Characterization and determination of the geographical origin of wines. Part III: multivariate discrimination and classification methods

Abstract

The aim of the European wine project was to test the possibility of determining the country of origin of wines based on their chemical composition. The results of descriptive and inductive univariate methods of data analysis are discussed in part II of this series of papers. Here the results of some selected multivariate methods of discrimination and classification such as classification and regression trees (CART), regularized discriminant analysis (RDA), and partial least squares discriminant analysis (PLS-DA) are compared and discussed. Special attention is paid to the development of models that are efficient both in terms of predictive performance and number of required variables. Using CART South African wines could be separated very easily from those of the East European countries by only one isotopic parameter, but it gives less good results for the discrimination of the East European wines. The application of RDA and PLS-DA, and its uninformative variable elimination variant (PLS-UVE) lead to discriminant models, which allow a correct classification of the wines from the East European countries with rates between 88 and 100%. Comparing RDA and PLS-DA, RDA-models contain somewhat fewer variables than PLS-DA-models, because PLS-DA is constrained to two-group comparisons (“one country against the other countries”).