Accurate discrimination of tea from multiple geographical regions by combining multi-elements with multivariate statistical analysis

文献类型： 外文期刊

第一作者： Zhang, Minglu

作者： Zhang, Minglu;Qin, Haoran;Yin, Jie;Huang, Congwei;Zhang, Jiangyang;Ma, Guicen;Liu, Xin;Huang, Congwei;Zhang, Jiangyang;Ma, Guicen;Liu, Xin

作者机构：

关键词： Tea; ICP-MS; Geographical origin; Multi-element analysis; Multivariate statistical analysis

期刊名称：JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION （ 影响因子：2.431； 五年影响因子：2.347 ）

ISSN： 2193-4126

年卷期： 2020 年 14 卷 6 期

页码：

收录情况： SCI

摘要： This study focused on building accurate and robust statistical models for geographical discrimination of teas, especially for the application on classification of the core geographical region teas from the non-core regions in tea. In order to achieve this goal, the content of mineral elements of tea samples was investigated. To be specific, takingDuyun maojian(DYMJ) tea as a sample, we applied inductively coupled plasma mass spectrometry (ICP-MS) combined with multivariate statistical analysis to profile the mineral elements of DYMJ collected from five adjacent production areas. Results demonstrated that a total of 39 elements were significantly different among the 5 regions, and in the stepwise linear discriminant analysis model (S-LDA), a satisfying prediction ability (88.3%) was obtained by cross-validation for the whole 5 regions, in which the correct classification rates for the geographical region (Duyun county) teas could reach 96.0%. The overall results suggested that the combination of ICP-MS with S-LDA could be successfully used as a rapid and reliable method for geographical origin identification of teas. At the end, the benefit of the essential elements supplement and also the risk of the toxic elements excess have been assessed during people's daily consumption of tea.

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