Wine characterisation according to geographical origin using analysis of mineral elements and rainfall correlation of oxygen isotope values

文献类型： 外文期刊

第一作者： Su, Yingyue

作者： Su, Yingyue;Wang, Fei;Zhang, Jinjie;Zhang, Ang;Su, Yingyue;Wang, Fei;Zhang, Jinjie;Zhang, Ang;Zhao, Yan;Cui, Kexu

作者机构：

关键词： Artificial neural network; Chinese wine; multielement; oxygen isotopic ratio; precipitation; traceability

期刊名称：INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY （ 影响因子：3.713； 五年影响因子：3.408 ）

ISSN： 0950-5423

年卷期：

页码：

收录情况： SCI

摘要： The wine industry has developed rapidly; however, wine fraud is a potential risk for consumers. In China, methods for detecting wine authenticity are far from perfect. To reduce the risk of counterfeit wines, Inductively Coupled Plasma-Mass Spectrometry and Isotope Ratio Mass Spectrometry were used to geographically classify 104 wines from four major production areas. In this paper, the naturally distributed characteristics of thirty-eight mineral elements contents and the effect of rainfall on the oxygen isotope values in wine were investigated. The result of delta O-18 ranged from -13 parts per thousand to 7 parts per thousand. The oxygen isotope of wine water in Northwest China is obviously more positive than that in South China. Linear discriminant analysis (LDA) showed 88.5% classification accuracy in training set and 81.7% in the cross-validation result. An artificial neural network (ANN) model determined origin of the wine with higher accuracy than LDA model. Furthermore, delta O-18 and Sr/Rb are important recognition elements in ANN, and the accuracy of region recognition can reach 90.9%. Thus, mineral elements and isotope ratios are effective in contributing to wine authenticity control in wine origin.

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