Replacing traditional coffee appraisers with inductively coupled plasma - mass spectrometry (ICP-MS): From manual sensory evaluation to scientific analysis

文献类型： 外文期刊

第一作者： Pan, Xiaowei

作者： Pan, Xiaowei;Ye, Jianzhi;Liang, Yaohui;Zhan, Guoyan;Pan, Xiaowei;Yan, Wenjing;Liao, Wenzhen;Yang, Xingfen;He, Qi;Dong, Hao;Wu, Xiaopeng

作者机构：

关键词： Coffee; Geographic origin; Plasma mass spectrometry (ICP-MS); Fisher discriminant analysis (FDA); Principal component analysis

期刊名称：FOOD CHEMISTRY-X （ 影响因子：8.2； 五年影响因子：8.2 ）

ISSN： 2590-1575

年卷期： 2024 年 24 卷

页码：

收录情况： SCI

摘要： Scientific development provides opportunities to replace many traditional manual methods to achieve more accurate results and higher efficiency. To scientifically ascertain the geographical origin of coffee, this study develops a method for the rapid determination of 16 inorganic elements in coffee using microwave digestion combined with the ICP-MS internal standard method. Principal component analysis (PCA), Fisher discriminant analysis (FDA), and Partial least squares discriminant analysis (PLS-DA) are employed to analyze 40 coffee samples from three production areas of Pu'er, Baoshan, and Wanning. The results show that the linear correlation coefficients of the 16 elements in this method are above 0.999, the detection limits are in the range of 0.0004-0.63 mg/kg, the RSD of the precision experiments are 4.5 %-13.5 %, the recovery rate of the peak experiment is 86.0 %-96.3 % with the RSD of 1.1 %-8.8 %, and the results of the standard substances are within the range of standard values. Using the discriminant analysis of inorganic elements in coffee (FDA and PLS-DA), coffee origin discrimination was realized, and six key elements (Al, Mn, Fe, Cu, Na, and Ba) are identified as effective discriminatory indexes. Accordingly, a coffee origin discrimination model is established, and the overall accuracy discrimination rate of the discrimination model are all more than 90.0 %, and FDA > PLS-DA. The findings indicated that the method has good accuracy and reliability, is suitable for analyzing and determining multiple elements in sample components as targets, and may have a positive impact on the development of related industries.

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