionnaire from 193 caregivers of frail elderly from January to February of 2005 were used. The discriminant analysis yielded a statistically significant function explaining 35.0% (Rc=0.59; d.f.=6; p=0.0001). The configuration indicated that the psychological predictors of change in caregiver burden with much perceived stress (1.47), high caregiver burden at baseline (1.28), emotional control (0.75), effort to achieve (-0.28), symptomatic depression (0.20) and "ikigai" (purpose in life) (0.18) made statistically significant contributions to the differentiation between no increase and increase in caregiver burden. The discriminant function showed a sensitivity of 86% and specificity of 81%, and successfully classified 83% of the caregivers. The function at baseline is a simple and useful method for screening of an increase in caregiver burden among caregivers for the frail elderly at home.
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Giacomelli, L; Zimbal, A; Reginatto, M; Tittelmeier, K
2011-01-01
A compact NE213 liquid scintillation neutron spectrometer with a new digital data acquisition (DAQ) system is now in operation at the Physikalisch-Technische Bundesanstalt (PTB). With the DAQ system, developed by ENEA Frascati, neutron spectrometry with high count rates in the order of 5×10(5) s(-1) is possible, roughly an order of magnitude higher than with an analog acquisition system. To validate the DAQ system, a new data analysis code was developed and tests were done using measurements with 14-MeV neutrons made at the PTB accelerator. Additional analysis was carried out to optimize the two-gate method used for neutron and gamma (n-γ) discrimination. The best results were obtained with gates of 35 ns and 80 ns. This indicates that the fast and medium decay time components of the NE213 light emission are the ones that are relevant for n-γ discrimination with the digital acquisition system. This differs from what is normally implemented in the analog pulse shape discrimination modules, namely, the fast and long decay emissions of the scintillating light.
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Improving Efficiency in Multi-Strange Baryon Reconstruction in d-Au at STAR
NASA Astrophysics Data System (ADS)
Leight, William
2003-10-01
We report preliminary multi-strange baryon measurements for d-Au collisions recorded at RHIC by the STAR experiment. After using classical topological analysis, in which cuts for each discriminating variable are adjusted by hand, we investigate improvements in signal-to-noise optimization using Linear Discriminant Analysis (LDA). LDA is an algorithm for finding, in the n-dimensional space of the n discriminating variables, the axis on which the signal and noise distributions are most separated. LDA is the first step in moving towards more sophisticated techniques for signal-to-noise optimization, such as Artificial Neural Nets. Due to the relatively low background and sufficiently high yields of d-Au collisions, they form an ideal system to study these possibilities for improving reconstruction methods. Such improvements will be extremely important for forthcoming Au-Au runs in which the size of the combinatoric background is a major problem in reconstruction efforts.
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NASA Astrophysics Data System (ADS)
de Siqueira e Oliveira, Fernanda S.; Giana, Hector E.; Silveira, Landulfo, Jr.
2012-03-01
It has been proposed a method based on Raman spectroscopy for identification of different microorganisms involved in bacterial urinary tract infections. Spectra were collected from different bacterial colonies (Gram negative: E. coli, K. pneumoniae, P. mirabilis, P. aeruginosa, E. cloacae and Gram positive: S. aureus and Enterococcus sp.), grown in culture medium (Agar), using a Raman spectrometer with a fiber Raman probe (830 nm). Colonies were scraped from Agar surface placed in an aluminum foil for Raman measurements. After pre-processing, spectra were submitted to a Principal Component Analysis and Mahalanobis distance (PCA/MD) discrimination algorithm. It has been found that the mean Raman spectra of different bacterial species show similar bands, being the S. aureus well characterized by strong bands related to carotenoids. PCA/MD could discriminate Gram positive bacteria with sensitivity and specificity of 100% and Gram negative bacteria with good sensitivity and high specificity.
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Asymmetry and irregularity border as discrimination factor between melanocytic lesions
NASA Astrophysics Data System (ADS)
Sbrissa, David; Pratavieira, Sebastião.; Salvio, Ana Gabriela; Kurachi, Cristina; Bagnato, Vanderlei Salvadori; Costa, Luciano Da Fontoura; Travieso, Gonzalo
2015-06-01
Image processing tools have been widely used in systems supporting medical diagnosis. The use of mobile devices for the diagnosis of melanoma can assist doctors and improve their diagnosis of a melanocytic lesion. This study proposes a method of image analysis for melanoma discrimination from other types of melanocytic lesions, such as regular and atypical nevi. The process is based on extracting features related with asymmetry and border irregularity. It were collected 104 images, from medical database of two years. The images were obtained with standard digital cameras without lighting and scale control. Metrics relating to the characteristics of shape, asymmetry and curvature of the contour were extracted from segmented images. Linear Discriminant Analysis was performed for dimensionality reduction and data visualization. Segmentation results showed good efficiency in the process, with approximately 88:5% accuracy. Validation results presents sensibility and specificity 85% and 70% for melanoma detection, respectively.
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Magagna, Federico; Guglielmetti, Alessandro; Liberto, Erica; Reichenbach, Stephen E; Allegrucci, Elena; Gobino, Guido; Bicchi, Carlo; Cordero, Chiara
2017-08-02
This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.
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Trace element analysis of rough diamond by LA-ICP-MS: a case of source discrimination?
Dalpé, Claude; Hudon, Pierre; Ballantyne, David J; Williams, Darrell; Marcotte, Denis
2010-11-01
Current profiling of rough diamond source is performed using different physical and/or morphological techniques that require strong knowledge and experience in the field. More recently, chemical impurities have been used to discriminate diamond source and with the advance of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) empirical profiling of rough diamonds is possible to some extent. In this study, we present a LA-ICP-MS methodology that we developed for analyzing ultra-trace element impurities in rough diamond for origin determination ("profiling"). Diamonds from two sources were analyzed by LA-ICP-MS and were statistically classified by accepted methods. For the two diamond populations analyzed in this study, binomial logistic regression produced a better overall correct classification than linear discriminant analysis. The results suggest that an anticipated matrix match reference material would improve the robustness of our methodology for forensic applications. © 2010 American Academy of Forensic Sciences.
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An approach to the language discrimination in different scripts using adjacent local binary pattern
NASA Astrophysics Data System (ADS)
Brodić, D.; Amelio, A.; Milivojević, Z. N.
2017-09-01
The paper proposes a language discrimination method of documents. First, each letter is encoded with the certain script type according to its status in baseline area. Such a cipher text is subjected to a feature extraction process. Accordingly, the local binary pattern as well as its expanded version called adjacent local binary pattern are extracted. Because of the difference in the language characteristics, the above analysis shows significant diversity. This type of diversity is a key aspect in the decision-making differentiation of the languages. Proposed method is tested on an example of documents. The experiments give encouraging results.
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Discriminative components of data.
Peltonen, Jaakko; Kaski, Samuel
2005-01-01
A simple probabilistic model is introduced to generalize classical linear discriminant analysis (LDA) in finding components that are informative of or relevant for data classes. The components maximize the predictability of the class distribution which is asymptotically equivalent to 1) maximizing mutual information with the classes, and 2) finding principal components in the so-called learning or Fisher metrics. The Fisher metric measures only distances that are relevant to the classes, that is, distances that cause changes in the class distribution. The components have applications in data exploration, visualization, and dimensionality reduction. In empirical experiments, the method outperformed, in addition to more classical methods, a Renyi entropy-based alternative while having essentially equivalent computational cost.
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NASA Astrophysics Data System (ADS)
Vítková, Gabriela; Prokeš, Lubomír; Novotný, Karel; Pořízka, Pavel; Novotný, Jan; Všianský, Dalibor; Čelko, Ladislav; Kaiser, Jozef
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.
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Preisner, Ornella; Guiomar, Raquel; Machado, Jorge; Menezes, José Cardoso; Lopes, João Almeida
2010-06-01
Fourier transform infrared (FT-IR) spectroscopy and chemometric techniques were used to discriminate five closely related Salmonella enterica serotype Enteritidis phage types, phage type 1 (PT1), PT1b, PT4b, PT6, and PT6a. Intact cells and outer membrane protein (OMP) extracts from bacterial cell membranes were subjected to FT-IR analysis in transmittance mode. Spectra were collected over a wavenumber range from 4,000 to 600 cm(-1). Partial least-squares discriminant analysis (PLS-DA) was used to develop calibration models based on preprocessed FT-IR spectra. The analysis based on OMP extracts provided greater separation between the Salmonella Enteritidis PT1-PT1b, PT4b, and PT6-PT6a groups than the intact cell analysis. When these three phage type groups were considered, the method based on OMP extract FT-IR spectra was 100% accurate. Moreover, complementary local models that considered only the PT1-PT1b and PT6-PT6a groups were developed, and the level of discrimination increased. PT1 and PT1b isolates were differentiated successfully with the local model using the entire OMP extract spectrum (98.3% correct predictions), whereas the accuracy of discrimination between PT6 and PT6a isolates was 86.0%. Isolates belonging to different phage types (PT19, PT20, and PT21) were used with the model to test its robustness. For the first time it was demonstrated that FT-IR analysis of OMP extracts can be used for construction of robust models that allow fast and accurate discrimination of different Salmonella Enteritidis phage types.
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Estimating the causes of traffic accidents using logistic regression and discriminant analysis.
Karacasu, Murat; Ergül, Barış; Altin Yavuz, Arzu
2014-01-01
Factors that affect traffic accidents have been analysed in various ways. In this study, we use the methods of logistic regression and discriminant analysis to determine the damages due to injury and non-injury accidents in the Eskisehir Province. Data were obtained from the accident reports of the General Directorate of Security in Eskisehir; 2552 traffic accidents between January and December 2009 were investigated regarding whether they resulted in injury. According to the results, the effects of traffic accidents were reflected in the variables. These results provide a wealth of information that may aid future measures toward the prevention of undesired results.
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Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis.
Hall, Gregory J; Clow, Kerin E; Kenny, Jonathan E
2005-10-01
As part of a strategy for preventing the introduction of aquatic nuisance species (ANS) to U.S. estuaries, ballast water exchange (BWE) regulations have been imposed. Enforcing these regulations requires a reliable method for determining the port of origin of water in the ballast tanks of ships entering U.S. waters. This study shows that a three-dimensional fluorescence fingerprinting technique, excitation emission matrix (EEM) spectroscopy, holds great promise as a ballast water analysis tool. In our technique, EEMs are analyzed by multivariate classification and curve resolution methods, such as N-way partial least squares Regression-discriminant analysis (NPLS-DA) and parallel factor analysis (PARAFAC). We demonstrate that classification techniques can be used to discriminate among sampling sites less than 10 miles apart, encompassing Boston Harbor and two tributaries in the Mystic River Watershed. To our knowledge, this work is the first to use multivariate analysis to classify water as to location of origin. Furthermore, it is shown that curve resolution can show seasonal features within the multidimensional fluorescence data sets, which correlate with difficulty in classification.
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Implementing Innovative Pedagogy and a Rainbow Curriculum to Expand Learning on Diversity
ERIC Educational Resources Information Center
Sumner, Steven W.; Sgoutas-Emch, Sandra; Nunn, Lisa; Kirkley, Evelyn
2017-01-01
A mixed methods approach analysis examines the impact of incorporating diversity education focusing on sexual diversity at an independent, Roman Catholic university, a site where Lesbian, Gay, Bisexual, Transgender, Questioning/Queer (LGBTQ) individuals face discrimination, harassment, and homophobia. The analysis demonstrates the positive impact…
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Moment tensor analysis of very shallow sources
Chiang, Andrea; Dreger, Douglas S.; Ford, Sean R.; ...
2016-10-11
An issue for moment tensor (MT) inversion of shallow seismic sources is that some components of the Green’s functions have vanishing amplitudes at the free surface, which can result in bias in the MT solution. The effects of the free surface on the stability of the MT method become important as we continue to investigate and improve the capabilities of regional full MT inversion for source–type identification and discrimination. It is important to understand free–surface effects on discriminating shallow explosive sources for nuclear monitoring purposes. It may also be important in natural systems that have very shallow seismicity, such asmore » volcanic and geothermal systems. We examine the effects of the free surface on the MT via synthetic testing and apply the MT–based discrimination method to three quarry blasts from the HUMMING ALBATROSS experiment. These shallow chemical explosions at ~10 m depth and recorded up to several kilometers distance represent rather severe source–station geometry in terms of free–surface effects. We show that the method is capable of recovering a predominantly explosive source mechanism, and the combined waveform and first–motion method enables the unique discrimination of these events. Furthermore, recovering the design yield using seismic moment estimates from MT inversion remains challenging, but we can begin to put error bounds on our moment estimates using the network sensitivity solution technique.« less
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ERIC Educational Resources Information Center
Williams, Lynne J.; Abdi, Herve; French, Rebecca; Orange, Joseph B.
2010-01-01
Purpose: In communication disorders research, clinical groups are frequently described based on patterns of performance, but researchers often study only a few participants described by many quantitative and qualitative variables. These data are difficult to handle with standard inferential tools (e.g., analysis of variance or factor analysis)…
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Joint recognition and discrimination in nonlinear feature space
NASA Astrophysics Data System (ADS)
Talukder, Ashit; Casasent, David P.
1997-09-01
A new general method for linear and nonlinear feature extraction is presented. It is novel since it provides both representation and discrimination while most other methods are concerned with only one of these issues. We call this approach the maximum representation and discrimination feature (MRDF) method and show that the Bayes classifier and the Karhunen- Loeve transform are special cases of it. We refer to our nonlinear feature extraction technique as nonlinear eigen- feature extraction. It is new since it has a closed-form solution and produces nonlinear decision surfaces with higher rank than do iterative methods. Results on synthetic databases are shown and compared with results from standard Fukunaga- Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem (discrimination) and to the classification and pose estimation of two similar objects (representation and discrimination).
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Stigma and discrimination against people living with HIV by healthcare providers, Southwest Ethiopia
2012-01-01
Background Stigma and discrimination against people living with human immunodeficiency virus (HIV) are obstacles in the way of effective responses to HIV. Understanding the extent of stigma / discrimination and the underlying causes is necessary for developing strategies to reduce them. This study was conducted to explore stigma and discrimination against PLHIV amongst healthcare providers in Jimma zone, Southwest Ethiopia. Methods A cross-sectional study, employing quantitative and qualitative methods, was conducted in 18 healthcare institutions of Jimma zone, during March 14 to April 14, 2011. A total of 255 healthcare providers responded to questionnaires asking about sociodemographic characteristics, HIV knowledge, perceived institutional support and HIV-related stigma and discrimination. Factor analysis was employed to create measurement scales for stigma and factor scores were used in one way analysis of variance (ANOVA), T-tests, Pearson’s correlation and multiple linear regression analyses. Qualitative data collected using key-informant interviews and Focus Group Discussions (FGDs) were employed to triangulate with the findings from the quantitative survey. Results Mean stigma scores (as the percentages of maximum scale scores) were: 66.4 for the extra precaution scale, 52.3 for the fear of work-related HIV transmission, 49.4 for the lack of feelings of safety, 39.0 for the value-driven stigma, 37.4 for unethical treatment of PLHIV, 34.4 for discomfort around PLHIV and 31.1 for unofficial disclosure. Testing and disclosing test results without consent, designating HIV clients and unnecessary referral to other healthcare institutions and refusal to treat clients were identified. Having in-depth HIV knowledge, the perception of institutional support, attending training on stigma and discrimination, educational level of degree or higher, high HIV case loads, the presence of ART service in the healthcare facility and claiming to be non-religious were negative predictors of stigma and discrimination as measured by the seven latent factors. Conclusions Higher levels of stigma and discrimination against PLHIV were associated with lack of in-depth knowledge on HIV and orientation about policies against stigma and discrimination. Hence, we recommend health managers to ensure institutional support through availing of clear policies and guidelines and the provision of appropriate training on the management of HIV/AIDS. PMID:22794201
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Plasma-laser ion discrimination by TOF technique applied to coupled SiC detectors.
NASA Astrophysics Data System (ADS)
Cavallaro, Salvatore
2018-01-01
The rate estimation of nuclear reactions induced in high intensity laser-target interaction (≥1016 W/cm2), is strongly depending on the neutron detection efficiency and ion charge discrimination, according to particles involved in exit open-channels. Ion discrimination is basically performed by means of analysis of pits observed on track detector, which is critically dependent on calibration and/or fast TOF devices based on SiC and diamond detectors. Last setup is used to determine the ion energy and to obtain a rough estimation of yields. However, for each TOF interval, the dependence of yield from the energy deposited in the detector sensitive region, introduces a distortion in the ion spectra. Moreover, if two ion species are present in the same spectrum, the discrimination of their contribution is not attainable. In this paper a new method is described which allows to discriminate the contribution of two ion species in the wide energy range of nuclear reactions induced in laser-target interactions. The method is based on charge response of two TOF-SiC detectors, of suitable thicknesses, placed in adjacent positions. In presence of two ion species, the response of the detectors, associated with different energy losses, can determine the ion specific contribution to each TOF interval.
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Luo, Huifang; Wang, Jierui; Zhang, Shuang; Mi, Congbo
2018-05-01
The frontal sinus, due to its unique anatomical features, has become an important element in research for individual identification. Previous studies have demonstrated the use of frontal sinus as an indicator for sex discrimination; however, the sex discrimination rate using frontal sinus was lower compared to that using the traditional morphological methods. In order to improve the sex discrimination percentage, we developed a new method involving the measurement of the frontal sinus index and frontal sinus area from lateral cephalogram radiographs. In this study, 475 digital lateral cephalograms of adult Han citizens from Xinjiang were included. The maximum height, depth, and area of the frontal sinus were calculated using the NemoCeph NX software. The frontal sinus index (ratio of the maximum height to the depth of frontal sinus) was also computed. Statistical analysis results showed significant differences in the frontal sinus index and area between males and females. Discriminant function equation derived from this study differentiated between sexes with 76.6% accuracy. The results demonstrated that the use of frontal sinus index and area for sex discrimination was more accurate than using the frontal sinus index alone. Copyright © 2017. Published by Elsevier Ltd.
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Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques
NASA Astrophysics Data System (ADS)
Palacios-Morillo, Ana; Alcázar, Ángela.; de Pablos, Fernando; Jurado, José Marcos
2013-02-01
Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.
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Comparison of two methods for composite score generation in dry eye syndrome.
See, Craig; Bilonick, Richard A; Feuer, William; Galor, Anat
2013-09-19
To compare two methods of composite score generation in dry eye syndrome (DES). Male patients seen in the Miami Veterans Affairs eye clinic with normal eyelid, corneal, and conjunctival anatomy were recruited to participate in the study. Patients filled out the Dry Eye Questionnaire 5 (DEQ5) and underwent measurement of tear film parameters. DES severity scores were generated by independent component analysis (ICA) and latent class analysis (LCA). A total of 247 men were included in the study. Mean age was 69 years (SD 9). Using ICA analysis, osmolarity was found to carry the largest weight, followed by eyelid vascularity and meibomian orifice plugging. Conjunctival injection and tear breakup time (TBUT) carried the lowest weights. Using LCA analysis, TBUT was found to be best at discriminating healthy from diseased eyes, followed closely by Schirmer's test. DEQ5, eyelid vascularity, and conjunctival injection were the poorest at discrimination. The adjusted correlation coefficient between the two generated composite scores was 0.63, indicating that the shared variance was less than 40%. Both ICA and LCA produced composite scores for dry eye severity, with weak to moderate agreement; however, agreement for the relative importance of single diagnostic tests was poor between the two methods.
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Caprihan, A; Pearlson, G D; Calhoun, V D
2008-08-15
Principal component analysis (PCA) is often used to reduce the dimension of data before applying more sophisticated data analysis methods such as non-linear classification algorithms or independent component analysis. This practice is based on selecting components corresponding to the largest eigenvalues. If the ultimate goal is separation of data in two groups, then these set of components need not have the most discriminatory power. We measured the distance between two such populations using Mahalanobis distance and chose the eigenvectors to maximize it, a modified PCA method, which we call the discriminant PCA (DPCA). DPCA was applied to diffusion tensor-based fractional anisotropy images to distinguish age-matched schizophrenia subjects from healthy controls. The performance of the proposed method was evaluated by the one-leave-out method. We show that for this fractional anisotropy data set, the classification error with 60 components was close to the minimum error and that the Mahalanobis distance was twice as large with DPCA, than with PCA. Finally, by masking the discriminant function with the white matter tracts of the Johns Hopkins University atlas, we identified left superior longitudinal fasciculus as the tract which gave the least classification error. In addition, with six optimally chosen tracts the classification error was zero.
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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 skin was discriminated from healthy tissue with 93.5% accuracy and post-PDT cutaneous lesions was discriminated from healthy tissue with 89.7% accuracy. PC-LDA was able to discriminate ATR-FTIR spectra of non-treated and post-PDT neoplastic lesions, as well as from healthy skin. Thus, the method can be used for early diagnosis of premalignant skin lesions, as well as to evaluate the response to photodynamic treatment. Lasers Surg. Med. 48:538-545, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
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Kalcsits, Lee A; Guy, Robert D
2016-02-01
Acquisition of mineral nitrogen by roots from the surrounding environment is often not completely efficient, in which a variable amount of leakage (efflux) relative to gross uptake (influx) occurs. The efflux/influx ratio (E/I) is, therefore, inversely related to the efficiency of nutrient uptake at the root level. Time-integrated estimates of E/I and other nitrogen-use traits may be obtainable from variation in stable isotope ratios or through compartmental analysis of tracer efflux (CATE) using radioactive or stable isotopes. To compare these two methods, Populus balsamifera L. genotypes were selected, a priori, for high or low nitrogen isotope discrimination. Vegetative cuttings were grown hydroponically, and E/I was calculated using an isotope mass balance model (IMB) and compared to E/I calculated using (15) N CATE. Both methods indicated that plants grown with ammonium had greater E/I than nitrate-grown plants. Genotypes with high or low E/I using CATE also had similarly high or low estimates of E/I using IMB, respectively. Genotype-specific means were linearly correlated (r = 0.77; P = 0.0065). Discrepancies in E/I between methods may reflect uncertainties in discrimination factors for the assimilatory enzymes, or temporal differences in uptake patterns. By utilizing genotypes with known variation in nitrogen isotope discrimination, a relationship between nitrogen isotope discrimination and bidirectional nitrogen fluxes at the root level was observed. © 2015 John Wiley & Sons Ltd.
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Mora-Ríos, Jazmín; Ortega-Ortega, Miriam; Medina-Mora, Maria Elena
2017-04-16
Social rejection of drug use is greater than that directed at other psychiatric conditions, like depression and anxiety, which may lead to social exclusion of substance users and prevent them from seeking and receiving treatment. The objective of this study is to investigate the stigma and discrimination related to addiction in treatment centers in Mexico City. Data are from a broad mixed methods study. The present study is a qualitative analysis of 35 in-depth interviews of drug users, family members, and providers, using guidelines for the exploration of experiences of stigma and discrimination related to substance abuse. A thematic analysis was carried out using specialized software (Atlas.ti 6.2.23). The common practices of stigma and discrimination (e.g., judging, mockery, inappropriate comments, overprotection, and hostile looks), were identified in participant testimonies. The narratives provide evidence of varied conditions of social and gender inequality, as well as experience of violence and abuse, that refer to a structural context of discrimination surrounding addiction, and that constitute obstacles to treatment. The findings allow for a description of the stigmatization process surrounding addiction as a shared problem that requires the implementation of coordinated treatment strategies. They also suggest the need for more comprehensive policies of inclusion, based on human rights, which favor prevention and treatment.
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NASA Astrophysics Data System (ADS)
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.
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Deport, Coralie; Ratel, Jérémy; Berdagué, Jean-Louis; Engel, Erwan
2006-05-26
The current work describes a new method, the comprehensive combinatory standard correction (CCSC), for the correction of instrumental signal drifts in GC-MS systems. The method consists in analyzing together with the products of interest a mixture of n selected internal standards, and in normalizing the peak area of each analyte by the sum of standard areas and then, select among the summation operator sigma(p = 1)(n)C(n)p possible sums, the sum that enables the best product discrimination. The CCSC method was compared with classical techniques of data pre-processing like internal normalization (IN) or single standard correction (SSC) on their ability to correct raw data from the main drifts occurring in a dynamic headspace-gas chromatography-mass spectrometry system. Three edible oils with closely similar compositions in volatile compounds were analysed using a device which performance was modulated by using new or used dynamic headspace traps and GC-columns, and by modifying the tuning of the mass spectrometer. According to one-way ANOVA, the CCSC method increased the number of analytes discriminating the products (31 after CCSC versus 25 with raw data or after IN and 26 after SSC). Moreover, CCSC enabled a satisfactory discrimination of the products irrespective of the drifts. In a factorial discriminant analysis, 100% of the samples (n = 121) were well-classified after CCSC versus 45% for raw data, 90 and 93%, respectively after IN and SSC.
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Franklin, Daniel; O'Higgins, Paul; Oxnard, Charles E; Dadour, Ian
2006-12-01
The determination of sex is a critical component in forensic anthropological investigation. The literature attests to numerous metrical standards, each utilizing diffetent skeletal elements, for sex determination in South A frican Blacks. Metrical standards are popular because they provide a high degree of expected accuracy and are less error-prone than subjective nonmetric visual techniques. We note, however, that there appears to be no established metric mandible discriminant function standards for sex determination in this population.We report here on a preliminary investigation designed to evaluate whether the mandible is a practical element for sex determination in South African Blacks. The sample analyzed comprises 40 nonpathological Zulu individuals drawn from the R.A. Dart Collection. Ten linear measurements, obtained from mathematically trans-formed three-dimensional landmark data, are analyzed using basic univariate statistics and discriminant function analyses. Seven of the 10 measurements examined are found to be sexually dimorphic; the dimensions of the ramus are most dimorphic. The sex classification accuracy of the discriminant functions ranged from 72.5 to 87.5% for the univariate method, 92.5% for the stepwise method, and 57.5 to 95% for the direct method. We conclude that the mandible is an extremely useful element for sex determination in this population.
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Talaska, Cara A.; Chaiken, Shelly
2013-01-01
Investigations of racial bias have emphasized stereotypes and other beliefs as central explanatory mechanisms and as legitimating discrimination. In recent theory and research, emotional prejudices have emerged as another, more direct predictor of discrimination. A new comprehensive meta-analysis of 57 racial attitude-discrimination studies finds a moderate relationship between overall attitudes and discrimination. Emotional prejudices are twices as closely related to racial discrimination as stereotypes and beliefs are. Moreover, emotional prejudices are closely related to both observed and self-reported discrimination, whereas stereotypes and beliefs are related only to self-reported discrimination. Implications for justifying discrimination are discussed. PMID:24052687
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Nakamura, Sayaka; Sato, Hiroaki; Tanaka, Reiko; Kusuya, Yoko; Takahashi, Hiroki; Yaguchi, Takashi
2017-04-26
Accurate identification of Aspergillus species is a very important subject. Mass spectral fingerprinting using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is generally employed for the rapid identification of fungal isolates. However, the results are based on simple mass spectral pattern-matching, with no peak assignment and no taxonomic input. We propose here a ribosomal subunit protein (RSP) typing technique using MALDI-TOF MS for the identification and discrimination of Aspergillus species. The results are concluded to be phylogenetic in that they reflect the molecular evolution of housekeeping RSPs. The amino acid sequences of RSPs of genome-sequenced strains of Aspergillus species were first verified and compared to compile a reliable biomarker list for the identification of Aspergillus species. In this process, we revealed that many amino acid sequences of RSPs (about 10-60%, depending on strain) registered in the public protein databases needed to be corrected or newly added. The verified RSPs were allocated to RSP types based on their mass. Peak assignments of RSPs of each sample strain as observed by MALDI-TOF MS were then performed to set RSP type profiles, which were then further processed by means of cluster analysis. The resulting dendrogram based on RSP types showed a relatively good concordance with the tree based on β-tubulin gene sequences. RSP typing was able to further discriminate the strains belonging to Aspergillus section Fumigati. The RSP typing method could be applied to identify Aspergillus species, even for species within section Fumigati. The discrimination power of RSP typing appears to be comparable to conventional β-tubulin gene analysis. This method would therefore be suitable for species identification and discrimination at the strain to species level. Because RSP typing can characterize the strains within section Fumigati, this method has potential as a powerful and reliable tool in the field of clinical microbiology.
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Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A
2016-08-01
The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.
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[Use of multiple locus variable number tandem repeats analysis for the Brucella systematization].
Kulakov, Iu K; Kovalev, D A; Misetova, E N; Golovneva, S I; Liapustina, L V; Zheludkov, M M
2012-01-01
The methods of molecular-genetic differentiation to strain level acquire increasing significance in the current system of struggle with brucellosis. MLVA (multiple locus variable number tandem repeats analysis) was selected for molecular-genetic differentiation to strain level and simultaneous establishment of the genetic relationship of investigated Brucella strains. The goal of this work was MLVA typing of three pathogenic Brucella species strains with the analysis of stability of chosen loci, discrimination power and concordance to conventional phenotypic methods of the Brucella differentiation for use in systematization of brucellosis causing agents. Twenty six Brucella strains representing reference (n = 15), vaccine (n = 2) and field strains of three pathogenic Brucella species were tested: B. melitensis (n = 3), B. abortus (n = 2), B. suis (n = 2), and isolates (n = 2) with unidentified taxonomic position using MLVA with 9 pairs primers on known variable loci of Brucella genome. The analysis of the stability of chosen loci, discrimination power on Hunter-Gaston discrimination index (HGDI) and consistency to phenotypic methods of identification was performed. MLVA was confirmed for the results of phenotypic methods of identification, stability of the chosen loci in majority reference, and vaccine strains with a high index of variability HGDI 0.9969 for all loci. A dendrogram was plotted on the basis of MLVA data on distributed Brucella strains in related clusters according to its taxonomic species and biovar positions and construction of 25 genotypes. B. melitensis strains formed cluster related to the reference strain of B. melitensis 63/9 biovar 2. Australian isolates of Brucella 83-4 and Brucella 83-6 isolated from rodents formed a cluster distant from other strains of Brucella. MLVA is a promising method for differentiation of Brucella strains with known and unresolved taxonomic status for their systematization and creation of MLVA genotype catalogue that will promote qualitative improvement of brucellosis surveillance system in Russia.
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Roine, Antti; Saviauk, Taavi; Kumpulainen, Pekka; Karjalainen, Markus; Tuokko, Antti; Aittoniemi, Janne; Vuento, Risto; Lekkala, Jukka; Lehtimäki, Terho; Tammela, Teuvo L; Oksala, Niku K J
2014-01-01
Urinary tract infection (UTI) is a common disease with significant morbidity and economic burden, accounting for a significant part of the workload in clinical microbiology laboratories. Current clinical chemisty point-of-care diagnostics rely on imperfect dipstick analysis which only provides indirect and insensitive evidence of urinary bacterial pathogens. An electronic nose (eNose) is a handheld device mimicking mammalian olfaction that potentially offers affordable and rapid analysis of samples without preparation at athmospheric pressure. In this study we demonstrate the applicability of ion mobility spectrometry (IMS) -based eNose to discriminate the most common UTI pathogens from gaseous headspace of culture plates rapidly and without sample preparation. We gathered a total of 101 culture samples containing four most common UTI bacteries: E. coli, S. saprophyticus, E. faecalis, Klebsiella spp and sterile culture plates. The samples were analyzed using ChemPro 100i device, consisting of IMS cell and six semiconductor sensors. Data analysis was conducted by linear discriminant analysis (LDA) and logistic regression (LR). The results were validated by leave-one-out and 5-fold cross validation analysis. In discrimination of sterile and bacterial samples sensitivity of 95% and specificity of 97% were achieved. The bacterial species were identified with sensitivity of 95% and specificity of 96% using eNose as compared to urine bacterial cultures. These findings strongly demonstrate the ability of our eNose to discriminate bacterial cultures and provides a proof of principle to use this method in urinanalysis of UTI.
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Lê, Laetitia Minh Mai; Eveleigh, Luc; Hasnaoui, Ikram; Prognon, Patrice; Baillet-Guffroy, Arlette; Caudron, Eric
2017-05-10
The aim of this study was to investigate near infrared spectroscopy (NIRS) combined to chemometric analysis to discriminate and quantify three antibiotics by direct measurement in plastic syringes.Solutions of benzylpenicillin (PENI), amoxicillin (AMOX) and amoxicillin/clavulanic acid (AMOX/CLAV) were analyzed at therapeutic concentrations in glass vials and plastic syringes with NIR spectrometer by direct measurement. Chemometric analysis using partial least squares regression and discriminative analysis was conducted to develop qualitative and quantitative calibration models. Discrimination of the three antibiotics was optimal for concentrated solutions with 100% of accuracy. For quantitative analysis, the three antibiotics furnished a linear response (R²>0.9994) for concentrations ranging from 0.05 to 0.2 g/mL for AMOX, 0.1 to 1.0 MUI/mL for PENI and 0.005 to 0.05 g/mL for AMOX/CLAV with excellent repeatability (maximum 1.3%) and intermediate precision (maximum of 3.2%). Based on proposed models, 94.4% of analyzed AMOX syringes, 80.0% of AMOX/CLAV syringes and 85.7% of PENI syringes were compliant with a relative error including the limit of ± 15%.NIRS as rapid, non-invasive and non-destructive analytical method represents a potentially powerful tool to further develop for securing the drug administration circuit of healthcare institutions to ensure that patients receive the correct product at the right dose. Copyright © 2017 Elsevier B.V. All rights reserved.
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Morphological resonances for multicomponent immunoassays
NASA Astrophysics Data System (ADS)
Whitten, W. B.; Shapiro, M. J.; Ramsey, J. M.; Bronk, B. V.
1995-06-01
An immunoassay technique capable of detecting and identifying a number of species of microorganisms in a single analysis is described. The method uses optical-resonance size discrimination of microspheres to identify antibodies to which stained microorganisms are bound.
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Drivelos, Spiros A; Higgins, Kevin; Kalivas, John H; Haroutounian, Serkos A; Georgiou, Constantinos A
2014-12-15
"Fava Santorinis", is a protected designation of origin (PDO) yellow split pea species growing only in the island of Santorini in Greece. Due to its nutritional quality and taste, it has gained a high monetary value. Thus, it is prone to adulteration with other yellow split peas. In order to discriminate "Fava Santorinis" from other yellow split peas, four classification methods utilising rare earth elements (REEs) measured through inductively coupled plasma-mass spectrometry (ICP-MS) are studied. The four classification processes are orthogonal projection analysis (OPA), Mahalanobis distance (MD), partial least squares discriminant analysis (PLS-DA) and k nearest neighbours (KNN). Since it is known that trace elements are often useful to determine geographical origin of food products, we further quantitated for trace elements using ICP-MS. Presented in this paper are results using the four classification processes based on the fusion of the REEs data with the trace element data. Overall, the OPA method was found to perform best with up to 100% accuracy using the fused data. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Methods for Kinetic and Thermodynamic Analysis of Aminoacyl-tRNA Synthetases
Francklyn, Christopher S.; First, Eric A.; Perona, John J.; Hou, Ya-Ming
2008-01-01
The accuracy of protein synthesis relies on the ability of aminoacyl-tRNA synthetases (aaRSs) to discriminate among true and near cognate substrates. To date, analysis of aaRSs function, including identification of residues of aaRS participating in amino acid and tRNA discrimination, has largely relied on the steady state kinetic pyrophosphate exchange and aminoacylation assays. Pre-steady state kinetic studies investigating a more limited set of aaRS systems have also been undertaken to assess the energetic contributions of individual enzyme-substrate interactions, particularly in the adenylation half reaction. More recently, a renewed interest in the use of rapid kinetics approaches for aaRSs has led to their application to several new aaRS systems, resulting in the identification of mechanistic differences that distinguish the two structurally distinct aaRS classes. Here, we review the techniques for thermodynamic and kinetic analysis of aaRS function. Following a brief survey of methods for the preparation of materials and for steady state kinetic analysis, this review will describe pre-steady state kinetic methods employing rapid quench and stopped-flow fluorescence for analysis of the activation and aminoacyl transfer reactions. Application of these methods to any aaRS system allows the investigator to derive detailed kinetic mechanisms for the activation and aminoacyl transfer reactions, permitting issues of substrate specificity, stereochemical mechanism, and inhibitor interaction to be addressed in a rigorous and quantitative fashion. PMID:18241792
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de Peinder, P; Vredenbregt, M J; Visser, T; de Kaste, D
2008-08-05
Research has been carried on the feasibility of near infrared (NIR) and Raman spectroscopy as rapid screening methods to discriminate between genuine and counterfeits of the cholesterol-lowering medicine Lipitor. Classification, based on partial least squares discriminant analysis (PLS-DA) models, appears to be successful for both spectroscopic techniques, irrespective of whether atorvastatine or lovastatine has been used as the active pharmaceutical ingredient (API). The discriminative power of the NIR model, in particular, largely relies on the spectral differences of the tablet matrix. This is due to the relative large sample volume that is probed with NIR and the strong spectroscopic activity of the excipients. PLS-DA models based on NIR or Raman spectra can also be applied to distinguish between atorvastatine and lovastatine as the API used in the counterfeits tested in this study. A disadvantage of Raman microscopy for this type of analysis is that it is primarily a surface technique. As a consequence spectra of the coating and the tablet core might differ. Besides, spectra may change with the position of the laser in case the sample is inhomogeneous. However, the robustness of the PLS-DA models turned out to be sufficiently large to allow a reliable discrimination. Principal component analysis (PCA) of the spectra revealed that the conditions, at which tablets have been stored, affect the NIR data. This effect is attributed to the adsorption of water from the atmosphere after unpacking from the blister. It implies that storage conditions should be taken into account when the NIR technique is used for discriminating purposes. However, in this study both models based on NIR spectra and Raman data enabled reliable discrimination between genuine and counterfeited Lipitor tablets, regardless of their storage conditions.
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Jiménez-Carvelo, Ana M; Pérez-Castaño, Estefanía; González-Casado, Antonio; Cuadros-Rodríguez, Luis
2017-04-15
A new method for differentiation of olive oil (independently of the quality category) from other vegetable oils (canola, safflower, corn, peanut, seeds, grapeseed, palm, linseed, sesame and soybean) has been developed. The analytical procedure for chromatographic fingerprinting of the methyl-transesterified fraction of each vegetable oil, using normal-phase liquid chromatography, is described and the chemometric strategies applied and discussed. Some chemometric methods, such as k-nearest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), support vector machine classification analysis (SVM-C), and soft independent modelling of class analogies (SIMCA), were applied to build classification models. Performance of the classification was evaluated and ranked using several classification quality metrics. The discriminant analysis, based on the use of one input-class, (plus a dummy class) was applied for the first time in this study. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Multisensor system for toxic gases detection generated on indoor environments
NASA Astrophysics Data System (ADS)
Durán, C. M.; Monsalve, P. A. G.; Mosquera, C. J.
2016-11-01
This work describes a wireless multisensory system for different toxic gases detection generated on indoor environments (i.e., Underground coal mines, etc.). The artificial multisensory system proposed in this study was developed through a set of six chemical gas sensors (MQ) of low cost with overlapping sensitivities to detect hazardous gases in the air. A statistical parameter was implemented to the data set and two pattern recognition methods such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA) were used for feature selection. The toxic gases categories were classified with a Probabilistic Neural Network (PNN) in order to validate the results previously obtained. The tests were carried out to verify feasibility of the application through a wireless communication model which allowed to monitor and store the information of the sensor signals for the appropriate analysis. The success rate in the measures discrimination was 100%, using an artificial neural network where leave-one-out was used as cross validation method.
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Forensic Comparison of Soil Samples Using Nondestructive Elemental Analysis.
Uitdehaag, Stefan; Wiarda, Wim; Donders, Timme; Kuiper, Irene
2017-07-01
Soil can play an important role in forensic cases in linking suspects or objects to a crime scene by comparing samples from the crime scene with samples derived from items. This study uses an adapted ED-XRF analysis (sieving instead of grinding to prevent destruction of microfossils) to produce elemental composition data of 20 elements. Different data processing techniques and statistical distances were evaluated using data from 50 samples and the log-LR cost (C llr ). The best performing combination, Canberra distance, relative data, and square root values, is used to construct a discriminative model. Examples of the spatial resolution of the method in crime scenes are shown for three locations, and sampling strategy is discussed. Twelve test cases were analyzed, and results showed that the method is applicable. The study shows how the combination of an analysis technique, a database, and a discriminative model can be used to compare multiple soil samples quickly. © 2016 American Academy of Forensic Sciences.
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Cognat, Claudine; Shepherd, Tom; Verrall, Susan R; Stewart, Derek
2012-10-01
Two different headspace sampling techniques were compared for analysis of aroma volatiles from freshly produced and aged plain oatcakes. Solid phase microextraction (SPME) using a Carboxen-Polydimethylsiloxane (PDMS) fibre and entrainment on Tenax TA within an adsorbent tube were used for collection of volatiles. The effects of variation in the sampling method were also considered using SPME. The data obtained using both techniques were processed by multivariate statistical analysis (PCA). Both techniques showed similar capacities to discriminate between the samples at different ages. Discrimination between fresh and rancid samples could be made on the basis of changes in the relative abundances of 14-15 of the constituents in the volatile profiles. A significant effect on the detection level of volatile compounds was observed when samples were crushed and analysed by SPME-GC-MS, in comparison to undisturbed product. The applicability and cost effectiveness of both methods were considered. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Gouvinhas, Irene; Machado, Nelson; Carvalho, Teresa; de Almeida, José M M M; Barros, Ana I R N A
2015-01-01
Extra virgin olive oils produced from three cultivars on different maturation stages were characterized using Raman spectroscopy. Chemometric methods (principal component analysis, discriminant analysis, principal component regression and partial least squares regression) applied to Raman spectral data were utilized to evaluate and quantify the statistical differences between cultivars and their ripening process. The models for predicting the peroxide value and free acidity of olive oils showed good calibration and prediction values and presented high coefficients of determination (>0.933). Both the R(2), and the correlation equations between the measured chemical parameters, and the values predicted by each approach are presented; these comprehend both PCR and PLS, used to assess SNV normalized Raman data, as well as first and second derivative of the spectra. This study demonstrates that a combination of Raman spectroscopy with multivariate analysis methods can be useful to predict rapidly olive oil chemical characteristics during the maturation process. Copyright © 2014 Elsevier B.V. All rights reserved.
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Perceived racial, socioeconomic and gender discrimination and its impact on contraceptive choice
Kossler, Karla; Kuroki, Lindsay M.; Allsworth, Jenifer E.; Secura, Gina M.; Roehl, Kimberly A.; Peipert, Jeffrey F.
2012-01-01
Background The study was conducted to determine whether perceived racial, economic, and gender discrimination has an impact on contraception use and choice of method. Methods We analyzed the first 2,500 women, aged 14–45 years enrolled in the Contraceptive CHOICE Project, a prospective cohort study aimed to reduce barriers to long-acting reversible contraception. Items from the “Experiences of Discrimination” (EOD) scale measured experienced race-, gender-, and economic-based discrimination. Results Overall, 57% of women reported a history of discrimination. Thirty-three percent reported gender- or race-based discrimination and 24% reported discrimination attributed to socioeconomic status (SES). Prior to study enrollment, women reporting discrimination were more likely to report any contraception use (61% vs. 51%, p<0.001), but were more likely to use less effective methods (e.g., barrier methods, natural family planning or withdrawal; 41% vs. 32%, p<0.001). In adjusted analyses, gender-, race- or SES-based discrimination were associated with increased current use of less effective methods (adjusted risk ratio (aRR) 1.22, CI 1.06–1.41; aRR 1.25, CI 1.08–1.45; aRR 1.23, CI 1.06–1.43, respectively). After enrollment, 67% of women with history of experience of discrimination chose a long-acting reversible contraceptive method (intrauterine device or implantable) and 33% chose a depo-medroxyprogesterone acetate or contraceptive pill, patch or ring. Conclusions Discrimination negatively impacts a woman’s use of contraception. However, after financial and structural barriers to contraceptive use were eliminated, women with EOD overwhelmingly selected effective methods of contraception. Future interventions to improve access and utilization of contraception should focus on eliminating barriers and targeting interventions that encompass race-, gender-, and economic-based discrimination. PMID:21843693
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Perceived racial, socioeconomic and gender discrimination and its impact on contraceptive choice.
Kossler, Karla; Kuroki, Lindsay M; Allsworth, Jenifer E; Secura, Gina M; Roehl, Kimberly A; Peipert, Jeffrey F
2011-09-01
The study was conducted to determine whether perceived racial, economic and gender discrimination has an impact on contraception use and choice of method. We analyzed the first 2,500 women aged 14-45 years enrolled in the Contraceptive CHOICE Project, a prospective cohort study aimed to reduce barriers to obtaining long-acting reversible contraception. Items from the "Experiences of Discrimination" (EOD) scale measured experienced race-, gender- and economic-based discrimination. Overall, 57% of women reported a history of discrimination. Thirty-three percent reported gender- or race-based discrimination, and 24% reported discrimination attributed to socioeconomic status (SES). Prior to study enrollment, women reporting discrimination were more likely to report any contraception use (61% vs. 52%, p<.001) but were more likely to use less effective methods (e.g., barrier methods, natural family planning or withdrawal; 41% vs. 32%, p<.001). In adjusted analyses, gender-, race- or SES-based discrimination were associated with increased current use of less effective methods [adjusted risk ratio (aRR) 1.22, 95% confidence interval (CI) 1.06-1.41; aRR 1.25, CI 1.08-1.45; aRR 1.23, CI 1.06-1.43, respectively]. After enrollment, 66% of women with a history of experience of discrimination chose a long-acting reversible contraceptive method (intrauterine device or implantable) and 35% chose a depo-medroxyprogesterone acetate or contraceptive pill, patch or ring. Discrimination negatively impacts a woman's use of contraception. However, after financial and structural barriers to contraceptive use were eliminated, women with EOD overwhelmingly selected effective methods of contraception. Future interventions to improve access and utilization of contraception should focus on eliminating barriers and targeting interventions that encompass race-, gender- and economic-based discrimination. Copyright © 2011 Elsevier Inc. All rights reserved.
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Methods for the scientific study of discrimination and health: an ecosocial approach.
Krieger, Nancy
2012-05-01
The scientific study of how discrimination harms health requires theoretically grounded methods. At issue is how discrimination, as one form of societal injustice, becomes embodied inequality and is manifested as health inequities. As clarified by ecosocial theory, methods must address the lived realities of discrimination as an exploitative and oppressive societal phenomenon operating at multiple levels and involving myriad pathways across both the life course and historical generations. An integrated embodied research approach hence must consider (1) the structural level-past and present de jure and de facto discrimination; (2) the individual level-issues of domains, nativity, and use of both explicit and implicit discrimination measures; and (3) how current research methods likely underestimate the impact of racism on health.
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Characterization of Microbiota in Children with Chronic Functional Constipation
de Meij, Tim G. J.; de Groot, Evelien F. J.; Eck, Anat; Budding, Andries E.; Kneepkens, C. M. Frank; Benninga, Marc A.; van Bodegraven, Adriaan A.; Savelkoul, Paul H. M.
2016-01-01
Objectives Disruption of the intestinal microbiota is considered an etiological factor in pediatric functional constipation. Scientifically based selection of potential beneficial probiotic strains in functional constipation therapy is not feasible due to insufficient knowledge of microbiota composition in affected subjects. The aim of this study was to describe microbial composition and diversity in children with functional constipation, compared to healthy controls. Study Design Fecal samples from 76 children diagnosed with functional constipation according to the Rome III criteria (median age 8.0 years; range 4.2–17.8) were analyzed by IS-pro, a PCR-based microbiota profiling method. Outcome was compared with intestinal microbiota profiles of 61 healthy children (median 8.6 years; range 4.1–17.9). Microbiota dissimilarity was depicted by principal coordinate analysis (PCoA), diversity was calculated by Shannon diversity index. To determine the most discriminative species, cross validated logistic ridge regression was performed. Results Applying total microbiota profiles (all phyla together) or per phylum analysis, no disease-specific separation was observed by PCoA and by calculation of diversity indices. By ridge regression, however, functional constipation and controls could be discriminated with 82% accuracy. Most discriminative species were Bacteroides fragilis, Bacteroides ovatus, Bifidobacterium longum, Parabacteroides species (increased in functional constipation) and Alistipes finegoldii (decreased in functional constipation). Conclusions None of the commonly used unsupervised statistical methods allowed for microbiota-based discrimination of children with functional constipation and controls. By ridge regression, however, both groups could be discriminated with 82% accuracy. Optimization of microbiota-based interventions in constipated children warrants further characterization of microbial signatures linked to clinical subgroups of functional constipation. PMID:27760208
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DiMichele, Daniel L; Spradley, M Katherine
2012-09-10
Reliable methods for sex estimation during the development of a biological profile are important to the forensic community in instances when the common skeletal elements used to assess sex are absent or damaged. Sex estimation from the calcaneus has potentially significant importance for the forensic community. Specifically, measurements of the calcaneus provide an additional reliable method for sex estimation via discriminant function analysis based on a North American forensic population. Research on a modern American sample was chosen in order to develop up-to-date population specific discriminant functions for sex estimation. The current study addresses this matter, building upon previous research and introduces a new measurement, posterior circumference that promises to advance the accuracy of use of this single, highly resistant bone in future instances of sex determination from partial skeletal remains. Data were collected from The William Bass Skeletal Collection, housed at The University of Tennessee. Sample size includes 320 adult individuals born between the years 1900 and 1985. The sample was comprised of 136 females and 184 males. Skeletons used for measurements were confined to those with fused diaphyses showing no signs of pathology or damage that may have altered measurements, and that also had accompanying records that included information on ancestry, age, and sex. Measurements collected and analyzed include maximum length, load-arm length, load-arm width, and posterior circumference. The sample was used to compute a discriminant function, based on all four variables, and was performed in SAS 9.1.3. The discriminant function obtained an overall cross-validated classification rate of 86.69%. Females were classified correctly in 88.64% of the cases and males were correctly classified in 84.75% of the cases. Due to the increasing heterogeneity of current populations further discussion on this topic will include the importance that the re-evaluation of past studies has on modern forensic populations. Due to secular and micro evolutionary changes among populations, the near future must include additional methods being updated, and new methods being examined, both which should cover a wide population spectrum. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
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Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.
Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin
2016-06-07
RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .
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Wixted, John T; Mickes, Laura
2018-01-01
Receiver operating characteristic (ROC) analysis was introduced to the field of eyewitness identification 5 years ago. Since that time, it has been both influential and controversial, and the debate has raised an issue about measuring discriminability that is rarely considered. The issue concerns the distinction between empirical discriminability (measured by area under the ROC curve) vs. underlying/theoretical discriminability (measured by d' or variants of it). Under most circumstances, the two measures will agree about a difference between two conditions in terms of discriminability. However, it is possible for them to disagree, and that fact can lead to confusion about which condition actually yields higher discriminability. For example, if the two conditions have implications for real-world practice (e.g., a comparison of competing lineup formats), should a policymaker rely on the area-under-the-curve measure or the theory-based measure? Here, we illustrate the fact that a given empirical ROC yields as many underlying discriminability measures as there are theories that one is willing to take seriously. No matter which theory is correct, for practical purposes, the singular area-under-the-curve measure best identifies the diagnostically superior procedure. For that reason, area under the ROC curve informs policy in a way that underlying theoretical discriminability never can. At the same time, theoretical measures of discriminability are equally important, but for a different reason. Without an adequate theoretical understanding of the relevant task, the field will be in no position to enhance empirical discriminability.
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NASA Astrophysics Data System (ADS)
Zhang, Linna; Ding, Hongyan; Lin, Ling; Wang, Yimin; Guo, Xin
2018-01-01
Noncontact discriminating human blood is significantly crucial for import-export ports and inspection and quarantine departments. We had already demonstrated that visible diffuse reflectance spectroscopy combining PLS-DA method can successfully realize noncontact human blood discrimination. However, the circulated blood vessels may be produced with different materials. The use of various kinds of blood tubes may have a negative effect on the discrimination, based on ;M+N; theory (Li et al., 2016). In this research, we explored the impact of different material of blood vessels, such as glass tube and plastic tube, on the prediction ability of the discrimination model. Furthermore, we searched for the modification method to reduce the influence from the blood tubes. Our work indicated that generalized diffuse reflectance method can greatly improve the discrimination accuracy. This research can greatly facilitate the application of noncontact discrimination method based on visible and near-infrared diffuse reflectance spectroscopy.
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Durakli Velioglu, Serap; Ercioglu, Elif; Boyaci, Ismail Hakki
2017-05-01
This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400-550 nm excitation range with Δλ of 10-100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.
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Space sickness predictors suggest fluid shift involvement and possible countermeasures
NASA Technical Reports Server (NTRS)
Simanonok, K. E.; Moseley, E. C.; Charles, J. B.
1992-01-01
Preflight data from 64 first time Shuttle crew members were examined retrospectively to predict space sickness severity (NONE, MILD, MODERATE, or SEVERE) by discriminant analysis. From 9 input variables relating to fluid, electrolyte, and cardiovascular status, 8 variables were chosen by discriminant analysis that correctly predicted space sickness severity with 59 pct. success by one method of cross validation on the original sample and 67 pct. by another method. The 8 variables in order of their importance for predicting space sickness severity are sitting systolic blood pressure, serum uric acid, calculated blood volume, serum phosphate, urine osmolality, environmental temperature at the launch site, red cell count, and serum chloride. These results suggest the presence of predisposing physiologic factors to space sickness that implicate a fluid shift etiology. Addition of a 10th input variable, hours spent in the Weightless Environment Training Facility (WETF), improved the prediction of space sickness severity to 66 pct. success by the first method of cross validation on the original sample and to 71 pct. by the second method. The data suggest that WETF training may reduce space sickness severity.
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Two-step Raman spectroscopy method for tumor diagnosis
NASA Astrophysics Data System (ADS)
Zakharov, V. P.; Bratchenko, I. A.; Kozlov, S. V.; Moryatov, A. A.; Myakinin, O. O.; Artemyev, D. N.
2014-05-01
Two-step Raman spectroscopy phase method was proposed for differential diagnosis of malignant tumor in skin and lung tissue. It includes detection of malignant tumor in healthy tissue on first step with identification of concrete cancer type on the second step. Proposed phase method analyze spectral intensity alteration in 1300-1340 and 1640-1680 cm-1 Raman bands in relation to the intensity of the 1450 cm-1 band on first step, and relative differences between RS intensities for tumor area and healthy skin closely adjacent to the lesion on the second step. It was tested more than 40 ex vivo samples of lung tissue and more than 50 in vivo skin tumors. Linear Discriminant Analysis, Quadratic Discriminant Analysis and Support Vector Machine were used for tumors type classification on phase planes. It is shown that two-step phase method allows to reach 88.9% sensitivity and 87.8% specificity for malignant melanoma diagnosis (skin cancer); 100% sensitivity and 81.5% specificity for adenocarcinoma diagnosis (lung cancer); 90.9% sensitivity and 77.8% specificity for squamous cell carcinoma diagnosis (lung cancer).
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Javidnia, Katayoun; Parish, Maryam; Karimi, Sadegh; Hemmateenejad, Bahram
2013-03-01
By using FT-IR spectroscopy, many researchers from different disciplines enrich the experimental complexity of their research for obtaining more precise information. Moreover chemometrics techniques have boosted the use of IR instruments. In the present study we aimed to emphasize on the power of FT-IR spectroscopy for discrimination between different oil samples (especially fat from vegetable oils). Also our data were used to compare the performance of different classification methods. FT-IR transmittance spectra of oil samples (Corn, Colona, Sunflower, Soya, Olive, and Butter) were measured in the wave-number interval of 450-4000 cm(-1). Classification analysis was performed utilizing PLS-DA, interval PLS-DA, extended canonical variate analysis (ECVA) and interval ECVA methods. The effect of data preprocessing by extended multiplicative signal correction was investigated. Whilst all employed method could distinguish butter from vegetable oils, iECVA resulted in the best performances for calibration and external test set with 100% sensitivity and specificity. Copyright © 2012 Elsevier B.V. All rights reserved.
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2018-01-01
Introduction Discrimination related to gender identity may directly influence vulnerability to HIV through increased exposure to unprotected receptive anal intercourse (URAI). Little is known about the relationship between gender-based discrimination (GBD) and URAI with stable partners among transgender women. Methods This mixed-methods research began with a cross-sectional survey conducted between 2014 and 2016 with transgender women in Salvador, the capital city in one of the poorest regions in Brazil. Respondent-driven sampling was used to recruit the study population. GBD was defined through Latent Class Analysis. Additionally, 19 semi-structured interviews with participants were transcribed and analyzed through thematic content analysis. Results URAI with stable partners was commonly reported (37.3%). GDB was positively associated with URAI among stable partners (OR = 6.47; IC 95%: 1.67–25.02). The analysis of the interviews illustrated how GBD impacted transgender women in diverse ways. Experiences with GBD perpetrated by the family often initiated a trajectory of economic vulnerability that led many to engage in survival sex work. The constant experience with GBD contributed to participants feeling an immense sense of trust with their stable partners, ultimately diminished their desire to use condoms. Further, the high frequency of GBD contributed to poor mental health overall, though some participants said engagement in transgender advocacy efforts provided a vital source of resilience and support. Conclusion Our mixed-method study capitalizes upon the strengths of diverse data sets to produce a holistic understanding of GBD and URAI with stable partners. Furthermore, by confirming the association between greater GBD and URAI, we have demonstrated how GBD can impact condom negotiation in diverse relationships. PMID:29641528
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The effect of perceived discrimination on the health of immigrant workers in Spain
2011-01-01
Background Discrimination is an important determinant of health inequalities, and immigrants may be more vulnerable to certain types of discrimination than the native-born. This study analyses the relationship between immigrants' perceived discrimination and various self-reported health indicators. Methods A cross-sectional survey was conducted (2008) amongst a non-random sample of 2434 immigrants from Ecuador, Morocco, Romania and Colombia in four Spanish cities: Barcelona, Huelva, Madrid and Valencia. A factorial analysis of variables revealed three dimensions of perceived discrimination (due to immigrant status, due to physical appearance, and workplace-related). The association of these dimensions with self-rated health, mental health (GHQ-12), change in self-rated health between origin and host country, and other self-reported health outcomes was analysed. Logistic regression was used adjusting for potential confounders (aOR-95%CI). Subjects with worsening self-reported health status potentially attributable to perceived discrimination was estimated (population attributable proportion, PAP %). Results 73.3% of men and 69.3% of women immigrants reported discrimination due to immigrant status. Moroccans showed the highest prevalence of perceived discrimination. Immigrants reporting discrimination were at significantly higher risk of reporting health problems than those not reporting discrimination. Workplace-related discrimination was associated with poor mental health (aOR 2.97 95%CI 2.45-3.60), and the worsening of self-rated health (aOR 2.20 95%CI 1.73- 2.80). 40% (95% CI 24-53) PAP of those reporting worse self-rated health could be attributable to discrimination due to immigrant status. Conclusions Discrimination may constitute a risk factor for health in immigrant workers in Spain and could explain some health inequalities among immigrant populations in Spanish society. PMID:21849020
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1992-04-10
and passive tracer concentrations, and their cross correlations have generally been used to estimate the magnitude of dispersive atmospheric transport...of gravity waves and turbulence. . 10 III. METHODS .......... ........................ 12 A. Data .......... ........................ 12 B. Analysis ...unstable, i.e., strange. For waves or even limit cycle motion about fixed attractors, self-similarity does not occur. Pertinent to time series analysis , this
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Rapid Detection & Identification of Bacillus Species using MALDI-TOF/TOF and Biomarker Database
2006-06-01
rRNA sequence analysis. Multilocus enzyme electrophoresis ( MEE ) and comparative DNA sequence analysis suggest that they may represent a single species...adaptation of the MEE method [63] but with greater discrimination [64]. All of these new PCR-based subtyping methods are certainly superior and more...Demirev, P.A., Lin, J.S., Pineda , F.J., and Fenselau, C. (2001). Bioinformatics and mass spectrometry for microorganism identification: proteome-wide
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NASA Technical Reports Server (NTRS)
Lee, K. (Principal Investigator); Raines, G. L.
1974-01-01
The author has identified the following significant results. With the advent of ERTS and Skylab satellites, multiband imagery and photography have become readily available to geologists. The ability of multiband photography to discriminate sedimentary rocks was examined. More than 8600 in situ measurements of band reflectance of the sedimentary rocks of the Front Range, Colorado, were acquired. Statistical analysis of these measurements showed that: (1) measurements from one site can be used at another site 100 miles away; (2) there is basically only one spectral reflectance curve for these rocks, with constant amplitude differences between the curves; and (3) the natural variation is so large that at least 150 measurements per formation are required to select best filters. These conclusions are supported by subjective tests with aerial multiband photography. The designed multiband photography concept for rock discrimination is not a practical method of improving sedimentary rock discrimination capabilities.
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ERIC Educational Resources Information Center
Hill, Susan C.; Lindsay, Gordon B.; Thomsen, Steve R.; Olsen, Astrid M.
2003-01-01
Media literacy education helps individuals become discriminating consumers of health information. Informed consumers are less likely to purchase useless health products if informed of misleading and deceptive advertising methods. The purpose of this study was to conduct a content analysis of health-related TV infomercials. An instrument…
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Homology and the optimization of DNA sequence data
NASA Technical Reports Server (NTRS)
Wheeler, W.
2001-01-01
Three methods of nucleotide character analysis are discussed. Their implications for molecular sequence homology and phylogenetic analysis are compared. The criterion of inter-data set congruence, both character based and topological, are applied to two data sets to elucidate and potentially discriminate among these parsimony-based ideas. c2001 The Willi Hennig Society.
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Schönweiler, R; Wübbelt, P; Tolloczko, R; Rose, C; Ptok, M
2000-01-01
Discriminant analysis (DA) and self-organizing feature maps (SOFM) were used to classify passively evoked auditory event-related potentials (ERP) P(1), N(1), P(2) and N(2). Responses from 16 children with severe behavioral auditory perception deficits, 16 children with marked behavioral auditory perception deficits, and 14 controls were examined. Eighteen ERP amplitude parameters were selected for examination of statistical differences between the groups. Different DA methods and SOFM configurations were trained to the values. SOFM had better classification results than DA methods. Subsequently, measures on another 37 subjects that were unknown for the trained SOFM were used to test the reliability of the system. With 10-dimensional vectors, reliable classifications were obtained that matched behavioral auditory perception deficits in 96%, implying central auditory processing disorder (CAPD). The results also support the assumption that CAPD includes a 'non-peripheral' auditory processing deficit. Copyright 2000 S. Karger AG, Basel.
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Surzhikov, V D; Surzhikov, D V
2014-01-01
The search and measurement of causal relationships between exposure to air pollution and health state of the population is based on the system analysis and risk assessment to improve the quality of research. With this purpose there is applied the modern statistical analysis with the use of criteria of independence, principal component analysis and discriminate function analysis. As a result of analysis out of all atmospheric pollutants there were separated four main components: for diseases of the circulatory system main principal component is implied with concentrations of suspended solids, nitrogen dioxide, carbon monoxide, hydrogen fluoride, for the respiratory diseases the main c principal component is closely associated with suspended solids, sulfur dioxide and nitrogen dioxide, charcoal black. The discriminant function was shown to be used as a measure of the level of air pollution.
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Metacarpophalangeal pattern profile analysis in Leri-Weill dyschondrosteosis.
Laurencikas, E; Soderman, E; Grigelioniene, G; Hagenäs, L; Jorulf, H
2005-04-01
To analyze the metacarpophalangeal profile (MCPP) in individuals with Leri-Weill dyschondrosteosis (LWD) and to assess its value as a possible contributor to early diagnosis. Hand profiles of 39 individuals with a diagnosis of LWD were calculated and analyzed. Discriminant analysis was applied to differentiate between LWD and normal individuals. There was a distinct pattern profile in LWD. Mean pattern profile showed two bone-shortening gradients, with increasing shortening from distal to proximal and from medial to lateral. Distal phalanx 2 was disproportionately long and second metacarpal was disproportionately short. Discriminant analysis yielded correct classification in 72% of analyzed cases. MCPP is not age-related and the analysis can be applied at any age, facilitating early diagnosis of LWD. In view of its availability, low costs, and diagnostic value, MCPP analysis should be considered as a routine method in the patients of short stature where LWD is suspected.
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Combined Raman spectroscopy and autofluoresence imaging method for in vivo skin tumor diagnosis
NASA Astrophysics Data System (ADS)
Zakharov, V. P.; Bratchenko, I. A.; Myakinin, O. O.; Artemyev, D. N.; Khristoforova, Y. A.; Kozlov, S. V.; Moryatov, A. A.
2014-09-01
The fluorescence and Raman spectroscopy (RS) combined method of in vivo detection of malignant human skin cancer was demonstrated. The fluorescence analysis was used for detection of abnormalities during fast scanning of large tissue areas. In suspected cases of malignancy the Raman spectrum analysis of biological tissue was performed to determine the type of neoplasm. A special RS phase method was proposed for in vivo identification of skin tumor. Quadratic Discriminant Analysis was used for tumor type classification on phase planes. It was shown that the application of phase method provides a diagnosis of malignant melanoma with a sensitivity of 89% and a specificity of 87%.
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Karabagias, Ioannis K; Karabournioti, Sofia
2018-05-03
Twenty-two honey samples, namely clover and citrus honeys, were collected from the greater Cairo area during the harvesting year 2014⁻2015. The main purpose of the present study was to characterize the aforementioned honey types and to investigate whether the use of easily assessable physicochemical parameters, including color attributes in combination with chemometrics, could differentiate honey floral origin. Parameters taken into account were: pH, electrical conductivity, ash, free acidity, lactonic acidity, total acidity, moisture content, total sugars (degrees Brix-°Bx), total dissolved solids and their ratio to total acidity, salinity, CIELAB color parameters, along with browning index values. Results showed that all honey samples analyzed met the European quality standards set for honey and had variations in the aforementioned physicochemical parameters depending on floral origin. Application of linear discriminant analysis showed that eight physicochemical parameters, including color, could classify Egyptian honeys according to floral origin ( p < 0.05). Correct classification rate was 95.5% using the original method and 90.9% using the cross validation method. The discriminatory ability of the developed model was further validated using unknown honey samples. The overall correct classification rate was not affected. Specific physicochemical parameter analysis in combination with chemometrics has the potential to enhance the differences in floral honeys produced in a given geographical zone.
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Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon
2015-01-01
Background: 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. Methods: 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. Results: 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. Conclusion: 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. PMID:26793655
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Karabournioti, Sofia
2018-01-01
Twenty-two honey samples, namely clover and citrus honeys, were collected from the greater Cairo area during the harvesting year 2014–2015. The main purpose of the present study was to characterize the aforementioned honey types and to investigate whether the use of easily assessable physicochemical parameters, including color attributes in combination with chemometrics, could differentiate honey floral origin. Parameters taken into account were: pH, electrical conductivity, ash, free acidity, lactonic acidity, total acidity, moisture content, total sugars (degrees Brix-°Bx), total dissolved solids and their ratio to total acidity, salinity, CIELAB color parameters, along with browning index values. Results showed that all honey samples analyzed met the European quality standards set for honey and had variations in the aforementioned physicochemical parameters depending on floral origin. Application of linear discriminant analysis showed that eight physicochemical parameters, including color, could classify Egyptian honeys according to floral origin (p < 0.05). Correct classification rate was 95.5% using the original method and 90.9% using the cross validation method. The discriminatory ability of the developed model was further validated using unknown honey samples. The overall correct classification rate was not affected. Specific physicochemical parameter analysis in combination with chemometrics has the potential to enhance the differences in floral honeys produced in a given geographical zone. PMID:29751543
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Pan, Yongke; Niu, Wenjia
2017-01-01
Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the kNN is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the k-nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines. PMID:28316616
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Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio
2015-12-01
This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.
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Classification and pose estimation of objects using nonlinear features
NASA Astrophysics Data System (ADS)
Talukder, Ashit; Casasent, David P.
1998-03-01
A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.
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Targeted sorting of single virus-infected cells of the coccolithophore Emiliania huxleyi.
Martínez Martínez, Joaquín; Poulton, Nicole J; Stepanauskas, Ramunas; Sieracki, Michael E; Wilson, William H
2011-01-01
Discriminating infected from healthy cells is the first step to understanding the mechanisms and ecological implications of viral infection. We have developed a method for detecting, sorting, and performing molecular analysis of individual, infected cells of the important microalga Emiliania huxleyi, based on known physiological responses to viral infection. Of three fluorescent dyes tested, FM 1-43 (for detecting membrane blebbing) gave the most unequivocal and earliest separation of cells. Furthermore, we were able to amplify the genomes of single infected cells using Multiple Displacement Amplification. This novel method to reliably discriminate infected from healthy cells in cultures will allow researchers to answer numerous questions regarding the mechanisms and implications of viral infection of E. huxleyi. The method may be transferable to other virus-host systems.
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Colniță, Alia; Dina, Nicoleta Elena; Leopold, Nicolae; Vodnar, Dan Cristian; Bogdan, Diana; Porav, Sebastian Alin; David, Leontin
2017-09-01
Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS), are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei ( L. casei ) and Listeria monocytogenes ( L. monocytogenes ) were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA) to their specific spectral data.
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Leopold, Nicolae; Vodnar, Dan Cristian; Bogdan, Diana; Porav, Sebastian Alin; David, Leontin
2017-01-01
Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS), are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei (L. casei) and Listeria monocytogenes (L. monocytogenes) were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA) to their specific spectral data. PMID:28862655
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Methods for the Scientific Study of Discrimination and Health: An Ecosocial Approach
2012-01-01
The scientific study of how discrimination harms health requires theoretically grounded methods. At issue is how discrimination, as one form of societal injustice, becomes embodied inequality and is manifested as health inequities. As clarified by ecosocial theory, methods must address the lived realities of discrimination as an exploitative and oppressive societal phenomenon operating at multiple levels and involving myriad pathways across both the life course and historical generations. An integrated embodied research approach hence must consider (1) the structural level—past and present de jure and de facto discrimination; (2) the individual level—issues of domains, nativity, and use of both explicit and implicit discrimination measures; and (3) how current research methods likely underestimate the impact of racism on health. PMID:22420803
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Basati, Zahra; Jamshidi, Bahareh; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef
2018-05-30
The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350-1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R 2 = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers. Copyright © 2018 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
HIRAMATSU, K.; MATSUI, T.; MIYAKITA, T.; ITO, A.; TOKUYAMA, T.; OSADA, Y.; YAMAMOTO, T.
2002-02-01
Discriminant function values of psychosomatics and neurosis are calculated using the 12 scale scores of the Todai Health Index, a general health questionnaire, obtained in the survey done around the Kadena and Futenma U.S. airfields in Okinawa, Japan. The total number of answers available for the analysis is 6301. Factor analysis is applied to the 12 scale scores by means of the principal factor method, and Oblimin rotation is done because the factors extracted are considered likely to correlate with each other to a greater or lesser extent. The logistic regression analysis is made with the independent variables of discriminant function (DF) values and factor scores and with the dependent variables of Ldn, age (six levels), sex, occupation (four categories) and the interaction of age and sex. Results indicate that the odds ratio of the DF values regarding psychosomatic disorder and of the score of somatic factor have clear dose-response relationship. The odds ratios of the DF value of neurosis and of the score of the mental factor increase in the area where noise exposure is very intense.
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Perceived Discrimination and Health: A Meta-Analytic Review
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 perceiving discrimination may affect health, including through psychological and physiological stress responses and health behaviors. Analysis of 134 samples suggests that when weighting each study’s contribution by sample size, perceived discrimination has a significant negative effect on both mental and physical health. Perceived discrimination also produces significantly heightened stress responses and is related to participation in unhealthy and nonparticipation in healthy behaviors. These findings suggest potential pathways linking perceived discrimination to negative health outcomes. PMID:19586161
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Multistate and multihypothesis discrimination with open quantum systems
NASA Astrophysics Data System (ADS)
Kiilerich, Alexander Holm; Mølmer, Klaus
2018-05-01
We show how an upper bound for the ability to discriminate any number N of candidates for the Hamiltonian governing the evolution of an open quantum system may be calculated by numerically efficient means. Our method applies an effective master-equation analysis to evaluate the pairwise overlaps between candidate full states of the system and its environment pertaining to the Hamiltonians. These overlaps are then used to construct an N -dimensional representation of the states. The optimal positive-operator valued measure (POVM) and the corresponding probability of assigning a false hypothesis may subsequently be evaluated by phrasing optimal discrimination of multiple nonorthogonal quantum states as a semidefinite programming problem. We provide three realistic examples of multihypothesis testing with open quantum systems.
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Oshima, Ryusei; Kotani, Akira; Kuroda, Minpei; Yamamoto, Kazuhiro; Mimaki, Yoshihiro; Hakamata, Hideki
2018-03-01
High-performance liquid chromatography with ultraviolet detection (HPLC-UV) using 20 mM phosphate mobile phase and an octadecylsilyl column (Triart C18, 150 × 3.0 mm i.d., 3 μm) has been developed for the analysis of hydrophilic compounds in the water extract of Schisandrae Fructus samples. The present HPLC-UV method permits the accurate and precise determination of malic, citric, and protocatechuic acids in the Japanese Pharmacopoeia (JP) Schisandrae Fructus, Schisandrae Chinensis Fructus and Schisandrae Sphenantherae Fructus. The JP Schisandrae Fructus studied contains 27.98 mg/g malic, 107.08 mg/g citric, and 0.42 mg/g protocatechuic acids, with a relative standard deviation (RSD) of repeatability of <0.9% (n = 6). The content of malic acids in Schisandrae Chinensis Fructus is approximately ten times that in Schisandrae Sphenantherae Fructus. To examine whether the HPLC-UV method is applicable to the fingerprint-based discrimination of Schisandrae Fructus samples obtained from Chinese markets, principal component analysis (PCA) was performed using the determined contents of organic acids and the ratio of six characteristic unknown peaks derived from hydrophilic components to internal standard peak areas. On the score plots, Schisandrae Chinensis Fructus and Schisandrae Sphenantherae Fructus samples are clearly discriminated. Therefore, the HPLC-UV method for the analysis of hydrophilic components coupled with PCA has been shown to be practical and useful in the quality control of Schisandrae Fructus.
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Sukumaran, Jeet; Economo, Evan P; Lacey Knowles, L
2016-05-01
Current statistical biogeographical analysis methods are limited in the ways ecology can be related to the processes of diversification and geographical range evolution, requiring conflation of geography and ecology, and/or assuming ecologies that are uniform across all lineages and invariant in time. This precludes the possibility of studying a broad class of macroevolutionary biogeographical theories that relate geographical and species histories through lineage-specific ecological and evolutionary dynamics, such as taxon cycle theory. Here we present a new model that generates phylogenies under a complex of superpositioned geographical range evolution, trait evolution, and diversification processes that can communicate with each other. We present a likelihood-free method of inference under our model using discriminant analysis of principal components of summary statistics calculated on phylogenies, with the discriminant functions trained on data generated by simulations under our model. This approach of model selection by classification of empirical data with respect to data generated under training models is shown to be efficient, robust, and performs well over a broad range of parameter space defined by the relative rates of dispersal, trait evolution, and diversification processes. We apply our method to a case study of the taxon cycle, that is testing for habitat and trophic level constraints in the dispersal regimes of the Wallacean avifaunal radiation. ©The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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Molecular Diagnosis and Biomarker Identification on SELDI proteomics data by ADTBoost method.
Wang, Lu-Yong; Chakraborty, Amit; Comaniciu, Dorin
2005-01-01
Clinical proteomics is an emerging field that will have great impact on molecular diagnosis, identification of disease biomarkers, drug discovery and clinical trials in the post-genomic era. Protein profiling in tissues and fluids in disease and pathological control and other proteomics techniques will play an important role in molecular diagnosis with therapeutics and personalized healthcare. We introduced a new robust diagnostic method based on ADTboost algorithm, a novel algorithm in proteomics data analysis to improve classification accuracy. It generates classification rules, which are often smaller and easier to interpret. This method often gives most discriminative features, which can be utilized as biomarkers for diagnostic purpose. Also, it has a nice feature of providing a measure of prediction confidence. We carried out this method in amyotrophic lateral sclerosis (ALS) disease data acquired by surface enhanced laser-desorption/ionization-time-of-flight mass spectrometry (SELDI-TOF MS) experiments. Our method is shown to have outstanding prediction capacity through the cross-validation, ROC analysis results and comparative study. Our molecular diagnosis method provides an efficient way to distinguish ALS disease from neurological controls. The results are expressed in a simple and straightforward alternating decision tree format or conditional format. We identified most discriminative peaks in proteomic data, which can be utilized as biomarkers for diagnosis. It will have broad application in molecular diagnosis through proteomics data analysis and personalized medicine in this post-genomic era.