Characterization of Selected Spanish Table Wine Samples According to Their Biogenic Amine Content from Liquid Chromatographic Determination ROBERTO ROMERO,MERCEDES SA Ä NCHEZ-VIN ˜ AS,* DOMINGO GA Ä ZQUEZ, AND M. GRACIA BAGUR Department of Analytical Chemistry, Faculty of Sciences, University of Granada, E-18071 Granada, Spain Pattern recognition techniques, such as principal component analysis, cluster analysis, and linear discriminant analysis, have been applied to samples of red, white, and rose ´ wines to determine whether some biogenic amines could be considered as chemical descriptors. Eight amines (tryptamine, phenylethylamine, putrescine, cadaverine, histamine, tyramine, spermidine, and spermine) were determined by RP-HPLC, after derivatization with dabsyl chloride. However, only putrescine, cadaverine, histamine, tyramine, spermidine, and spermine were found in the wines analyzed. From the association between variables obtained by principal component analysis and clustering and from the relationship found by linear discriminant analysis, it can be deduced that the amines generated during malolactic fermentation (putrescine, histamine, and tyramine) could be used as chemical descriptors to characterize table wine samples. KEYWORDS: Wines; biogenic amines; malolactic fermentation; multivariate analysis INTRODUCTION Biogenic amines are organic base compounds that occur in different kinds of food, such as fish products (1), cheese (2), wine (3), cured meats, and other fermented foods (4). These compounds are produced during and after wine-making, although some are present in small amounts in grape juice. They can be present in the must or formed by yeasts during alcoholic fermentation. Chemically, the main factors involved in the generation of these compounds are malolactic fermentation, during which the main biogenic amines generated by decar- boxylation of the corresponding amino acids are putrescine, histamine, and tyramine (5), and the pH of the wine. At high pH, biogenic amines are always produced in large amounts (5). Thus, red wines, which are generally less acidic, contain higher biogenic amine concentrations than white wines. Furthermore, in wine-making, malolactic fermentation usually has a greater importance in red wines than in white wines. There is also a third type of wine, rose ´ wine, made from red grapes or from a mixture of red and white grapes, of which the juices are fermented like white wines, that is, without their skins, showing properties between those of red and white wines (6). Consequently, as a result of the extent of malolactic fermen- tation, red, rose ´, and white wines can be expected to have different biogenic amine contents, which would permit the classification of the wines as well as to explain a misclassifi- cation related to the wine-making. An attractive possibility for this purpose of wines is based on unsupervised and supervised pattern recognition techniques (7-9), which make it possible to extract information from analytical parameters, allowing us (a) to verify associations among variables, (b) to group or to cluster samples (objects) with respect to comparable chemical descriptors, and (c) to search multivariate data classification on the basis of known class membership of those objects. In parallel with these studies, data analysis can be carried out using analysis of variance and correlation studies and by establishing the discriminant capacities of the variables, one by one, through the Fisher index (10). This univariate approach implies that each variable has been studied separately from the others, by calculating and comparing mean values and standard deviations. It can be used as a first approach to establish the possibility of a pattern recognition study. The aim of this work is to demonstrate that the content of those biogenic amines closely related to malolactic fermentation could be used as a chemical descriptor to differentiate between the types of wine and thus to discover if the wine has been irregularly processed or not. The biogenic amine content was determined by RP-HPLC after derivatization with dabsyl chloride (11-13). MATERIALS AND METHODS Apparatus and Software. The liquid chromatograph consisted of a Hewlett-Packard 1050 series equipped with a UV-visible variable- wavelength detector, a 3396-A integrator, and a Rheodyne (Rheodyne, Inc., Cotati, CA) 7125 loop injector with a 20-μL sample loop. A * Author to whom correspondence should be addressed [fax +34958243328; e-mail mercedes@ugr.es] J. Agric. Food Chem. 2002, 50, 4713-4717 4713 10.1021/jf025514r CCC: $22.00 © 2002 American Chemical Society Published on Web 07/02/2002