Metal element drivers of rice sensory variation revealed by ICP-MS and electronic tongue predictive modeling.

Metal accumulation in rice grains influenced both nutritional composition and consumer-perceived eating quality. Concentrations of 26 metal elements were quantified in 36 rice samples by ICP-MS, and hierarchical cluster analysis grouped the samples into three categories. ANOVA revealed significant differences in essential elements (Ca, Mg, Zn, Cu) and non-essential or potentially toxic elements (Al, Ba, B), highlighting their contribution to classification. Sensory evaluation of representative samples demonstrated pronounced variation in odor, taste, palatability, and overall eating quality. Pearson correlation and PLSR-VIP analyses identified Ag, Al, B, Ba, Co, and V as strongly and negatively associated with sensory traits, whereas Ca and Fe exerted attribute-specific effects. Electronic tongue analysis with PCA achieved clear group separation, and a SVM model reached 93% overall accuracy. These results established a close linkage between elemental profiles and sensory performance, providing a framework for rapid, objective, and non-destructive rice quality assessment.