Differentiation of wine vinegars based on phenolic composition

Phenolic composition of 92 wine vinegars produced from different wines from the south of Spain (Jerez, Montilla, El Condado) is determined by HPLC with diode array detection. Pattern recognition techniques were applied to distinguish between different methods of elaboration (slow traditional methods with surface culture or quick methods carried out in bioreactors with submerged culture) or wines employed as substrate. Multivariate analysis of data includes principal component analysis, cluster analysis, and linear discriminant analysis (LDA) as well as artificial neural networks trained by back-propagation (BPANN). The classification depending on the acetification process leads to good recalling rates in both LDA (mean = 92.5) and BPANN (mean = 99.6). With respect to the classification on the basis of the geographical origin, the obtained recalling rates were 88.8 for LDA and of 96.5 for BPANN (mean values). Keywords: Vinegar; phenols; discriminant analysis; artificial neural network; HPLC