黑海东北海岸葡萄酒元素特征研究

L. Oganesyants, Alexandr L. Panasyuk, D. Sviridov, O. S. Egorova, D. R. Akbulatova, M. Ganin, Aleksey A. Shilkin, Alexandr A. Il’in
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引用次数: 0

摘要

由于消费者对具有受控原产地、PGI(受保护的地理标志)和 PDO(受保护的原产地名称)的葡萄酒越来越感兴趣,最尖锐的问题是如何识别它们。在全球实践中,确认葡萄酒原产地的最有效方法之一是利用统计分析方法对元素特征进行综合研究。在 2020 年至 2023 年期间,从克里米亚和库班的不同酒厂收集了 152 份葡萄样本。从中获得的葡萄汁在实验室条件下进行了发酵。对酿制的葡萄酒进行了元素分析,其中包括 71 项指标。研究结果表明,克里米亚和库班的葡萄酒在 B、Ca、Cu、Mn、Na、Ni、Re、Si、Sn 和 U 元素的含量上存在显著的统计学差异。同时,单变量和多变量统计方法并不能可靠地将克里米亚和库班的葡萄酒样品按产地进行分类。为了揭示所研究的葡萄酒指标与葡萄种植地理位置的非线性关系,我们使用了随机森林监督学习方法。在数据集上训练该模型后,其预测正确率为 96%。该模型使用了 61 个参数,其中最重要的是 Ni、Re、Ba、Rb、Na、U、Sb、Zn、Bi、Ag 和 Ti。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study of the Elemental Profiles of Wines from the North-Eastern Coast of the Black Sea
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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