Application of multivariate analysis and artificial neural networks for the differentiation of red wines from the Canary Islands according to the island of origin.

Eleven metals (K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li, and Rb) were determined in 83 red wines from the Canary Islands. The wines presented high concentrations of Na, and the concentrations of Cu and Zn were much lower than the maximum concentrations established by the International Office of Vine and Wine (OIV). Applying principal component analysis, the dimension space was reduced to five principal components that explain 76.4% of the total variance, and the wines tend to separate on the basis of the island of production. Linear discriminant analysis (LDA) allowed a reasonable classification of wines according to the island of production. When artificial neural networks (Kohonen self-organizing maps and back-propagation feed-forward as unsupervised and supervised techniques, respectively) were applied on the matrix of data constituted by the analyzed metals, the results improved in relation to those obtained by other multivariate methods observing a differentiation of wines according to island of production.