Abstract
Due to the increasing consumer interest in wines with a controlled place of origin, PGI (Protected Geographical Indication) and PDO (Protected Designation of Origin), the most acute question is how to identify them. One of the most effective ways to confirm the place of origin of wine in global practice is a comprehensive study of the elemental profile using statistical analysis methods. In the period from 2020 to 2023, 152 grape samples of grapes were collected from various wineries in Crimea and Kuban. The grape must that was obtained from them was fermented in laboratory conditions. The elemental profile was determined in the prepared wines, which included 71 indicators. In the conducted work, it was revealed that wines from Crimea and Kuban differ statistically significantly in the concentration of the elements B, Ca, Cu, Mn, Na, Ni, Re, Si, Sn and U. At the same time, the contents of the elements U, Sn and Re prevail in wines from Crimea, and those of B, Ca, Cu, Mn, Na, Ni and Si prevail in wines from Kuban. At the same time, methods of univariate and multivariate statistics do not allow us to reliably classify wine samples from Crimea and Kuban by their place of origin. In order to reveal the non-linear dependence of the studied indicators in wines on the geographical place of grape growing, the method of a supervised learning Random Forest was used. After training the model on the dataset, the proportion of its correct predictions was 96%. The model used 61 parameters, among which the most important were Ni, Re, Ba, Rb, Na, U, Sb, Zn, Bi, Ag and Ti.
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