Abstract:

With the rapid development of modern agriculture, the demand for high-quality rice production and consumption has been increasingly growing, becoming an urgent requirement for the current rice industry development. This study employed principal component analysis (PCA) to comprehensively evaluate 12 quality indicators of 31 japonica rice varieties from Shangyu region of Zhejiang Province, and utilized cluster analysis for systematic classification. The results showed that there were significant differences in various quality indicators among different japonica rice varieties in Shangyu, with varying degrees of correlation observed between indicators. Among them, the coefficients of variation for chalkiness degree and chalky grain rate were the highest across all quality indicators, and amylose content showed a highly significant positive correlation with chalky grain rate. The four principal components extracted by PCA reflected appearance quality, processing quality, nutritional quality, and eating/cooking quality, respectively, providing an effective basis for comprehensively evaluating the quality of different rice varieties. Comprehensive analysis revealed that Nangeng 46, Xiushui 1717, and Jia 67 performed excellently in multiple quality indicators, ranking in the top three overall. Furthermore, cluster analysis divided the 31 varieties into three groups and the 12 indicators into three groups, with significant correlations observed between variety clustering and quality indicator clustering. This study establishes a reliable technical foundation for rice quality assessment, systematic evaluation, and scientific classification.

Key words: rice, quality evaluation, principal component analysis (PCA), cluster analysis