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Nonlinear classification of commercial Mexican tequilas
Author(s) -
Andrade Jose Manuel,
Ballabio Davide,
GómezCarracedo Maria Paz,
PérezCaballero Guadalupe
Publication year - 2017
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2939
Subject(s) - quadratic classifier , linear discriminant analysis , mathematics , artificial intelligence , kernel (algebra) , nonlinear system , artificial neural network , discriminant , pattern recognition (psychology) , partial least squares regression , quadratic equation , statistics , machine learning , support vector machine , computer science , physics , geometry , combinatorics , quantum mechanics
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.