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Study of the elemental composition of the soil-grape-wine chain and correlation relationships between the chain links is presented. The objects of the study were grapes of the Muscat, Cabernet and Merlot varieties, wine samples produced from them and soils of the viticultural areas. Concentrations of Li, Mg, Al, K, Ca, V, Mn, Fe, Ni, Co, Cu, Zn, Rb, Cd, Pb, Ba, Na, Ti and Sr in soils, grapes and wines were determined by inductively coupled plasma atomic emission spectrometry (ICP-AES). Relationships between the elemental composition of the objects under study were assessed by statistical modelling using the STATISTICA software. The character of metal distribution in the soil-grape chain was studied for three forms of the element extraction from soils. We determined their gross content, the content of acid-soluble and mobile forms. The degree of absorption of mobile forms of metals by grapes was estimated using «biological absorption coefficient»., Values of the biological absorption coefficient (BAC) of different metals depend on the grape variety. High BAC values are observed for Rb, Ti, Mg, Zn, Cu, Na, Fe, Al, and Sr in Merlot grapes; K, Pbm and Ni in Muscat; and for V and Mn in Cabernet. The lowest BAC values were observed for Co, Ba, and Ca in all grape varieties under study. Each grape variety formed its own individual elemental image due to the different nature of absorption of the studied metals. The transfer of metals from grape to wine for all considered varieties was accompanied by a decrease in the concentrations of Mg, Al, K, Ca, Mn, Ni, Cu, Zn, Rb, Ba, Ti, and Sr and by an increase in the content of V, Fe, Co, Pb, and Na. Discriminant analysis revealed the metals with the highest identification properties, considering their form present in the soil. The results obtained can be used when setting markers determining the varietal and regional origin of wines.

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This paper shows a possibility of establishing the authenticity and geographic origin of wines by neural networks based on multi-element analysis. The study used 144 samples of Cabernet and Merlot dry red wines pro- duced in Krasnodar Region according to traditional technologies. The wines were provided by the producers or pur- chased in retail stores. The concentrations of 20 micro- and macroelements in red wines were determined by atomic emission spectroscopy with inductively coupled plasma. The analysis of average elemental contents showed a signi- ficant dependence of wine composition on the grape variety and place of origin, which enabled us to examine inter- relations between the elements and think of a way to identify them by means of classification models. The software STATISTICA Neural Networks was used to assess a possibility of determining the grape variety and geographical origin. The neural networks constructed in the study contained five variables corresponding to the elements with sta- tistically significant correlations between the names of the regions and the wine samples, namely Fe, Mg, Rb, Ti, and Na. These predictors were able to determine the grape variety and place of growth with a sufficiently high accuracy. In the test sample set, the accuracy reached 95.24% and 100% for variety and region identification, respectively. A software product was developed to automate the calculations based on the neural networks. The program can estab- lish the grape variety from a minimal set of microelements, and then, based on the variety and the same set of micro- elements, determine its place of origin.