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- W2766004531 abstract "Discriminant partial least squares (PLS-DA)—a de facto standard classification method—was found to behave poorly when 3 classes of tequilas were modeled to study a collection of 170 commercial Mexican spirits measured by UV-Vis spectroscopy. This result was compared with other linear and nonlinear supervised classification methods (PLS with variable selection by SRI index and genetic algorithms; kernel-PLS—modified in this paper to handle simultaneously several classes, quadratic discriminant analysis (QDA), support vectors machines, and counter-propagation artificial neural networks). All linear models performed worse than nonlinear ones, and this was attributed to the quite different inner dispersion of the classes and the intermediate position of 1 class. Considering the overall classification results and parsimony, QDA was selected for routine assessments thanks to its simplicity and broad availability." @default.
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- W2766004531 date "2017-10-17" @default.
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- W2766004531 title "Nonlinear classification of commercial Mexican tequilas" @default.
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- W2766004531 doi "https://doi.org/10.1002/cem.2939" @default.
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