Improved organic and pesticide-free rice ( L.) authentication based on multiple stable isotope ratio analysis and rice milling state.

This study looked at the application of multiple bulk stable isotope ratio analysis to accurately authenticate organic rice and counteract organic fraud within the expanding global organic market. Variations of δC, δN, δO, and δS in organic, pesticide-free, and conventional rice were assessed across different milling states (brown, milled, and bran). Individual stable isotope ratio alone such as δN demonstrated limited capacity to correctly differentiate organic, pesticide-free, and conventional rice. A support vector machine model-incorporating δC, δN, δO, and δS in milled rice-yielded overall predictability (95%) in distinguishing organic, pesticide-free, and conventional rice, where δO emerged as the pivotal variable based on the feature weights in the SVM model. These findings suggest the potential of multi-isotope and advanced statistical approaches in combating organic fraud and ensuring authenticity in the food supply chain.