• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.225-232
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• 2000

The linear and quadratic discrimination functions based on normal theory are widely used to classify an observation to one of predefined groups. But the discriminant functions are sensitive to outliers. A high breakdown procedure to estimate location and scatter of multivariate data is the minimum volume ellipsoid or MVE estimator To obtain high breakdown classifiers outliers in multivariate data are detected by using the robust Mahalanobis distance based on MVE estimators and the weighted estimators are inserted in the functions for classification. A samll-sample MOnte Carlo study shows that the high breakdown robust procedures perform better than the classical classifiers.

• Korean Journal of Food Science and Technology
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• v.28 no.2
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• pp.298-304
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• 1996

Thirty-nine samples of roasted sesame seed oils were sensorially evaluated in terms of nutty odor, burnt odor and overall desirability, and their volatile compounds quantitatively analysed using direct sampling capillary GLC. Five volatile compounds were appeared to be significant for the sensory Properties of sesame oils through the multivariate analytical techniques such as stepwise discriminant analysis. canonical discriminant analysis, discriminant analysis and principal component analysis. The most important compounds were 2,5-dimethylpyrazine and 2-methylpyrazine which could be effectively used as chemical indicators related to nutty and burnt odor of sesame oils, respectively. The sesame oils which have represented a good grade of overall desirability have been always kept $35.82{\sim}4.43$ ppm of 2,5-dimethylpyrazine and also $28.90{\sim}6.35$ ppm of 2-methylpyrazine.

• Korean Journal of Pharmacognosy
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• v.44 no.4
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• pp.332-335
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• 2013

Geographical classification of A. gigas was performed in the present study using UHPLC-DAD combined with multivariate data analysis techniques. Six active constituents were isolated from A. gigas; nodakenin, marmesin, decursinol, demethylsuberosin, decursin and decursinol angelate. One hundred sixty eight A. gigas samples were simultaneously determined using UHPLC-DAD. A principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) was used to classify the samples according to geographical origins (Korea and China). The origins of A. gigas from Korea and China were correctly classified by 81.6% and 93.8% using PLS-DA Y prediction. This result demonstrates the potential use of UHPLC-DAD combined with multivariate analysis techniques as an accurate and rapid method to classify A. gigas according to their geographical origin.

• Asian-Australasian Journal of Animal Sciences
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• v.9 no.1
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• pp.83-89
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• 1996

Multivariate statistical procedures were used to analyse data on the chemical composition and in vitro digestibility of four varienties of rice straw after treatment with 4% NaOH solution, 4% urea solution or distilled water (control) for 48 hours. For each treatment, stepwise discriminant analysis identified the variables which maximized differences between varieties and the eigenvectors from principal component analysis quantified the contribution of these criterion variables to varietal differences. The overall response of varieties to chemical treatment was demonstrated qualitatively, by cluster analysis, and quantitatively, from the magnitude of the principal component scores. The analysis revealed that the urea and control treatments elicited the same response whereas NaOH had the greatest effect on the poorest straw variety. Similar analyses conducted on the botanical fractions of the varieties showed that the relative response of the inflorescence, stem, leaf blade and leaf sheath fractions was not altered by chemical treatment.

• Korean Journal of Medicinal Crop Science
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• v.24 no.2
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• pp.93-100
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• 2016

Background: A set of logical criteria that can accurately identify and verify the cultivation region of raw materials is a critical tool for the scientific management of traditional herbal medicine. Methods and Results: Volatile compounds were obtained from 19 and 32 samples of Angelica gigas Nakai cultivated in Korea and China, respectively, by using steam distillation extraction. The metabolites were identified using GC/MS by querying against the NIST reference library. Data binning was performed to normalize the number of variables used in statistical analysis. Multivariate statistical analyses, such as Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) were performed using the SIMCA-P software. Significant variables with a Variable Importance in the Projection (VIP) score higher than 1.0 as obtained through OPLS-DA and those that resulted in p-values less than 0.05 through one-way ANOVA were selected to verify the marker compounds. Among the 19 variables extracted, styrene, ${\alpha}$-pinene, and ${\beta}$-terpinene were selected as markers to indicate the origin of A. gigas. Conclusions: The statistical model developed was suitable for determination of the geographical origin of A. gigas. The cultivation regions of six Korean and eight Chinese A. gigas. samples were predicted using the established OPLS-DA model and it was confirmed that 13 of the 14 samples were accurately classified.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.662-666
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• 2005

Using data gathered from expert opinion of plant and equipment professionals; this paper presents the key variables that may constitute a maintenance proficient plant operator. The Multivariate Discriminant Analysis (MDA) was applied to generate data and was tested for sensitivity analysis. Results showed that the MDA model was able to classify plant operators' proficiency at 94.10 percent accuracy and determined nine (9) key variables of a maintenance proficient plant operator. The key variables included: i) number of years of experience as equipment operator (PQ1); ii) eye-hand coordination (PQ9); iii) eye-hand-foot coordination (PQ10); iv) planning skills (TE16); v) pay/wage (MQ1); vi) work satisfaction (MQ4); vii) operator responsibilities as defined by management (MF1); viii) clear management policies (MF4); and ix) management pay scheme (MF5). The classification procedure of nine variables formed the general model with the equation viz: OMP (general) = 0.516PQ1 + 0.309PQ9 + 0.557PQ10 + 0.831TE16 + 0.8MQ1 + 0.0216MQ4 + 0.136MF1 + 0.28MF4 + 0.332MF5 - 4.387

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.633-644
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• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• Information Systems Review
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• v.8 no.3
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• pp.125-134
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• 2006

This article adopts a decision tree algorithm(C5.0) to predict customer churning in online auto insurance environment. Using a sample of on-line auto insurance customers contracts sold between 2003 and 2004, we test how decision tree-based model(C5.0) works on the prediction of customer churning. We compare the result of C5.0 with those of logistic regression model(LRM), multivariate discriminant analysis(MDA) model. The result shows C5.0 outperforms other models in the predictability. Based on the result, this study suggests a way of setting marketing strategy and of developing online auto insurance business.

• The Journal of Information Systems
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• v.27 no.2
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• pp.115-134
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• 2018

Purpose In recent years, mobile payment service users have been rapidly increasing. Previous researchers focused on the mobile usage situation such as the elements of mobile payment service, usage pattern, and user behaviors, and the research that is approached from the viewpoint of the user is still insufficient. The aim of this study is to suggest a acceptance-resistance motivation model of choosing a mobile payment service based on the Herzbergs Two-Factor Theory by investigating users' motivation and hygiene factors. Design/methodology/approach For the purpose, literature reviews on factors of choosing a mobile payment service were conducted and classified motivation and hygiene factors. Two hypotheses were set as follows: Hypothesis I is that motivation factors have a positive impact on the choice of mobile payment service, and Hypothesis II is that hygiene factors have a negative impact on the choice of mobile payment service. To test two hypotheses, this study conducted an online questionnaire survey and a multivariate discriminant analysis. Findings The result found that mobile payment service is more likely to be replaced with mobile by improving convenience, simplicity, and ease of use that affect the acceptance motivation of mobile payment service. This result supported the Hypothesis I but not Hypothesis II and contributed to provide implications for future mobile payment service development and marketing utilization.

• Journal of the military operations research society of Korea
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• v.6 no.2
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• pp.89-127
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• 1980

The multivariate analysis techniques of cluster analysis are examined in this article. The theory and applications of the techniques and computer software concerning these techniques are discussed and sample jobs are included. A hierarchical cluster analysis algorithm, available in the IMSL software package, is applied to a set of data extracted from a group of subjects for the purpose of partitioning a collection of 26 attributes of a weapon system into six clusters of superattributes. A nonhierarchical clustering procedure were applied to a collection of data of tanks considering of twenty-four observations of ten attributes of tanks. The cluster analysis shows that the tanks cluster somewhat naturally by nationality. The principal componant analysis and the discriminant analysis show that tank weight is the single most important discriminator among nationality although they are not shown in this article because of the space restriction. This is a part of thesis for master's degree in operations research.