Enological Parameters and Pattern Recognition Methods in the Geographic Differentiation of Spanish Red Wines

Abstract

Two hundred thirty wines, 1976 to 1983 vintages, from four different Spanish regions have been analyzed by pattern recognition methods in order to characterize the groups and classify unknown samples according to their geographic origin. The nine physico-chemical parameters determined are termed inexpensive variables since they can be evaluated at any enological station. The variables lactic, tartaric, and malic acids, titratable acidity, and potassium content are the most relevant in this study. The effect of factors such as varietal diversity, climatic conditions, or winemaking techniques, which account for the differences in the mean values of the variables for each region, are discussed. Coincident classification results have been found for all three supervised methods of analysis used: statistical linear discriminant analysis (SLDA), linear learning machine (LLM), and K-nearest neighbor (KNN). The correct classification percentage obtained has been <i>ca</i> 90% for all procedures employed.