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A total of 190 japonica rice samples from six origins in Heilongjiang Province were analyzed, yielding 46 metabolite features. After mahalanobis distance quality control to eliminate outliers, 120 valid samples were retained and visualized via uniform manifold approximation and projection. Six representative machine learning algorithms were systematically evaluated, and genetic algorithm and simulated annealing were employed for feature optimization. The results show that the random forest algorithm combined with genetic algorithm achieved the highest performance (validation accuracy\u2009=\u200999.5%, AUC\u2009=\u20090.998, F\u2010measure\u2009=\u20090.995), outperforming existing spectral and isotope\u2010based methods. Twenty\u2010eight key metabolites were identified, each closely linked to origin\u2010specific environmental factors. Statistical tests confirmed significant performance differences between algorithms. This work provides a robust, interpretable, and cost\u2010effective solution for rice origin traceability, with implications for food safety supervision and high\u2010quality agricultural product authentication.<\/jats:p>","DOI":"10.1002\/cpe.70575","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T02:59:36Z","timestamp":1768964376000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning\u2013Discriminant Analysis of Rice Origin Traceability"],"prefix":"10.1002","volume":"38","author":[{"given":"Runzhong","family":"Yu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology Harbin Engineering University  Harbin People's Republic of China"},{"name":"College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University  Daqing People's Republic of 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