Intelligent discrimination of organic and conventional rice via electrochemiluminescence-enhanced Vis-NIR spectral analysis.

Rapid authentication of organic rice is crucial for food quality and consumer protection. Here, we propose a novel approach integrating electrochemiluminescence (ECL) with visible and near-infrared (Vis-NIR) spectroscopy to discriminate between organic and conventional rice. ECL analysis revealed higher albumin content in organic rice, resulting in stronger quenching of luminol-H2O2 chemiluminescence. Direct Vis-NIR analysis of rice supernatants with machine learning achieved 77.78% accuracy, whereas coupling ECL with Vis-NIR markedly enhanced classification, yielding 96.11% accuracy. PCA loading plot further confirmed the detection mechanism, showing strong contributions in the 400-500 nm range corresponding to the emission band of oxidized luminol products (e.g., 3-aminophthalate at ∼425 nm). ROC analysis of PLS-DA models demonstrated excellent robustness (AUC = 0.9967). This study is the first to integrate the ECL principle into Vis-NIR spectroscopy, providing a rapid, intelligent, and high-throughput strategy for organic rice authenticity testing.