Near-Infrared Spectroscopy Combined with PLSR, Ridge Regression, and Extremely Randomized Trees for Predicting Quality Indicators in Chinese Japonica Rice

Given the diversity and richness of China’s grain varieties, traditional physicochemical quality testing methods for rice, while providing accurate results, suffer from drawbacks such as time-consuming procedures, high costs, substantial reagent consumption, cumbersome sample preparation, and reliance on destructive or semi-destructive techniques. This study aims to employ near-infrared spectroscopy technology to establish rapid and non-destructive predictive models for key quality indicators of japonica rice. The research analyzed 133 samples from 71 widely cultivated japonica rice varieties across five major production regions in China, utilizing spectral data within a wavelength range of 660–1080 nm. Predictive models for moisture, protein, amylose, and fatty acid values were constructed using three algorithms—partial least squares regression (PLSR), ridge regression (RR), and extremely randomized trees (ERT)—linear regression and the extreme randomization tree (ERT)—their optimal parameters were determined using a 10-fold cross-validation optimization method. Eighty percent of the total dataset served as the training set, while the remaining 20% formed the test set, yielding a final test set comprising 26 samples. Performance comparisons revealed that the PLSR and RR models demonstrated superior predictive performance: the coefficient of determination (Rp2) exceeded 0.9 for all four indicators, with the R2 value for fatty acid prediction reaching as high as 0.99; the root mean square error (RMSEP) of the PLSR and RR models ranged between 0.0534% and 0.3360%, confirming their high predictive accuracy. Although all ERT models (except the protein model) achieved Rp2 values exceeding 0.9, their overall performance was slightly inferior to the first two methods. The protein ERT model demonstrated relatively low performance, with an Rp2 value of 0.6984 on the test set, which may be attributed to the limited sample size and weak protein spectral response signals. Although the samples covered five major production regions and 71 japonica rice varieties, their distribution was uneven (multiple varieties were represented by only one or a few samples). This study provides an efficient rapid quality assessment method for japonica rice; however, the generalization ability of the models requires further validation in future studies employing larger and more balanced sample sizes.