Hyperspectral Imaging-Based Rapid Assessment of Chinese Yam Quality for Dried Slice Processing

Abstract

Chinese yam (Dioscorea spp.) is widely used as food and traditional medicine, but varietal differences may affect dried yam slice quality. This study compared ten Chinese yam varieties, examined relationships between raw-material quality indicators and dried yam slice quality, and evaluated hyperspectral imaging for rapid assessment before processing. Significant varietal differences were observed in reducing sugar content, total phenolic content, and texture properties (p < 0.05), and trait–quality relationships were variety-dependent. Spectral preprocessing, variable selection, and regression modelling were used to predict the reference values of reducing sugar content and total phenolic content obtained using the specified analytical procedures, together with fresh-slice hardness. The best models combined principal component analysis with decision tree regression (PCA-DTR), competitive adaptive reweighted sampling with partial least squares regression (CARS-PLSR), and CARS with random forest regression (CARS-RFR), respectively. Prediction-set coefficients of determination (RP2) were 0.9891, 0.9335, and 0.9314, with root mean square errors of prediction (RMSEP) of 0.0981%, 0.0905 mg gallic acid equivalents/100 g dry weight, and 130.3973 gf, and residual predictive deviation (RPD) values of 9.7535, 3.9438, and 3.8820, respectively. These results support hyperspectral imaging with chemometrics for rapid assessment and selection of yam raw materials for dried yam slice processing.