In this paper, the differentiation of green, black, Oolong, white, and Pu-erh teas has been carried out according to their free amino acid contents. Alanine, arginine, asparagine, aspartic acid, glutamic acid, isoleucine, histidine, leucine, phenylalanine, serine, theanine, threonine, and tyrosine have been determined by liquid chromatography with derivatization with o-phthalaldehyde and fluorescence detection. The chromatographic separation was achieved with a Hypersil ODS column and gradient elution. The amino acid contents were used as chemometric descriptors for classification purposes of different tea varieties. Principal component analysis, k-nearest neighbors, linear discriminant analysis, and artificial neural networks were applied to differentiate tea varieties. Using back-propagation multilayer perceptron artificial neural networks, 100% success in the classification was obtained. The most differentiating amino acids were glutamic acid, asparagine, serine, alanine, leucine, and isoleucine.

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In this paper the differentiation of silver, gold, aged and extra-aged tequila and mezcal has been carried out according to their metal content. Aluminum, barium, calcium, copper, iron, magnesium, manganese, potassium, sodium, strontium, zinc, and sulfur were determined by inductively coupled plasma optical emission spectrometry. The concentrations found for each element in the samples were used as chemical descriptors for characterization purposes. Principal component analysis, linear discriminant analysis and artificial neural networks were applied to differentiate types of tequila and mezcal. Using probabilistic neural networks 100% of success in the classification was obtained for silver, gold, extra-aged tequila and mezcal. In the case of aged tequila 90% of samples were successfully classified. Sodium, potassium, calcium, sulfur, magnesium, iron, strontium, copper and zinc were the most discriminant elements.

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