Sample records for analysis discriminant analysis
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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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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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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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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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[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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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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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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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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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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Faradji, Farhad; Ward, Rabab K; Birch, Gary E
2009-06-15
The feasibility of having a self-paced brain-computer interface (BCI) based on mental tasks is investigated. The EEG signals of four subjects performing five mental tasks each are used in the design of a 2-state self-paced BCI. The output of the BCI should only be activated when the subject performs a specific mental task and should remain inactive otherwise. For each subject and each task, the feature coefficient and the classifier that yield the best performance are selected, using the autoregressive coefficients as the features. The classifier with a zero false positive rate and the highest true positive rate is selected as the best classifier. The classifiers tested include: linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, support vector machine, and radial basis function neural network. The results show that: (1) some classifiers obtained the desired zero false positive rate; (2) the linear discriminant analysis classifier does not yield acceptable performance; (3) the quadratic discriminant analysis classifier outperforms the Mahalanobis discriminant analysis classifier and performs almost as well as the radial basis function neural network; and (4) the support vector machine classifier has the highest true positive rates but unfortunately has nonzero false positive rates in most cases.
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Discrimination against Latina/os: A Meta-Analysis of Individual-Level Resources and Outcomes
ERIC Educational Resources Information Center
Lee, Debbiesiu L.; Ahn, Soyeon
2012-01-01
This meta-analysis synthesizes the findings of 60 independent samples from 51 studies examining racial/ethnic discrimination against Latina/os in the United States. The purpose was to identify individual-level resources and outcomes that most strongly relate to discrimination. Discrimination against Latina/os significantly results in outcomes…
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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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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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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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Discrimination Enhancement with Transient Feature Analysis of a Graphene Chemical Sensor.
Nallon, Eric C; Schnee, Vincent P; Bright, Collin J; Polcha, Michael P; Li, Qiliang
2016-01-19
A graphene chemical sensor is subjected to a set of structurally and chemically similar hydrocarbon compounds consisting of toluene, o-xylene, p-xylene, and mesitylene. The fractional change in resistance of the sensor upon exposure to these compounds exhibits a similar response magnitude among compounds, whereas large variation is observed within repetitions for each compound, causing a response overlap. Therefore, traditional features depending on maximum response change will cause confusion during further discrimination and classification analysis. More robust features that are less sensitive to concentration, sampling, and drift variability would provide higher quality information. In this work, we have explored the advantage of using transient-based exponential fitting coefficients to enhance the discrimination of similar compounds. The advantages of such feature analysis to discriminate each compound is evaluated using principle component analysis (PCA). In addition, machine learning-based classification algorithms were used to compare the prediction accuracies when using fitting coefficients as features. The additional features greatly enhanced the discrimination between compounds while performing PCA and also improved the prediction accuracy by 34% when using linear discrimination analysis.
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EXTRACTING PRINCIPLE COMPONENTS FOR DISCRIMINANT ANALYSIS OF FMRI IMAGES
Liu, Jingyu; Xu, Lai; Caprihan, Arvind; Calhoun, Vince D.
2009-01-01
This paper presents an approach for selecting optimal components for discriminant analysis. Such an approach is useful when further detailed analyses for discrimination or characterization requires dimensionality reduction. Our approach can accommodate a categorical variable such as diagnosis (e.g. schizophrenic patient or healthy control), or a continuous variable like severity of the disorder. This information is utilized as a reference for measuring a component’s discriminant power after principle component decomposition. After sorting each component according to its discriminant power, we extract the best components for discriminant analysis. An application of our reference selection approach is shown using a functional magnetic resonance imaging data set in which the sample size is much less than the dimensionality. The results show that the reference selection approach provides an improved discriminant component set as compared to other approaches. Our approach is general and provides a solid foundation for further discrimination and classification studies. PMID:20582334
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EXTRACTING PRINCIPLE COMPONENTS FOR DISCRIMINANT ANALYSIS OF FMRI IMAGES.
Liu, Jingyu; Xu, Lai; Caprihan, Arvind; Calhoun, Vince D
2008-05-12
This paper presents an approach for selecting optimal components for discriminant analysis. Such an approach is useful when further detailed analyses for discrimination or characterization requires dimensionality reduction. Our approach can accommodate a categorical variable such as diagnosis (e.g. schizophrenic patient or healthy control), or a continuous variable like severity of the disorder. This information is utilized as a reference for measuring a component's discriminant power after principle component decomposition. After sorting each component according to its discriminant power, we extract the best components for discriminant analysis. An application of our reference selection approach is shown using a functional magnetic resonance imaging data set in which the sample size is much less than the dimensionality. The results show that the reference selection approach provides an improved discriminant component set as compared to other approaches. Our approach is general and provides a solid foundation for further discrimination and classification studies.
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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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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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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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Analysis of laser printer and photocopier toners by spectral properties and chemometrics
NASA Astrophysics Data System (ADS)
Verma, Neha; Kumar, Raj; Sharma, Vishal
2018-05-01
The use of printers to generate falsified documents has become a common practice in today's world. The examination and identification of the printed matter in the suspected documents (civil or criminal cases) may provide important information about the authenticity of the document. In the present study, a total number of 100 black toner samples both from laser printers and photocopiers were examined using diffuse reflectance UV-Vis Spectroscopy. The present research is divided into two parts; visual discrimination and discrimination by using multivariate analysis. A comparison between qualitative and quantitative analysis showed that multivariate analysis (Principal component analysis) provides 99.59%pair-wise discriminating power for laser printer toners while 99.84% pair-wise discriminating power for photocopier toners. The overall results obtained confirm the applicability of UV-Vis spectroscopy and chemometrics, in the nondestructive analysis of toner printed documents while enhancing their evidential value for forensic applications.
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De Luca, Michele; Restuccia, Donatella; Clodoveo, Maria Lisa; Puoci, Francesco; Ragno, Gaetano
2016-07-01
Chemometric discrimination of extra virgin olive oils (EVOO) from whole and stoned olive pastes was carried out by using Fourier transform infrared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach. Four Italian commercial EVOO brands, all in both whole and stoned version, were considered in this study. The adopted chemometric methodologies were able to describe the different chemical features in phenolic and volatile compounds contained in the two types of oil by using unspecific IR spectral information. Principal component analysis (PCA) was employed in cluster analysis to capture data patterns and to highlight differences between technological processes and EVOO brands. The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extraction procedures. Discriminant analysis was extended to the evaluation of possible adulteration by addition of aliquots of oil from whole paste to the most valuable oil from stoned olives. The statistical parameters from external validation of all the PLS models were very satisfactory, with low root mean square error of prediction (RMSEP) and relative error (RE%). Copyright © 2016 Elsevier Ltd. All rights reserved.
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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)
Aidi, Muhammad Nur; Sari, Resty Indah
2012-05-01
A decision of credit that given by bank or another creditur must have a risk and it called credit risk. Credit risk is an investor's risk of loss arising from a borrower who does not make payments as promised. The substantial of credit risk can lead to losses for the banks and the debtor. To minimize this problem need a further study to identify a potential new customer before the decision given. Identification of debtor can using various approaches analysis, one of them is by using discriminant analysis. Discriminant analysis in this study are used to classify whether belonging to the debtor's good credit or bad credit. The result of this study are two discriminant functions that can identify new debtor. Before step built the discriminant function, selection of explanatory variables should be done. Purpose of selection independent variable is to choose the variable that can discriminate the group maximally. Selection variables in this study using different test, for categoric variable selection of variable using proportion chi-square test, and stepwise discriminant for numeric variable. The result of this study are two discriminant functions that can identify new debtor. The selected variables that can discriminating two groups of debtor maximally are status of existing checking account, credit history, credit amount, installment rate in percentage of disposable income, sex, age in year, other installment plans, and number of people being liable to provide maintenance. This classification produce a classification accuracy rate is good enough, that is equal to 74,70%. Debtor classification using discriminant analysis has risk level that is small enough, and it ranged beetwen 14,992% and 17,608%. Based on that credit risk rate, using discriminant analysis on the classification of credit status can be used effectively.
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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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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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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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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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Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis
ERIC Educational Resources Information Center
Jarrell, Stephen B.; Stanley, T. D.
2004-01-01
The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.
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From Equal to Equivalent Pay: Salary Discrimination in Academia
ERIC Educational Resources Information Center
Greenfield, Ester
1977-01-01
Examines the federal statutes barring sex discrimination in employment and argues that the work of any two professors is comparable but not equal. Suggests using regression analysis to prove salary discrimination and discusses the legal justification for adopting regression analysis and the standard of comparable pay for comparable work.…
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Perceived Discrimination and Health: A Meta-Analytic Review
ERIC Educational Resources Information Center
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…
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Laursen, K H; Mihailova, A; Kelly, S D; Epov, V N; Bérail, S; Schjoerring, J K; Donard, O F X; Larsen, E H; Pedentchouk, N; Marca-Bell, A D; Halekoh, U; Olesen, J E; Husted, S
2013-12-01
Novel procedures for analytical authentication of organic plant products are urgently needed. Here we present the first study encompassing stable isotopes of hydrogen, carbon, nitrogen, oxygen, magnesium and sulphur as well as compound-specific nitrogen and oxygen isotope analysis of nitrate for discrimination of organically and conventionally grown plants. The study was based on wheat, barley, faba bean and potato produced in rigorously controlled long-term field trials comprising 144 experimental plots. Nitrogen isotope analysis revealed the use of animal manure, but was unable to discriminate between plants that were fertilised with synthetic nitrogen fertilisers or green manures from atmospheric nitrogen fixing legumes. This limitation was bypassed using oxygen isotope analysis of nitrate in potato tubers, while hydrogen isotope analysis allowed complete discrimination of organic and conventional wheat and barley grains. It is concluded, that multi-isotopic analysis has the potential to disclose fraudulent substitutions of organic with conventionally cultivated plants. Copyright © 2013 Elsevier Ltd. All rights reserved.
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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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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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Discrimination between smiling faces: Human observers vs. automated face analysis.
Del Líbano, Mario; Calvo, Manuel G; Fernández-Martín, Andrés; Recio, Guillermo
2018-05-11
This study investigated (a) how prototypical happy faces (with happy eyes and a smile) can be discriminated from blended expressions with a smile but non-happy eyes, depending on type and intensity of the eye expression; and (b) how smile discrimination differs for human perceivers versus automated face analysis, depending on affective valence and morphological facial features. Human observers categorized faces as happy or non-happy, or rated their valence. Automated analysis (FACET software) computed seven expressions (including joy/happiness) and 20 facial action units (AUs). Physical properties (low-level image statistics and visual saliency) of the face stimuli were controlled. Results revealed, first, that some blended expressions (especially, with angry eyes) had lower discrimination thresholds (i.e., they were identified as "non-happy" at lower non-happy eye intensities) than others (especially, with neutral eyes). Second, discrimination sensitivity was better for human perceivers than for automated FACET analysis. As an additional finding, affective valence predicted human discrimination performance, whereas morphological AUs predicted FACET discrimination. FACET can be a valid tool for categorizing prototypical expressions, but is currently more limited than human observers for discrimination of blended expressions. Configural processing facilitates detection of in/congruence(s) across regions, and thus detection of non-genuine smiling faces (due to non-happy eyes). Copyright © 2018 Elsevier B.V. All rights reserved.
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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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Richard. D. Wood-Smith; John M. Buffington
1996-01-01
Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...
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Principal Component Clustering Approach to Teaching Quality Discriminant Analysis
ERIC Educational Resources Information Center
Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan
2016-01-01
Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…
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The Coopersmith Self-Esteem Inventory in an Adult Sample.
ERIC Educational Resources Information Center
Noller, Patricia; Shugm, David
1988-01-01
The reliability and validity of the Self-Esteem Inventory developed by S. C. Coopersmith (1975) were evaluated via item-total correlation, discriminant analysis, factor analysis, and analysis of variance of data for 352 Australian adults. The instrument had high internal consistency and discriminated well between subjects with high and low…
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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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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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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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Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria
2015-02-15
Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination. Copyright © 2014 Elsevier Ltd. All rights reserved.
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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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Kwon, Yong-Kook; Ahn, Myung Suk; Park, Jong Suk; Liu, Jang Ryol; In, Dong Su; Min, Byung Whan; Kim, Suk Weon
2013-01-01
To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng. PMID:24558311
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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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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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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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Discriminant Analysis of Student Loan Applications
ERIC Educational Resources Information Center
Dyl, Edward A.; McGann, Anthony F.
1977-01-01
The use of discriminant analysis in identifying potentially "good" versus potentially "bad" student loans is explained. The technique is applied to a sample of 200 student loan applications at the University of Wyoming. (LBH)
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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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Ro, Annie E; Choi, Kyung-Hee
2009-01-01
The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in North California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design.
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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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ERIC Educational Resources Information Center
Ahrens, Steve
Predictor variables that could be used effectively to place entering freshmen methematics students into courses of instruction in mathematics were investigated at West Virginia University. Multiple discriminant analysis was used with nearly 6,000 student records collected over a three-year period, and a series of predictive equations were…
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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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Smith, Andrew M; Wells, Gary L; Lindsay, R C L; Penrod, Steven D
2017-04-01
Receiver Operating Characteristic (ROC) analysis has recently come in vogue for assessing the underlying discriminability and the applied utility of lineup procedures. Two primary assumptions underlie recommendations that ROC analysis be used to assess the applied utility of lineup procedures: (a) ROC analysis of lineups measures underlying discriminability, and (b) the procedure that produces superior underlying discriminability produces superior applied utility. These same assumptions underlie a recently derived diagnostic-feature detection theory, a theory of discriminability, intended to explain recent patterns observed in ROC comparisons of lineups. We demonstrate, however, that these assumptions are incorrect when ROC analysis is applied to lineups. We also demonstrate that a structural phenomenon of lineups, differential filler siphoning, and not the psychological phenomenon of diagnostic-feature detection, explains why lineups are superior to showups and why fair lineups are superior to biased lineups. In the process of our proofs, we show that computational simulations have assumed, unrealistically, that all witnesses share exactly the same decision criteria. When criterial variance is included in computational models, differential filler siphoning emerges. The result proves dissociation between ROC curves and underlying discriminability: Higher ROC curves for lineups than for showups and for fair than for biased lineups despite no increase in underlying discriminability. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
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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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NASA Astrophysics Data System (ADS)
Hahn, Federico
1996-03-01
Statistical discriminative analysis and neural networks were used to prove that crop/weed/soil discrimination by optical reflectance was feasible. The wavelengths selected as inputs on those neural networks were ten nanometers width, reducing the total collected radiation for the sensor. Spectral data collected from several farms having different weed populations were introduced to discriminant analysis. The best discriminant wavelengths were used to build a wavelength histogram which selected the three best spectral broadbands for broccoli/weed/soil discrimination. The broadbands were analyzed using a new single broadband discriminator index named the discriminative integration index, DII, and the DII values obtained were used to train a neural network. This paper introduces the index concept, its results and its use for minimizing artificial lightning requirements with broadband spectral measurements for broccoli/weed/soil discrimination.
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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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Finn, James E.; Burger, Carl V.; Holland-Bartels, Leslie E.
1997-01-01
We used otolith banding patterns formed during incubation to discriminate among hatchery- and wild-incubated fry of sockeye salmon Oncorhynchus nerka from Tustumena Lake, Alaska. Fourier analysis of otolith luminance profiles was used to describe banding patterns: the amplitudes of individual Fourier harmonics were discriminant variables. Correct classification of otoliths to either hatchery or wild origin was 83.1% (cross-validation) and 72.7% (test data) with the use of quadratic discriminant function analysts on 10 Fourier amplitudes. Overall classification rates among the six test groups (one hatchery and five wild groups) were 46.5% (cross-validation) and 39.3% (test data) with the use of linear discriminant function analysis on 16 Fourier amplitudes. Although classification rates for wild-incubated fry from any one site never exceeded 67% (cross-validation) or 60% (test data), location-specific information was evident for all groups because the probability of classifying an individual to its true incubation location was significantly greater than chance. Results indicate phenotypic differences in otolith microstructure among incubation sites separated by less than 10 km. Analysis of otolith luminance profiles is a potentially useful technique for discriminating among and between various populations of hatchery and wild fish.
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NASA Technical Reports Server (NTRS)
Ballew, G.
1977-01-01
The ability of Landsat multispectral digital data to differentiate among 62 combinations of rock and alteration types at the Goldfield mining district of Western Nevada was investigated by using statistical techniques of cluster and discriminant analysis. Multivariate discriminant analysis was not effective in classifying each of the 62 groups, with classification results essentially the same whether data of four channels alone or combined with six ratios of channels were used. Bivariate plots of group means revealed a cluster of three groups including mill tailings, basalt and all other rock and alteration types. Automatic hierarchical clustering based on the fourth dimensional Mahalanobis distance between group means of 30 groups having five or more samples was performed. The results of the cluster analysis revealed hierarchies of mill tailings vs. natural materials, basalt vs. non-basalt, highly reflectant rocks vs. other rocks and exclusively unaltered rocks vs. predominantly altered rocks. The hierarchies were used to determine the order in which sets of multiple discriminant analyses were to be performed and the resulting discriminant functions were used to produce a map of geology and alteration which has an overall accuracy of 70 percent for discriminating exclusively altered rocks from predominantly altered rocks.
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Discrimination surfaces with application to region-specific brain asymmetry analysis.
Martos, Gabriel; de Carvalho, Miguel
2018-05-20
Discrimination surfaces are here introduced as a diagnostic tool for localizing brain regions where discrimination between diseased and nondiseased participants is higher. To estimate discrimination surfaces, we introduce a Mann-Whitney type of statistic for random fields and present large-sample results characterizing its asymptotic behavior. Simulation results demonstrate that our estimator accurately recovers the true surface and corresponding interval of maximal discrimination. The empirical analysis suggests that in the anterior region of the brain, schizophrenic patients tend to present lower local asymmetry scores in comparison with participants in the control group. Copyright © 2018 John Wiley & Sons, Ltd.
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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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ERIC Educational Resources Information Center
Lee, Barbara A.
1990-01-01
Questions assumptions by Schoenfeld and Zirkel in a study reviewing gender discrimination cases against institutions of higher education. Critiques the methodology used in that study, cautions about the overall utility of "outcomes analysis," and reports more promising routes of empirical legal research. (15 references) (MLF)
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Fruehwald-Pallamar, J; Hesselink, J R; Mafee, M F; Holzer-Fruehwald, L; Czerny, C; Mayerhoefer, M E
2016-02-01
To evaluate whether texture-based analysis of standard MRI sequences can help in the discrimination between benign and malignant head and neck tumors. The MR images of 100 patients with a histologically clarified head or neck mass, from two different institutions, were analyzed. Texture-based analysis was performed using texture analysis software, with region of interest measurements for 2 D and 3 D evaluation independently for all axial sequences. COC, RUN, GRA, ARM, and WAV features were calculated for all ROIs. 10 texture feature subsets were used for a linear discriminant analysis, in combination with k-nearest-neighbor classification. Benign and malignant tumors were compared with regard to texture-based values. There were differences in the images from different field-strength scanners, as well as from different vendors. For the differentiation of benign and malignant tumors, we found differences on STIR and T2-weighted images for 2 D, and on contrast-enhanced T1-TSE with fat saturation for 3 D evaluation. In a separate analysis of the subgroups 1.5 and 3 Tesla, more discriminating features were found. Texture-based analysis is a useful tool in the discrimination of benign and malignant tumors when performed on one scanner with the same protocol. We cannot recommend this technique for the use of multicenter studies with clinical data. 2 D/3 D texture-based analysis can be performed in head and neck tumors. Texture-based analysis can differentiate between benign and malignant masses. Analyzed MR images should originate from one scanner with an identical protocol. © Georg Thieme Verlag KG Stuttgart · New York.
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Item validity vs. item discrimination index: a redundancy?
NASA Astrophysics Data System (ADS)
Panjaitan, R. L.; Irawati, R.; Sujana, A.; Hanifah, N.; Djuanda, D.
2018-03-01
In several literatures about evaluation and test analysis, it is common to find that there are calculations of item validity as well as item discrimination index (D) with different formula for each. Meanwhile, other resources said that item discrimination index could be obtained by calculating the correlation between the testee’s score in a particular item and the testee’s score on the overall test, which is actually the same concept as item validity. Some research reports, especially undergraduate theses tend to include both item validity and item discrimination index in the instrument analysis. It seems that these concepts might overlap for both reflect the test quality on measuring the examinees’ ability. In this paper, examples of some results of data processing on item validity and item discrimination index were compared. It would be discussed whether item validity and item discrimination index can be represented by one of them only or it should be better to present both calculations for simple test analysis, especially in undergraduate theses where test analyses were included.
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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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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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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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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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2007-03-01
state failure, and Discriminant Analysis to classify states as Stable, Borderline, or Failing based on these indicators. Furthermore, each...nation’s discriminant function scores are used to determine their degree of instability. The methodology is applied to 200 countries for which open source...and go for a long walk. Finally, to my wonderful wife, who now knows more about Discriminant Analysis than any Legal Assistant on the planet, thank
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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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Ro, Annie E.; Choi, Kyung-Hee
2009-01-01
The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in Northern California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design. PMID:19485231
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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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Tian, Yunfei; Wu, Peng; Wu, Xi; Jiang, Xiaoming; Xu, Kailai; Hou, Xiandeng
2013-04-21
A simple and economical multi-channel optical sensor using corona discharge radical emission spectroscopy is developed and explored as an optical nose for discrimination analysis of volatile organic compounds, wines, and even isomers.
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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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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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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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Shen, Fei; Wu, Jian; Ying, Yibin; Li, Bobin; Jiang, Tao
2013-12-15
Discrimination of Chinese rice wines from three well-known wineries ("Guyuelongshan", "Kuaijishan", and "Pagoda") in China has been carried out according to mineral element contents in this study. Nineteen macro and trace mineral elements (Na, Mg, Al, K, Ca, Mn, Fe, Cu, Zn, V, Cr, Co, Ni, As, Se, Mo, Cd, Ba and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in 117 samples. Then the experimental data were subjected to analysis of variance (ANOVA) and principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Stepwise linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA) were applied to develop classification models and achieved correct classified rates of 100% and 97.4% for the prediction sample set, respectively. The discrimination could be attributed to different raw materials (mainly water) and elaboration processes employed. The results indicate that the element compositions combined with multivariate analysis can be used as fingerprinting techniques to protect prestigious wineries and enable the authenticity of Chinese rice wine. Copyright © 2013 Elsevier Ltd. All rights reserved.
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NASA Technical Reports Server (NTRS)
Ballew, G.
1977-01-01
The ability of Landsat multispectral digital data to differentiate among 62 combinations of rock and alteration types at the Goldfield mining district of Western Nevada was investigated by using statistical techniques of cluster and discriminant analysis. Multivariate discriminant analysis was not effective in classifying each of the 62 groups, with classification results essentially the same whether data of four channels alone or combined with six ratios of channels were used. Bivariate plots of group means revealed a cluster of three groups including mill tailings, basalt and all other rock and alteration types. Automatic hierarchical clustering based on the fourth dimensional Mahalanobis distance between group means of 30 groups having five or more samples was performed using Johnson's HICLUS program. The results of the cluster analysis revealed hierarchies of mill tailings vs. natural materials, basalt vs. non-basalt, highly reflectant rocks vs. other rocks and exclusively unaltered rocks vs. predominantly altered rocks. The hierarchies were used to determine the order in which sets of multiple discriminant analyses were to be performed and the resulting discriminant functions were used to produce a map of geology and alteration which has an overall accuracy of 70 percent for discriminating exclusively altered rocks from predominantly altered rocks.
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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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Multari, Rosalie A.; Cremers, David A.; Bostian, Melissa L.; Dupre, Joanne M.
2013-01-01
Laser-Induced Breakdown Spectroscopy (LIBS) is a rapid, in situ, diagnostic technique in which light emissions from a laser plasma formed on the sample are used for analysis allowing automated analysis results to be available in seconds to minutes. This speed of analysis coupled with little or no sample preparation makes LIBS an attractive detection tool. In this study, it is demonstrated that LIBS can be utilized to discriminate both the bacterial species and strains of bacterial colonies grown on blood agar. A discrimination algorithm was created based on multivariate regression analysis of spectral data. The algorithm was deployed on a simulated LIBS instrument system to demonstrate discrimination capability using 6 species. Genetically altered Staphylococcus aureus strains grown on BA, including isogenic sets that differed only by the acquisition of mutations that increase fusidic acid or vancomycin resistance, were also discriminated. The algorithm successfully identified all thirteen cultures used in this study in a time period of 2 minutes. This work provides proof of principle for a LIBS instrumentation system that could be developed for the rapid discrimination of bacterial species and strains demonstrating relatively minor genomic alterations using data collected directly from pathogen isolation media. PMID:24109513
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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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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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Stemp, W James; Chung, Steven
2011-01-01
This pilot study tests the reliability of laser scanning confocal microscopy (LSCM) to quantitatively measure wear on experimental obsidian tools. To our knowledge, this is the first use of confocal microscopy to study wear on stone flakes made from an amorphous silicate like obsidian. Three-dimensional surface roughness or texture area scans on three obsidian flakes used on different contact materials (hide, shell, wood) were documented using the LSCM to determine whether the worn surfaces could be discriminated using area-scale analysis, specifically relative area (RelA). When coupled with the F-test, this scale-sensitive fractal analysis could not only discriminate the used from unused surfaces on individual tools, but was also capable of discriminating the wear histories of tools used on different contact materials. Results indicate that such discriminations occur at different scales. Confidence levels for the discriminations at different scales were established using the F-test (mean square ratios or MSRs). In instances where discrimination of surface roughness or texture was not possible above the established confidence level based on MSRs, photomicrographs and RelA assisted in hypothesizing why this was so. Copyright © 2011 Wiley Periodicals, Inc.
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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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Neural mechanisms of coarse-to-fine discrimination in the visual cortex.
Purushothaman, Gopathy; Chen, Xin; Yampolsky, Dmitry; Casagrande, Vivien A
2014-12-01
Vision is a dynamic process that refines the spatial scale of analysis over time, as evidenced by a progressive improvement in the ability to detect and discriminate finer details. To understand coarse-to-fine discrimination, we studied the dynamics of spatial frequency (SF) response using reverse correlation in the primary visual cortex (V1) of the primate. In a majority of V1 cells studied, preferred SF either increased monotonically with time (group 1) or changed nonmonotonically, with an initial increase followed by a decrease (group 2). Monotonic shift in preferred SF occurred with or without an early suppression at low SFs. Late suppression at high SFs always accompanied nonmonotonic SF dynamics. Bayesian analysis showed that SF discrimination performance and best discriminable SF frequencies changed with time in different ways in the two groups of neurons. In group 1 neurons, SF discrimination performance peaked on both left and right flanks of the SF tuning curve at about the same time. In group 2 neurons, peak discrimination occurred on the right flank (high SFs) later than on the left flank (low SFs). Group 2 neurons were also better discriminators of high SFs. We examined the relationship between the time at which SF discrimination performance peaked on either flank of the SF tuning curve and the corresponding best discriminable SFs in both neuronal groups. This analysis showed that the population best discriminable SF increased with time in V1. These results suggest neural mechanisms for coarse-to-fine discrimination behavior and that this process originates in V1 or earlier. Copyright © 2014 the American Physiological Society.
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Ranking procedure for partial discriminant analysis
DOE Office of Scientific and Technical Information (OSTI.GOV)
Beckman, R.J.; Johnson, M.E.
1981-09-01
A rank procedure developed by Broffitt, Randles, and Hogg (1976) is modified to control the conditional probability of misclassification given that classification has been attempted. This modification leads to a useful solution to the two-population partial discriminant analysis problem for even moderately sized training sets.
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Feature extraction with deep neural networks by a generalized discriminant analysis.
Stuhlsatz, André; Lippel, Jens; Zielke, Thomas
2012-04-01
We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.
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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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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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Evaluation of FTIR spectroscopy as diagnostic tool for colorectal cancer using spectral analysis
NASA Astrophysics Data System (ADS)
Dong, Liu; Sun, Xuejun; Chao, Zhang; Zhang, Shiyun; Zheng, Jianbao; Gurung, Rajendra; Du, Junkai; Shi, Jingsen; Xu, Yizhuang; Zhang, Yuanfu; Wu, Jinguang
2014-03-01
The aim of this study is to confirm FTIR spectroscopy as a diagnostic tool for colorectal cancer. 180 freshly removed colorectal samples were collected from 90 patients for spectrum analysis. The ratios of spectral intensity and relative intensity (/I1460) were calculated. Principal component analysis (PCA) and Fisher's discriminant analysis (FDA) were applied to distinguish the malignant from normal. The FTIR parameters of colorectal cancer and normal tissues were distinguished due to the contents or configurations of nucleic acids, proteins, lipids and carbohydrates. Related to nitrogen containing, water, protein and nucleic acid were increased significantly in the malignant group. Six parameters were selected as independent factors to perform discriminant functions. The sensitivity for FTIR in diagnosing colorectal cancer was 96.6% by discriminant analysis. Our study demonstrates that FTIR can be a useful technique for detection of colorectal cancer and may be applied in clinical colorectal cancer diagnosis.
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Alkarkhi, Abbas F M; Ramli, Saifullah Bin; Easa, Azhar Mat
2009-01-01
Major (sodium, potassium, calcium, magnesium) and minor elements (iron, copper, zinc, manganese) and one heavy metal (lead) of Cavendish banana flour and Dream banana flour were determined, and data were analyzed using multivariate statistical techniques of factor analysis and discriminant analysis. Factor analysis yielded four factors explaining more than 81% of the total variance: the first factor explained 28.73%, comprising magnesium, sodium, and iron; the second factor explained 21.47%, comprising only manganese and copper; the third factor explained 15.66%, comprising zinc and lead; while the fourth factor explained 15.50%, comprising potassium. Discriminant analysis showed that magnesium and sodium exhibited a strong contribution in discriminating the two types of banana flour, affording 100% correct assignation. This study presents the usefulness of multivariate statistical techniques for analysis and interpretation of complex mineral content data from banana flour of different varieties.
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Tahir, Haroon Elrasheid; Xiaobo, Zou; Xiaowei, Huang; Jiyong, Shi; Mariod, Abdalbasit Adam
2016-09-01
Aroma profiles of six honey varieties of different botanical origins were investigated using colorimetric sensor array, gas chromatography-mass spectrometry (GC-MS) and descriptive sensory analysis. Fifty-eight aroma compounds were identified, including 2 norisoprenoids, 5 hydrocarbons, 4 terpenes, 6 phenols, 7 ketones, 9 acids, 12 aldehydes and 13 alcohols. Twenty abundant or active compounds were chosen as key compounds to characterize honey aroma. Discrimination of the honeys was subsequently implemented using multivariate analysis, including hierarchical clustering analysis (HCA) and principal component analysis (PCA). Honeys of the same botanical origin were grouped together in the PCA score plot and HCA dendrogram. SPME-GC/MS and colorimetric sensor array were able to discriminate the honeys effectively with the advantages of being rapid, simple and low-cost. Moreover, partial least squares regression (PLSR) was applied to indicate the relationship between sensory descriptors and aroma compounds. Copyright © 2016 Elsevier Ltd. All rights reserved.
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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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Ganga, G M D; Esposto, K F; Braatz, D
2012-01-01
The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.
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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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NASA Technical Reports Server (NTRS)
Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.
1984-01-01
An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.
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Ajayi, Alex A; Syed, Moin
2014-10-01
This study used a person-oriented analytic approach to identify meaningful patterns of barriers-focused racial socialization and perceived racial discrimination experiences in a sample of 295 late adolescents. Using cluster analysis, three distinct groups were identified: Low Barrier Socialization-Low Discrimination, High Barrier Socialization-Low Discrimination, and High Barrier Socialization-High Discrimination clusters. These groups were substantively unique in terms of the frequency of racial socialization messages about bias preparation and out-group mistrust its members received and their actual perceived discrimination experiences. Further, individuals in the High Barrier Socialization-High Discrimination cluster reported significantly higher depressive symptoms than those in the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. However, no differences in adjustment were observed between the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. Overall, the findings highlight important individual differences in how young people of color experience their race and how these differences have significant implications on psychological adjustment. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.
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The Categorical Perception Deficit in Dyslexia: A Meta-Analysis
ERIC Educational Resources Information Center
Noordenbos, Mark W.; Serniclaes, Willy
2015-01-01
Speech perception in dyslexia is characterized by a categorical perception (CP) deficit, demonstrated by weaker discrimination of acoustic differences between phonemic categories in conjunction with better discrimination of acoustic differences within phonemic categories. We performed a meta-analysis of studies that examined the reliability of the…
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NASA Astrophysics Data System (ADS)
Diedrich, Jonathan; Rehse, Steven J.; Palchaudhuri, Sunil
2007-04-01
Three strains of Escherichia coli, one strain of environmental mold, and one strain of Candida albicans yeast have been analyzed by laser-induced breakdown spectroscopy using nanosecond laser pulses. All microorganisms were analyzed while still alive and with no sample preparation. Nineteen atomic and ionic emission lines have been identified in the spectrum, which is dominated by calcium, magnesium, and sodium. A discriminant function analysis has been used to discriminate between the biotypes and E. coli strains. This analysis showed efficient discrimination between laser-induced breakdown spectroscopy spectra from different strains of a single bacteria species.
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Gender Wage Inequality and Economic Growth: Is There Really a Puzzle?-A Comment.
Schober, Thomas; Winter-Ebmer, Rudolf
2011-08-01
Seguino (2000) shows that gender wage discrimination in export-oriented semi-industrialized countries might be fostering investment and growth in general. While the original analysis does not have internationally comparable wage discrimination data, we replicate the analysis using data from a meta-study on gender wage discrimination and do not find any evidence that more discrimination might further economic growth-on the contrary: if anything the impact of gender inequality is negative for growth. Standing up for more gender equality-also in terms of wages-is good for equity considerations and at least not negative for growth.
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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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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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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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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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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)
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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Some observations on the use of discriminant analysis in ecology
Williams, B.K.
1983-01-01
The application of discriminant analysis in ecological investigations is discussed. The appropriate statistical assumptions for discriminant analysis are illustrated, and both classification and group separation approaches are outlined. Three assumptions that are crucial in ecological studies are discussed at length, and the consequences of their violation are developed. These assumptions are: equality of dispersions, identifiability of prior probabilities, and precise and accurate estimation of means and dispersions. The use of discriminant functions for purposes of interpreting ecological relationships is also discussed. It is suggested that the common practice of imputing ecological 'meaning' to the signs and magnitudes of coefficients be replaced by an assessment of 'structure coefficients.' Finally, the potential and limitations of representation of data in canonical space are considered, and some cautionary points are made concerning ecological interpretation of patterns in canonical space.
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Assessment of craniometric traits in South Indian dry skulls for sex determination.
Ramamoorthy, Balakrishnan; Pai, Mangala M; Prabhu, Latha V; Muralimanju, B V; Rai, Rajalakshmi
2016-01-01
The skeleton plays an important role in sex determination in forensic anthropology. The skull bone is considered as the second best after the pelvic bone in sex determination due to its better retention of morphological features. Different populations have varying skeletal characteristics, making population specific analysis for sex determination essential. Hence the objective of this investigation is to obtain the accuracy of sex determination using cranial parameters of adult skulls to the highest percentage in South Indian population and to provide a baseline data for sex determination in South India. Seventy adult preserved human skulls were taken and based on the morphological traits were classified into 43 male skulls and 27 female skulls. A total of 26 craniometric parameters were studied. The data were analyzed by using the SPSS discriminant function. The analysis of stepwise, multivariate, and univariate discriminant function gave an accuracy of 77.1%, 85.7%, and 72.9% respectively. Multivariate direct discriminant function analysis classified skull bones into male and female with highest levels of accuracy. Using stepwise discriminant function analysis, the most dimorphic variable to determine sex of the skull, was biauricular breadth followed by weight. Subjecting the best dimorphic variables to univariate discriminant analysis, high levels of accuracy of sexual dimorphism was obtained. Percentage classification of high accuracies were obtained in this study indicating high level of sexual dimorphism in the crania, setting specific discriminant equations for the gender determination in South Indian people. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.
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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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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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Stigma in the mental health workplace: perceptions of peer employees and clinicians.
Stromwall, Layne K; Holley, Lynn C; Bashor, Kathy E
2011-08-01
Informed by a structural theory of workplace discrimination, mental health system employees' perceptions of mental health workplace stigma and discrimination against service recipients and peer employees were investigated. Fifty-one peer employees and 52 licensed behavioral health clinicians participated in an online survey. Independent variables were employee status (peer or clinician), gender, ethnicity, years of mental health employment, age, and workplace social inclusion of peer employees. Analysis of covariance on workplace discrimination against service recipients revealed that peer employees perceived more discrimination than clinicians and whites perceived more discrimination than employees of color (corrected model F = 9.743 [16, 87], P = .000, partial ŋ (2) = .644). Analysis of covariance on workplace discrimination against peer employees revealed that peer employees perceived more discrimination than clinicians (F = 4.593, [6, 97], P = .000, partial ŋ (2) = .223).
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Sequential Ideal-Observer Analysis of Visual Discriminations.
ERIC Educational Resources Information Center
Geisler, Wilson S.
1989-01-01
A new analysis, based on the concept of the ideal observer in signal detection theory, is described. It allows: tracing of the flow of discrimination information through the initial physiological stages of visual processing for arbitrary spatio-chromatic stimuli, and measurement of the information content of said visual stimuli. (TJH)
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Prediction of Recidivism in Juvenile Offenders Based on Discriminant Analysis.
ERIC Educational Resources Information Center
Proefrock, David W.
The recent development of strong statistical techniques has made accurate predictions of recidivism possible. To investigate the utility of discriminant analysis methodology in making predictions of recidivism in juvenile offenders, the court records of 271 male and female juvenile offenders, aged 12-16, were reviewed. A cross validation group…
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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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Keshtkaran, Mohammad Reza; Yang, Zhi
2017-06-01
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.
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NASA Astrophysics Data System (ADS)
Keshtkaran, Mohammad Reza; Yang, Zhi
2017-06-01
Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.
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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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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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Dackehag, Margareta; Gerdtham, Ulf-G; Nordin, Martin
2015-07-01
This article investigates the excess-weight penalty in income for men and women in the Swedish labor market, using longitudinal data. It compares two identification strategies, OLS and individual fixed effects, and distinguishes between two main sources of excess-weight penalties, lower productivity because of bad health and discrimination. For men, the analysis finds a significant obesity penalty related to discrimination when applying individual fixed effects. We do not find any significant excess-weight penalty for women.
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Harassment and discrimination in medical training: a systematic review and meta-analysis.
Fnais, Naif; Soobiah, Charlene; Chen, Maggie Hong; Lillie, Erin; Perrier, Laure; Tashkhandi, Mariam; Straus, Sharon E; Mamdani, Muhammad; Al-Omran, Mohammed; Tricco, Andrea C
2014-05-01
Harassment and discrimination include a wide range of behaviors that medical trainees perceive as being humiliating, hostile, or abusive. To understand the significance of such mistreatment and to explore potential preventive strategies, the authors conducted a systematic review and meta-analysis to examine the prevalence, risk factors, and sources of harassment and discrimination among medical trainees. In 2011, the authors identified relevant studies by searching MEDLINE and EMBASE, scanning reference lists of relevant studies, and contacting experts. They included studies that reported the prevalence, risk factors, and sources of harassment and discrimination among medical trainees. Two reviewers independently screened all articles and abstracted study and participant characteristics and study results. The authors assessed the methodological quality in individual studies using the Newcastle-Ottawa Scale. They also conducted a meta-analysis. The authors included 57 cross-sectional and 2 cohort studies in their review. The meta-analysis of 51 studies demonstrated that 59.4% of medical trainees had experienced at least one form of harassment or discrimination during their training (95% confidence interval [CI]: 52.0%-66.7%). Verbal harassment was the most commonly cited form of harassment (prevalence: 63.0%; 95% CI: 54.8%-71.2%). Consultants were the most commonly cited source of harassment and discrimination, followed by patients or patients' families (34.4% and 21.9%, respectively). This review demonstrates the surprisingly high prevalence of harassment and discrimination among medical trainees that has not declined over time. The authors recommend both drafting policies and promoting cultural change within academic institutions to prevent future abuse.
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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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Comparative decision models for anticipating shortage of food grain production in India
NASA Astrophysics Data System (ADS)
Chattopadhyay, Manojit; Mitra, Subrata Kumar
2018-01-01
This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage ( true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability.
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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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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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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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Britt-Spells, Angelitta M.; Slebodnik, Maribeth; Sands, Laura P.; Rollock, David
2016-01-01
Research reports that perceived discrimination is positively associated with depressive symptoms. The literature is limited when examining this relationship among Black men. This meta-analysis systematically examines the current literature and investigates the relationship of perceived discrimination on depressive symptoms among Black men residing in the United States. Using a random-effects model, study findings indicate a positive association between perceived discrimination and depressive symptoms among Black men (r = .29). Several potential moderators were also examined in this study; however, there were no significant moderation effects detected. Recommendations and implications for future research and practice are discussed. PMID:26742988
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Gender Wage Inequality and Economic Growth: Is There Really a Puzzle?—A Comment
Schober, Thomas; Winter-Ebmer, Rudolf
2011-01-01
Summary Seguino (2000) shows that gender wage discrimination in export-oriented semi-industrialized countries might be fostering investment and growth in general. While the original analysis does not have internationally comparable wage discrimination data, we replicate the analysis using data from a meta-study on gender wage discrimination and do not find any evidence that more discrimination might further economic growth—on the contrary: if anything the impact of gender inequality is negative for growth. Standing up for more gender equality—also in terms of wages—is good for equity considerations and at least not negative for growth. PMID:21857765
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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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ERIC Educational Resources Information Center
Myers, Greeley; Siera, Steven
1980-01-01
Default on guaranteed student loans has been increasing. The use of discriminant analysis as a technique to identify "good" v "bad" student loans based on information available from the loan application is discussed. Research to test the ability of models to such predictions is reported. (Author/MLW)
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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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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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The Economic Status of Migrant Wives: An Application of Discriminant Analysis.
ERIC Educational Resources Information Center
Rytina, Nancy F.
1981-01-01
Lower work rate of migrant wives after moving is a function of moving for husbands' job opportunities and being in the childrearing stage of life. Discriminant analysis reveals that socioeconomic characteristics are lower for nonworking than migrant wives. Working migrant wives have more education and higher status jobs than nonmigrants.…
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Practitioner Representations of Environmental Uncertainty: An Application of Discriminant Analysis.
ERIC Educational Resources Information Center
Acharya, Lalit
Multiple discriminant analysis was used to analyze the structure of a perceived environmental uncertainty variable employed previously in research on public relations roles. Data came from a subset (N=229) of a national sample of public relations practitioners belonging to the Public Relations Society of America, who completed a set of scaled…
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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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[Phoneme analysis and phoneme discrimination of juvenile speech therapy school students].
Franz, S; Rosanowski, F; Eysholdt, U; Hoppe, U
2011-05-01
Phoneme analysis and phoneme discrimination, important factors in acquiring spoken and written language, have been evaluated in juvenile speech therapy school students. The results have been correlated with the results of a school achievement test. The following questions were of interest: Do students in the lower verbal skill segment show pathological phoneme analysis and phoneme discrimination skills? Do the results of the school achievement test differ from the results by students visiting German "Hauptschule"? How does phoneme analysis and phoneme discrimination performance correlate to other tested parameters? 74 students of a speech therapy school ranging from 7 (th) to 9 (th) grade were examined (ages 12;10-17;04) with the Heidelberg Phoneme Discrimination Test H-LAD and the school achievement test "Prüfsystem für Schul- und Bildungsberatung PSB-R 6-13". Compared to 4 (th) graders the juvenile speech therapy school students showed worse results in the H-LAD test with good differentiation in the lower measuring range. In the PSB-R 6-13 test the examined students did worse compared to students visiting German "Hauptschule" for all grades except 9 (th) grade. Comparing H-LAD and PSB-R 6-13 shows a significant correlation for the sub-tests covering language competence and intelligence but not for the concentration tests. Pathological phoneme analysis and phoneme discrimination skills suggest elevated need for counseling, but this needs to corroborated through additional linguistic parameters and measuring non-verbal intelligence. Further trails are needed in order to clarify whether the results can lead to sophisticated therapy algorithms for educational purposes. © Georg Thieme Verlag KG Stuttgart · New York.
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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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[Men who have sex with men and human immunodeficiency virus testing in dental practice].
Elizondo, Jesús Eduardo; Treviño, Ana Cecilia; Violant, Deborah; Rivas-Estilla, Ana María; Álvarez, Mario Moisés
To explore the attitudes of men who have sex with men (MSM) towards the implementation of rapid HIV-1/2 testing in the dental practice, and to evaluate MSM's perceptions of stigma and discrimination related to sexual orientation by dental care professionals. Cross-sectional study using a self-administered, anonymous, structured analytical questionnaire answered by 185 MSM in Mexico. The survey included sociodemographic variables, MSM's perceptions towards public and private dental providers, and dental services, as well as their perception towards rapid HIV-1/2 testing in the dental practice. In addition, the perception of stigma and discrimination associated with their sexual orientation was explored by designing a psychometric Likert-type scale. The statistical analysis included factor analysis and non-hierarchical cluster analysis. 86.5% of the respondents expressed their willingness to take a rapid HIV-1/2 screening test during their dental visit. Nevertheless, 91.9% of them considered it important that dental professionals must be well-trained before administering any rapid HIV-1/2 tests. Factor analysis revealed two factors: experiences of sexual orientation stigma and discrimination in dental settings, and feelings of concern about the attitude of the dentist and dental staff towards their sexual orientation. Based on these factors and cluster analysis, three user profiles were identified: users who have not experienced stigma and discrimination (90.3%); users who have not experienced stigma and discrimination, but feel a slight concern (8.1%), and users who have experienced some form of discrimination and feel concern (1.6%). The dental practice may represent a potential location for rapid HIV-1/2 testing contributing to early HIV infection diagnosis. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.
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West, M M
1998-11-01
This meta-analysis of 12 studies assesses the efficacy of projective techniques to discriminate between sexually abused children and nonsexually abused children. A literature search was conducted to identify published studies that used projective instruments with sexually abused children. Those studies that reported statistics that allowed for an effect size to be calculated, were then included in the meta-analysis. There were 12 studies that fit the criteria. The projectives reviewed include The Rorschach, The Hand Test, The Thematic Apperception Test (TAT), the Kinetic Family Drawings, Human Figure Drawings, Draw Your Favorite Kind of Day, The Rosebush: A Visualization Strategy, and The House-Tree-Person. The results of this analysis gave an over-all effect size of d = .81, which is a large effect. Six studies included only a norm group of nondistressed, nonabused children with the sexual abuse group. The average effect size was d = .87, which is impressive. Six studies did include a clinical group of distressed nonsexually abused subjects and the effect size lowered to d = .76, which is a medium to large effect. This indicates that projective instruments can discriminate distressed children from nondistressed subjects, quite well. In the studies that included a clinical group of distressed children who were not sexually abused, the lower effect size indicates that the instruments were less able to discriminate the type of distress. This meta-analysis gives evidence that projective techniques have the ability to discriminate between children who have been sexually abused and those who were not abused sexually. However, further research that is designed to include clinical groups of distressed children is needed in order to determine how well the projectives can discriminate the type of distress.
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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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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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Keith, Verna M; Nguyen, Ann W; Taylor, Robert Joseph; Mouzon, Dawne M; Chatters, Linda M
2017-05-01
Data from the 2001-2003National Survey of American Life are used to investigate the effects of phenotype on everyday experiences with discrimination among African Americans (N=3343). Latent class analysis is used to identify four classes of discriminatory treatment: 1) low levels of discrimination, 2) disrespect and condescension, 3) character-based discrimination, and 4) high levels of discrimination. We then employ latent class multinomial logistic regression to evaluate the association between skin tone and body weight and these four classes of discrimination. Designating the low level discrimination class as the reference group, findings revealed that respondents with darker skin were more likely to be classified into the disrespect/condescension and the high level microaggression types. BMI was unrelated to the discrimination type, although there was a significant interaction effect between gender and BMI. BMI was strongly and positively associated with membership in the disrespect and condescension type among men but not among women. These findings indicate that skin tone and body weight are two phenotypic characteristics that influence the type and frequency of discrimination experienced by African Americans.
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Acoustic emission spectral analysis of fiber composite failure mechanisms
NASA Technical Reports Server (NTRS)
Egan, D. M.; Williams, J. H., Jr.
1978-01-01
The acoustic emission of graphite fiber polyimide composite failure mechanisms was investigated with emphasis on frequency spectrum analysis. Although visual examination of spectral densities could not distinguish among fracture sources, a paired-sample t statistical analysis of mean normalized spectral densities did provide quantitative discrimination among acoustic emissions from 10 deg, 90 deg, and plus or minus 45 deg, plus or minus 45 deg sub s specimens. Comparable discrimination was not obtained for 0 deg specimens.
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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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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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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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Velioğlu, Hasan Murat; Temiz, Havva Tümay; Boyaci, Ismail Hakki
2015-04-01
The potential of Raman spectroscopy was investigated in terms of its capability to discriminate the species of the fish samples and determine their freshness according to the number of freezing/thawing cycles they exposed. Species discrimination analysis was carried out on sixty-four fish samples from six different species, namely horse mackerel (Trachurus trachurus), European anchovy (Engraulis encrasicolus), red mullet (Mullus surmuletus), Bluefish (Pomatamus saltatrix), Atlantic salmon (Salmo salar) and flying gurnard (Trigla lucerna). Afterwards, fish samples were exposed to different numbers of freezing/thawing cycles and separated into three batches, namely (i) fresh, (ii) once frozen-thawed (OF) and (iii) twice frozen-thawed (TF) samples, in order to perform the freshness analysis. Raman data collected were used as inputs for chemometric analysis, which enabled us to develop two main PCA models to successfully terminate the studies for both species discrimination and freshness determination analysis. Copyright © 2014 Elsevier Ltd. 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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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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Romero-Flores, Adrian; McConnell, Laura L; Hapeman, Cathleen J; Ramirez, Mark; Torrents, Alba
2017-11-01
Electronic noses have been widely used in the food industry to monitor process performance and quality control, but use in wastewater and biosolids treatment has not been fully explored. Therefore, we examined the feasibility of an electronic nose to discriminate between treatment conditions of alkaline stabilized biosolids and compared its performance with quantitative analysis of key odorants. Seven lime treatments (0-30% w/w) were prepared and the resultant off-gas was monitored by GC-MS and by an electronic nose equipped with ten metal oxide sensors. A pattern recognition model was created using linear discriminant analysis (LDA) and principal component analysis (PCA) of the electronic nose data. In general, LDA performed better than PCA. LDA showed clear discrimination when single tests were evaluated, but when the full data set was included, discrimination between treatments was reduced. Frequency of accurate recognition was tested by three algorithms with Euclidan and Mahalanobis performing at 81% accuracy and discriminant function analysis at 70%. Concentrations of target compounds by GC-MS were in agreement with those reported in literature and helped to elucidate the behavior of the pattern recognition via comparison of individual sensor responses to different biosolids treatment conditions. Results indicated that the electronic nose can discriminate between lime percentages, thus providing the opportunity to create classes of under-dosed and over-dosed relative to regulatory requirements. Full scale application will require careful evaluation to maintain accuracy under variable process and environmental conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.
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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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Religious Discrimination Discourse in the Mono-Cultural School: The Case of Poland
ERIC Educational Resources Information Center
Anczyk, Adam; Grzymala-Moszczynska, Joanna
2018-01-01
The article forms an analysis of the religious discrimination discourse in Polish public schools, with special attention paid to the culturally specific, Polish understanding of the notion of religious discrimination. The introductory part presents the concept of religious discrimination as present in anti-discriminatory policies. The following…
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Synthesis and analysis of discriminators under influence of broadband non-Gaussian noise
NASA Astrophysics Data System (ADS)
Artyushenko, V. M.; Volovach, V. I.
2018-01-01
We considered the problems of the synthesis and analysis of discriminators, when the useful signal is exposed to non-Gaussian additive broadband noise. It is shown that in this case, the discriminator of the tracking meter should contain the nonlinear transformation unit, the characteristics of which are determined by the Fisher information relative to the probability density function of the mixture of non-Gaussian broadband noise and mismatch errors. The parameters of the discriminatory and phase characteristics of the discriminators working under the above conditions are obtained. It is shown that the efficiency of non-linear processing depends on the ratio of power of FM noise to the power of Gaussian noise. The analysis of the information loss of signal transformation caused by the linear section of discriminatory characteristics of the unit of nonlinear transformations of the discriminator is carried out. It is shown that the average slope of the nonlinear transformation characteristic is determined by the Fisher information relative to the probability density function of the mixture of non-Gaussian noise and mismatch errors.
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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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Cluster analysis and prediction of treatment outcomes for chronic rhinosinusitis.
Soler, Zachary M; Hyer, J Madison; Rudmik, Luke; Ramakrishnan, Viswanathan; Smith, Timothy L; Schlosser, Rodney J
2016-04-01
Current clinical classifications of chronic rhinosinusitis (CRS) have weak prognostic utility regarding treatment outcomes. Simplified discriminant analysis based on unsupervised clustering has identified novel phenotypic subgroups of CRS, but prognostic utility is unknown. We sought to determine whether discriminant analysis allows prognostication in patients choosing surgery versus continued medical management. A multi-institutional prospective study of patients with CRS in whom initial medical therapy failed who then self-selected continued medical management or surgical treatment was used to separate patients into 5 clusters based on a previously described discriminant analysis using total Sino-Nasal Outcome Test-22 (SNOT-22) score, age, and missed productivity. Patients completed the SNOT-22 at baseline and for 18 months of follow-up. Baseline demographic and objective measures included olfactory testing, computed tomography, and endoscopy scoring. SNOT-22 outcomes for surgical versus continued medical treatment were compared across clusters. Data were available on 690 patients. Baseline differences in demographics, comorbidities, objective disease measures, and patient-reported outcomes were similar to previous clustering reports. Three of 5 clusters identified by means of discriminant analysis had improved SNOT-22 outcomes with surgical intervention when compared with continued medical management (surgery was a mean of 21.2 points better across these 3 clusters at 6 months, P < .05). These differences were sustained at 18 months of follow-up. Two of 5 clusters had similar outcomes when comparing surgery with continued medical management. A simplified discriminant analysis based on 3 common clinical variables is able to cluster patients and provide prognostic information regarding surgical treatment versus continued medical management in patients with CRS. Copyright © 2015 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.
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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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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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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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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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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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Sex Discrimination in Higher Education Employment: An Empirical Analysis of the Case Law.
ERIC Educational Resources Information Center
Schoenfeld, Sage L.; Zirkel, Perry A.
1989-01-01
Analysis of 132 court decisions for the period of 1976-86 concerning sex discrimination of faculty and nonfaculty employees in higher education indicates that the overall odds favor the defendant-institutions over the plaintiff-employees by a four-to-one ratio. The most frequently litigated institutional action was salary, followed by hiring and…
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ERIC Educational Resources Information Center
Lee, Debbiesiu L.; Ahn, Soyeon
2013-01-01
This meta-analysis synthesized the results of 27 studies examining the relations of racial identity, ethnic identity, and racial socialization to discrimination-distress for Black Americans. The purpose was to uncover which constructs connected to racial identity, ethnic identity, and racial socialization most strongly correlate with racial…
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NASA Astrophysics Data System (ADS)
Qiu, Sufang; Li, Chao; Lin, Jinyong; Xu, Yuanji; Lu, Jun; Huang, Qingting; Zou, Changyan; Chen, Chao; Xiao, Nanyang; Lin, Duo; Chen, Rong; Pan, Jianji; Feng, Shangyuan
2016-12-01
Surface-enhanced Raman spectroscopy (SERS) was employed to detect deoxyribose nucleic acid (DNA) variations associated with the development of nasopharyngeal carcinoma (NPC). Significant SERS spectral differences between the DNA extracted from early NPC, advanced NPC, and normal nasopharyngeal tissue specimens were observed at 678, 729, 788, 1337, 1421, 1506, and 1573 cm-1, which reflects the genetic variations in NPC. Principal component analysis combined with discriminant function analysis for early NPC discrimination yielded a diagnostic accuracy of 86.8%, 92.3%, and 87.9% for early NPC, advanced NPC, and normal nasopharyngeal tissue DNA, respectively. In this exploratory study, we demonstrated the potential of SERS for early detection of NPC based on the DNA molecular study of biopsy tissues.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Mann, L.K.; Shugart, H.H.; Kitchings, J.T.
1978-03-01
The purpose of this study was to evaluate the effectiveness of using discriminant analysis in assessing plant niches. As a component of research by the Environmental Research Park Project at Oak Ridge, Tennessee, five sites were inventoried for herbaceous species. From this inventory, four sympatric species of Galium and seventeen co-occurring herbaceous species were selected for discriminant analysis. The four species of Galium were treated as two data sets: one was composed of information collected at one site (a mesic hardwood area) and the other contained data from two cedar sites of shallow soil over limestone bedrock. The seventeen herbaceousmore » species all occurred in the mesic hardwood area.« 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)
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)
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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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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Filipiak, Anna; Hasiów-Jaroszewska, Beata
2016-04-01
The real-time PCR-HRM analysis was developed for the detection and discrimination of the quarantine nematode Bursaphelenchus xylophilus and Bursaphelenchus mucronatus. A set of primers was designed to target the ITS region of rDNA. The results have demonstrated that this analysis is a valuable tool for differentiation of these both species. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Wavelet and receiver operating characteristic analysis of heart rate variability
NASA Astrophysics Data System (ADS)
McCaffery, G.; Griffith, T. M.; Naka, K.; Frennaux, M. P.; Matthai, C. C.
2002-02-01
Multiresolution wavelet analysis has been used to study the heart rate variability in two classes of patients with different pathological conditions. The scale dependent measure of Thurner et al. was found to be statistically significant in discriminating patients suffering from hypercardiomyopathy from a control set of normal subjects. We have performed Receiver Operating Characteristc (ROC) analysis and found the ROC area to be a useful measure by which to label the significance of the discrimination, as well as to describe the severity of heart dysfunction.
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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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Soybean varieties discrimination using non-imaging hyperspectral sensor
NASA Astrophysics Data System (ADS)
da Silva Junior, Carlos Antonio; Nanni, Marcos Rafael; Shakir, Muhammad; Teodoro, Paulo Eduardo; de Oliveira-Júnior, José Francisco; Cezar, Everson; de Gois, Givanildo; Lima, Mendelson; Wojciechowski, Julio Cesar; Shiratsuchi, Luciano Shozo
2018-03-01
Infrared region of electromagnetic spectrum has remarkable applications in crop studies. Infrared along with Red band has been used to develop certain vegetation indices. These indices like NDVI, EVI provide important information on any crop physiological stages. The main objective of this research was to discriminate 4 different soybean varieties (BMX Potência, NA5909, FT Campo Mourão and Don Mario) using non-imaging hyperspectral sensor. The study was conducted in four agricultural areas in the municipality of Deodápolis (MS), Brazil. For spectral analysis, 2400 field samples were taken from soybean leaves by means of FieldSpec 3 JR spectroradiometer in the range from 350 to 2500 nm. The data were evaluated through multivariate analysis with the whole set of spectral curves isolated by blue, green, red and near infrared wavelengths along with the addition of vegetation indices like (Enhanced Vegetation Index - EVI, Normalized Difference Vegetation Index - NDVI, Green Normalized Difference Vegetation Index - GNDVI, Soil-adjusted Vegetation Index - SAVI, Transformed Vegetation Index - TVI and Optimized Soil-Adjusted Vegetation Index - OSAVI). A number of the analysis performed where, discriminant (60 and 80% of the data), simulated discriminant (40 and 20% of data), principal component (PC) and cluster analysis (CA). Discriminant and simulated discriminant analyze presented satisfactory results, with average global hit rates of 99.28 and 98.77%, respectively. The results obtained by PC and CA revealed considerable associations between the evaluated variables and the varieties, which indicated that each variety has a variable that discriminates it more effectively in relation to the others. There was great variation in the sample size (number of leaves) for estimating the mean of variables. However, it was possible to observe that 200 leaves allow to obtain a maximum error of 2% in relation to the mean.
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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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Digital pulse-shape analysis with a TRACE early silicon prototype
NASA Astrophysics Data System (ADS)
Mengoni, D.; Dueñas, J. A.; Assié, M.; Boiano, C.; John, P. R.; Aliaga, R. J.; Beaumel, D.; Capra, S.; Gadea, A.; Gonzáles, V.; Gottardo, A.; Grassi, L.; Herrero-Bosch, V.; Houdy, T.; Martel, I.; Parkar, V. V.; Perez-Vidal, R.; Pullia, A.; Sanchis, E.; Triossi, A.; Valiente Dobón, J. J.
2014-11-01
A highly segmented silicon-pad detector prototype has been tested to explore the performance of the digital pulse shape analysis in the discrimination of the particles reaching the silicon detector. For the first time a 200 μm thin silicon detector, grown using an ordinary floating zone technique, has been shown to exhibit a level discrimination thanks to the fine segmentation. Light-charged particles down to few MeV have been separated, including their punch-through. A coaxial HPGe detector in time coincidence has further confirmed the quality of the particle discrimination.
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Hu, Boran; Yue, Yaqing; Zhu, Yong; Wen, Wen; Zhang, Fengmin; Hardie, Jim W
2015-01-01
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. 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. 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. 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.
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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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Development and validation of the stigma scale for epilepsy in Turkey.
Baybaş, Sevim; Yıldırım, Zerrin; Ertem, Devrimsel Harika; Dirican, Ayten; Dirican, Ahmet
2017-02-01
Epilepsy is a chronic disease with an increased risk of stigma. The aim of this study was to investigate the efficacy of a scale developed by the authors to determine the level of stigma in Turkish patients with epilepsy and their relatives. In this pilot study, two scales were developed, one consisting of 32 questions for the patients and one of 20 questions for the patients' relatives. Initially, a total of 30 patients with epilepsy and 30 relatives of the patients were included. The Cronbach's alpha coefficient was calculated in a reliability analysis of validity applying the scales to 302 patients and 201 relatives of the patients. The Pearson correlation coefficient was used for the reliability analysis of the test-retest. The t-test was used in paired series, and factor analysis was conducted. The correlation between the clinical and demographical data and the stigma scores was evaluated. The scales were applied to participants twice under the same conditions in one-week interval. In the test-retest analysis, the internal consistency of the scales was high and reliable. In the analysis of the patients, the Cronbach's alpha value of the scale was found to be 0.915. In the factor analysis, the questions were grouped into five factors including social isolation, discrimination, insufficiency, false beliefs, and stigma resistance. The factors with the highest contribution to the stigma level were social isolation and discrimination. In the stigma scores, a significant correlation was found between the age of the patient, frequency of seizures, education status, level of income, and the amount of antiepileptic drugs used. In the analysis of the patients' relatives, the Cronbach's alpha value of the scale was found to be 0.892. In the factor analysis, the questions were classified as discrimination, prejudgments, and false beliefs. The factor which most contributed to the stigma level was discrimination. A significant correlation was found in the stigma scores between sex, education status, marital status, and income distribution. According to our study results, it is clearly seen that both patients and their relatives suffer from epilepsy-associated stigma. Patients with epilepsy and their relatives are faced with discrimination in society, resulting in social isolation. We, therefore, believe that both patients and their relatives should be informed in detail about discrimination to overcome this challenge. Copyright © 2016 Elsevier Inc. All rights reserved.
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Kin discrimination within honey bee (Apis mellifera) colonies: An analysis of the evidence.
Breed, M D; Welch, C K; Cruz, R
1994-12-01
Compelling evolutionary arguments lead to the prediction that honey bee workers should discriminate between supersisters and half-sisters within colonies. We review the theoretical support for discrimination during swarming, queen rearing, feeding, and grooming. A survey of the data that tests whether such discrimination takes place shows that, despite substantial effort in a number of laboratories, there is no conclusive evidence for intracolony discrimination in any of the postulated contexts. The strongest suggestive data is in the critical context of queen rearing, but flaws in experimental design or analysis make the best available tests inconclusive. We present new data that shows that cues exist on which discriminations can be made among adult workers in nestmate recognition interactions and in feeding interactions, but our data does not differentiate between subfamily recognition and recognition associated with color phenotypes. We conclude that while selection may favor discrimination between supersisters and half-sisters, as a practical matter such discriminations play no role, or only a minor role, in the biology of the honey bee. Copyright © 1994. Published by Elsevier B.V.
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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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Romano, Federica; Meoni, Gaia; Manavella, Valeria; Baima, Giacomo; Tenori, Leonardo; Cacciatore, Stefano; Aimetti, Mario
2018-06-07
Recent findings about the differential gene expression signature of periodontal lesions have raised the hypothesis of distinctive biological phenotypes expressed by generalized chronic periodontitis (GCP) and generalized aggressive periodontitis (GAgP) patients. Therefore, this cross-sectional investigation was planned, primarily, to determine the ability of nuclear magnetic resonance (NMR) spectroscopic analysis of unstimulated whole saliva to discriminate GCP and GAgP disease-specific metabolomic fingerprint and, secondarily, to assess potential metabolites discriminating periodontitis patients from periodontally healthy individuals (HI). NMR-metabolomics spectra were acquired from salivary samples of patients with a clinical diagnosis of GCP (n = 33) or GAgP (n = 28) and from HI (n = 39). The clustering of HI, GCP and GAgP patients was achieved by using a combination of the Principal Component Analysis and Canonical Correlation Analysis on the NMR profiles. These analyses revealed a significant predictive accuracy discriminating HI from GCP, and discriminating HI from GAgP patients (both 81%). In contrast, the GAgP and GCP saliva samples seem to belong to the same metabolic space (60% predictive accuracy). Significantly lower levels (P < 0.05) of pyruvate, N-acetyl groups and lactate and higher levels (P < 0.05) of proline, phenylalanine, and tyrosine were found in GCP and GAgP patients compared with HI. Within the limitations of this study, CGP and GAgP metabolomic profiles were not unequivocally discriminated through a NMR-based spectroscopic analysis of saliva. This article is protected by copyright. All rights reserved. This article is protected by copyright. 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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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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Keita, S O
1992-03-01
An analysis of First Dynasty crania from Abydos was undertaken using multiple discriminant functions. The results demonstrate greater affinity with Upper Nile Valley patterns, but also suggest change from earlier craniometric trends. Gene flow and movement of northern officials to the important southern city may explain the findings.
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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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ERIC Educational Resources Information Center
Spearing, Debra; Woehlke, Paula
To assess the effect on discriminant analysis in terms of correct classification into two groups, the following parameters were systematically altered using Monte Carlo techniques: sample sizes; proportions of one group to the other; number of independent variables; and covariance matrices. The pairing of the off diagonals (or covariances) with…
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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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Garnett, Bernice Raveche; Masyn, Katherine E; Austin, S Bryn; Miller, Matthew; Williams, David R; Viswanath, Kasisomayajula
2014-08-01
Discrimination is commonly experienced among adolescents. However, little is known about the intersection of multiple attributes of discrimination and bullying. We used a latent class analysis (LCA) to illustrate the intersections of discrimination attributes and bullying, and to assess the associations of LCA membership to depressive symptoms, deliberate self harm and suicidal ideation among a sample of ethnically diverse adolescents. The data come from the 2006 Boston Youth Survey where students were asked whether they had experienced discrimination based on four attributes: race/ethnicity, immigration status, perceived sexual orientation and weight. They were also asked whether they had been bullied or assaulted for these attributes. A total of 965 (78%) students contributed to the LCA analytic sample (45% Non-Hispanic Black, 29% Hispanic, 58% Female). The LCA revealed that a 4-class solution had adequate relative and absolute fit. The 4-classes were characterized as: low discrimination (51%); racial discrimination (33%); sexual orientation discrimination (7%); racial and weight discrimination with high bullying (intersectional class) (7%). In multivariate models, compared to the low discrimination class, individuals in the sexual orientation discrimination class and the intersectional class had higher odds of engaging in deliberate self-harm. Students in the intersectional class also had higher odds of suicidal ideation. All three discrimination latent classes had significantly higher depressive symptoms compared to the low discrimination class. Multiple attributes of discrimination and bullying co-occur among adolescents. Research should consider the co-occurrence of bullying and discrimination.
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Hartman, C. Alex; Ackerman, Joshua T.; Eagles-Smith, Collin A.; Herzog, Mark
2016-01-01
In birds where males and females are similar in size and plumage, sex determination by alternative means is necessary. Discriminant function analysis based on external morphometrics was used to distinguish males from females in two closely related species: Western Grebe (Aechmophorus occidentalis) and Clark's Grebe (A. clarkii). Additionally, discriminant function analysis was used to evaluate morphometric divergence between Western and Clark's grebe adults and eggs. Aechmophorus grebe adults (n = 576) and eggs (n = 130) were sampled across 29 lakes and reservoirs throughout California, USA, and adult sex was determined using molecular analysis. Both Western and Clark's grebes exhibited considerable sexual size dimorphism. Males averaged 6–26% larger than females among seven morphological measurements, with the greatest sexual size dimorphism occurring for bill morphometrics. Discriminant functions based on bill length, bill depth, and short tarsus length correctly assigned sex to 98% of Western Grebes, and a function based on bill length and bill depth correctly assigned sex to 99% of Clark's Grebes. Further, a simplified discriminant function based only on bill depth correctly assigned sex to 96% of Western Grebes and 98% of Clark's Grebes. In contrast, external morphometrics were not suitable for differentiating between Western and Clark's grebe adults or their eggs, with correct classification rates of discriminant functions of only 60%, 63%, and 61% for adult males, adult females, and eggs, respectively. Our results indicate little divergence in external morphology between species of Aechmophorus grebes, and instead separation is much greater between males and females.
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NASA Astrophysics Data System (ADS)
Zhao, Bo; Liu, Jinhu; Song, Junjie; Cao, Liang; Dou, Shuozeng
2017-08-01
The otolith morphology of two croaker species (Collichthys lucidus and Collichthys niveatus) from three areas (Liaodong Bay, LD; Huanghe (Yellow) River estuary, HRE; Jiaozhou Bay, JZ) along the northern Chinese coast were investigated for species identification and stock discrimination. The otolith contour shape described by elliptic Fourier coefficients (EFC) were analysed using principal components analysis (PCA) and stepwise canonical discriminant analysis (CDA) to identify species and stocks. The two species were well differentiated, with an overall classification success rate of 97.8%. And variations in the otolith shapes were significant enough to discriminate among the three geographical samples of C. lucidus (67.7%) or C. niveatus (65.2%). Relatively high mis-assignment occurred between the geographically adjacent LD and HRE samples, which implied that individual mixing may exist between the two samples. This study yielded information complementary to that derived from genetic studies and provided information for assessing the stock structure of C. lucidus and C. niveatus in the Bohai Sea and the Yellow Sea.
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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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Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.
Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V
2007-01-01
The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.
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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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Robust L1-norm two-dimensional linear discriminant analysis.
Li, Chun-Na; Shao, Yuan-Hai; Deng, Nai-Yang
2015-05-01
In this paper, we propose an L1-norm two-dimensional linear discriminant analysis (L1-2DLDA) with robust performance. Different from the conventional two-dimensional linear discriminant analysis with L2-norm (L2-2DLDA), where the optimization problem is transferred to a generalized eigenvalue problem, the optimization problem in our L1-2DLDA is solved by a simple justifiable iterative technique, and its convergence is guaranteed. Compared with L2-2DLDA, our L1-2DLDA is more robust to outliers and noises since the L1-norm is used. This is supported by our preliminary experiments on toy example and face datasets, which show the improvement of our L1-2DLDA over L2-2DLDA. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Discrimination of rectal cancer through human serum using surface-enhanced Raman spectroscopy
NASA Astrophysics Data System (ADS)
Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Zhang, Su; Jin, Lili
2015-05-01
In this paper, surface-enhanced Raman spectroscopy (SERS) was used to detect the changes in blood serum components that accompany rectal cancer. The differences in serum SERS data between rectal cancer patients and healthy controls were examined. Postoperative rectal cancer patients also participated in the comparison to monitor the effects of cancer treatments. The results show that there are significant variations at certain wavenumbers which indicates alteration of corresponding biological substances. Principal component analysis (PCA) and parameters of intensity ratios were used on the original SERS spectra for the extraction of featured variables. These featured variables then underwent linear discriminant analysis (LDA) and classification and regression tree (CART) for the discrimination analysis. Accuracies of 93.5 and 92.4 % were obtained for PCA-LDA and parameter-CART, respectively.
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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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ERIC Educational Resources Information Center
McFarland, Renee L.
2013-01-01
The experience of discrimination is a complex phenomenon. At present, there are few studies that have captured the experience of discrimination on a predominately white university campus. This study was designed to investigate the association between perceived discrimination and self-reported health outcomes among university students in Southwest…
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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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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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Panneton, Bernard; Guillaume, Serge; Roger, Jean-Michel; Samson, Guy
2010-01-01
Precision weeding by spot spraying in real time requires sensors to discriminate between weeds and crop without contact. Among the optical based solutions, the ultraviolet (UV) induced fluorescence of the plants appears as a promising alternative. In a first paper, the feasibility of discriminating between corn hybrids, monocotyledonous, and dicotyledonous weeds was demonstrated on the basis of the complete spectra. Some considerations about the different sources of fluorescence oriented the focus to the blue-green fluorescence (BGF) part, ignoring the chlorophyll fluorescence that is inherently more variable in time. This paper investigates the potential of performing weed/crop discrimination on the basis of several large spectral bands in the BGF area. A partial least squares discriminant analysis (PLS-DA) was performed on a set of 1908 spectra of corn and weed plants over 3 years and various growing conditions. The discrimination between monocotyledonous and dicotyledonous plants based on the blue-green fluorescence yielded robust models (classification error between 1.3 and 4.6% for between-year validation). On the basis of the analysis of the PLS-DA model, two large bands were chosen in the blue-green fluorescence zone (400-425 nm and 425-490 nm). A linear discriminant analysis based on the signal from these two bands also provided very robust inter-year results (classification error from 1.5% to 5.2%). The same selection process was applied to discriminate between monocotyledonous weeds and maize but yielded no robust models (up to 50% inter-year error). Further work will be required to solve this problem and provide a complete UV fluorescence based sensor for weed-maize discrimination.
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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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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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Clark, Trenette T.; Salas-Wright, Christopher P.; Vaughn, Michael G.; Whitfield, Keith E.
2016-01-01
Perceived discrimination is a major source of health-related stress. The purpose of this study was to model the heterogeneity of everyday-discrimination experiences among African American and Caribbean Blacks and to identify differences in the prevalence of mood and substance use outcomes, including generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder among the identified subgroups. The study uses data from the National Survey of American Life obtained from a sample of African American and Caribbean Black respondents (N = 4,462) between 18 and 65 years. We used latent profile analysis and multinomial regression analyses to identify and validate latent subgroups and test hypotheses, yielding 4 classes of perceived everyday discrimination: Low Discrimination, Disrespect and Condescension, General Discrimination, and Chronic Discrimination. Findings show significant differences exist between the Low Discrimination and General Discrimination classes for major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Moreover, we find significant differences exist between the Low Discrimination and Chronic Discrimination classes for the four disorders examined. Compared with the Chronic Discrimination class, members of the other classes were significantly less likely to meet criteria for generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Findings suggest elevated levels of discrimination increase risk for mood and substance-use disorders. Importantly, results suggest the prevalence of mood and substance-use disorders is a function of the type and frequency of discrimination that individuals experience. PMID:25254321
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NASA Astrophysics Data System (ADS)
Kumar, Amit; Manjunath, K. R.; Meenakshi; Bala, Renu; Sud, R. K.; Singh, R. D.; Panigrahy, Sushma
2013-08-01
The quality and yield of tea depends upon management of tea plantations, which takes into account the factors like type, age of plantation, growth stage, pruning status, light conditions, and disease incidence. Recognizing the importance of hyperspectral data in detecting minute spectral variations in vegetation, the present study was conducted to explore applicability of such data in evaluating these factors. Also stepwise discriminant analysis and principal component analysis were conducted to identify the appropriate bands for accessing the above mentioned factors. The Green region followed by NIR region was found as most appropriate best band for discriminating different types of tea plants, and the tea in sunlit and shade condition. For discriminating age of plantation, growth stage of tea, and diseased and healthy bush, Blue region was most appropriate. The Red and NIR regions were best bands to discriminate pruned and unpruned tea. The study concluded that field hyperspectral data can be efficiently used to know the plantation that need special care and may be an indicator of tea productivity. The spectral signature of these characteristics of tea plantations may also be used to classify the hyperspectral satellite data to derive these parameters at regional scale.
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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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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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NASA Technical Reports Server (NTRS)
Lent, P. C. (Principal Investigator)
1973-01-01
The author has identified the following significant results. Step-wise discriminate analysis has demonstrated the feasibility of feature identification using linear discriminate functions of ERTS-1 MSS band densities and their ratios. The analysis indicated that features such as small streams can be detected even when they are in dark mountain shadow. The potential utility of this and similar analytic techniques appears considerable, and the limits it can be applied to analysis of ERTS-1 imagery are not yet fully known.
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NASA Astrophysics Data System (ADS)
Tulip, David F.; Lucas, Keith B.
1991-12-01
At a time when recruitment into preservice teacher education courses in mathematics and science is difficult, one strategy to increase the number of graduates is to minimise the number of students who fail to complete their university courses. This study sought to determine factors which distinguish withdrawers from persisters in the first semester of a B.Ed course. Discriminant analysis was employed; a discriminant function employing seven factors resulted in correct classification in 81% of cases. Further analysis distinguishing between dropouts and transferees resulted in two discriminant functions with some common variables.
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Oka, Hiroshi; Tanaka, Masaru; Kobayashi, Seiichiro; Argenziano, Giuseppe; Soyer, H Peter; Nishikawa, Takeji
2004-04-01
As a first step to develop a screening system for pigmented skin lesions, we performed digital discriminant analyses between early melanomas and Clark naevi. A total of 59 cases of melanoma, including 23 melanoma in situ and 36 thin invasive melanomas (Breslow thickness < or =0.75 mm), and 188 clinically equivocal, histopathologically diagnosed Clark naevi were used in our study. After calculating 62 mathematical variables related to the colour, texture, asymmetry and circularity based on the dermoscopic findings of the pigmented skin lesions, we performed multivariate stepwise discriminant analysis using these variables to differentiate melanomas from naevi. The sensitivities and specificities of our model were 94.4 and 98.4%, respectively, for discriminating between melanomas (Breslow thickness < or =0.75 mm) and Clark naevi, and 73.9 and 85.6%, respectively, for discriminating between melanoma in situ and Clark naevi. Our algorithm accurately discriminated invasive melanomas from Clark naevi, but not melanomas in situ from Clark naevi.
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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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NASA Astrophysics Data System (ADS)
Harvey, T. J.; Hughes, C.; Ward, A. D.; Gazi, E.; Faria, E. Correia; Clarke, N. W.; Brown, M.; Snook, R.; Gardner, P.
2008-11-01
Here we report on investigations into using Raman optical tweezers to analyse both live and chemically fixed prostate and bladder cells. Spectra were subjected to chemometric analysis to discriminate and classify the cell types based on their spectra. Subsequent results revealed the potential of Raman tweezers as a potential clinical diagnostic tool.
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ERIC Educational Resources Information Center
Kissel, Mary Ann
The use of stepwise discriminant analysis as a means to select entering students who would benefit from a special program for the disadvantaged was studied. In fall 1984, 278 full-time black students were admitted as first-time students to a large urban university. Of the total, 200 entered a special program for the disadvantaged and 78 entered…
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2010-09-01
Discrimination of Closely-Spaced Geosynchronous Satellites – Phase Curve Analysis & New Small Business Innovative Research (SBIR) Efforts...such objects from one time epoch to another showcases the deficiencies in associating individual objects before and after the configuration change...1]) have emphasized examples of multiple satellites occupying the same geosynchronous slot, with individual satellites maneuvering about one another
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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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GRace: a MATLAB-based application for fitting the discrimination-association model.
Stefanutti, Luca; Vianello, Michelangelo; Anselmi, Pasquale; Robusto, Egidio
2014-10-28
The Implicit Association Test (IAT) is a computerized two-choice discrimination task in which stimuli have to be categorized as belonging to target categories or attribute categories by pressing, as quickly and accurately as possible, one of two response keys. The discrimination association model has been recently proposed for the analysis of reaction time and accuracy of an individual respondent to the IAT. The model disentangles the influences of three qualitatively different components on the responses to the IAT: stimuli discrimination, automatic association, and termination criterion. The article presents General Race (GRace), a MATLAB-based application for fitting the discrimination association model to IAT data. GRace has been developed for Windows as a standalone application. It is user-friendly and does not require any programming experience. The use of GRace is illustrated on the data of a Coca Cola-Pepsi Cola IAT, and the results of the analysis are interpreted and discussed.
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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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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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Chung, Ill-Min; Kim, Jae-Kwang; Lee, Kyoung-Jin; Park, Sung-Kyu; Lee, Ji-Hee; Son, Na-Young; Jin, Yong-Ik; Kim, Seung-Hyun
2018-02-01
Rice (Oryza sativa L.) is the world's third largest food crop after wheat and corn. Geographic authentication of rice has recently emerged asan important issue for enhancing human health via food safety and quality assurance. Here, we aimed to discriminate rice of six Asian countries through geographic authentication using combinations of elemental/isotopic composition analysis and chemometric techniques. Principal components analysis could distinguish samples cultivated from most countries, except for those cultivated in the Philippines and Japan. Furthermore, orthogonal projection to latent structure-discriminant analysis provided clear discrimination between rice cultivated in Korea and other countries. The major common variables responsible for differentiation in these models were δ 34 S, Mn, and Mg. Our findings contribute to understanding the variations of elemental and isotopic compositions in rice depending on geographic origins, and offer valuable insight into the control of fraudulent labeling regarding the geographic origins of rice traded among Asian countries. Copyright © 2017 Elsevier Ltd. All rights reserved.
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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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Analysis of human nails by laser-induced breakdown spectroscopy
NASA Astrophysics Data System (ADS)
Hosseinimakarem, Zahra; Tavassoli, Seyed Hassan
2011-05-01
Laser-induced breakdown spectroscopy (LIBS) is applied to analyze human fingernails using nanosecond laser pulses. Measurements on 45 nail samples are carried out and 14 key species are identified. The elements detected with the present system are: Al, C, Ca, Fe, H, K, Mg, N, Na, O, Si, Sr, Ti as well as CN molecule. Sixty three emission lines have been identified in the spectrum that are dominated by calcium lines. A discriminant function analysis is used to discriminate among different genders and age groups. This analysis demonstrates efficient discrimination among these groups. The mean concentration of each element is compared between different groups. Correlation between concentrations of elements in fingernails is calculated. A strong correlation is found between sodium and potassium while calcium and magnesium levels are inversely correlated. A case report on high levels of sodium and potassium in patients with hyperthyroidism is presented. It is shown that LIBS could be a promising technique for the analysis of nails and therefore identification of health problems.
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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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Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification.
Wen, Zaidao; Hou, Biao; Jiao, Licheng
2017-05-03
Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.
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Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Dwirahmadi, Febi; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Do, Cuong Manh; Nguyen, Trung Hieu; Dinh, Tuan Anh Diep
2015-05-01
The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.
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Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques
NASA Astrophysics Data System (ADS)
Gulgundi, Mohammad Shahid; Shetty, Amba
2018-03-01
Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.
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ERIC Educational Resources Information Center
Bishop, Dorothy V. M.; Hardiman, Mervyn J.; Barry, Johanna G.
2011-01-01
Behavioural and electrophysiological studies give differing impressions of when auditory discrimination is mature. Ability to discriminate frequency and speech contrasts reaches adult levels only around 12 years of age, yet an electrophysiological index of auditory discrimination, the mismatch negativity (MMN), is reported to be as large in…
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Lin, Chenghe; Jiao, Benzheng; Liu, Shanshan; Guan, Feng; Chung, Nak-Eun; Han, Seung-Ho; Lee, U-Young
2014-03-01
It has been known that mandible ramus flexure is an important morphologic trait for sex determination. However, it will be unavailable when mandible is incomplete or fragmented. Therefore, the anthropometric analysis on incomplete or fragmented mandible becomes more important. The aim of this study is to investigate the sex-discriminant potential of mandible ramus flexure on the Korean three-dimensional (3D) mandible models with anthropometric analysis. The sample consists of 240 three dimensional mandibular models obtained from Korean population (M:F; 120:120, mean age 46.2 y), collected by The Catholic Institute for Applied Anatomy, The Catholic University of Korea. Anthropometric information about 11 metric was taken with Mimics, anthropometry libraries toolkit. These parameters were subjected to different discriminant function analyses using SPSS 17.0. Univariate analyses showed that the resubstitution accuracies for sex determination range from 50.4 to 77.1%. Mandibular flexure upper border (MFUB), maximum ramus vertical height (MRVH), and upper ramus vertical height (URVH) expressed the greatest dimorphism, 72.1 to 77.1%. Bivariate analyses indicated that the combination of MFUB and MRVH hold even higher resubstitution accuracy of 81.7%. Furthermore, the direct and stepwise discriminant analyses with the variables on the upper ramus above flexure could predict sex in 83.3 and 85.0%, respectively. When all variables of mandibular ramus flexure were input in stepwise discriminant analysis, the resubstitution accuracy arrived as high as 88.8%. Therefore, we concluded that the upper ramus above flexure hold the larger potentials than the mandibular ramus flexure itself to predict sexes, and that the equations in bivariate and multivariate analysis from our study will be helpful for sex determination on Korean population in forensic science and law. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
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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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Mostafa, Hamza; Amin, Arwa M; Teh, Chin-Hoe; Murugaiyah, Vikneswaran; Arif, Nor Hayati; Ibrahim, Baharudin
2016-12-01
Alcohol-dependence (AD) is a ravaging public health and social problem. AD diagnosis depends on questionnaires and some biomarkers, which lack specificity and sensitivity, however, often leading to less precise diagnosis, as well as delaying treatment. This represents a great burden, not only on AD individuals but also on their families. Metabolomics using nuclear magnetic resonance spectroscopy (NMR) can provide novel techniques for the identification of novel biomarkers of AD. These putative biomarkers can facilitate early diagnosis of AD. To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics. Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC). The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0). This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Chung, Ill-Min; Kim, Jae-Kwang; Prabakaran, Mayakrishnan; Yang, Jin-Hee; Kim, Seung-Hyun
2016-05-01
Although rice (Oryza sativa L.) is the third largest food crop, relatively fewer studies have been reported on rice geographical origin based on light element isotope ratios in comparison with other foods such as wine, beef, juice, oil and milk. Therefore this study tries to discriminate the geographical origin of the same rice cultivars grown in different Asian countries using the analysis of C, N, O and S stable isotope ratios and chemometrics. The δ(15) NAIR , δ(18) OVSMOW and δ(34) SVCDT values of brown rice were more markedly influenced by geographical origin than was the δ(13) CVPDB value. In particular, the combination of δ(18) OVSMOW and δ(34) SVCDT more efficiently discriminated rice geographical origin than did the remaining combinations. Principal component analysis (PCA) revealed a clear discrimination between different rice geographical origins but not between rice genotypes. In particular, the first components of PCA discriminated rice cultivated in the Philippines from rice cultivated in China and Korea. The present findings suggest that analysis of the light element isotope composition combined with chemometrics can be potentially applicable to discriminate rice geographical origin and also may provide a valuable insight into the control of improper or fraudulent labeling regarding the geographical origin of rice worldwide. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.
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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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El-Deftar, Moteaa M; Speers, Naomi; Eggins, Stephen; Foster, Simon; Robertson, James; Lennard, Chris
2014-08-01
A commercially available laser-induced breakdown spectroscopy (LIBS) instrument was evaluated for the determination of elemental composition of twenty Australian window glass samples, consisting of 14 laminated samples and 6 non-laminated samples (or not otherwise specified) collected from broken windows at crime scenes. In this study, the LIBS figures of merit were assessed in terms of accuracy, limits of detection and precision using three standard reference materials (NIST 610, 612, and 1831). The discrimination potential of LIBS was compared to that obtained using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), X-ray microfluorescence spectroscopy (μXRF) and scanning electron microscopy energy dispersive X-ray spectrometry (SEM-EDX) for the analysis of architectural window glass samples collected from crime scenes in the Canberra region, Australia. Pairwise comparisons were performed using a three-sigma rule, two-way ANOVA and Tukey's HSD test at 95% confidence limit in order to investigate the discrimination power for window glass analysis. The results show that the elemental analysis of glass by LIBS provides a discrimination power greater than 97% (>98% when combined with refractive index data), which was comparable to the discrimination powers obtained by LA-ICP-MS and μXRF. These results indicate that LIBS is a feasible alternative to the more expensive LA-ICP-MS and μXRF options for the routine forensic analysis of window glass samples. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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Sanchez, Delida; Whittaker, Tiffany A; Hamilton, Emma; Zayas, Luis H
2016-07-01
This study explored the relation between perceived discrimination and sexual precursor behaviors among 205 Mexican American preadolescent middle school girls. In addition, this study examined whether psychological distress and sexual attitudes mediated and whether marianismo beliefs moderated this relation. A categorical confirmatory factor analysis (CCFA) of the Marianismo Beliefs Scale (MBS) was conducted to test the factor structure with a preadolescent Mexican American population (ages 11-14). A path analysis of analytic models was then performed to examine the hypothesized relations between perceived discrimination, psychological distress, sexual attitudes, marianismo beliefs, and sexual precursor behaviors. Results of the CCFA did not support the original 5-factor structure of the MBS for preadolescent Latina girls. However, a revised version of the MBS indicated an acceptable model fit, and findings from the path analysis indicated that perceived discrimination was both directly and indirectly linked to sexual precursor behaviors via psychological distress. Marianismo was not found to moderate the relation between perceived discrimination and sexual risk behaviors, however certain marianismo pillars were significantly negatively linked with sexual attitudes and precursor behaviors. This study underscores the importance of psychological distress in the perceived discrimination and sexual precursor link as well as the compensatory aspects of marianismo against sexual precursor behaviors in Mexican American preadolescent girls. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
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[Identification of Dendrobium varieties by infrared spectroscopy].
Liu, Fei; Wang, Yuan-Zhong; Yang, Chun-Yan; Jin, Hang
2014-11-01
The difference of Dendrobium varieties were analyzed by Fourier transform infrared (FTIR) spectroscopy. The infrared spectra of 206 stems from 30 Dendrobium varieties were obtained, and showed that polysaccharides, especially fiber, were the main components in Dendrobium plants. FTIR combined with Wilks' Lambda stepwise discriminative analysis was used to identify Dendrobium varieties. The effects of spectral range and number of training samples on the discrimination results were also analysed. Two hundred eighty seven variables in the spectral range of 1 800-1 250 cm(-1) were studied, and showed that the return discrimination is 100% correct when the training samples number of each species was 2, 3, 4, 5, and 6, respectively, whereas for the remaining samples the correct rates of identification were equal to 79.4%, 91.3%, 93.0%, 98.2%, and 100%, respectively. The same discriminative analyses on five different training samples in the spectral range of 1 800-1 500, 1 500-1 250, 1 250-600, 1 250-950 and 950-650 cm(-1) were compared, which showed that the variables in the range of 1 800-1 250, 1 800-1 500 and 950-600 cm(-1) were more suitable for variety identification, and one can obtain the satisfactory result for discriminative analysis when the training sample is more than 3. Our results indicate that FTIR combined with stepwise discriminative analysis is an effective way to distinguish different Dendrobium varieties.
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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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Prabitha, Vasumathi Gopala; Suchetha, Sambasivan; Jayanthi, Jayaraj Lalitha; Baiju, Kamalasanan Vijayakumary; Rema, Prabhakaran; Anuraj, Koyippurath; Mathews, Anita; Sebastian, Paul; Subhash, Narayanan
2016-01-01
Diffuse reflectance (DR) spectroscopy is a non-invasive, real-time, and cost-effective tool for early detection of malignant changes in squamous epithelial tissues. The present study aims to evaluate the diagnostic power of diffuse reflectance spectroscopy for non-invasive discrimination of cervical lesions in vivo. A clinical trial was carried out on 48 sites in 34 patients by recording DR spectra using a point-monitoring device with white light illumination. The acquired data were analyzed and classified using multivariate statistical analysis based on principal component analysis (PCA) and linear discriminant analysis (LDA). Diagnostic accuracies were validated using random number generators. The receiver operating characteristic (ROC) curves were plotted for evaluating the discriminating power of the proposed statistical technique. An algorithm was developed and used to classify non-diseased (normal) from diseased sites (abnormal) with a sensitivity of 72 % and specificity of 87 %. While low-grade squamous intraepithelial lesion (LSIL) could be discriminated from normal with a sensitivity of 56 % and specificity of 80 %, and high-grade squamous intraepithelial lesion (HSIL) from normal with a sensitivity of 89 % and specificity of 97 %, LSIL could be discriminated from HSIL with 100 % sensitivity and specificity. The areas under the ROC curves were 0.993 (95 % confidence interval (CI) 0.0 to 1) and 1 (95 % CI 1) for the discrimination of HSIL from normal and HSIL from LSIL, respectively. The results of the study show that DR spectroscopy could be used along with multivariate analytical techniques as a non-invasive technique to monitor cervical disease status in real time.
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Regional Moment Tensor Source-Type Discrimination Analysis
2015-11-16
Research Laboratory Space Vehicles Directorate 3550 Aberdeen Avenue SE Kirtland AFB, NM 87117-5776 AFRL /RVBYE 11. SPONSOR/MONITOR’S REPORT...OCP 8725 John J. Kingman Rd, Suite 0944 Ft Belvoir, VA 22060-6218 1 cy AFRL /RVIL Kirtland AFB, NM 87117-5776 2 cys Official Record Copy... AFRL -RV-PS- AFRL -RV-PS- TR-2016-0014 TR-2016-0014 REGIONAL MOMENT TENSOR SOURCE-TYPE DISCRIMINATION ANALYSIS Douglas S. Dreger, et al
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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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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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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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Prediction of hospital failure: a post-PPS analysis.
Gardiner, L R; Oswald, S L; Jahera, J S
1996-01-01
This study investigates the ability of discriminant analysis to provide accurate predictions of hospital failure. Using data from the period following the introduction of the Prospective Payment System, we developed discriminant functions for each of two hospital ownership categories: not-for-profit and proprietary. The resulting discriminant models contain six and seven variables, respectively. For each ownership category, the variables represent four major aspects of financial health (liquidity, leverage, profitability, and efficiency) plus county marketshare and length of stay. The proportion of closed hospitals misclassified as open one year before closure does not exceed 0.05 for either ownership type. Our results show that discriminant functions based on a small set of financial and nonfinancial variables provide the capability to predict hospital failure reliably for both not-for-profit and proprietary hospitals.
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Lee, Debbiesiu L; Ahn, Soyeon
2013-01-01
This meta-analysis synthesized the results of 27 studies examining the relations of racial identity, ethnic identity, and racial socialization to discrimination-distress for Black Americans. The purpose was to uncover which constructs connected to racial identity, ethnic identity, and racial socialization most strongly correlate with racial discrimination and psychological distress. Discrimination significantly related to aspects of racial identity, including immersion-emersion, public regard, encounter, Afrocentricity/racial centrality/private regard, and internalization. Distress significantly correlated with preencounter/assimilation, encounter, public regard, immersion-emersion, and Afrocentricity/racial centrality/private regard. Several of these relationships were significantly moderated by the measure of racial identity or demographic variables (gender or age). Implications of these findings are discussed. PsycINFO Database Record (c) 2013 APA, all rights reserved.
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Linear discriminant analysis with misallocation in training samples
NASA Technical Reports Server (NTRS)
Chhikara, R. (Principal Investigator); Mckeon, J.
1982-01-01
Linear discriminant analysis for a two-class case is studied in the presence of misallocation in training samples. A general appraoch to modeling of mislocation is formulated, and the mean vectors and covariance matrices of the mixture distributions are derived. The asymptotic distribution of the discriminant boundary is obtained and the asymptotic first two moments of the two types of error rate given. Certain numerical results for the error rates are presented by considering the random and two non-random misallocation models. It is shown that when the allocation procedure for training samples is objectively formulated, the effect of misallocation on the error rates of the Bayes linear discriminant rule can almost be eliminated. If, however, this is not possible, the use of Fisher rule may be preferred over the Bayes rule.
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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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Japanese International Female Students' Experience of Discrimination, Prejudice, and Stereotypes
ERIC Educational Resources Information Center
Bonazzo, Claude; Wong, Y. Joel
2007-01-01
This qualitative study examined four Japanese international female college students' experience of discrimination, prejudice, and stereotypes in a predominately white university. Four themes emerged from the analysis of data: (1) overt forms of prejudice and discrimination; (2) stereotypes common to Asians; (3) stereotypes unique to the Japanese;…
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ERIC Educational Resources Information Center
Chambers, Brittany D.; Erausquin, Jennifer Toller
2018-01-01
Background: Adolescence is a critical phase of development and experimentation with delinquent behaviors. There is a growing body of literature exploring individual and structural impacts of discrimination on health outcomes and delinquent behaviors. However, there is limited research assessing how school diversity and discrimination impact…
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Ethiopian Emerging Adult Immigrants in Israel: Coping with Discrimination and Racism
ERIC Educational Resources Information Center
Walsh, Sophie D.; Tuval-Mashiach, Rivka
2012-01-01
Experiences of discrimination and racism and individual coping strategies were examined among 22 emerging adult Ethiopian immigrants in Israel. In-depth interviews explored the way they perceive, understand, respond to, and cope with experiences of discrimination. Qualitative analysis identified an initial contrast between those interviewees who…
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Racial Discrimination and Asian Mental Health: A Meta-Analysis
ERIC Educational Resources Information Center
Lee, Debbiesiu L.; Ahn, Soyeon
2011-01-01
Although research on racial discrimination and mental health has proliferated, findings are varied and dispersed. This study explored the critical question of how Asians, in particular, deal with discrimination and how this relates to Asian mental health. With 99 correlations from 23 independent studies, the overall relationship between racial…
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ERIC Educational Resources Information Center
Banks, Kira Hudson; Kohn-Wood, Laura P.
2007-01-01
This study examined the association between racial identity profiles, discrimination, and mental health outcomes. African American college students (N = 194) completed measures of racial discrimination, racial identity, college hassles, and depressive symptoms. Four meaningful profiles emerged through a cluster analysis of seven dimensions of…
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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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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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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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Clark, Trenette T; Salas-Wright, Christopher P; Vaughn, Michael G; Whitfield, Keith E
2015-01-01
Perceived discrimination is a major source of health-related stress. The purpose of this study was to model the heterogeneity of everyday-discrimination experiences among African American and Caribbean Blacks and to identify differences in the prevalence of mood and substance use outcomes, including generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder among the identified subgroups. The study uses data from the National Survey of American Life obtained from a sample of African American and Caribbean Black respondents (N=4,462) between 18 and 65 years. We used latent profile analysis and multinomial regression analyses to identify and validate latent subgroups and test hypotheses, yielding 4 classes of perceived everyday discrimination: Low Discrimination, Disrespect and Condescension, General Discrimination, and Chronic Discrimination. Findings show significant differences exist between the Low Discrimination and General Discrimination classes for major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Moreover, we find significant differences exist between the Low Discrimination and Chronic Discrimination classes for the four disorders examined. Compared with the Chronic Discrimination class, members of the other classes were significantly less likely to meet criteria for generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Findings suggest elevated levels of discrimination increase risk for mood and substance-use disorders. Importantly, results suggest the prevalence of mood and substance-use disorders is a function of the type and frequency of discrimination that individuals experience. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Determination of authenticity of brand perfume using electronic nose prototypes
NASA Astrophysics Data System (ADS)
Gebicki, Jacek; Szulczynski, Bartosz; Kaminski, Marian
2015-12-01
The paper presents the practical application of an electronic nose technique for fast and efficient discrimination between authentic and fake perfume samples. Two self-built electronic nose prototypes equipped with a set of semiconductor sensors were employed for that purpose. Additionally 10 volunteers took part in the sensory analysis. The following perfumes and their fake counterparts were analysed: Dior—Fahrenheit, Eisenberg—J’ose, YSL—La nuit de L’homme, 7 Loewe and Spice Bomb. The investigations were carried out using the headspace of the aqueous solutions. Data analysis utilized multidimensional techniques: principle component analysis (PCA), linear discrimination analysis (LDA), k-nearest neighbour (k-NN). The results obtained confirmed the legitimacy of the electronic nose technique as an alternative to the sensory analysis as far as the determination of authenticity of perfume is concerned.
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Soleimani, Mohammad Ali; Yaghoobzadeh, Ameneh; Bahrami, Nasim; Sharif, Saeed Pahlevan; Sharif Nia, Hamid
2016-10-01
In this study, 398 Iranian cancer patients completed the 15-item Templer's Death Anxiety Scale (TDAS). Tests of internal consistency, principal components analysis, and confirmatory factor analysis were conducted to assess the internal consistency and factorial validity of the Persian TDAS. The construct reliability statistic and average variance extracted were also calculated to measure construct reliability, convergent validity, and discriminant validity. Principal components analysis indicated a 3-component solution, which was generally supported in the confirmatory analysis. However, acceptable cutoffs for construct reliability, convergent validity, and discriminant validity were not fulfilled for the three subscales that were derived from the principal component analysis. This study demonstrated both the advantages and potential limitations of using the TDAS with Persian-speaking cancer patients.
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Kim, Yugyun; Son, Inseo; Wie, Dainn; Muntaner, Carles; Kim, Hyunwoo; Kim, Seung-Sup
2016-07-19
Ethnic discrimination is increasingly common nowadays in South Korea with the influx of migrants. Despite the growing body of evidences suggests that ethnic discrimination negatively impacts health, only few researches have been conducted on the association between ethnic discrimination and health outcomes among marriage migrants in Korea. This study sought to examine how ethnic discrimination and response to the discrimination are related to self-rated health and whether the association differs by victim's gender. We conducted two-step analysis using cross-sectional dataset from the 'National Survey of Multicultural Families 2012'. First, we examined the association between perceived ethnic discrimination and self-rated health among 14,406 marriage migrants in Korea. Second, among the marriage migrants who experienced ethnic discrimination (n=5,880), we examined how response to discrimination (i.e., whether or not asking for fair treatment) is related to poor self-rated health. All analyses were conducted after being stratified by the migrant's gender. This research found the significant association between ethnic discrimination and poor self-rated health among female marriage migrants (OR: 1.53, 95 % CI: 1.32, 1.76), but not among male marriage migrants (OR: 1.16, 95 % CI: 0.81, 1.66). In the restricted analysis with marriage migrants who experienced ethnic discrimination, compared to the group who did not ask for fair treatment, female marriage migrants who asked for fair treatment were more likely to report poor self-rated health (OR: 1.21, 95 % CI: 0.98, 1.50); however, male marriage migrants who asked for fair treatment were less likely to report poor self-rated health (OR: 0.65, 95 % CI: 0.36, 1.04) although both were not statistically significant. This is the first study to investigate gender difference in the association between response to ethnic discrimination and self-rated health in South Korea. We discussed that gender may play an important role in the association between response to discrimination and self-rated health among marriage migrants in Korea. In order to prevent discrimination which could endanger the health of ethnic minorities including marriage migrants, relevant policies are needed.
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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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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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Kortesniemi, Maaria; Sinkkonen, Jari; Yang, Baoru; Kallio, Heikki
2014-03-15
¹H NMR spectroscopy and multivariate data analysis were applied to the metabolic profiling and discrimination of wild sea buckthorn (Hippophaë rhamnoides L.) berries from different locations in Finland (subspecies (ssp.) rhamnoides) and China (ssp. sinensis). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) showed discrimination of the two subspecies and different growth sites. The discrimination of ssp. rhamnoides was mainly associated with typically higher temperature, radiation and humidity and lower precipitation in the south, yielding higher levels of O-ethyl β-d-glucopyranoside and d-glucose, and lower levels of malic, quinic and ascorbic acids. Significant metabolic differences (p<0.05) in genetically identical berries were observed between latitudes 60° and 67° north in Finland. High altitudes (> 2,000 m) correlated with greater levels of malic and ascorbic acids in ssp. sinensis. The NMR metabolomics approach applied here is effective for identification of metabolites, geographical origin and subspecies of sea buckthorn berries. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Detection of non-milk fat in milk fat by gas chromatography and linear discriminant analysis.
Gutiérrez, R; Vega, S; Díaz, G; Sánchez, J; Coronado, M; Ramírez, A; Pérez, J; González, M; Schettino, B
2009-05-01
Gas chromatography was utilized to determine triacylglycerol profiles in milk and non-milk fat. The values of triacylglycerol were subjected to linear discriminant analysis to detect and quantify non-milk fat in milk fat. Two groups of milk fat were analyzed: A) raw milk fat from the central region of Mexico (n = 216) and B) ultrapasteurized milk fat from 3 industries (n = 36), as well as pork lard (n = 2), bovine tallow (n = 2), fish oil (n = 2), peanut (n = 2), corn (n = 2), olive (n = 2), and soy (n = 2). The samples of raw milk fat were adulterated with non-milk fats in proportions of 0, 5, 10, 15, and 20% to form 5 groups. The first function obtained from the linear discriminant analysis allowed the correct classification of 94.4% of the samples with levels <10% of adulteration. The triacylglycerol values of the ultrapasteurized milk fats were evaluated with the discriminant function, demonstrating that one industry added non-milk fat to its product in 80% of the samples analyzed.
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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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Aursand, Marit; Standal, Inger B; Praël, Angelika; McEvoy, Lesley; Irvine, Joe; Axelson, David E
2009-05-13
(13)C nuclear magnetic resonance (NMR) in combination with multivariate data analysis was used to (1) discriminate between farmed and wild Atlantic salmon ( Salmo salar L.), (2) discriminate between different geographical origins, and (3) verify the origin of market samples. Muscle lipids from 195 Atlantic salmon of known origin (wild and farmed salmon from Norway, Scotland, Canada, Iceland, Ireland, the Faroes, and Tasmania) in addition to market samples were analyzed by (13)C NMR spectroscopy and multivariate analysis. Both probabilistic neural networks (PNN) and support vector machines (SVM) provided excellent discrimination (98.5 and 100.0%, respectively) between wild and farmed salmon. Discrimination with respect to geographical origin was somewhat more difficult, with correct classification rates ranging from 82.2 to 99.3% by PNN and SVM, respectively. In the analysis of market samples, five fish labeled and purchased as wild salmon were classified as farmed salmon (indicating mislabeling), and there were also some discrepancies between the classification and the product declaration with regard to geographical origin.
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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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Plural-wavelength flame detector that discriminates between direct and reflected radiation
NASA Technical Reports Server (NTRS)
Hall, Gregory H. (Inventor); Barnes, Heidi L. (Inventor); Medelius, Pedro J. (Inventor); Simpson, Howard J. (Inventor); Smith, Harvey S. (Inventor)
1997-01-01
A flame detector employs a plurality of wavelength selective radiation detectors and a digital signal processor programmed to analyze each of the detector signals, and determine whether radiation is received directly from a small flame source that warrants generation of an alarm. The processor's algorithm employs a normalized cross-correlation analysis of the detector signals to discriminate between radiation received directly from a flame and radiation received from a reflection of a flame to insure that reflections will not trigger an alarm. In addition, the algorithm employs a Fast Fourier Transform (FFT) frequency spectrum analysis of one of the detector signals to discriminate between flames of different sizes. In a specific application, the detector incorporates two infrared (IR) detectors and one ultraviolet (UV) detector for discriminating between a directly sensed small hydrogen flame, and reflections from a large hydrogen flame. The signals generated by each of the detectors are sampled and digitized for analysis by the digital signal processor, preferably 250 times a second. A sliding time window of approximately 30 seconds of detector data is created using FIFO memories.
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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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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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NASA Technical Reports Server (NTRS)
Quattrochi, D. A.
1984-01-01
An initial analysis of LANDSAT 4 Thematic Mapper (TM) data for the discrimination of agricultural, forested wetland, and urban land covers is conducted using a scene of data collected over Arkansas and Tennessee. A classification of agricultural lands derived from multitemporal LANDSAT Multispectral Scanner (MSS) data is compared with a classification of TM data for the same area. Results from this comparative analysis show that the multitemporal MSS classification produced an overall accuracy of 80.91% while the TM classification yields an overall classification accuracy of 97.06% correct.
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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)
Vasefi, Fartash; Kittle, David S.; Nie, Zhaojun; Falcone, Christina; Patil, Chirag G.; Chu, Ray M.; Mamelak, Adam N.; Black, Keith L.; Butte, Pramod V.
2016-04-01
We have developed and tested a system for real-time intra-operative optical identification and classification of brain tissues using time-resolved fluorescence spectroscopy (TRFS). A supervised learning algorithm using linear discriminant analysis (LDA) employing selected intrinsic fluorescence decay temporal points in 6 spectral bands was employed to maximize statistical significance difference between training groups. The linear discriminant analysis on in vivo human tissues obtained by TRFS measurements (N = 35) were validated by histopathologic analysis and neuronavigation correlation to pre-operative MRI images. These results demonstrate that TRFS can differentiate between normal cortex, white matter and glioma.
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Fraysse, Bodvaël; Barthélémy, Inès; Qannari, El Mostafa; Rouger, Karl; Thorin, Chantal; Blot, Stéphane; Le Guiner, Caroline; Chérel, Yan; Hogrel, Jean-Yves
2017-04-12
Accelerometric analysis of gait abnormalities in golden retriever muscular dystrophy (GRMD) dogs is of limited sensitivity, and produces highly complex data. The use of discriminant analysis may enable simpler and more sensitive evaluation of treatment benefits in this important preclinical model. Accelerometry was performed twice monthly between the ages of 2 and 12 months on 8 healthy and 20 GRMD dogs. Seven accelerometric parameters were analysed using linear discriminant analysis (LDA). Manipulation of the dependent and independent variables produced three distinct models. The ability of each model to detect gait alterations and their pattern change with age was tested using a leave-one-out cross-validation approach. Selecting genotype (healthy or GRMD) as the dependent variable resulted in a model (Model 1) allowing a good discrimination between the gait phenotype of GRMD and healthy dogs. However, this model was not sufficiently representative of the disease progression. In Model 2, age in months was added as a supplementary dependent variable (GRMD_2 to GRMD_12 and Healthy_2 to Healthy_9.5), resulting in a high overall misclassification rate (83.2%). To improve accuracy, a third model (Model 3) was created in which age was also included as an explanatory variable. This resulted in an overall misclassification rate lower than 12%. Model 3 was evaluated using blinded data pertaining to 81 healthy and GRMD dogs. In all but one case, the model correctly matched gait phenotype to the actual genotype. Finally, we used Model 3 to reanalyse data from a previous study regarding the effects of immunosuppressive treatments on muscular dystrophy in GRMD dogs. Our model identified significant effect of immunosuppressive treatments on gait quality, corroborating the original findings, with the added advantages of direct statistical analysis with greater sensitivity and more comprehensible data representation. Gait analysis using LDA allows for improved analysis of accelerometry data by applying a decision-making analysis approach to the evaluation of preclinical treatment benefits in GRMD dogs.
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Hahn, Allison H; Campbell, Kimberley A; Congdon, Jenna V; Hoang, John; McMillan, Neil; Scully, Erin N; Yong, Joshua J H; Elie, Julie E; Sturdy, Christopher B
2017-07-01
Chickadees produce a multi-note chick-a-dee call in multiple socially relevant contexts. One component of this call is the D note, which is a low-frequency and acoustically complex note with a harmonic-like structure. In the current study, we tested black-capped chickadees on a between-category operant discrimination task using vocalizations with acoustic structures similar to black-capped chickadee D notes, but produced by various songbird species, in order to examine the role that phylogenetic distance plays in acoustic perception of vocal signals. We assessed the extent to which discrimination performance was influenced by the phylogenetic relatedness among the species producing the vocalizations and by the phylogenetic relatedness between the subjects' species (black-capped chickadees) and the vocalizers' species. We also conducted a bioacoustic analysis and discriminant function analysis in order to examine the acoustic similarities among the discrimination stimuli. A previous study has shown that neural activation in black-capped chickadee auditory and perceptual brain regions is similar following the presentation of these vocalization categories. However, we found that chickadees had difficulty discriminating between forward and reversed black-capped chickadee D notes, a result that directly corresponded to the bioacoustic analysis indicating that these stimulus categories were acoustically similar. In addition, our results suggest that the discrimination between vocalizations produced by two parid species (chestnut-backed chickadees and tufted titmice) is perceptually difficult for black-capped chickadees, a finding that is likely in part because these vocalizations contain acoustic similarities. Overall, our results provide evidence that black-capped chickadees' perceptual abilities are influenced by both phylogenetic relatedness and acoustic structure.
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Facial patterns in a tropical social wasp correlate with colony membership
NASA Astrophysics Data System (ADS)
Baracchi, David; Turillazzi, Stefano; Chittka, Lars
2016-10-01
Social insects excel in discriminating nestmates from intruders, typically relying on colony odours. Remarkably, some wasp species achieve such discrimination using visual information. However, while it is universally accepted that odours mediate a group level recognition, the ability to recognise colony members visually has been considered possible only via individual recognition by which wasps discriminate `friends' and `foes'. Using geometric morphometric analysis, which is a technique based on a rigorous statistical theory of shape allowing quantitative multivariate analyses on structure shapes, we first quantified facial marking variation of Liostenogaster flavolineata wasps. We then compared this facial variation with that of chemical profiles (generated by cuticular hydrocarbons) within and between colonies. Principal component analysis and discriminant analysis applied to sets of variables containing pure shape information showed that despite appreciable intra-colony variation, the faces of females belonging to the same colony resemble one another more than those of outsiders. This colony-specific variation in facial patterns was on a par with that observed for odours. While the occurrence of face discrimination at the colony level remains to be tested by behavioural experiments, overall our results suggest that, in this species, wasp faces display adequate information that might be potentially perceived and used by wasps for colony level recognition.
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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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Laurencikas, E; Sävendahl, L; Jorulf, H
2006-06-01
To assess the value of the metacarpophalangeal pattern profile (MCPP) analysis as a diagnostic tool for differentiating between patients with dyschondrosteosis, Turner syndrome, and hypochondroplasia. Radiographic and clinical data from 135 patients between 1 and 51 years of age were collected and analyzed. The study included 25 patients with hypochondroplasia (HCP), 39 with dyschondrosteosis (LWD), and 71 with Turner syndrome (TS). Hand pattern profiles were calculated and compared with those of 110 normal individuals. Pearson correlation coefficient (r) and multivariate discriminant analysis were used for pattern profile analysis. Pattern variability index, a measure of dysmorphogenesis, was calculated for LWD, TS, HCP, and normal controls. Our results demonstrate that patients with LWD, TS, or HCP have distinct pattern profiles that are significantly different from each other and from those of normal controls. Discriminant analysis yielded correct classification of normal versus abnormal individuals in 84% of cases. Classification of the patients into LWD, TS, and HCP groups was successful in 75%. The correct classification rate was higher (85%) when differentiating two pathological groups at a time. Pattern variability index was not helpful for differential diagnosis of LWD, TS, and HCP. Patients with LWD, TS, or HCP have distinct MCPPs and can be successfully differentiated from each other using advanced MCPP analysis. Discriminant analysis is to be preferred over Pearson correlation coefficient because it is a more sensitive and specific technique. MCPP analysis is a helpful tool for differentiating between syndromes with similar clinical and radiological abnormalities.
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NASA Astrophysics Data System (ADS)
Feller, Jens; Feller, Sebastian; Mauersberg, Bernhard; Mergenthaler, Wolfgang
2009-09-01
Many applications in plant management require close monitoring of equipment performance, in particular with the objective to prevent certain critical events. At each point in time, the information available to classify the criticality of the process, is represented through the historic signal database as well as the actual measurement. This paper presents an approach to detect and predict critical events, based on pattern recognition and discriminance analysis.
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Typification of cider brandy on the basis of cider used in its manufacture.
Rodríguez Madrera, Roberto; Mangas Alonso, Juan J
2005-04-20
A study of typification of cider brandies on the basis of the origin of the raw material used in their manufacture was conducted using chemometric techniques (principal component analysis, linear discriminant analysis, and Bayesian analysis) together with their composition in volatile compounds, as analyzed by gas chromatography with flame ionization to detect the major volatiles and by mass spectrometric to detect the minor ones. Significant principal components computed by a double cross-validation procedure allowed the structure of the database to be visualized as a function of the raw material, that is, cider made from fresh apple juice versus cider made from apple juice concentrate. Feasible and robust discriminant rules were computed and validated by a cross-validation procedure that allowed the authors to classify fresh and concentrate cider brandies, obtaining classification hits of >92%. The most discriminating variables for typifying cider brandies according to their raw material were 1-butanol and ethyl hexanoate.
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NMR-based metabolomic analysis of spatial variation in soft corals.
He, Qing; Sun, Ruiqi; Liu, Huijuan; Geng, Zhufeng; Chen, Dawei; Li, Yinping; Han, Jiao; Lin, Wenhan; Du, Shushan; Deng, Zhiwei
2014-03-28
Soft corals are common marine organisms that inhabit tropical and subtropical oceans. They are shown to be rich source of secondary metabolites with biological activities. In this work, soft corals from two geographical locations were investigated using ¹H-NMR spectroscopy coupled with multivariate statistical analysis at the metabolic level. A partial least-squares discriminant analysis showed clear separation among extracts of soft corals grown in Sanya Bay and Weizhou Island. The specific markers that contributed to discrimination between soft corals in two origins belonged to terpenes, sterols and N-containing compounds. The satisfied precision of classification obtained indicates this approach using combined ¹H-NMR and chemometrics is effective to discriminate soft corals collected in different geographical locations. The results revealed that metabolites of soft corals evidently depended on living environmental condition, which would provide valuable information for further relevant coastal marine environment evaluation.
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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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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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Noh, Jin-Won; Park, Hyunchun; Kim, Minji; Kwon, Young Dae; Kim, Jin-Seok; Yu, Shieun
2018-01-01
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. 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. 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. 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.
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Fluorescent polymer sensor array for detection and discrimination of explosives in water.
Woodka, Marc D; Schnee, Vincent P; Polcha, Michael P
2010-12-01
A fluorescent polymer sensor array (FPSA) was made from commercially available fluorescent polymers coated onto glass beads and was tested to assess the ability of the array to discriminate between different analytes in aqueous solution. The array was challenged with exposures to 17 different analytes, including the explosives trinitrotoluene (TNT), tetryl, and RDX, various explosive-related compounds (ERCs), and nonexplosive electron-withdrawing compounds (EWCs). The array exhibited a natural selectivity toward EWCs, while the non-electron-withdrawing explosive 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) produced no response. Response signatures were visualized by principal component analysis (PCA), and classified by linear discriminant analysis (LDA). RDX produced the same response signature as the sampled blanks and was classified accordingly. The array exhibited excellent discrimination toward all other compounds, with the exception of the isomers of nitrotoluene and aminodinitrotoluene. Of particular note was the ability of the array to discriminate between the three isomers of dinitrobenzene. The natural selectivity of the FPSA toward EWCs, plus the ability of the FPSA to discriminate between different EWCs, could be used to design a sensor with a low false alarm rate and an excellent ability to discriminate between explosives and explosive-related compounds.
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Perceived Discrimination Experiences and Mental Health of Latina/o American Persons
ERIC Educational Resources Information Center
Moradi, Bonnie; Risco, Cristina
2006-01-01
With a sample of 128 Latina/o persons, the present study examined a model that tested direct, indirect, and mediated relations among perceived discrimination, psychological distress, self-esteem, sense of personal control, and acculturation to Latina/o and U.S. cultures. Path analysis of the model indicated that (a) perceived discrimination was…
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Their Personal Side of Street-Level Bureaucracy: Discrimination or Neutral Competence?
ERIC Educational Resources Information Center
Thomas, John Clayton
1986-01-01
Analysis of data on citizen contacts with the Cincinnati municipal bureaucracy reveals some possible racial and social discrimination in treatment of citizens, but no discrimination by income. However, municipal administrators appear mostly to be exercising neutral competence in their dealings with citizens, and not always to the disadvantage of…
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An Analysis of Discrimination by Real Estate Brokers.
ERIC Educational Resources Information Center
Yinger, John
This paper focuses on designing policies to eliminate discrimination in the sale of single-family houses by analyzing the behavior of the agents who actually do most of the discriminating, namely real estate agents. Discriminatory practices are said to be supported by policies of house builders, lending institutions, and government, and by the…
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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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Liu, Tsang-Sen; Lin, Jhen-Nan; Peng, Tsung-Ren
2018-01-16
Isotopic compositions of δ 2 H, δ 18 O, δ 13 C, and δ 15 N and concentrations of 22 trace elements from garlic samples were analyzed and processed with stepwise principal component analysis (PCA) to discriminate garlic's country of origin among Asian regions including South Korea, Vietnam, Taiwan, and China. Results indicate that there is no single trace-element concentration or isotopic composition that can accomplish the study's purpose and the stepwise PCA approach proposed does allow for discrimination between countries on a regional basis. Sequentially, Step-1 PCA distinguishes garlic's country of origin among Taiwanese, South Korean, and Vietnamese samples; Step-2 PCA discriminates Chinese garlic from South Korean garlic; and Step-3 and Step-4 PCA, Chinese garlic from Vietnamese garlic. In model tests, countries of origin of all audit samples were correctly discriminated by stepwise PCA. Consequently, this study demonstrates that stepwise PCA as applied is a simple and effective approach to discriminating country of origin among Asian garlics. © 2018 American Academy of Forensic Sciences.
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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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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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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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2010-01-01
Background Discrimination between clinical and environmental strains within many bacterial species is currently underexplored. Genomic analyses have clearly shown the enormous variability in genome composition between different strains of a bacterial species. In this study we have used Legionella pneumophila, the causative agent of Legionnaire's disease, to search for genomic markers related to pathogenicity. During a large surveillance study in The Netherlands well-characterized patient-derived strains and environmental strains were collected. We have used a mixed-genome microarray to perform comparative-genome analysis of 257 strains from this collection. Results Microarray analysis indicated that 480 DNA markers (out of in total 3360 markers) showed clear variation in presence between individual strains and these were therefore selected for further analysis. Unsupervised statistical analysis of these markers showed the enormous genomic variation within the species but did not show any correlation with a pathogenic phenotype. We therefore used supervised statistical analysis to identify discriminating markers. Genetic programming was used both to identify predictive markers and to define their interrelationships. A model consisting of five markers was developed that together correctly predicted 100% of the clinical strains and 69% of the environmental strains. Conclusions A novel approach for identifying predictive markers enabling discrimination between clinical and environmental isolates of L. pneumophila is presented. Out of over 3000 possible markers, five were selected that together enabled correct prediction of all the clinical strains included in this study. This novel approach for identifying predictive markers can be applied to all bacterial species, allowing for better discrimination between strains well equipped to cause human disease and relatively harmless strains. PMID:20630115
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Demographic and clinical features related to perceived discrimination in schizophrenia.
Fresán, Ana; Robles-García, Rebeca; Madrigal, Eduardo; Tovilla-Zarate, Carlos-Alfonso; Martínez-López, Nicolás; Arango de Montis, Iván
2018-04-01
Perceived discrimination contributes to the development of internalized stigma among those with schizophrenia. Evidence on demographic and clinical factors related to the perception of discrimination among this population is both contradictory and scarce in low- and middle-income countries. Accordingly, the main purpose of this study is to determine the demographic and clinical factors predicting the perception of discrimination among Mexican patients with schizophrenia. Two hundred and seventeen adults with paranoid schizophrenia completed an interview on their demographic status and clinical characteristics. Symptom severity was assessed using the Positive and Negative Syndrome Scale; and perceived discrimination using 13 items from the King's Internalized Stigma Scale. Bivariate linear associations were determined to identify the variables of interest to be included in a linear regression analysis. Years of education, age of illness onset and length of hospitalization were associated with discrimination. However, only age of illness onset and length of hospitalization emerged as predictors of perceived discrimination in the final regression analysis, with longer length of hospitalization being the independent variable with the greatest contribution. Fortunately, this is a modifiable factor regarding the perception of discrimination and self-stigma. Strategies for achieving this as part of community-based mental health care are also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.
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Desalu, Jessica M; Kim, Jueun; Zaso, Michelle J; Corriders, Sydnee R; Loury, Jacoby A; Minter, Monique L; Park, Aesoon
2017-09-21
Experiences of racial discrimination have been associated with diverse negative health outcomes among racial minorities. However, extant findings of the association between racial discrimination and alcohol behaviors among Black college students are mixed. The current study examined mediating roles of depressive symptoms and coping drinking motives in the association of perceived racial discrimination with binge drinking and negative drinking consequences. Data were obtained from a cross-sectional study of Black college students attending a predominantly White institution in the northeastern US (N = 251, 66% female, mean age = 20 years). Results from path analysis showed that, when potential mediators were not considered, perceived racial discrimination was positively associated with negative drinking consequences but not frequency of binge drinking. Serial multiple mediation analysis showed that depressive symptoms and in turn coping drinking motives partially mediated the associations of perceived racial discrimination with both binge drinking frequency and negative drinking consequences (after controlling for sex, age, and negative life events). Perceived racial discrimination is directly associated with experiences of alcohol-related problems, but not binge drinking behaviors among Black college students. Affective responses to perceived racial discrimination experiences and drinking to cope may serve as risk mechanisms for alcohol-related problems in this population. Implications for prevention and intervention efforts are discussed.
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Discriminant analysis for predictor of falls in stroke patients by using the Berg Balance Scale.
Maeda, Noriaki; Urabe, Yukio; Murakami, Masahito; Itotani, Keisuke; Kato, Junichi
2015-05-01
An observational study was carried out to estimate the strength of the relationships among balance, mobility and falls in hemiplegic stroke inpatients. The objective was to examine factors that may aid in the prediction of the likelihood of falls in stroke patients. A total of 53 stroke patients (30 male, 23 female) aged 67.0 ± 11.1 years were interviewed regarding their fall history. Physical performance was assessed using the Berg Balance Scale (BBS) and the Functional Independence Measure (FIM) scale. Variables that differed between fallers and non-fallers were identified, and a discriminant function analysis was carried out to determine the combination of variables that effectively predicted fall status. Of the 53 stroke patients, 19 were fallers. Compared with the non-fallers, the fallers scored low on the FIM, and differed with respect to age, time from stroke onset, length of hospital stay, Brunnstrom recovery stage and admission BBS score. Discriminant analysis for predicting falls in stroke patients showed that admission BBS score was significantly related to the likelihood of falls. Moreover, discriminant analysis showed that the use of a significant BBS score to classify fallers and non-fallers had an accuracy of 81.1%. The discriminating criterion between the two groups was a score of 31 points on the BBS. The results of this study suggest that BBS score is a strong predictor of falls in stroke patients. As balance is closely related to the risk of falls in hospitalised stroke patients, BBS might be useful in the prediction of falls.
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NASA Astrophysics Data System (ADS)
Silveira, Landulfo, Jr.; Silveira, Fabrício L.; Bodanese, Benito; Pacheco, Marcos Tadeu T.; Zângaro, Renato A.
2012-02-01
This work demonstrated the discrimination among basal cell carcinoma (BCC) and normal human skin in vivo using near-infrared Raman spectroscopy. Spectra were obtained in the suspected lesion prior resectional surgery. After tissue withdrawn, biopsy fragments were submitted to histopathology. Spectra were also obtained in the adjacent, clinically normal skin. Raman spectra were measured using a Raman spectrometer (830 nm) with a fiber Raman probe. By comparing the mean spectra of BCC with the normal skin, it has been found important differences in the 800-1000 cm-1 and 1250-1350 cm-1 (vibrations of C-C and amide III, respectively, from lipids and proteins). A discrimination algorithm based on Principal Components Analysis and Mahalanobis distance (PCA/MD) could discriminate the spectra of both tissues with high sensitivity and specificity.
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Mais, Enos; Alolga, Raphael N; Wang, Shi-Lei; Linus, Loveth O; Yin, Xiaojin; Qi, Lian-Wen
2018-02-01
Ginger, the rhizome of Zingiber officinale Roscoe, is a popular spice used in the food, beverage and confectionary industries. In this study, we report an untargeted UPLC-Q/TOF-MS-based metabolomics approach for comprehensively discriminating between ginger from two geographical locations, Ghana in West Africa and China. Forty batches of fresh ginger from both countries were discriminated using principal component analysis and orthogonal partial least squares discrimination analysis. Sixteen differential metabolites were identified between the gingers from the two geographical locations, six of which were identified as the marker compounds responsible for the discrimination. Our study highlights the essence and predictive power of metabolomics in detecting minute differences in same varieties of plants/plant samples based on the levels and composition of their metabolites. Copyright © 2017 Elsevier Ltd. All rights reserved.
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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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Methodologies for Evaluating the Impact of Contraceptive Social Marketing Programs.
ERIC Educational Resources Information Center
Bertrand, Jane T.; And Others
1989-01-01
An overview of the evaluation issues associated with contraceptive social marketing programs is provided. Methodologies covered include survey techniques, cost-effectiveness analyses, retail audits of sales data, time series analysis, nested logit analysis, and discriminant analysis. (TJH)
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Advanced microwave soil moisture studies. [Big Sioux River Basin, Iowa
NASA Technical Reports Server (NTRS)
Dalsted, K. J.; Harlan, J. C.
1983-01-01
Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: soil map and land cover data; direct soil moisture measurement; and a computer generated contour map were statistically evaluated using regression analysis and linear discriminant analysis. Regression analysis of footprint data shows that statistical groupings of ground variables (soil features and land cover) hold promise for qualitative assessment of soil moisture and for reducing variance within the sampling space. Dry conditions appear to be more conductive to producing meaningful statistics than wet conditions. Regression analysis using field averaged TB and TIR data did not approach the higher sq R values obtained using within-field variations. The linear discriminant analysis indicates some capacity to distinguish categories with the results being somewhat better on a field basis than a footprint basis.
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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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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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Pieterse, Alex L; Carter, Robert T; Evans, Sarah A; Walter, Rebecca A
2010-07-01
In this study, we examined the association among perceptions of racial and/or ethnic discrimination, racial climate, and trauma-related symptoms among 289 racially diverse college undergraduates. Study measures included the Perceived Stress Scale, the Perceived Ethnic Discrimination Questionnaire, the Posttraumatic Stress Disorder Checklist-Civilian Version, and the Racial Climate Scale. Results of a multivariate analysis of variance (MANOVA) indicated that Asian and Black students reported more frequent experiences of discrimination than did White students. Additionally, the MANOVA indicated that Black students perceived the campus racial climate as being more negative than did White and Asian students. A hierarchical regression analysis showed that when controlling for generic life stress, perceptions of discrimination contributed an additional 10% of variance in trauma-related symptoms for Black students, and racial climate contributed an additional 7% of variance in trauma symptoms for Asian students. (c) 2010 APA, all rights reserved.
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Grossman, R A
1995-09-01
The purpose of this study was to determine whether women can discriminate better from less effective paracervical block techniques applied to opposite sides of the cervix. If this discrimination could be made, it would be possible to compare different techniques and thus improve the quality of paracervical anesthesia. Two milliliters of local anesthetic was applied to one side and 6 ml to the other side of volunteers' cervices before cervical dilation. Statistical examination was by sequential analysis. The study was stopped after 47 subjects had entered, when sequential analysis found that there was no significant difference in women's perception of pain. Nine women reported more pain on the side with more anesthesia and eight reported more pain on the side with less anesthesia. Because the amount of anesthesia did not make a difference, the null hypothesis (that women cannot discriminate between different anesthetic techniques) was accepted. Women are not able to discriminate different doses of local anesthetic when applied to opposite sides of the cervix.
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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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Byrd, Christy M; Carter Andrews, Dorinda J
2016-08-01
Although there exists a healthy body of literature related to discrimination in schools, this research has primarily focused on racial or ethnic discrimination as perceived and experienced by students of color. Few studies examine students' perceptions of discrimination from a variety of sources, such as adults and peers, their descriptions of the discrimination, or the frequency of discrimination in the learning environment. Middle and high school students in a Midwestern school district (N=1468) completed surveys identifying whether they experienced discrimination from seven sources (e.g., peers, teachers, administrators), for seven reasons (e.g., gender, race/ethnicity, religion), and in eight forms (e.g., punished more frequently, called names, excluded from social groups). The sample was 52% White, 15% Black/African American, 14% Multiracial, and 17% Other. Latent class analysis was used to cluster individuals based on reported sources of, reasons for, and forms of discrimination. Four clusters were found, and ANOVAs were used to test for differences between clusters on perceptions of school climate, relationships with teachers, perceptions that the school was a "good school," and engagement. The Low Discrimination cluster experienced the best outcomes, whereas an intersectional cluster experienced the most discrimination and the worst outcomes. The results confirm existing research on the negative effects of discrimination. Additionally, the paper adds to the literature by highlighting the importance of an intersectional approach to examining students' perceptions of in-school discrimination. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.
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Chocholova, Erika; Bertok, Tomas; Jane, Eduard; Lorencova, Lenka; Holazova, Alena; Belicka, Ludmila; Belicky, Stefan; Mislovicova, Danica; Vikartovska, Alica; Imrich, Richard; Kasak, Peter; Tkac, Jan
2018-06-01
In this study, one hundred serum samples from healthy people and patients with rheumatoid arthritis (RA) were analyzed. Standard immunoassays for detection of 10 different RA markers and analysis of glycan markers on antibodies in 10 different assay formats with several lectins were applied for each serum sample. A dataset containing 2000 data points was data mined using artificial neural networks (ANN). We identified key RA markers, which can discriminate between healthy people and seropositive RA patients (serum containing autoantibodies) with accuracy of 83.3%. Combination of RA markers with glycan analysis provided much better discrimination accuracy of 92.5%. Immunoassays completely failed to identify seronegative RA patients (serum not containing autoantibodies), while glycan analysis correctly identified 43.8% of these patients. Further, we revealed other critical parameters for successful glycan analysis such as type of a sample, format of analysis and orientation of captured antibodies for glycan analysis. Copyright © 2018 Elsevier B.V. All rights reserved.
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Perfume Fragrance Discrimination Using Resistance And Capacitance Responses Of Polymer Sensors
NASA Astrophysics Data System (ADS)
Lima, John Paul Hempel; Vandendriessche, Thomas; Fonseca, Fernando J.; Lammertyn, Jeroen; Nicolai, Bart M.; de Andrade, Adnei Melges
2009-05-01
This work shows a comparison between electrical resistance and capacitance responses of ethanol and five different fragrances using an electronic nose based on conducting polymers. Gas chromatography—mass spectrometry (GC-MS) measurements were performed to evaluate the main differences between the analytes. It is shown that although the fragrances are quite similar in their compositions the sensors are able to discriminate them through PCA (Principal Component Analysis) and ANNs (Artificial Neural Network) analysis.
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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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Stewart, C M; Newlands, S D; Perachio, A A
2004-12-01
Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.
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Yu, HaiYan; Zhao, Jie; Li, Fenghua; Tian, Huaixiang; Ma, Xia
2015-08-01
To evaluate the taste characteristics of Chinese rice wine, wine samples sourced from different vintage years were analyzed using liquid chromatographic analysis, sensory evaluation, and an electronic tongue. Six organic acids and seventeen amino acids were measured using high performance liquid chromatography (HPLC). Five monosaccharides were measured using anion-exchange chromatography. The global taste attributes were analyzed using an electronic tongue (E-tongue). The correlations between the 28 taste-active compounds and the sensory attributes, and the correlations between the E-tongue response and the sensory attributes were established via partial least square discriminant analysis (PLSDA). E-tongue response data combined with linear discriminant analysis (LDA) were used to discriminate the Chinese rice wine samples sourced from different vintage years. Sensory evaluation indicated significant differences in the Chinese rice wine samples sourced from 2003, 2005, 2008, and 2010 vintage years in the sensory attributes of harmony and mellow. The PLSDA model for the taste-active compounds and the sensory attributes showed that proline, fucose, arabinose, lactic acid, glutamic acid, arginine, isoleucine, valine, threonine, and lysine had an influence on the taste characteristic of Chinese rice wine. The Chinese rice wine samples were all correctly classified using the E-tongue and LDA. The electronic tongue was an effective tool for rapid discrimination of Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.
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Carballido-Gamio, Julio; Krug, Roland; Huber, Markus B; Hyun, Ben; Eckstein, Felix; Majumdar, Sharmila; Link, Thomas M
2009-02-01
In vivo assessment of trabecular bone microarchitecture could improve the prediction of fracture risk and the efficacy of osteoporosis treatment and prevention. Geodesic topological analysis (GTA) is introduced as a novel technique to quantify the trabecular bone microarchitecture from high-spatial resolution magnetic resonance (MR) images. Trabecular bone parameters that quantify the scale, topology, and anisotropy of the trabecular bone network in terms of its junctions are the result of GTA. The reproducibility of GTA was tested with in vivo images of human distal tibiae and radii (n = 6) at 1.5 Tesla; and its ability to discriminate between subjects with and without vertebral fracture was assessed with ex vivo images of human calcanei at 1.5 and 3.0 Tesla (n = 30). GTA parameters yielded an average reproducibility of 4.8%, and their individual areas under the curve (AUC) of the receiver operating characteristic curve analysis for fracture discrimination performed better at 3.0 than at 1.5 Tesla reaching values of up to 0.78 (p < 0.001). Logistic regression analysis demonstrated that fracture discrimination was improved by combining GTA parameters, and that GTA combined with bone mineral density (BMD) allow for better discrimination than BMD alone (AUC = 0.95; p < 0.001). Results indicate that GTA can substantially contribute in studies of osteoporosis involving imaging of the trabecular bone microarchitecture. Copyright 2009 Wiley-Liss, Inc.
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Pardeck, J T
1998-01-01
People with psychiatric disabilities are often victims of job discrimination. The Americans with Disabilities Act (ADA) of 1990 makes it very clear that job discrimination based on a psychiatric impairment is illegal. The Equal Employment Opportunity Commission (EEOC) reports that psychiatric disability is one of the leading causes why persons file discrimination complaints. Given this situation, an analysis is offered of what constitutes a psychiatric disability under the ADA. An overview, an analysis, and examples of the kinds of accommodations employers can be expected to provide people with psychiatric disabilities are offered. The policy and practice implications of the employment provisions (Title I) in the area of psychiatric disabilities are presented.
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ERIC Educational Resources Information Center
Beauchamp, Chris M.; Stelmack, Robert M.
2006-01-01
The relation between intelligence and speed of auditory discrimination was investigated during an auditory oddball task with backward masking. In target discrimination conditions that varied in the interval between the target and the masking stimuli and in the tonal frequency of the target and masking stimuli, higher ability participants (HA)…
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Sex Discrimination--Court Narrows Gilbert--Some Pregnancy Discrimination Is Sex Related.
ERIC Educational Resources Information Center
Allen, Claudia G.; Powers, Jean C.
1978-01-01
Issues concerning sex discrimination based on pregnancy, presented in Nashville Gas Co. vs. Satty, and the Supreme Court's treatment of the issues are examined. The way in which the Satty opinion limits the scope of the General Electric Co. vs. Gilbert decision, and an analysis of the implications of the Satty decision are included. (JMD)
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ERIC Educational Resources Information Center
Caldwell, Julia T.; Takahashi, Lois M.
2014-01-01
Existing research suggests that religious institutions play a significant role in improving the health of communities, particularly those coping with racial and ethnic discrimination. Using the California Health Interview Survey, this article examines the relationship of self-reported experiences of racial/ethnic discrimination, worship…
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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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Bias of phencyclidine discrimination by the schedule of reinforcement.
McMillan, D E; Wenger, G R
1984-01-01
Pigeons, trained to discriminate phencyclidine from saline under a procedure requiring the bird to track the location of a color, received cumulative doses of phencyclidine, pentobarbital, or d-amphetamine with a variety of schedules of reinforcement in effect (across phases). When the same second-order schedules were used to reinforce responding after either saline or phencyclidine administration, stimulus control by phencyclidine did not depend on the schedule parameter. When different second-order schedules were used that biased responding toward the phencyclidine-correlated key color, pigeons responded on the phencyclidine-correlated key at lower doses of phencyclidine and pentobarbital than when the second-order schedule biased responding toward the saline key color. A similar but less marked effect was obtained with d-amphetamine. Attempts to produce bias by changing reinforcement magnitude (duration of food availability) were less successful. A signal-detection analysis of dose-effect curves for phencyclidine under two of the second-order schedules employed suggested that at low doses of phencyclidine, response bias is a major determinant of responding. As doses were increased, position preferences occurred and response bias decreased; at higher doses both response bias and position preference decreased and discriminability increased. With low doses of pentobarbital, responding again was biased but increasing doses produced position preference with only small increases in discriminability. At low doses of d-amphetamine responding also was biased, but bias did not decrease consistently with dose nor did discriminability increase. These experiments suggest that the schedule of reinforcement can be used to bias responding toward or away from making the drug-correlated response in drug discrimination experiments, and that signal-detection analysis and analysis of responding at a position can be used to separate the discriminability of the drug state from other effects of the drug on responding. PMID:6481300
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Hoff, Michael H.
2004-01-01
The lake herring (Coregonus artedi) was one of the most commercially and ecologically valuable Lake Superior fishes, but declined in the second half of the 20th century as the result of overharvest of putatively discrete stocks. No tools were previously available that described lake herring stock structure and accurately classified lake herring to their spawning stocks. The accuracy of discriminating among spawning aggregations was evaluated using whole-body morphometrics based on a truss network. Lake herring were collected from 11 spawning aggregations in Lake Superior and two inland Wisconsin lakes to evaluate morphometrics as a stock discrimination tool. Discriminant function analysis correctly classified 53% of all fish from all spawning aggregations, and fish from all but one aggregation were classified at greater rates than were possible by chance. Discriminant analysis also correctly classified 66% of fish to nearest neighbor groups, which were groups that accounted for the possibility of mixing among the aggregations. Stepwise discriminant analysis showed that posterior body length and depth measurements were among the best discriminators of spawning aggregations. These findings support other evidence that discrete stocks of lake herring exist in Lake Superior, and fishery managers should consider all but one of the spawning aggregations as discrete stocks. Abundance, annual harvest, total annual mortality rate, and exploitation data should be collected from each stock, and surplus production of each stock should be estimated. Prudent management of stock surplus production and exploitation rates will aid in restoration of stocks and will prevent a repeat of the stock collapses that occurred in the middle of the 20th century, when the species was nearly extirpated from the lake.
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Huart, C; Rombaux, Ph; Hummel, T; Mouraux, A
2013-09-01
The clinical usefulness of olfactory event-related brain potentials (OERPs) to assess olfactory function is limited by the relatively low signal-to-noise ratio of the responses identified using conventional time-domain averaging. Recently, it was shown that time-frequency analysis of the obtained EEG signals can markedly improve the signal-to-noise ratio of OERPs in healthy controls, because it enhances both phase-locked and non phase-locked EEG responses. The aim of the present study was to investigate the clinical usefulness of this approach and evaluate its feasibility in a clinical setting. We retrospectively analysed EEG recordings obtained from 45 patients (15 anosmic, 15 hyposmic and 15 normos- mic). The responses to olfactory stimulation were analysed using conventional time-domain analysis and joint time-frequency analysis. The ability of the two methods to discriminate between anosmic, hyposmic and normosmic patients was assessed using a Receiver Operating Characteristic analysis. The discrimination performance of OERPs identified using conventional time-domain averaging was poor. In contrast, the discrimination performance of the EEG response identified in the time-frequency domain was relatively high. Furthermore, we found a significant correlation between the magnitude of this response and the psychophysical olfactory score. Time-frequency analysis of the EEG responses to olfactory stimulation could be used as an effective and reliable diagnostic tool for the objective clinical evaluation of olfactory function in patients.
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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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Hussain, Hazilia; Yusoff, Mohd Kamil; Ramli, Mohd Firuz; Abd Latif, Puziah; Juahir, Hafizan; Zawawi, Mohamed Azwan Mohammed
2013-11-15
Nitrate-nitrogen leaching from agricultural areas is a major cause for groundwater pollution. Polluted groundwater with high levels of nitrate is hazardous and cause adverse health effects. Human consumption of water with elevated levels of NO3-N has been linked to the infant disorder methemoglobinemia and also to non-Hodgkin's disease lymphoma in adults. This research aims to study the temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy soil at Ladang Merdeka Ismail Mulong in Kelantan, Malaysia. The complex data matrix (128 x 16) of nitrate-nitrogen parameters was subjected to multivariate analysis mainly Principal Component Analysis (PCA) and Discriminant Analysis (DA). PCA extracted four principal components from this data set which explained 86.4% of the total variance. The most important contributors were soil physical properties confirmed using Alyuda Forecaster software (R2 = 0.98). Discriminant analysis was used to evaluate the temporal variation in soil nitrate-nitrogen on leaching process. Discriminant analysis gave four parameters (hydraulic head, evapotranspiration, rainfall and temperature) contributing more than 98% correct assignments in temporal analysis. DA allowed reduction in dimensionality of the large data set which defines the four operating parameters most efficient and economical to be monitored for temporal variations. This knowledge is important so as to protect the precious groundwater from contamination with nitrate.
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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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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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ERIC Educational Resources Information Center
Glover, Robert H.; Mills, Michael R.
A research design, decision support system, and results of a comparative analysis of enrollment and financial strength (of private institutions granting masters and doctoral degrees) are presented. Cluster analysis, discriminant analysis, multiple regression, and an interactive decision support system are used to compare the enrollment and…
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[Discrimination among different brands of coffee by using vis-near infrared spectra].
Wang, Yan-Yan; He, Yong; Shao, Yong-Ni; Zhang, Zhi-Fei
2007-04-01
Near infrared spectroscopy technology was used to distinguish three different brands of coffee bought from the supermarket. Two models, PCA-BP and WT-BP, were employed for the analysis and prediction of the samples. The discrimination among the different brands of coffee was based on the combination of the function of data compression in the PCA and WT technology and the ability of learning and prediction of the artificial neural network. In the experiment, 60 samples were used for model calibration and 30 for brand prediction. The result showed that both the PCA-BP and WT-BP models achieved 100% discrimination rate, and the wavelet transforms technology is superior to the principal component analysis both in time-consuming and the capability of data compression. It is indicated that the model set up by the combination of WT technology and BP neural network in the present study is rapid in analysis and precise in pattern discrimination. It can be concluded that a new approach to distinguishing pure coffee was of fered and the result of this experiment established the foundation for the determination of the raw material (coffee bean) of different brands of coffee in the market.
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Impairments of colour vision induced by organic solvents: a meta-analysis study.
Paramei, Galina V; Meyer-Baron, Monika; Seeber, Andreas
2004-09-01
The impairment of colour discrimination induced by occupational exposure to toluene, styrene and mixtures of organic solvents is reviewed and analysed using a meta-analytical approach. Thirty-nine studies were surveyed covering a wide range of exposure conditions. Those studies using the Lanthony Panel D-15 desaturated test (D-15d) were further considered. From these for 15 samples data on colour discrimination ability (Colour Confusion Index, CCI) and exposure levels were provided, required for the meta-analysis. In accordance with previously reported higher CCI values for the exposed groups, the computations yielded positive effect sizes for 13 of the 15 samples, indicating that in the great majority of the studies the exposed groups showed inferior colour discrimination. However, the meta-analysis showed great variation in effect sizes across the studies. Possible reasons for inconsistency among the reported findings are discussed. These pertain to exposure-related parameters, as well as to confounders such as conditions of test administration and characteristics of subject samples. Those factors vary considerably among the studies and might have greatly contributed to divergence in measured colour vision capacity, thereby obscuring consistent effects of organic solvents on colour discrimination.
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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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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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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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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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Gender-Related Differences in Pelvic Morphometrics of the Retriever Dog Breed.
Nganvongpanit, K; Pitakarnnop, T; Buddhachat, K; Phatsara, M
2017-02-01
This study presents the results from a morphometric analysis of 52 dry Retriever dog pelvic bones (30 male, 22 female). A total of 20 parameters were measured using an osteometric board and digital vernier caliper. Six parameters were found to be significantly higher (P < 0.05) in males than in females, while one parameter was significantly higher (P < 0.05) in females than in males. However, none of the measured parameters demonstrated clear cut-off values with no intersect between males and females. Therefore, we generated a stepwise discriminant analysis from all 20 parameters in order to develop a possible working equation to discriminate gender from a dog pelvic bone. Stepwise discriminant analysis was used to create a discrimination function: Y = [82.1*PS/AII] - [50.72*LIS/LI] - [23.09*OTD/SP] + [7.69*SP/IE] + [6.52*IC/OW] + [7.67*ISA/OW] + [20.77*AII/PS] + [504.71*OW/ISA] - [90.84*PS/ISA] - [148.95], which showed an accuracy rate of 86.27%. This is the first study presenting an equation/function for use in discriminating gender from a dog's pelvic measurements. The results can be used in veterinary forensic anthropology and also show that a dog's pelvis presents sexual dimorphisms, as in humans. © 2016 Blackwell Verlag GmbH.
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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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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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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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Zaghloul, Mohamed A. S.; Wang, Mohan; Milione, Giovanni; Li, Ming-Jun; Li, Shenping; Huang, Yue-Kai; Wang, Ting; Chen, Kevin P.
2018-01-01
Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores’ temperature and strain coefficients are such that temperature (strain) changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μϵ/MHz), which is 2.63 (3.67) times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain) changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%). PMID:29649148
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Davis, Philip A.; Grolier, Maurice J.
1984-01-01
Landsat multispectral scanner (MSS) band and band-ratio databases of two scenes covering the Midyan region of northwestern Saudi Arabia were examined quantitatively and qualitatively to determine which databases best discriminate the geologic units of this semi-arid and arid region. Unsupervised, linear-discriminant cluster-analysis was performed on these two band-ratio combinations and on the MSS bands for both scenes. The results for granitoid-rock discrimination indicated that the classification images using the MSS bands are superior to the band-ratio classification images for two reasons, discussed in the paper. Yet, the effects of topography and material type (including desert varnish) on the MSS-band data produced ambiguities in the MSS-band classification results. However, these ambiguities were clarified by using a simulated natural-color image in conjunction with the MSS-band classification image.
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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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Forensic discrimination of vaginal epithelia by DNA methylation analysis through pyrosequencing.
Antunes, Joana; Silva, Deborah S B S; Balamurugan, Kuppareddi; Duncan, George; Alho, Clarice S; McCord, Bruce
2016-10-01
The accurate identification of body fluids from crime scenes can aid in the discrimination between criminal and innocent intent. This research aimed to determine if the levels of DNA methylation in the locus PFN3A could be used to discriminate vaginal epithelia from other body fluids. In this work we bisulfite-modified and amplified DNA samples from blood, saliva, semen, and vaginal epithelia using primers for PFN3A. Through pyrosequencing we were able to show that vaginal epithelia present distinct methylation levels when compared to other body fluids. Mixtures of different body fluids present methylation values that correlate with single-source body fluid samples and the primers for PFN3A are specific for primates. This report successfully demonstrated that the analysis of methylation in the PFN3A locus can be used for vaginal epithelia discrimination in forensic samples. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Zaghloul, Mohamed A S; Wang, Mohan; Milione, Giovanni; Li, Ming-Jun; Li, Shenping; Huang, Yue-Kai; Wang, Ting; Chen, Kevin P
2018-04-12
Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores' temperature and strain coefficients are such that temperature (strain) changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μ ϵ /MHz), which is 2.63 (3.67) times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain) changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%).
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A Quantitative Approach to Scar Analysis
Khorasani, Hooman; Zheng, Zhong; Nguyen, Calvin; Zara, Janette; Zhang, Xinli; Wang, Joyce; Ting, Kang; Soo, Chia
2011-01-01
Analysis of collagen architecture is essential to wound healing research. However, to date no consistent methodologies exist for quantitatively assessing dermal collagen architecture in scars. In this study, we developed a standardized approach for quantitative analysis of scar collagen morphology by confocal microscopy using fractal dimension and lacunarity analysis. Full-thickness wounds were created on adult mice, closed by primary intention, and harvested at 14 days after wounding for morphometrics and standard Fourier transform-based scar analysis as well as fractal dimension and lacunarity analysis. In addition, transmission electron microscopy was used to evaluate collagen ultrastructure. We demonstrated that fractal dimension and lacunarity analysis were superior to Fourier transform analysis in discriminating scar versus unwounded tissue in a wild-type mouse model. To fully test the robustness of this scar analysis approach, a fibromodulin-null mouse model that heals with increased scar was also used. Fractal dimension and lacunarity analysis effectively discriminated unwounded fibromodulin-null versus wild-type skin as well as healing fibromodulin-null versus wild-type wounds, whereas Fourier transform analysis failed to do so. Furthermore, fractal dimension and lacunarity data also correlated well with transmission electron microscopy collagen ultrastructure analysis, adding to their validity. These results demonstrate that fractal dimension and lacunarity are more sensitive than Fourier transform analysis for quantification of scar morphology. PMID:21281794
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Vigli, Georgia; Philippidis, Angelos; Spyros, Apostolos; Dais, Photis
2003-09-10
A combination of (1)H NMR and (31)P NMR spectroscopy and multivariate statistical analysis was used to classify 192 samples from 13 types of vegetable oils, namely, hazelnut, sunflower, corn, soybean, sesame, walnut, rapeseed, almond, palm, groundnut, safflower, coconut, and virgin olive oils from various regions of Greece. 1,2-Diglycerides, 1,3-diglycerides, the ratio of 1,2-diglycerides to total diglycerides, acidity, iodine value, and fatty acid composition determined upon analysis of the respective (1)H NMR and (31)P NMR spectra were selected as variables to establish a classification/prediction model by employing discriminant analysis. This model, obtained from the training set of 128 samples, resulted in a significant discrimination among the different classes of oils, whereas 100% of correct validated assignments for 64 samples were obtained. Different artificial mixtures of olive-hazelnut, olive-corn, olive-sunflower, and olive-soybean oils were prepared and analyzed by (1)H NMR and (31)P NMR spectroscopy. Subsequent discriminant analysis of the data allowed detection of adulteration as low as 5% w/w, provided that fresh virgin olive oil samples were used, as reflected by their high 1,2-diglycerides to total diglycerides ratio (D > or = 0.90).
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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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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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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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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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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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Liu, Xiaona; Zhang, Qiao; Wu, Zhisheng; Shi, Xinyuan; Zhao, Na; Qiao, Yanjiang
2015-01-01
Laser-induced breakdown spectroscopy (LIBS) was applied to perform a rapid elemental analysis and provenance study of Blumea balsamifera DC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were implemented to exploit the multivariate nature of the LIBS data. Scores and loadings of computed principal components visually illustrated the differing spectral data. The PLS-DA algorithm showed good classification performance. The PLS-DA model using complete spectra as input variables had similar discrimination performance to using selected spectral lines as input variables. The down-selection of spectral lines was specifically focused on the major elements of B. balsamifera samples. Results indicated that LIBS could be used to rapidly analyze elements and to perform provenance study of B. balsamifera. PMID:25558999
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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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Taylor, J V; DiBennardo, R; Linares, G H; Goldman, A D; DeForest, P R
1984-07-01
A case study is presented to demonstrate the utility of the team approach to the identification of human remains, and to illustrate a methodological innovation developed by MFAT. Case 1 represents the first of several planned case studies, each designed to present new methodological solutions to standard problems in identification. The present case describes a test, by application, of race and sex assessment of the postcranial skeleton by discriminant function analysis.
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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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2008-10-20
Dr. Stephen Billings Dr. Leonard Pasion Sky Research, Inc) Laurens Beran UBC-GIF Approved for public release; distribution...Sites 5. FUNDING NUMBERS Contract # W912HQ-05-C-0018 6. AUTHOR(S) Dr. Stephen Billings, Dr. Leonard Pasion (Sky Research, Inc.) Laurens Beran...representative of the test datasets. ESTCP MM-0504: Additional Analysis of Camp Sibert Data Sky Research Inc October 2008 References [1] L.R. Pasion , N
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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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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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Gender discrimination in the elderly and its impact on the elderly health.
Keskinoglu, Pembe; Ucuncu, Tugba; Yildirim, Idris; Gurbuz, Turgut; Ur, Ismail; Ergor, Gul
2007-01-01
The aim of this study was to determine gender discrimination and risk factors in the elderly population and to assess the impact of that discrimination on elderly health. One hundred and sixty-eight elderly individuals who were selected from the records by simple randomized sampling were enrolled in the study. Data were obtained by face-to-face interviews at the residence of the elderly individuals. Chi(2)-Analysis, t-test, Mann-Whitney U-test, and logistic regression were used for data analysis. 81.1% of the elderly were married and 40.5% were middle or high school graduates, and 93.9% of the subjects had at least one living child. It was determined that 51.7% of the females, and 21.3% of the males were exposed to negative gender discrimination. This discrimination was higher among women in all sub-groups. In fact, older women and elderly individuals with only primary school education or less were significantly more exposed to gender discrimination (p=0.008 and p=0.043, respectively). It was found that only economical variables were related to poor health status, without gender discrimination. Despite the fact that the freedom has been obtained in some areas such as participation in household decision-making and dressing, the patriarchal family structure and sexual inequality continue in older age.
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NASA Astrophysics Data System (ADS)
Giniyatullin, K. G.; Valeeva, A. A.; Smirnova, E. V.
2017-08-01
Particle-size distribution in soddy-podzolic and light gray forest soils of the Botanical Garden of Kazan Federal University has been studied. The cluster analysis of data on the samples from genetic soil horizons attests to the lithological heterogeneity of the profiles of all the studied soils. It is probable that they are developed from the two-layered sediments with the upper colluvial layer underlain by the alluvial layer. According to the discriminant analysis, the major contribution to the discrimination of colluvial and alluvial layers is that of the fraction >0.25 mm. The results of canonical analysis show that there is only one significant discriminant function that separates alluvial and colluvial sediments on the investigated territory. The discriminant function correlates with the contents of fractions 0.05-0.01, 0.25-0.05, and >0.25 mm. Classification functions making it possible to distinguish between alluvial and colluvial sediments have been calculated. Statistical assessment of particle-size distribution data obtained for the plow horizons on ten plowed fields within the garden indicates that this horizon is formed from colluvial sediments. We conclude that the contents of separate fractions and their ratios cannot be used as a universal criterion of the lithological heterogeneity. However, adequate combination of the cluster and discriminant analyses makes it possible to give a comprehensive assessment of the lithology of soil samples from data on the contents of sand and silt fractions, which considerably increases the information value and reliability of the results.
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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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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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Choi, Young Hae; Sertic, Sarah; Kim, Hye Kyong; Wilson, Erica G; Michopoulos, Filippos; Lefeber, Alfons W M; Erkelens, Cornelis; Prat Kricun, Sergio D; Verpoorte, Robert
2005-02-23
The metabolomic analysis of 11 Ilex species, I. argentina, I. brasiliensis, I. brevicuspis, I. dumosavar. dumosa, I. dumosa var. guaranina, I. integerrima, I. microdonta, I. paraguariensis var. paraguariensis, I. pseudobuxus, I. taubertiana, and I. theezans, was carried out by NMR spectroscopy and multivariate data analysis. The analysis using principal component analysis and classification of the (1)H NMR spectra showed a clear discrimination of those samples based on the metabolites present in the organic and aqueous fractions. The major metabolites that contribute to the discrimination are arbutin, caffeine, phenylpropanoids, and theobromine. Among those metabolites, arbutin, which has not been reported yet as a constituent of Ilex species, was found to be a biomarker for I. argentina,I. brasiliensis, I. brevicuspis, I. integerrima, I. microdonta, I. pseudobuxus, I. taubertiana, and I. theezans. This reliable method based on the determination of a large number of metabolites makes the chemotaxonomical analysis of Ilex species possible.
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ERIC Educational Resources Information Center
Park, Irene J. K.; Wang, Lijuan; Williams, David R.; Alegría, Margarita
2017-01-01
Although prior research has consistently documented the association between racial/ethnic discrimination and poor mental health outcomes, the mechanisms that underlie this link are still unclear. The present 3-wave longitudinal study tested the mediating role of anger regulation in the discrimination-mental health link among 269 Mexican-origin…
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ERIC Educational Resources Information Center
Brody, Gene H.; Lei, Man-Kit; Chae, David H.; Yu, Tianyi; Kogan, Steven M.; Beach, Steven R. H.
2014-01-01
This study was designed to examine the prospective relations of perceived racial discrimination with allostatic load (AL), along with a possible buffer of the association. A sample of 331 African Americans in the rural South provided assessments of perceived discrimination from ages 16 to 18 years. When youth were 18 years, caregivers reported…
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Bucci, Melanie E.; Callahan, Peggy; Koprowski, John L.; Polfus, Jean L.; Krausman, Paul R.
2015-01-01
Stable isotope analysis of diet has become a common tool in conservation research. However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable. PMID:25803664
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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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Derbridge, Jonathan J; Merkle, Jerod A; Bucci, Melanie E; Callahan, Peggy; Koprowski, John L; Polfus, Jean L; Krausman, Paul R
2015-01-01
Stable isotope analysis of diet has become a common tool in conservation research. However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable.
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Citrus species and hybrids depicted by near- and mid-infrared spectroscopy.
Páscoa, Ricardo Nmj; Moreira, Silvana; Lopes, João A; Sousa, Clara
2018-01-31
Citrus trees are among the most cultivated plants in the world, with a high economic impact. The wide sexual compatibility among relatives gave rise to a large number of hybrids that are difficult to discriminate. This work sought to explore the ability of infrared spectroscopy to discriminate among Citrus species and/or hybrids and to contribute to the elucidation of its relatedness. Adult leaves of 18 distinct Citrus plants were included in this work. Near- and mid-infrared (NIR and FTIR) spectra were acquired from leaves after harvesting and a drying period of 1 month. Spectra were modelled by principal component analysis and partial least squares discriminant analysis. Both techniques revealed a high discrimination potential (78.5-95.9%), being the best results achieved with NIR spectroscopy and air-dried leaves (95.9%). Infrared spectroscopy was able to successfully discriminate several Citrus species and/or hybrids. Our results contributed also to enhance insights regarding the studied Citrus species and/or hybrids. Despite the benefit of including additional samples, the results herein obtained clearly pointed infrared spectroscopy as a reliable technique for Citrus species and/or hybrid discrimination. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.
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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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A simple and fast representation space for classifying complex time series
NASA Astrophysics Data System (ADS)
Zunino, Luciano; Olivares, Felipe; Bariviera, Aurelio F.; Rosso, Osvaldo A.
2017-03-01
In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease.
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Assessment of sampling stability in ecological applications of discriminant analysis
Williams, B.K.; Titus, K.
1988-01-01
A simulation study was undertaken to assess the sampling stability of the variable loadings in linear discriminant function analysis. A factorial design was used for the factors of multivariate dimensionality, dispersion structure, configuration of group means, and sample size. A total of 32,400 discriminant analyses were conducted, based on data from simulated populations with appropriate underlying statistical distributions. A review of 60 published studies and 142 individual analyses indicated that sample sizes in ecological studies often have met that requirement. However, individual group sample sizes frequently were very unequal, and checks of assumptions usually were not reported. The authors recommend that ecologists obtain group sample sizes that are at least three times as large as the number of variables measured.
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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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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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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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Singularity and Nonnormality in the Classification of Compositional Data
Bohling, Geoffrey C.; Davis, J.C.; Olea, R.A.; Harff, Jan
1998-01-01
Geologists may want to classify compositional data and express the classification as a map. Regionalized classification is a tool that can be used for this purpose, but it incorporates discriminant analysis, which requires the computation and inversion of a covariance matrix. Covariance matrices of compositional data always will be singular (noninvertible) because of the unit-sum constraint. Fortunately, discriminant analyses can be calculated using a pseudo-inverse of the singular covariance matrix; this is done automatically by some statistical packages such as SAS. Granulometric data from the Darss Sill region of the Baltic Sea is used to explore how the pseudo-inversion procedure influences discriminant analysis results, comparing the algorithm used by SAS to the more conventional Moore-Penrose algorithm. Logratio transforms have been recommended to overcome problems associated with analysis of compositional data, including singularity. A regionalized classification of the Darss Sill data after logratio transformation is different only slightly from one based on raw granulometric data, suggesting that closure problems do not influence severely regionalized classification of compositional data.
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Paolini, Mauro; Ziller, Luca; Laursen, Kristian Holst; Husted, Søren; Camin, Federica
2015-07-01
We present a study deploying compound-specific nitrogen and carbon isotope analysis of amino acids to discriminate between organically and conventionally grown plants. We focused on grain samples of common wheat and durum wheat grown using synthetic nitrogen fertilizers, animal manures, or green manures from nitrogen-fixing legumes. The measurement of amino acid δ(15)N and δ(13)C values, after protein hydrolysis and derivatization, was carried out using gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). Our results demonstrated that δ(13)C of glutamic acid and glutamine in particular, but also the combination of δ(15)N and δ(13)C of 10 amino acids, can improve the discrimination between conventional and organic wheat compared to stable isotope bulk tissue analysis. We concluded that compound-specific stable isotope analysis of amino acids represents a novel analytical tool with the potential to support and improve the certification and control procedures in the organic sector.
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Discrimination of radiation quality through second harmonic out-of-phase cw-ESR detection.
Marrale, Maurizio; Longo, Anna; Brai, Maria; Barbon, Antonio; Brustolon, Marina
2014-02-01
The ability to discriminate the quality of ionizing radiation is important because the biological effects produced in tissue strongly depends on both absorbed dose and linear energy transfer (LET) of ionizing particles. Here we present an experimental electron spin resonance (ESR) analysis aimed at discriminating the effective LETs of various radiation beams (e.g., 19.3 MeV protons, (60)Co photons and thermal neutrons). The measurement of the intensities of the continuous wave spectrometer signal channel first harmonic in-phase and the second harmonic out-of-phase components are used to distinguish the radiation quality. A computational analysis, was carried out to evaluate the dependence of the first harmonic in-phase and second harmonic out-of-phase components on microwave power, modulation amplitude and relaxation times, and highlights that these components could be used to point out differences in the relaxation times. On the basis of this numerical analysis the experimental results are discussed. The methodology described in this study has the potential to provide information on radiation quality.
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Traceability of 'Limone di Siracusa PGI' by a multidisciplinary analytical and chemometric approach.
Amenta, M; Fabroni, S; Costa, C; Rapisarda, P
2016-11-15
Food traceability is increasingly relevant with respect to safety, quality and typicality issues. Lemon fruits grown in a typical lemon-growing area of southern Italy (Siracusa), have been awarded the PGI (Protected Geographical Indication) recognition as 'Limone di Siracusa'. Due to its peculiarity, consumers have an increasing interest about this product. The detection of potential fraud could be improved by using the tools linking the composition of this production to its typical features. This study used a wide range of analytical techniques, including conventional techniques and analytical approaches, such as spectral (NIR spectra), multi-elemental (Fe, Zn, Mn, Cu, Li, Sr) and isotopic ((13)C/(12)C, (18)O/(16)O) marker investigations, joined with multivariate statistical analysis, such as PLS-DA (Partial Least Squares Discriminant Analysis) and LDA (Linear Discriminant Analysis), to implement a traceability system to verify the authenticity of 'Limone di Siracusa' production. The results demonstrated a very good geographical discrimination rate. Copyright © 2016 Elsevier Ltd. All rights reserved.
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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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The Raman spectrum character of skin tumor induced by UVB
NASA Astrophysics Data System (ADS)
Wu, Shulian; Hu, Liangjun; Wang, Yunxia; Li, Yongzeng
2016-03-01
In our study, the skin canceration processes induced by UVB were analyzed from the perspective of tissue spectrum. A home-made Raman spectral system with a millimeter order excitation laser spot size combined with a multivariate statistical analysis for monitoring the skin changed irradiated by UVB was studied and the discrimination were evaluated. Raman scattering signals of the SCC and normal skin were acquired. Spectral differences in Raman spectra were revealed. Linear discriminant analysis (LDA) based on principal component analysis (PCA) were employed to generate diagnostic algorithms for the classification of skin SCC and normal. The results indicated that Raman spectroscopy combined with PCA-LDA demonstrated good potential for improving the diagnosis of skin cancers.
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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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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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Ayala, George; Bingham, Trista; Kim, Junyeop; Wheeler, Darrell P; Millett, Gregorio A
2012-05-01
We examined the impact of social discrimination and financial hardship on unprotected anal intercourse with a male sex partner of serodiscordant or unknown HIV status in the past 3 months among 1081 Latino and 1154 Black men who have sex with men (MSM; n = 2235) residing in Los Angeles County, California; New York, New York; and Philadelphia, Pennsylvania. We administered HIV testing and a questionnaire assessing 6 explanatory variables. We combined traditional mediation analysis with the results of a path analysis to simultaneously examine the direct, indirect, and total effects of these variables on the outcome variable. Bivariate analysis showed that homophobia, racism, financial hardship, and lack of social support were associated with unprotected anal intercourse with a serodiscordant or sero-unknown partner. Path analysis determined that these relations were mediated by participation in risky sexual situations and lack of social support. However, paths between the explanatory variable and 2 mediating variables varied by participants' serostatus. Future prevention research and program designs should specifically address the differential impact of social discrimination and financial hardship on lack of social support and risky sexual situations among Latino and Black MSM.
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Parent, Eric C; Hill, Doug; Mahood, Jim; Moreau, Marc; Raso, Jim; Lou, Edmond
2009-10-15
Prospective cross-sectional measurement study. To determine the ability of the Scoliosis Research Society (SRS)-22 questionnaire to discriminate among management and scoliosis severity subgroups and to correlate with internal and external measures of curve severity. In earlier studies of the SRS-22 discriminative ability, age was not a controlled factor. The ability of the SRS-22 to predict curve severity has not been thoroughly examined. The SRS-22 was completed by 227 females with adolescent idiopathic scoliosis. Using Analysis of covariance analyses controlling for age, the SRS-22 scores were compared among management subgroups (observation, brace, presurgery, and postsurgery) and curve-severity subgroups (in nonoperated subjects: Cobb angles of <30 degrees, 30 degrees -50 degrees, and >50 degrees). A stepwise discriminant analysis was used to identify the SRS-22 domains most discriminative for curve-severity categories. Correlation between SRS-22 scores and radiographic or surface topography measurements was used to determine the predictive ability of the questionnaire. Pain was better for subjects treated with braces than for those planning surgery. Self-image was better for subjects under observation or postsurgery than for those planning surgery. Satisfaction was better for the brace and postsurgery subgroups than for the observation or presurgery subgroups. Statistically significant mean differences between subgroups were all larger than 0.5, which is within the range of minimal clinically important differences recommended for each of the 5-point SRS-22 domain scoring scales. Pain and mental health were worse for those with Cobb angles of >50 degrees than with Cobb angles of 30 degrees to 50 degrees. Self-image and total scores were worse for those with Cobb angles of >50 degrees than both other subgroups. Using discriminant analysis, self-image was the only SRS-22 domain score selected to classify subjects within curve severity subgroups. The percentage of patients accurately classified was 54% when trying to classify within 3 curve severity subgroups. The percentage of patients accurately classified was 73% when classifying simply as those with curves larger or smaller than 50 degrees . Pain, self-image, and satisfaction scores could discriminate among management subgroups, but function, mental health and total scores could not. The total score and all domain scores except satisfaction discriminated among curve-severity subgroups. Using discriminant analysis, self-image was the only domain retained in a model predicting curve-severity categories.
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Santolaria, Pilar; Pauciullo, Alfredo; Silvestre, Miguel A; Vicente-Fiel, Sandra; Villanova, Leyre; Pinton, Alain; Viruel, Juan; Sales, Ester; Yániz, Jesús L
2016-01-01
This study was designed to determine the ability of computer-assisted sperm morphometry analysis (CASA-Morph) with fluorescence to discriminate between spermatozoa carrying different sex chromosomes from the nuclear morphometrics generated and different statistical procedures in the bovine species. The study was divided into two experiments. The first was to study the morphometric differences between X- and Y-chromosome-bearing spermatozoa (SX and SY, respectively). Spermatozoa from eight bulls were processed to assess simultaneously the sex chromosome by FISH and sperm morphometry by fluorescence-based CASA-Morph. SX cells were larger than SY cells on average (P < 0.001) although with important differences between bulls. A simultaneous evaluation of all the measured features by discriminant analysis revealed that nuclear area and average fluorescence intensity were the variables selected by stepwise discriminant function analysis as the best discriminators between SX and SY. In the second experiment, the sperm nuclear morphometric results from CASA-Morph in nonsexed (mixed SX and SY) and sexed (SX) semen samples from four bulls were compared. FISH allowed a successful classification of spermatozoa according to their sex chromosome content. X-sexed spermatozoa displayed a larger size and fluorescence intensity than nonsexed spermatozoa (P < 0.05). We conclude that the CASA-Morph fluorescence-based method has the potential to find differences between X- and Y-chromosome-bearing spermatozoa in bovine species although more studies are needed to increase the precision of sex determination by this technique.
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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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Study of the cerrado vegetation in the Federal District area from orbital data. M.S. Thesis
NASA Technical Reports Server (NTRS)
Dejesusparada, N. (Principal Investigator); Aoki, H.; Dossantos, J. R.
1980-01-01
The physiognomic units of cerrado in the area of Distrito Federal (DF) were studied through the visual and automatic analysis of products provided by Multispectral Scanning System (MSS) of LANDSAT. The visual analysis of the multispectral images in black and white, at the 1:250,000 scale, was made based on the texture and tonal patterns. The automatic analysis of the compatible computer tapes (CCT) was made by means of IMAGE-100 system. The following conclusions were obtained: (1) the delimitation of cerrado vegetation forms can be made by the visual and automatic analysis; (2) in the visual analysis, the principal parameter used to discriminate the cerrado forms was the tonal pattern, independently of the year's seasons, and the channel 5 gave better information; (3) in the automatic analysis, the data of the four channels of MSS can be used in the discrimination of the cerrado forms; and (4) in the automatic analysis, the four channels combination possibilities gave more information in the separation of cerrado units when soil types were considered.
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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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Palazón, L; Navas, A
2017-06-01
Information on sediment contribution and transport dynamics from the contributing catchments is needed to develop management plans to tackle environmental problems related with effects of fine sediment as reservoir siltation. In this respect, the fingerprinting technique is an indirect technique known to be valuable and effective for sediment source identification in river catchments. Large variability in sediment delivery was found in previous studies in the Barasona catchment (1509 km 2 , Central Spanish Pyrenees). Simulation results with SWAT and fingerprinting approaches identified badlands and agricultural uses as the main contributors to sediment supply in the reservoir. In this study the <63 μm sediment fraction from the surface reservoir sediments (2 cm) are investigated following the fingerprinting procedure to assess how the use of different statistical procedures affects the amounts of source contributions. Three optimum composite fingerprints were selected to discriminate between source contributions based in land uses/land covers from the same dataset by the application of (1) discriminant function analysis; and its combination (as second step) with (2) Kruskal-Wallis H-test and (3) principal components analysis. Source contribution results were different between assessed options with the greatest differences observed for option using #3, including the two step process: principal components analysis and discriminant function analysis. The characteristics of the solutions by the applied mixing model and the conceptual understanding of the catchment showed that the most reliable solution was achieved using #2, the two step process of Kruskal-Wallis H-test and discriminant function analysis. The assessment showed the importance of the statistical procedure used to define the optimum composite fingerprint for sediment fingerprinting applications. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Figueira, José; Câmara, Hugo; Pereira, Jorge; Câmara, José S
2014-02-15
To gain insights on the effects of cultivar on the volatile metabolomic expression of different tomato (Lycopersicon esculentum L.) cultivars--Plum, Campari, Grape, Cherry and Regional, cultivated under similar edafoclimatic conditions, and to identify the most discriminate volatile marker metabolites related to the cultivar, the chromatographic profiles resulting from headspace solid phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-qMS) analysis, combined with multivariate analysis were investigated. The data set composed by the 77 volatile metabolites identified in the target tomato cultivars, 5 of which (2,2,6-trimethylcyclohexanone, 2-methyl-6-methyleneoctan-2-ol, 4-octadecyl-morpholine, (Z)-methyl-3-hexenoate and 3-octanone) are reported for the first time in tomato volatile metabolomic composition, was evaluated by chemometrics. Firstly, principal component analysis was carried out in order to visualise data trends and clusters, and then, linear discriminant analysis in order to detect the set of volatile metabolites able to differentiate groups according to tomato cultivars. The results obtained revealed a perfect discrimination between the different Lycopersicon esculentum L. cultivars considered. The assignment success rate was 100% in classification and 80% in prediction ability by using "leave-one-out" cross-validation procedure. The volatile profile was able to differentiate all five cultivars and revealed complex interactions between them including the participation in the same biosynthetic pathway. The volatile metabolomic platform for tomato samples obtained by HS-SPME/GC-qMS here described, and the interrelationship detected among the volatile metabolites can be used as a roadmap for biotechnological applications, namely to improve tomato aroma and their acceptance in the final consumer, and for traceability studies. Copyright © 2013 Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Neto, Lázaro P. M.; Martin, Aírton A.; Soto, Claudio A. T.; Santos, André B. O.; Mello, Evandro S.; Pereira, Marina A.; Cernea, Cláudio R.; Brandão, Lenine G.; Canevari, Renata A.
2016-02-01
Thyroid carcinomas represent the main endocrine malignancy and their diagnosis may produce inconclusive results. Raman spectroscopy and gene expression analysis have shown excellent results on the differentiation of carcinomas. This study aimed to improve the discrimination between different thyroid pathologies combining of both analyses. A total of 35 thyroid tissues samples including normal tissue (n=10), goiter (n=10), papillary (n=10) and follicular carcinomas (n=5) were analyzed. Confocal Raman spectra was obtain by using a Rivers Diagnostic System, 785 nm laser excitation and CCD detector. The data was processed by the software Labspec5 and Origin 8.5 and analyzed by Minitab® program. The gene expression analysis was performed by qRT-PCR technique for TG, TPO, PDGFB, SERPINA1, LGALS3 and TFF3 genes and statistically analyzed by Mann-Whitney test. The confocal Raman spectroscopy allowed a maximum discrimination of 91.1% between normal and tumor tissues, 84.8% between benign and malignant pathologies and 84.6% among carcinomas analyzed. Significant differences was observed for TG, LGALS3, SERPINA1 and TFF3 genes between benign lesions and carcinomas, and SERPINA1 and TFF3 genes between papillary and follicular carcinomas. Principal component analysis was performed using PC1 and PC2 in the papillary carcinoma samples that showed over gene expression when compared with normal sample, where 90% of discrimination was observed at the Amide 1 (1655 cm-1), and at the tyrosine spectra region (856 cm-1). The discrimination of tissues thyroid carried out by confocal Raman spectroscopy and gene expression analysis indicate that these techniques are promising tools to be used in the diagnosis of thyroid lesions.
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Elemental analysis of forensic glasses by inductively coupled plasma mass spectrometry
NASA Astrophysics Data System (ADS)
Almirall, Jose R.; Duckworth, Douglas C.; Bayne, Charles K.; Morton, Sherman A.; Smith, David H.; Koons, Robert D.; Furton, Kenneth G.
1999-02-01
Flat glass is a common type of evidence collected from the scenes of crimes such as burglaries, vandalism, and hit-and- run accidents. The usefulness of such evidence lies in the ability to associate the glass from the scene (or a suspect) to the original source. Physical and chemical analysis of the glass can be used for discrimination between the possible sources of glass. If the sample is large enough, physical attributes such as fracture matches, density, color, and thickness can be employed for comparison between a recovered fragment(s) to the suspect source. More commonly, refractive index (RI) comparisons are employed. Due to the improved control over glass manufacturing processes, RI values often cannot differentiate glasses where approximately 6 - 9% of casework samples are not expected to be distinguished by RI alone even if they originated from different sources. Employing methods such as NAA, XRF, ICP-AES, and ICP-MS for the comparison of trace elemental compositions has been shown to be more discriminating than RI comparisons. The multielement capability and the sensitivity of ICP-AES and ICP-MS provide for excellent discrimination power. In this work, the sources of variability in ICP-MS of glass analysis are investigated to determine possible sources of variation. The sources of variation examined include errors due to sample preparation, instrument accuracy and precision, and interlaboratory reproducibility. Other sources of variation include inhomogeneity across a sheet of glass from the same source. Analysis of variance has been applied to our ICP-MS analysis of NIST standards and to the interlaboratory comparisons of float glass samples collected across a sheet in a production facility. The results of these experiments allows for a more accurate interpretation of forensic glass data and a better understanding of the discriminating power (absolute and practical) of ICP-MS.
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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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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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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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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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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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Li, Yan; Zhang, Ji; Zhao, Yanli; Liu, Honggao; Wang, Yuanzhong; Jin, Hang
2016-01-01
In this study the geographical differentiation of dried sclerotia of the medicinal mushroom Wolfiporia extensa, obtained from different regions in Yunnan Province, China, was explored using Fourier-transform infrared (FT-IR) spectroscopy coupled with multivariate data analysis. The FT-IR spectra of 97 samples were obtained for wave numbers ranging from 4000 to 400 cm-1. Then, the fingerprint region of 1800-600 cm-1 of the FT-IR spectrum, rather than the full spectrum, was analyzed. Different pretreatments were applied on the spectra, and a discriminant analysis model based on the Mahalanobis distance was developed to select an optimal pretreatment combination. Two unsupervised pattern recognition procedures- principal component analysis and hierarchical cluster analysis-were applied to enhance the authenticity of discrimination of the specimens. The results showed that excellent classification could be obtained after optimizing spectral pretreatment. The tested samples were successfully discriminated according to their geographical locations. The chemical properties of dried sclerotia of W. extensa were clearly dependent on the mushroom's geographical origins. Furthermore, an interesting finding implied that the elevations of collection areas may have effects on the chemical components of wild W. extensa sclerotia. Overall, this study highlights the feasibility of FT-IR spectroscopy combined with multivariate data analysis in particular for exploring the distinction of different regional W. extensa sclerotia samples. This research could also serve as a basis for the exploitation and utilization of medicinal mushrooms.
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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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ERIC Educational Resources Information Center
Höhle, Barbara; Pauen, Sabina; Hesse, Volker; Weissenborn, Jürgen
2014-01-01
In this article we report on early rhythmic discrimination performance of children who participated in a longitudinal study following children from birth to their 6th year of life. Thirty-four children including 8 children with a family risk for developmental language impairment were tested on the discrimination of trochaic and iambic disyllabic…
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Yang, Tse-Chuan; Chen, Danhong
2018-04-01
The objective of this study was to answer three questions: (1) Is perceived discrimination adversely related to self-rated stress via the social capital and health care system distrust pathways? (2) Does the relationship between perceived discrimination and self-rated stress vary across race/ethnicity groups? and (3) Do the two pathways differ by one's race/ethnicity background? Using the Philadelphia Health Management Corporation's Southeastern Pennsylvania Household Survey, we classified 9831 respondents into 4 race/ethnicity groups: non-Hispanic White (n = 6621), non-Hispanic Black (n = 2359), Hispanic (n = 505), and non-Hispanic other races (n = 346). Structural equation modeling was employed to simultaneously estimate five sets of equations, including the confirmatory factor analysis for both social capital and health care distrust and both direct and indirect effects from perceived discrimination to self-rated stress. The key findings drawn from the analysis include the following: (1) in general, people who experienced racial discrimination have higher distrust and weaker social capital than those without perceived discrimination and both distrust and social capital are ultimately related to self-rated stress. (2) The direct relationship between perceived discrimination and self-rated stress is found for all race/ethnicity groups (except non-Hispanic other races) and it does not vary across groups. (3) The two pathways can be applied to non-Hispanic White and Black, but for Hispanic and non-Hispanic other races, we found little evidence for the social capital pathway. For non-Hispanic White, non-Hispanic Black, and Hispanic, perceived discrimination is negatively related to self-rated stress. This finding highlights the importance of reducing interpersonal discriminatory behavior even for non-Hispanic White. The health care system distrust pathway can be used to address the racial health disparity in stress as it holds true for all four race/ethnicity groups. On the other hand, the social capital pathway seems to better help non-Hispanic White and Black to mediate the adverse effect of perceived discrimination on stress.
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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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Multitrait-Multimethod Analyses of Two Self-Concept Instruments.
ERIC Educational Resources Information Center
Marsh, Herbert W.; Smith, Ian D.
1982-01-01
The multidimensionality of self-concept and the use of factor analysis in the development of self-concept instruments are supported in multitrait-multimethod analyses of the Sears and Coopersmith instruments. Convergent validity and discriminate validity of subscales in factor analysis and multitrait-multimethod analysis of longitudinal data are…
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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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Speller, Nicholas C; Siraj, Noureen; Regmi, Bishnu P; Marzoughi, Hassan; Neal, Courtney; Warner, Isiah M
2015-01-01
Herein, we demonstrate an alternative strategy for creating QCM-based sensor arrays by use of a single sensor to provide multiple responses per analyte. The sensor, which simulates a virtual sensor array (VSA), was developed by depositing a thin film of ionic liquid, either 1-octyl-3-methylimidazolium bromide ([OMIm][Br]) or 1-octyl-3-methylimidazolium thiocyanate ([OMIm][SCN]), onto the surface of a QCM-D transducer. The sensor was exposed to 18 different organic vapors (alcohols, hydrocarbons, chlorohydrocarbons, nitriles) belonging to the same or different homologous series. The resulting frequency shifts (Δf) were measured at multiple harmonics and evaluated using principal component analysis (PCA) and discriminant analysis (DA) which revealed that analytes can be classified with extremely high accuracy. In almost all cases, the accuracy for identification of a member of the same class, that is, intraclass discrimination, was 100% as determined by use of quadratic discriminant analysis (QDA). Impressively, some VSAs allowed classification of all 18 analytes tested with nearly 100% accuracy. Such results underscore the importance of utilizing lesser exploited properties that influence signal transduction. Overall, these results demonstrate excellent potential of the virtual sensor array strategy for detection and discrimination of vapor phase analytes utilizing the QCM. To the best of our knowledge, this is the first report on QCM VSAs, as well as an experimental sensor array, that is based primarily on viscoelasticity, film thickness, and harmonics.
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Ivezić, Slađana Štrkalj; Sesar, Marijan Alfonso; Mužinić, Lana
2017-03-01
Self-stigma adversely affects recovery from schizophrenia. Analyses of self stigma reduction programs discovered that few studies have investigated the impact of education about the illness on self-stigma reduction. The objective of this study was to determine whether psychoeducation based on the principles of recovery and empowerment using therapeutic group factors assists in reduction of self-stigma, increased empowerment and reduced perception of discrimination in patients with schizophrenia. 40 patients participated in psychoeducation group program and were compared with a control group of 40 patients placed on the waiting list for the same program. A Solomon four group design was used to control the influence of the pretest. Rating scales were used to measure internalized stigma, empowerment and perception of discrimination. Two-way analysis of variance was used to determine the main effects and interaction between the treatment and pretest. Simple analysis of variance with repeated measures was used to additionally test effect of treatment onself-stigma, empowerment and perceived discrimination. The participants in the psychoeducation group had lower scores on internalized stigma (F(1,76)=8.18; p<0.01) than the patients treated as usual. Analysis also confirmed the same effect with comparing experimental group before and after psychoeducation (F(1,19)=5.52; p<0.05). All participants showed a positive trend for empowerment. Psychoeducation did not influence perception of discrimination. Group psychoeducation decreased the level of self stigma. This intervention can assist in recovery from schizophrenia.
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NASA Astrophysics Data System (ADS)
Luo, Congpei; He, Tao; Chun, Ze
2013-04-01
Dendrobium is a commonly used and precious herb in Traditional Chinese Medicine. The high biodiversity of Dendrobium and the therapeutic needs require tools for the correct and fast discrimination of different Dendrobium species. This study investigates Fourier transform infrared spectroscopy followed by cluster analysis for discrimination and chemical phylogenetic study of seven Dendrobium species. Despite the general pattern of the IR spectra, different intensities, shapes, peak positions were found in the IR spectra of these samples, especially in the range of 1800-800 cm-1. The second derivative transformation and alcoholic extracting procedure obviously enlarged the tiny spectral differences among these samples. The results indicated each Dendrobium species had a characteristic IR spectra profile, which could be used to discriminate them. The similarity coefficients among the samples were analyzed based on their second derivative IR spectra, which ranged from 0.7632 to 0.9700, among the seven Dendrobium species, and from 0.5163 to 0.9615, among the ethanol extracts. A dendrogram was constructed based on cluster analysis the IR spectra for studying the chemical phylogenetic relationships among the samples. The results indicated that D. denneanum and D. crepidatum could be the alternative resources to substitute D. chrysotoxum, D. officinale and D. nobile which were officially recorded in Chinese Pharmacopoeia. In conclusion, with the advantages of high resolution, speediness and convenience, the experimental approach can successfully discriminate and construct the chemical phylogenetic relationships of the seven Dendrobium species.
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Atypical Face Perception in Autism: A Point of View?
Morin, Karine; Guy, Jacalyn; Habak, Claudine; Wilson, Hugh R; Pagani, Linda; Mottron, Laurent; Bertone, Armando
2015-10-01
Face perception is the most commonly used visual metric of social perception in autism. However, when found to be atypical, the origin of face perception differences in autism is contentious. One hypothesis proposes that a locally oriented visual analysis, characteristic of individuals with autism, ultimately affects performance on face tasks where a global analysis is optimal. The objective of this study was to evaluate this hypothesis by assessing face identity discrimination with synthetic faces presented with and without changes in viewpoint, with the former condition minimizing access to local face attributes used for identity discrimination. Twenty-eight individuals with autism and 30 neurotypical participants performed a face identity discrimination task. Stimuli were synthetic faces extracted from traditional face photographs in both front and 20° side viewpoints, digitized from 37 points to provide a continuous measure of facial geometry. Face identity discrimination thresholds were obtained using a two-alternative, temporal forced choice match-to-sample paradigm. Analyses revealed an interaction between group and condition, with group differences found only for the viewpoint change condition, where performance in the autism group was decreased compared to that of neurotypical participants. The selective decrease in performance for the viewpoint change condition suggests that face identity discrimination in autism is more difficult when access to local cues is minimized, and/or when dependence on integrative analysis is increased. These results lend support to a perceptual contribution of atypical face perception in autism. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.
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ERIC Educational Resources Information Center
Cullen, John B.; Perrewe, Pamela L.
1981-01-01
Used factors identified in the literature as predictors of centralization/decentralization as potential discriminating variables among several decision making configurations in university affiliated professional schools. The model developed from multiple discriminant analysis had reasonable success in classifying correctly only the decentralized…
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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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ERIC Educational Resources Information Center
Bernstein, Michael I.
1982-01-01
Steps a school board can take to minimize the risk of age discrimination suits include reviewing all written policies, forms, files, and collective bargaining agreements for age discriminatory items; preparing a detailed statistical analysis of the age of personnel; and reviewing reduction-in-force procedures. (Author/MLF)
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ERIC Educational Resources Information Center
Marie, S. Maria Josephine Arokia; Edannur, Sreekala
2015-01-01
This paper focused on the analysis of test items constructed in the paper of teaching Physical Science for B.Ed. class. It involved the analysis of difficulty level and discrimination power of each test item. Item analysis allows selecting or omitting items from the test, but more importantly item analysis is a tool to help the item writer improve…
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Using a virtual reality temporal bone simulator to assess otolaryngology trainees.
Zirkle, Molly; Roberson, David W; Leuwer, Rudolf; Dubrowski, Adam
2007-02-01
The objective of this study is to determine the feasibility of computerized evaluation of resident performance using hand motion analysis on a virtual reality temporal bone (VR TB) simulator. We hypothesized that both computerized analysis and expert ratings would discriminate the performance of novices from experienced trainees. We also hypothesized that performance on the virtual reality temporal bone simulator (VR TB) would differentiate based on previous drilling experience. The authors conducted a randomized, blind assessment study. Nineteen volunteers from the Otolaryngology-Head and Neck Surgery training program at the University of Toronto drilled both a cadaveric TB and a simulated VR TB. Expert reviewers were asked to assess operative readiness of the trainee based on a blind video review of their performance. Computerized hand motion analysis of each participant's performance was conducted. Expert raters were able to discriminate novices from experienced trainees (P < .05) on cadaveric temporal bones, and there was a trend toward discrimination on VR TB performance. Hand motion analysis showed that experienced trainees had better movement economy than novices (P < .05) on the VR TB. Performance, as measured by hand motion analysis on the VR TB simulator, reflects trainees' previous drilling experience. This study suggests that otolaryngology trainees could accomplish initial temporal bone training on a VR TB simulator, which can provide feedback to the trainee, and may reduce the need for constant faculty supervision and evaluation.
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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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Moscetti, Roberto; Radicetti, Emanuele; Monarca, Danilo; Cecchini, Massimo; Massantini, Riccardo
2015-10-01
This study investigates the possibility of using near infrared spectroscopy for the authentication of the 'Nocciola Romana' hazelnut (Corylus avellana L. cvs Tonda Gentile Romana and Nocchione) as a Protected Designation of Origin (PDO) hazelnut from central Italy. Algorithms for the selection of the optimal pretreatments were tested in combination with the following discriminant routines: k-nearest neighbour, soft independent modelling of class analogy, partial least squares discriminant analysis and support vector machine discriminant analysis. The best results were obtained using a support vector machine discriminant analysis routine. Thus, classification performance rates with specificities, sensitivities and accuracies as high as 96.0%, 95.0% and 95.5%, respectively, were achieved. Various pretreatments, such as standard normal variate, mean centring and a Savitzky-Golay filter with seven smoothing points, were used. The optimal wavelengths for classification were mainly correlated with lipids, although some contribution from minor constituents, such as proteins and carbohydrates, was also observed. Near infrared spectroscopy could classify hazelnut according to the PDO 'Nocciola Romana' designation. Thus, the experimentation lays the foundations for a rapid, online, authentication system for hazelnut. However, model robustness should be improved taking into account agro-pedo-climatic growing conditions. © 2014 Society of Chemical Industry.
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NASA Astrophysics Data System (ADS)
Priya, Mallika; Rao, Bola Sadashiva Satish; Chandra, Subhash; Ray, Satadru; Mathew, Stanley; Datta, Anirbit; Nayak, Subramanya G.; Mahato, Krishna Kishore
2016-02-01
In spite of many efforts for early detection of breast cancer, there is still lack of technology for immediate implementation. In the present study, the potential photoacoustic spectroscopy was evaluated in discriminating breast cancer from normal, involving blood serum samples seeking early detection. Three photoacoustic spectra in time domain were recorded from each of 20 normal and 20 malignant samples at 281nm pulsed laser excitations and a total of 120 spectra were generated. The time domain spectra were then Fast Fourier Transformed into frequency domain and 116.5625 - 206.875 kHz region was selected for further analysis using a combinational approach of wavelet, PCA and logistic regression. Initially, wavelet analysis was performed on the FFT data and seven features (mean, median, area under the curve, variance, standard deviation, skewness and kurtosis) from each were extracted. PCA was then performed on the feature matrix (7x120) for discriminating malignant samples from the normal by plotting a decision boundary using logistic regression analysis. The unsupervised mode of classification used in the present study yielded specificity and sensitivity values of 100% in each respectively with a ROC - AUC value of 1. The results obtained have clearly demonstrated the capability of photoacoustic spectroscopy in discriminating cancer from the normal, suggesting its possible clinical implications.
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Park, Yu Min; Lee, Cheong Mi; Hong, Joon Ho; Jamila, Nargis; Khan, Naeem; Jung, Jong-Hyun; Jung, Young-Chul; Kim, Kyong Su
2018-09-01
This study verified the origin of 346 defatted Korean and non-Korean pork samples via trace elements profiling, and C and N stable isotope ratios analysis. The analyzed elements were 6 Li, 7 Li, 10 B, 11 B, 51 V , 50 Cr, 52 Cr, 53 Cr, 55 Mn, 58 Ni, 60 Ni, 59 Co, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 69 Ga, 71 Ga, 75 As, 82 Se, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 85 Rb, 94 Mo, 95 Mo, 97 Mo, 107 Ag, 109 Ag, 110 Cd, 111 Cd, 113 Cd, 112 Cd, 114 Cd, 116 Cd, 133 Cs, 206 Pb, 207 Pb, and 208 Pb. Content (mg/kg) of 51 V (0.012), 50 Cr (0.882), 75 As (0.017), 85 Rb (57.7), and 87 Sr (46.3) were high in Korean pork samples whereas 6 Li, 7 Li, 59 Co, 55 Mn, 58 Ni, 84 Sr, 86 Sr, 88 Sr, 111 Cd, and 133 Cs were found higher in non-Korean samples. The results of discriminant analysis showed that the trace elements content and stable isotope ratios were significant for the discrimination of geographical origins with a perfect discrimination rate of 100%. Copyright © 2018 Elsevier Ltd. All rights reserved.
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[Nondestructive discrimination of strawberry varieties by NIR and BP-ANN].
Niu, Xiao-ying; Shao, Li-min; Zhao, Zhi-lei; Zhang, Xiao-yu
2012-08-01
Strawberry variety is a main factor that can influence strawberry fruit quality. The use of near-infrared reflectance spectroscopy was explored discriminate among samples of strawberry of different varieties. And the significance of difference among different varieties was analyzed by comparison of the chemical composition of the different varieties samples. The performance of models established using back propagation-artificial neural networks (BP-ANN), least squares-support vector machine and discriminant analysis were evaluated on spectra range of 4545-9090 cm(-1). The optimal model was obtained by BP-ANN with a topology of 12-18-3, which correctly classified 96.68% of calibration set and 97.14% of prediction set. And the 94.95%, 97% and 98.29% classifications were given respectively for "Tianbao" (n=99), "Fengxiang" (n=100) and "Mingxing" (n=117). One-way analysis of variance was made for comparison of the mean values for soluble solids content (SSC), titratable acid (TA), pH value and SSC-TA ratio, and the statistically significant differences were found. Principal component analysis was performed on the four chemical compositions, and obvious clustering tendencies for different varieties were found. These results showed that NIR combined with BP-ANN can discriminate strawberry of different varieties effectively, and the difference in chemical compositions of different varieties strawberry might be a chemical validation for NIR results.
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Tactical metrics that discriminate winning, drawing and losing teams in UEFA Euro 2012®.
Winter, Christian; Pfeiffer, Mark
2016-01-01
The objectives of this article are twofold: first, an innovative approach to notational analysis in football is outlined. By considering the important theoretical requirements for the analysis of sports games (like the interaction between two parties, the procedural sequence of action or the significance of tactical behaviour) the meaning of the introduced parameters, called tactical metrics, is illustrated. In a second step, the validity of this approach is tested using matches of the Union of European Football Associations (UEFA) Euro 2012® to investigate a connection between these metrics and success. The results show that 11 tactical metrics model tactical behaviour in 4 different dimensions (game speed, transition play after ball recovery, transition play after ball loss and offence efficiency (OE)). Discriminant analysis based on the factor values leads to a correct classification of 64.8% identifying winners, losers and drawers. This successful discrimination reveals a connection between match success and the presented metrics. Especially, the transition play after losing the ball and the OE seem to be factors connected directly with the result of a match, since those were important values for a successful discrimination. Furthermore, the procedural description of tactical behaviour provides the opportunity to conduct meaningful recommendations for the training and coaching process.
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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)
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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Analysis of Greek small coinage from the classic period
NASA Astrophysics Data System (ADS)
Šmit, Ž.; Šemrov, A.
2018-02-01
A series of 25 Greek coins from the 6th to 4th centuries BC was studied by PIXE for their trace element composition, with an aim to discover the origin of their silver ore. The procedure revealed a counterfeited coin, and then concentrated on distinguishing the coins minted from the ore of Laurion on the Attica peninsula and the coins minted from other sources. Linear discriminant analysis based on the impurities and alloying elements of copper, gold, lead and bismuth revealed that discrimination is indeed possible according to a single canonical variable.
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Roth, Zvi N
2016-01-01
Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream.
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Roth, Zvi N.
2016-01-01
Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream. PMID:27242455
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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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NASA Astrophysics Data System (ADS)
Francisco, Arthur; Blondel, Cécile; Brunetière, Noël; Ramdarshan, Anusha; Merceron, Gildas
2018-03-01
Tooth wear and, more specifically, dental microwear texture is a dietary proxy that has been used for years in vertebrate paleoecology and ecology. DMTA, dental microwear texture analysis, relies on a few parameters related to the surface complexity, anisotropy and heterogeneity of the enamel facets at the micrometric scale. Working with few but physically meaningful parameters helps in comparing published results and in defining levels for classification purposes. Other dental microwear approaches are based on ISO parameters and coupled with statistical tests to find the more relevant ones. The present study roughly utilizes most of the aforementioned parameters in their more or less modified form. But more than parameters, we here propose a new approach: instead of a single parameter characterizing the whole surface, we sample the surface and thus generate 9 derived parameters in order to broaden the parameter set. The identification of the most discriminative parameters is performed with an automated procedure which is an extended and refined version of the workflows encountered in some studies. The procedure in its initial form includes the most common tools, like the ANOVA and the correlation analysis, along with the required mathematical tests. The discrimination results show that a simplified form of the procedure is able to more efficiently identify the desired number of discriminative parameters. Also highlighted are some trends like the relevance of working with both height and spatial parameters, as well as the potential benefits of dimensionless surfaces. On a set of 45 surfaces issued from 45 specimens of three modern ruminants with differences in feeding preferences (grazing, leaf-browsing and fruit-eating), it is clearly shown that the level of wear discrimination is improved with the new methodology compared to the other ones.
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Vavougios, George D; Doskas, Triantafyllos; Konstantopoulos, Kostas
2018-05-01
Dysarthrophonia is a predominant symptom in many neurological diseases, affecting the quality of life of the patients. In this study, we produced a discriminant function equation that can differentiate MS patients from healthy controls, using electroglottographic variables not analyzed in a previous study. We applied stepwise linear discriminant function analysis in order to produce a function and score derived from electroglottographic variables extracted from a previous study. The derived discriminant function's statistical significance was determined via Wilk's λ test (and the associated p value). Finally, a 2 × 2 confusion matrix was used to determine the function's predictive accuracy, whereas the cross-validated predictive accuracy is estimated via the "leave-one-out" classification process. Discriminant function analysis (DFA) was used to create a linear function of continuous predictors. DFA produced the following model (Wilk's λ = 0.043, χ2 = 388.588, p < 0.0001, Tables 3 and 4): D (MS vs controls) = 0.728*DQx1 mean monologue + 0.325*CQx monologue + 0.298*DFx1 90% range monologue + 0.443*DQx1 90% range reading - 1.490*DQx1 90% range monologue. The derived discriminant score (S1) was used subsequently in order to form the coordinates of a ROC curve. Thus, a cutoff score of - 0.788 for S1 corresponded to a perfect classification (100% sensitivity and 100% specificity, p = 1.67e -22 ). Consistent with previous findings, electroglottographic evaluation represents an easy to implement and potentially important assessment in MS patients, achieving adequate classification accuracy. Further evaluation is needed to determine its use as a biomarker.
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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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Sex differences in discriminative power of volleyball game-related statistics.
João, Paulo Vicente; Leite, Nuno; Mesquita, Isabel; Sampaio, Jaime
2010-12-01
To identify sex differences in volleyball game-related statistics, the game-related statistics of several World Championships in 2007 (N=132) were analyzed using the software VIS from the International Volleyball Federation. Discriminant analysis was used to identify the game-related statistics which better discriminated performances by sex. Analysis yielded an emphasis on fault serves (SC = -.40), shot spikes (SC = .40), and reception digs (SC = .31). Specific robust numbers represent that considerable variability was evident in the game-related statistics profile, as men's volleyball games were better associated with terminal actions (errors of service), and women's volleyball games were characterized by continuous actions (in defense and attack). These differences may be related to the anthropometric and physiological differences between women and men and their influence on performance profiles.
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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 and Acculturative Stress among First-Generation Dominicans
ERIC Educational Resources Information Center
Dawson, Beverly Araujo; Panchanadeswaran, Subadra
2010-01-01
The present study examined the relationship between discriminatory experiences and acculturative stress levels among a sample of 283 Dominican immigrants. Findings from a linear regression analysis revealed that experiences of daily racial discrimination and major racist events were significant predictors of acculturative stress after controlling…
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The consequences of perceived discrimination for psychological well-being: a meta-analytic review.
Schmitt, Michael T; Branscombe, Nyla R; Postmes, Tom; Garcia, Amber
2014-07-01
In 2 meta-analyses, we examined the relationship between perceived discrimination and psychological well-being and tested a number of moderators of that relationship. In Meta-Analysis 1 (328 independent effect sizes, N = 144,246), we examined correlational data measuring both perceived discrimination and psychological well-being (e.g., self-esteem, depression, anxiety, psychological distress, life satisfaction). Using a random-effects model, the mean weighted effect size was significantly negative, indicating harm (r = -.23). Effect sizes were larger for disadvantaged groups (r = -.24) compared to advantaged groups (r = -.10), larger for children compared to adults, larger for perceptions of personal discrimination compared to group discrimination, and weaker for racism and sexism compared to other stigmas. The negative relationship was significant across different operationalizations of well-being but was somewhat weaker for positive outcomes (e.g., self-esteem, positive affect) than for negative outcomes (e.g., depression, anxiety, negative affect). Importantly, the effect size was significantly negative even in longitudinal studies that controlled for prior levels of well-being (r = -.15). In Meta-Analysis 2 (54 independent effect sizes, N = 2,640), we examined experimental data from studies manipulating perceptions of discrimination and measuring well-being. We found that the effect of discrimination on well-being was significantly negative for studies that manipulated general perceptions of discrimination (d = -.25), but effects did not differ from 0 when attributions to discrimination for a specific negative event were compared to personal attributions (d = .06). Overall, results support the idea that the pervasiveness of perceived discrimination is fundamental to its harmful effects on psychological well-being. PsycINFO Database Record (c) 2014 APA, all rights reserved.
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NASA Astrophysics Data System (ADS)
De Lucia, Frank C., Jr.; Gottfried, Jennifer L.
2011-02-01
Using a series of thirteen organic materials that includes novel high-nitrogen energetic materials, conventional organic military explosives, and benign organic materials, we have demonstrated the importance of variable selection for maximizing residue discrimination with partial least squares discriminant analysis (PLS-DA). We built several PLS-DA models using different variable sets based on laser induced breakdown spectroscopy (LIBS) spectra of the organic residues on an aluminum substrate under an argon atmosphere. The model classification results for each sample are presented and the influence of the variables on these results is discussed. We found that using the whole spectra as the data input for the PLS-DA model gave the best results. However, variables due to the surrounding atmosphere and the substrate contribute to discrimination when the whole spectra are used, indicating this may not be the most robust model. Further iterative testing with additional validation data sets is necessary to determine the most robust model.
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Drivelos, Spiros A; Danezis, Georgios P; Haroutounian, Serkos A; Georgiou, Constantinos A
2016-12-15
This study examines the trace and rare earth elemental (REE) fingerprint variations of PDO (Protected Designation of Origin) "Fava Santorinis" over three consecutive harvesting years (2011-2013). Classification of samples in harvesting years was studied by performing discriminant analysis (DA), k nearest neighbours (κ-NN), partial least squares (PLS) analysis and probabilistic neural networks (PNN) using rare earth elements and trace metals determined using ICP-MS. DA performed better than κ-NN, producing 100% discrimination using trace elements and 79% using REEs. PLS was found to be superior to PNN, achieving 99% and 90% classification for trace and REEs, respectively, while PNN achieved 96% and 71% classification for trace and REEs, respectively. The information obtained using REEs did not enhance classification, indicating that REEs vary minimally per harvesting year, providing robust geographical origin discrimination. The results show that seasonal patterns can occur in the elemental composition of "Fava Santorinis", probably reflecting seasonality of climate. Copyright © 2016 Elsevier Ltd. All rights reserved.
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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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NASA Technical Reports Server (NTRS)
Wolf, S. F.; Lipschutz, M. E.
1993-01-01
Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.
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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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NASA Astrophysics Data System (ADS)
Ahn, Jae-Jun; Akram, Kashif; Kwak, Ji-Young; Jeong, Mi-Seon; Kwon, Joong-Ho
2013-10-01
Cost-effective and time-efficient analytical techniques are required to screen large food lots in accordance to their irradiation status. Gamma-irradiated (0-10 kGy) cinnamon, red pepper, black pepper, and fresh paprika were investigated using photostimulated luminescence (PSL), direct epifluorescent filter technique/the aerobic plate count (DEFT/APC), and electronic-nose (e-nose) analyses. The screening results were also confirmed with thermoluminescence analysis. PSL analysis discriminated between irradiated (positive, >5000 PCs) and non-irradiated (negative, <700 PCs) cinnamon and red peppers. Black pepper had intermediate results (700-5000 PCs), while paprika had low sensitivity (negative results) upon irradiation. The DEFT/APC technique also showed clear screening results through the changes in microbial profiles, where the best results were found in paprika, followed by red pepper and cinnamon. E-nose analysis showed a dose-dependent discrimination in volatile profiles upon irradiation through principal component analysis. These methods can be used considering their potential applications for the screening analysis of irradiated foods.
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NASA Astrophysics Data System (ADS)
Zanello, Marc; Poulon, Fanny; Pallud, Johan; Varlet, Pascale; Hamzeh, H.; Abi Lahoud, Georges; Andreiuolo, Felipe; Ibrahim, Ali; Pages, Mélanie; Chretien, Fabrice; di Rocco, Federico; Dezamis, Edouard; Nataf, François; Turak, Baris; Devaux, Bertrand; Abi Haidar, Darine
2017-02-01
Delineating tumor margins as accurately as possible is of primordial importance in surgical oncology: extent of resection is associated with survival but respect of healthy surrounding tissue is necessary for preserved quality of life. The real-time analysis of the endogeneous fluorescence signal of brain tissues is a promising tool for defining margins of brain tumors. The present study aims to demonstrate the feasibility of multimodal optical analysis to discriminate fresh samples of gliomas, metastases and meningiomas from their appropriate controls. Tumor samples were studied on an optical fibered endoscope using spectral and fluorescence lifetime analysis and then on a multimodal set-up for acquiring spectral, one and two-photon fluorescence images, second harmonic generation signals and two-photon fluorescence lifetime datasets. The obtained data allowed us to differentiate healthy samples from tumor samples. These results confirmed the possible clinical relevance of this real-time multimodal optical analysis. This technique can be easily applied to neurosurgical procedures for a better delineation of surgical margins.
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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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Gosetti, Fabio; Chiuminatto, Ugo; Mazzucco, Eleonora; Mastroianni, Rita; Marengo, Emilio
2015-01-15
The study investigates the sunlight photodegradation process of carminic acid, a natural red colourant used in beverages. For this purpose, both carminic acid aqueous standard solutions and sixteen different commercial beverages, ten containing carminic acid and six containing E120 dye, were subjected to photoirradiation. The results show different patterns of degradation, not only between the standard solutions and the beverages, but also from beverage to beverage. Due to the different beverage recipes, unpredictable reactions take place between the dye and the other ingredients. To identify the dye degradation products in a very complex scenario, a methodology was used, based on the combined use of principal component analysis with discriminant analysis and ultra-high-performance liquid chromatography coupled with tandem high resolution mass spectrometry. The methodology is unaffected by beverage composition and allows the degradation products of carminic acid dye to be identified for each beverage. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Relationships between vegetation and terrain variables in southeastern Arizona. Ph.D. Thesis
NASA Technical Reports Server (NTRS)
Mouat, D. A. (Principal Investigator)
1974-01-01
The author has identified the following significant results. Relationships were established between eight terrain variables and plant species and 31 vegetation types. Certain plant species are better than others for differentiating or discriminating groups of specified terrain variables. Certain terrain variables are better than others for differentiating or discriminating groups of vegetation types. Stepwise discriminant analysis was shown to be a useful tool in plant ecological studies.
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Feng, Danjun; Xu, Lingzhong
2015-01-01
This study described the prevalence of psychological distress and examined the moderating effect of self-esteem in the relationship between perceived discrimination and psychological distress among Chinese pulmonary tuberculosis (TB) patients. Seven hundred and twenty patients with TB from three cities of Shandong Province in eastern China participated in a cross-sectional survey. Patients were measured with the Kessler 10 (K10), the Rosenberg self-esteem scale, and a self-developed perceived discrimination questionnaire. A total of 58.6% of patients with TB scored above 16 on the K10, indicating moderate and serious psychological distress. Chi-square test revealed that female patients reported higher psychological distress than male patients. The structural equation modeling (SEM) analysis among the whole sample indicated that perceived discrimination was significantly related with psychological distress (β = .28, p ≤ .01). The multiple group analysis of SEM showed that perceived discrimination had a significantly substantial (β = .50, p ≤ .001), significantly moderate (β = .15, p ≤ .01), and insignificant effect (β = .05, p ≥ .05) on psychological distress among low self-esteem, moderate self-esteem, and high self-esteem patients with TB, respectively, which verified the moderating effect of self-esteem in the relationship between perceived discrimination and psychological distress.
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Chambers, Brittany D; Erausquin, Jennifer Toller
2018-02-01
Adolescence is a critical phase of development and experimentation with delinquent behaviors. There is a growing body of literature exploring individual and structural impacts of discrimination on health outcomes and delinquent behaviors. However, there is limited research assessing how school diversity and discrimination impact students' delinquent behaviors. In response, the purpose of this study was to assess if individual- and school-level indicators of discrimination and diversity were associated with student delinquent behaviors among African American and White students. We analyzed Wave I (1994-1995) data from the National Longitudinal Study of Adolescent Health. Our analysis was limited to 8947 African American and White students (73% White, 48% male, and 88% parent ≥ high school education). We used multilevel zero-inflated negative binomial regression to test the association of individual- and school characteristics and discrimination with the number of self-reported delinquent behaviors. Race, sex, perceived peer inclusion, and teacher discrimination were predictors of students' delinquent behaviors. The average school perceived peer inclusion and percentage of African Americans in teaching roles were associated with delinquent behaviors. Findings from this study highlight the potential for intervention at the interpersonal and school levels to reduce delinquency among African American and White students. © 2018, American School Health Association.
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Mu, Chun-sun; Zhang, Ping; Kong, Chun-yan; Li, Yang-ning
2015-09-01
To study the application of Bayes probability model in differentiating yin and yang jaundice syndromes in neonates. Totally 107 jaundice neonates who admitted to hospital within 10 days after birth were assigned to two groups according to syndrome differentiation, 68 in the yang jaundice syndrome group and 39 in the yin jaundice syndrome group. Data collected for neonates were factors related to jaundice before, during and after birth. Blood routines, liver and renal functions, and myocardial enzymes were tested on the admission day or the next day. Logistic regression model and Bayes discriminating analysis were used to screen factors important for yin and yang jaundice syndrome differentiation. Finally, Bayes probability model for yin and yang jaundice syndromes was established and assessed. Factors important for yin and yang jaundice syndrome differentiation screened by Logistic regression model and Bayes discriminating analysis included mothers' age, mother with gestational diabetes mellitus (GDM), gestational age, asphyxia, or ABO hemolytic diseases, red blood cell distribution width (RDW-SD), platelet-large cell ratio (P-LCR), serum direct bilirubin (DBIL), alkaline phosphatase (ALP), cholinesterase (CHE). Bayes discriminating analysis was performed by SPSS to obtain Bayes discriminant function coefficient. Bayes discriminant function was established according to discriminant function coefficients. Yang jaundice syndrome: y1= -21. 701 +2. 589 x mother's age + 1. 037 x GDM-17. 175 x asphyxia + 13. 876 x gestational age + 6. 303 x ABO hemolytic disease + 2.116 x RDW-SD + 0. 831 x DBIL + 0. 012 x ALP + 1. 697 x LCR + 0. 001 x CHE; Yin jaundice syndrome: y2= -33. 511 + 2.991 x mother's age + 3.960 x GDM-12. 877 x asphyxia + 11. 848 x gestational age + 1. 820 x ABO hemolytic disease +2. 231 x RDW-SD +0. 999 x DBIL +0. 023 x ALP +1. 916 x LCR +0. 002 x CHE. Bayes discriminant function was hypothesis tested and got Wilks' λ =0. 393 (P =0. 000). So Bayes discriminant function was proved to be with statistical difference. To check Bayes probability model in discriminating yin and yang jaundice syndromes, coincidence rates for yin and yang jaundice syndromes were both 90% plus. Yin and yang jaundice syndromes in neonates could be accurately judged by Bayesian discriminating functions.
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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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Musical Abilities and Sex Differences in the Analysis of Aural-Musical Capacities
ERIC Educational Resources Information Center
Whellams, Frederick S.
1973-01-01
By means of a multiple discrimination function analysis it is possible to reject the hypothesis that musicality, as measured by the tests in Wing's battery, is entirely influenced by hereditary factors. (Author)
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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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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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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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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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NASA Astrophysics Data System (ADS)
Chernomyrdin, Nikita V.; Zaytsev, Kirill I.; Lesnichaya, Anastasiya D.; Kudrin, Konstantin G.; Cherkasova, Olga P.; Kurlov, Vladimir N.; Shikunova, Irina A.; Perchik, Alexei V.; Yurchenko, Stanislav O.; Reshetov, Igor V.
2016-09-01
In present paper, an ability to differentiate basal cell carcinoma (BCC) and healthy skin by combining multi-spectral autofluorescence imaging, principle component analysis (PCA), and linear discriminant analysis (LDA) has been demonstrated. For this purpose, the experimental setup, which includes excitation and detection branches, has been assembled. The excitation branch utilizes a mercury arc lamp equipped with a 365-nm narrow-linewidth excitation filter, a beam homogenizer, and a mechanical chopper. The detection branch employs a set of bandpass filters with the central wavelength of spectral transparency of λ = 400, 450, 500, and 550 nm, and a digital camera. The setup has been used to study three samples of freshly excised BCC. PCA and LDA have been implemented to analyze the data of multi-spectral fluorescence imaging. Observed results of this pilot study highlight the advantages of proposed imaging technique for skin cancer diagnosis.
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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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Classification of adulterated honeys by multivariate analysis.
Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad
2017-06-01
In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.
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Zifan, Ali; Ledgerwood-Lee, Melissa; Mittal, Ravinder K
2016-12-01
Three-dimensional high-definition anorectal manometry (3D-HDAM) is used to assess anal sphincter function; it determines profiles of regional pressure distribution along the length and circumference of the anal canal. There is no consensus, however, on the best way to analyze data from 3D-HDAM to distinguish healthy individuals from persons with sphincter dysfunction. We developed a computer analysis system to analyze 3D-HDAM data and to aid in the diagnosis and assessment of patients with fecal incontinence (FI). In a prospective study, we performed 3D-HDAM analysis of 24 asymptomatic healthy subjects (control subjects; all women; mean age, 39 ± 10 years) and 24 patients with symptoms of FI (all women; mean age, 58 ± 13 years). Patients completed a standardized questionnaire (FI severity index) to score the severity of FI symptoms. We developed and evaluated a robust prediction model to distinguish patients with FI from control subjects using linear discriminant, quadratic discriminant, and logistic regression analyses. In addition to collecting pressure information from the HDAM data, we assessed regional features based on shape characteristics and the anal sphincter pressure symmetry index. The combination of pressure values, anal sphincter area, and reflective symmetry values was identified in patients with FI versus control subjects with an area under the curve value of 1.0. In logistic regression analyses using different predictors, the model identified patients with FI with an area under the curve value of 0.96 (interquartile range, 0.22). In discriminant analysis, results were classified with a minimum error of 0.02, calculated using 10-fold cross-validation; different combinations of predictors produced median classification errors of 0.16 in linear discriminant analysis (interquartile range, 0.25) and 0.08 in quadratic discriminant analysis (interquartile range, 0.25). We developed and validated a novel prediction model to analyze 3D-HDAM data. This system can accurately distinguish patients with FI from control subjects. Copyright © 2016 AGA Institute. Published by Elsevier Inc. All rights reserved.
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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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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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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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Wang, Wei; Heitschmidt, Gerald W; Windham, William R; Feldner, Peggy; Ni, Xinzhi; Chu, Xuan
2015-01-01
The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel. © 2014 Institute of Food Technologists®
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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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A Signal Detection Theory Analysis of Behavioral Pattern Separation Paradigms
ERIC Educational Resources Information Center
Loiotile, Rita E.; Courtney, Susan M.
2015-01-01
Behavioral pattern separation (BPS) paradigms ask participants to discriminate previously encoded (old) stimuli from highly similar (lure) and categorically distinct (novel) stimuli. The lure-old discrimination, thought to uniquely reflect pattern separation in the hippocampal formation, is typically pitted against the traditional novel-old…
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An Analysis of an Autoclitic Analogue in Pigeons
ERIC Educational Resources Information Center
Kuroda, Toshikazu; Lattal, Kennon A.; García-Penagos, Andrés
2014-01-01
Using a conditional discrimination procedure, pigeons were exposed to a nonverbal analogue of qualifying autoclitics such as "definitely" and "maybe." It has been suggested that these autoclitics are similar to tacts except that they are under the control of private discriminative stimuli. Instead of the conventional assumption…
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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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García-Molina, María Dolores; García-Olmo, Juan; Barro, Francisco
2016-01-01
The aim of this work was to assess the ability of Near Infrared Spectroscopy (NIRS) to distinguish wheat lines with low gliadin content, obtained by RNA interference (RNAi), from non-transgenic wheat lines. The discriminant analysis was performed using both whole grain and flour. The transgenic sample set included 409 samples for whole grain sorting and 414 samples for flour experiments, while the non-transgenic set consisted of 126 and 156 samples for whole grain and flour, respectively. Samples were scanned using a Foss-NIR Systems 6500 System II instrument. Discrimination models were developed using the entire spectral range (400-2500 nm) and ranges of 400-780 nm, 800-1098 nm and 1100-2500 nm, followed by analysis of means of partial least square (PLS). Two external validations were made, using samples from the years 2013 and 2014 and a minimum of 99% of the flour samples and 96% of the whole grain samples were classified correctly. The results demonstrate the ability of NIRS to successfully discriminate between wheat samples with low-gliadin content and wild types. These findings are important for the development and analysis of foodstuff for celiac disease (CD) patients to achieve better dietary composition and a reduction in disease incidence.
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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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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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Bingham, Trista; Kim, Junyeop; Wheeler, Darrell P.; Millett, Gregorio A.
2012-01-01
Objectives. We examined the impact of social discrimination and financial hardship on unprotected anal intercourse with a male sex partner of serodiscordant or unknown HIV status in the past 3 months among 1081 Latino and 1154 Black men who have sex with men (MSM; n = 2235) residing in Los Angeles County, California; New York, New York; and Philadelphia, Pennsylvania. Methods. We administered HIV testing and a questionnaire assessing 6 explanatory variables. We combined traditional mediation analysis with the results of a path analysis to simultaneously examine the direct, indirect, and total effects of these variables on the outcome variable. Results. Bivariate analysis showed that homophobia, racism, financial hardship, and lack of social support were associated with unprotected anal intercourse with a serodiscordant or sero-unknown partner. Path analysis determined that these relations were mediated by participation in risky sexual situations and lack of social support. However, paths between the explanatory variable and 2 mediating variables varied by participants’ serostatus. Conclusions. Future prevention research and program designs should specifically address the differential impact of social discrimination and financial hardship on lack of social support and risky sexual situations among Latino and Black MSM. PMID:22401516
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Inci, Ercan; Ekizoglu, Oguzhan; Turkay, Rustu; Aksoy, Sema; Can, Ismail Ozgur; Solmaz, Dilek; Sayin, Ibrahim
2016-10-01
Morphometric analysis of the mandibular ramus (MR) provides highly accurate data to discriminate sex. The objective of this study was to demonstrate the utility and accuracy of MR morphometric analysis for sex identification in a Turkish population.Four hundred fifteen Turkish patients (18-60 y; 201 male and 214 female) who had previously had multidetector computed tomography scans of the cranium were included in the study. Multidetector computed tomography images were obtained using three-dimensional reconstructions and a volume-rendering technique, and 8 linear and 3 angular values were measured. Univariate, bivariate, and multivariate discriminant analyses were performed, and the accuracy rates for determining sex were calculated.Mandibular ramus values produced high accuracy rates of 51% to 95.6%. Upper ramus vertical height had the highest rate at 95.6%, and bivariate analysis showed 89.7% to 98.6% accuracy rates with the highest ratios of mandibular flexure upper border and maximum ramus breadth. Stepwise discrimination analysis gave a 99% accuracy rate for all MR variables.Our study showed that the MR, in particular morphometric measures of the upper part of the ramus, can provide valuable data to determine sex in a Turkish population. The method combines both anthropological and radiologic studies.
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Weakly Supervised Dictionary Learning
NASA Astrophysics Data System (ADS)
You, Zeyu; Raich, Raviv; Fern, Xiaoli Z.; Kim, Jinsub
2018-05-01
We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly learning a dictionary and corresponding sparse coefficients to provide accurate data representation. This approach is useful for denoising and signal restoration, but may lead to sub-optimal classification performance. By contrast, analysis dictionary learning provides a transform that maps data to a sparse discriminative representation suitable for classification. We consider the problem of analysis dictionary learning for time-series data under a weak supervision setting in which signals are assigned with a global label instead of an instantaneous label signal. We propose a discriminative probabilistic model that incorporates both label information and sparsity constraints on the underlying latent instantaneous label signal using cardinality control. We present the expectation maximization (EM) procedure for maximum likelihood estimation (MLE) of the proposed model. To facilitate a computationally efficient E-step, we propose both a chain and a novel tree graph reformulation of the graphical model. The performance of the proposed model is demonstrated on both synthetic and real-world data.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Baumgardt, D.
1998-03-31
The International Monitoring System (IMS) for the Comprehensive Test Ban Treaty (CTBT) faces the serious challenge of being able to accurately and reliably identify seismic events in any region of the world. Extensive research has been performed in recent years on developing discrimination techniques which appear to classify seismic events into broad categories of source types, such as nuclear explosion, earthquake, and mine blast. This report examines in detail the problem of effectiveness of regional discrimination procedures in the application of waveform discriminants to Special Event identification and the issue of discriminant transportability.
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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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Application of FT-IR spectroscopy on breast cancer serum analysis
NASA Astrophysics Data System (ADS)
Elmi, Fatemeh; Movaghar, Afshin Fayyaz; Elmi, Maryam Mitra; Alinezhad, Heshmatollah; Nikbakhsh, Novin
2017-12-01
Breast cancer is regarded as the most malignant tumor among women throughout the world. Therefore, early detection and proper diagnostic methods have been known to help save women's lives. Fourier Transform Infrared (FT-IR) spectroscopy, coupled with PCA-LDA analysis, is a new technique to investigate the characteristics of serum in breast cancer. In this study, 43 breast cancer and 43 healthy serum samples were collected, and the FT-IR spectra were recorded for each one. Then, PCA analysis and linear discriminant analysis (LDA) were used to analyze the spectral data. The results showed that there were differences between the spectra of the two groups. Discriminating wavenumbers were associated with several spectral differences over the 950-1200 cm- 1(sugar), 1190-1350 cm- 1 (collagen), 1475-1710 cm- 1 (protein), 1710-1760 cm- 1 (ester), 2800-3000 cm- 1 (stretching motions of -CH2 & -CH3), and 3090-3700 cm- 1 (NH stretching) regions. PCA-LDA performance on serum IR could recognize changes between the control and the breast cancer cases. The diagnostic accuracy, sensitivity, and specificity of PCA-LDA analysis for 3000-3600 cm- 1 (NH stretching) were found to be 83%, 84%, 74% for the control and 80%, 76%, 72% for the breast cancer cases, respectively. The results showed that the major spectral differences between the two groups were related to the differences in protein conformation in serum samples. It can be concluded that FT-IR spectroscopy, together with multivariate data analysis, is able to discriminate between breast cancer and healthy serum samples.
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Textural Maturity Analysis and Sedimentary Environment Discrimination Based on Grain Shape Data
NASA Astrophysics Data System (ADS)
Tunwal, M.; Mulchrone, K. F.; Meere, P. A.
2017-12-01
Morphological analysis of clastic sedimentary grains is an important source of information regarding the processes involved in their formation, transportation and deposition. However, a standardised approach for quantitative grain shape analysis is generally lacking. In this contribution we report on a study where fully automated image analysis techniques were applied to loose sediment samples collected from glacial, aeolian, beach and fluvial environments. A range of shape parameters are evaluated for their usefulness in textural characterisation of populations of grains. The utility of grain shape data in ranking textural maturity of samples within a given sedimentary environment is evaluated. Furthermore, discrimination of sedimentary environment on the basis of grain shape information is explored. The data gathered demonstrates a clear progression in textural maturity in terms of roundness, angularity, irregularity, fractal dimension, convexity, solidity and rectangularity. Textural maturity can be readily categorised using automated grain shape parameter analysis. However, absolute discrimination between different depositional environments on the basis of shape parameters alone is less certain. For example, the aeolian environment is quite distinct whereas fluvial, glacial and beach samples are inherently variable and tend to overlap each other in terms of textural maturity. This is most likely due to a collection of similar processes and sources operating within these environments. This study strongly demonstrates the merit of quantitative population-based shape parameter analysis of texture and indicates that it can play a key role in characterising both loose and consolidated sediments. This project is funded by the Irish Petroleum Infrastructure Programme (www.pip.ie)
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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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NASA Astrophysics Data System (ADS)
Arulbalaji, Palanisamy; Balasubramanian, Gurugnanam
2017-07-01
This study uses advanced spaceborne thermal emission and reflection radiometer (ASTER) hyperspectral remote sensing techniques to discriminate rock types composing Kanjamalai hill located in the Salem district of Tamil Nadu, India. Kanjamalai hill is of particular interest because it contains economically viable iron ore deposits. ASTER hyperspectral data were subjected to principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) to improve identification of lithologies remotely and to compare these digital data results with published geologic maps. Hyperspectral remote sensing analysis indicates that PCA (R∶G∶B=2∶1∶3), MNF (R∶G∶B=3∶2∶1), and ICA (R∶G∶B=1∶3∶2) provide the best band combination for effective discrimination of lithological rock types composing Kanjamalai hill. The remote sensing-derived lithological map compares favorably with a published geological map from Geological Survey of India and has been verified with ground truth field investigations. Therefore, ASTER data-based lithological mapping provides fast, cost-effective, and accurate geologic data useful for lithological discrimination and identification of ore deposits.