数字农科院2.0

Isotope-assisted data mining techniques for authenticating rice across Chinese markets

文献类型： 外文期刊

作者： Syed Abdul Wadood;Weixing Zhang;Yongzhi Zhang;Chunlin Li;Karyne M. Rogers;Wahab Ali Khan;Muhammad Azeem;Wu Lina;Yuwei Yuan

作者机构：

关键词： Machine learning;Market rice traceability;PCA;Stable isotopes;T-SNE

期刊名称： Food Chemistry: X

ISSN： 2590-1575

年卷期： 2025 年 30 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： This study aims to authenticate the geographical origin and cultivar (Japonica vs Indica) labeling of rice sold across Chinese markets using stable isotope analysis coupled with multivariate analyses and machine learning models. δ13C, δ15N, δ18O, and δ2H were investigated in rice using isotope ratio mass spectrometry (IRMS) and analyzed through PCA and t-SNE to visualize regional separation and cultivar differences. Different machine learning models (kNN, SVM, RF) were developed using authentic field samples and tested with market-sourced samples for origin verification. Results revealed significant isotopic discrepancies between field and market labelled samples, suggesting potential mislabeling of cultivar and origin. t-SNE demonstrated superior performance in resolving nonlinear patterns in isotope data compared to PCA. Among the different models, RF showed slightly better classification performance. Although the models achieved perfect classification accuracy on field-sourced samples, a considerable decline in performance was observed when applied to market-sourced samples, suggesting widespread origin mislabeling issues.

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