title: Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines.
abstract: Classical multivariate analysis techniques such as factor analysis and stepwise linear discriminant analysis and artificial neural networks method (ANN) have been applied to the classification of Spanish denomination of origin (DO) rose wines according to their geographical origin. Seventy commercial rose wines from four different Spanish DO (Ribera del Duero, Rioja, Valdepeñas and La Mancha) and two successive vintages were studied. Nineteen different variables were measured in these wines. The stepwise linear discriminant analyses (SLDA) model selected 10 variables obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. The ANN model selected seven variables, five of which were also selected by the SLDA model, and it gave a 100% of correct classification for training and prediction. So, both models can be considered satisfactory and acceptable, being the selected variables useful to classify and differentiate these wines by their origin. Furthermore, the casual index analysis gave information that can be easily explained from an enological point of view.
authors.affiliation: Department of Biotechnology and Food Science, University of Burgos, Plaza Misael Bañuelos s/n, 09001 Burgos, Spain.
authors.forename: S
authors.initials: S
authors.lastname: Pérez-Magariño
authors.forename: M
authors.initials: M
authors.lastname: Ortega-Heras
authors.forename: M L
authors.initials: ML
authors.lastname: González-San José
authors.forename: Z
authors.initials: Z
authors.lastname: Boger