Nonlinear classification of commercial Mexican tequilas

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. The rising success of tequila made its counterfeiting a profitable business that affects both consumers and reputed brands. To streamline a simple methodology to ascertain the class of a tequila, UV‐Vis spectroscopy is proposed combined to chemometric treatments. A suite of supervised classification methods were compared where from quadratic discriminant analysis is proposed for routine applications. Genetic algorithms implemented in discriminant PLS demonstrated to be a good option for high‐level confirmatory studies (using UV‐Vis data).