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.

Andrade, J., Ballabio, D., Gomez-Carracedo, M., Perez-Caballero, G. (2017). Nonlinear classification of commercial Mexican tequilas. JOURNAL OF CHEMOMETRICS, 31(12) [10.1002/cem.2939].

Nonlinear classification of commercial Mexican tequilas

Ballabio, D;
2017

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.
Articolo in rivista - Articolo scientifico
Counter-propagation artificial neural networks; Kernel partial least squares; PLS-DA; Supervised classification; Support vectors machines; Tequila; Analytical Chemistry; Applied Mathematics
English
2017
31
12
e2939
none
Andrade, J., Ballabio, D., Gomez-Carracedo, M., Perez-Caballero, G. (2017). Nonlinear classification of commercial Mexican tequilas. JOURNAL OF CHEMOMETRICS, 31(12) [10.1002/cem.2939].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/176959
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