Exploring the feasibility of multi-elements coupled with chemometrics for discriminating the geographical origins of oysters (Crassostrea ariakensis)

文献类型： 外文期刊

第一作者： Li, Danyi

作者： Li, Danyi;Rao, Yiyong;Wang, Xunuo;Wang, Zenghuan;Huang, Ke;Li, Danyi;Wang, Xunuo;Wang, Zenghuan;Huang, Ke;Rao, Yiyong

作者机构：

关键词： Oyster; Geographical authentication; Multi-element analysis; Chemometrics

期刊名称：FOOD CHEMISTRY （ 影响因子：8.5； 五年影响因子：8.2 ）

ISSN： 0308-8146

年卷期： 2024 年 460 卷

页码：

收录情况： SCI

摘要： This study explored the efficacy of multi-elements combined with chemometrics to discriminate the geographical origins of oysters (Crassostrea ariakensi). We determined 52 elements in 166 samples from four regions along the southeast coast of China. Significant regional variations of 51 elements were revealed (P < 0.05), while the principal component analysis (PCA) provided no clear regional delineations. The training models (n = 117) established on linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), and random forest (RF) uniformly achieved 100% predictive accuracy. The cross-validation accuracies of the final models (n = 166) derived from LDA, PLS-DA, and RF were 100%, 100%, and 99.4%, respectively. Even with the models simplified to 8 elements (Zn, Al, K, Cd, Cu, Rb, B, and Ag), high predictive and cross-validation accuracies were maintained, underscoring the robustness and algorithm flexibility of elemental profiling for accurately identifying the geographical origins of oysters.

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