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Phenolic compounds in 46 Spanish cider apple varieties were determined by RP-HPLC with direct injection. Several pattern recognition procedures, including principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares (PLS-1), were applied to the data in an attempt to classify the samples into bitter and nonbitter categories. Reliable decision rules were obtained by both LDA and PLS-1. LDA achieved 91.3 and 85.7% correct classification respectively, for internal and external evaluation of the model.

Ninety-four samples of Asturian natural cider were analyzed for titratable and volatile acidities, pH, alcoholic, total polyphenol, and acidic polysaccharide contents, nonvolatile acids, polyalcohols, residual sugars, and major volatile compounds. A partial least-squares regression analysis (PLR-1) was performed to correlate the chemical composition and the origin of the raw material, the cider samples being grouped into two categories: an "odd" class, cider made from foreign apples (1995 and 1997), and an "even" class, ciders made from Asturian apples (1996 and 1998). The mathematical model has a multiple linear correlation coefficient of 80%.

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An improved analytical method was developed for the determination of polyphenols in the apple products must and cider. Phenolic compounds were fractionated into neutral and acidic groups by means of a solid-phase extraction method. The analytical method proposed was effective for the quantitation of phenolic compounds; recoveries between 84% and 111% were obtained, and the relative standard deviation was usually less than 5%.