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Authentication of the geographical origin of Guizhou green tea using stable isotope and mineral element signatures combined with chemometric analysis

文献类型： 外文期刊

作者： Liu, Wenwen 1 ; Chen, Yan 1 ; Liao, Ruoxin 1 ; Zhao, Jie 1 ; Yang, Hui 1 ; Wang, Fuhua 1 ;

作者机构： 1.Guangdong Acad Agr Sci, Publ Monitoring Ctr Agroprod, Guangzhou 510640, Peoples R China

2.Minist Agr & Rural Affairs, Qual Supervis & Inspect Ctr Agr Prod, Guangzhou 510640, Peoples R China

关键词： Geographical origin; Green tea; Stable isotope ratio; Mineral element content

期刊名称：FOOD CONTROL （ 影响因子：4.258； 五年影响因子：4.421 ）

ISSN： 0956-7135

年卷期： 2021 年 125 卷

页码：

收录情况： SCI

摘要： Guizhou green tea is a provincial-scale protected geographical indication (PGI) product with high value and good reputation. However, induced by economic benefit, deliberate origin mislabeling and adulteration of Guizhou green tea become significant in the market. Reliable methods for authenticating the origins of green tea are therefore needed to counteract deliberate mislabeling and protect genuine Guizhou green tea. In this study, we analyze the signatures of four stable isotope and thirty-one mineral element in green tea samples collected from different provinces of China (Guizhou, Hubei and Guangdong) and different counties of Guizhou province (Duyun, Leishan and Meitan). We show that the 82H and 818O values are significantly higher in green tea samples collected from Hubei, which may be attributed to the strong secondary evaporation induced by low relative humidity. We also show that mineral element contents in green tea samples exhibit obvious regional differences, e.g. Fe, Mn, Cu, rare earth elements. These differences are mainly explained by the soil environment (i.e. soil properties, geological background and mineral resources) and are probrally correlated with climate change. Moreover, we investage the possibility of authenticating Guizhou green tea at the provincial and prefectural scales, using stable isotope and mineral element signatures combined with principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal projection to latent structures discriminant analysis (OPLSDA). The prediction accuracies of the LDA and OPLS-DA methods are all greater than 97% under the validation by cross-validation and external validation. The 82H value and the contents of K, V, Co, Cu, Zn and Ce, are identified as important variables for discrimination between green tea samples collected from the three different provinces. The contents of Dy, Er, Gd, Ho, Sm, Tb, Tm and Yb are key factors for secondary classification of Guizhou green tea samples collected from the three different counties. This strategy combining stable isotope and mineral element analysis is thus a powerful tool for authenticating Guizhou green tea from green tea with differing geographical origins, including those from nearby regions.

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