Vis/NIR Spectral Sensing-Based Quality Prediction for Postharvest Sweet Potatoes

Abstract

Rapid and non-destructive assessment of sweet potato quality is important for postharvest management, processing suitability evaluation, and market quality control. In this study, a 12-channel visible/near-infrared (Vis/NIR) spectral sensing system was applied to predict multiple physicochemical quality attributes of postharvest sweet potatoes. Sixty independent sweet potato storage roots were measured at three representative positions, producing 180 position-specific observations; measurements from the same root were retained within the same validation group. The measured attributes included dry matter content (DMC), starch content (SC), soluble solids content (SSC), and the CIE 1976 L*a*b* (CIELAB) color coordinates L*, a*, and b*. Four spectral treatment conditions, including original spectra, normalization, standardization, and first-derivative transformation, were combined with partial least squares regression (PLSR), multiple linear regression (MLR), extreme gradient boosting (XGBoost), and random forest (RF), generating 16 prediction strategies for each quality attribute. Root-grouped five-fold cross-validation showed that the optimal models achieved coefficients of determination for cross-validation (R2CV) ranging from 0.9083 to 0.9190 and residual predictive deviation (RPD) values ranging from 3.3112 to 3.5230. Repeated grouped cross-validation produced mean R2CV values of 0.9113–0.9176, and root-block Y-scrambling yielded empirical p values of 0.005 for all six attributes. PLSR provided the highest cross-validated performance for all six quality attributes, although MLR showed comparable performance for several targets. These results provide preliminary evidence that discrete Vis/NIR spectral sensing can support simultaneous non-destructive estimation of multiple sweet potato quality attributes. External multi-batch and multi-cultivar validation is required before the models can be considered robust for practical deployment.