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In order to determine the geographical origin and wine variety of white wines, we studied 153 samples of the white wines Riesling (49), Chardonnay (56), and Muscat (48) produced in the territory of the main wineries of geographical zones in the Krasnodar krai. The concentrations of trace and macro elements in wines were determined by inductively coupled plasma atomic emission spectrometry. Chemometric studies were performed using the STATISTICA Neural Networks. From a set of 15 trace and macro elements determined, 5 trace elements (Fe, Mg, Rb, Ti, and Na) were recognized by correlation analysis as the predictors of a constructed neural network model, which successfully identified the brands of wines. To determine the region of grape growing, a neural network model was constructed based on six predictors: five trace elements and a specified wine brand. A software was developed to automate the computations required. [ABSTRACT FROM AUTHOR]

We have estimated the possibility of identifying the provenance of some natural wines produced in the Krasnodar region. The macro- and microelement composition of wines and soils from the place of grape origin has been used as the classification criterion. The main elements indicating the wine provenance, Al, Co, Cu, Mn, Ti, Zn, Ba, and Rb have been determined by inductively coupled plasma atomic emission spectroscopy. We have revealed the possibility of distinguishing wines by their brand and provenance using pair-wise comparison of the classification function parameters for soils and wines produced from grape grown on those soils. [ABSTRACT FROM AUTHOR]