LAUSR

Sweet potatoes are a substantial source of nutrition and can be added to processed foods in the form of paste. The moisture and starch contents of these potatoes affect the physicochemical properties of sweet potato paste. In this study, the changes in the moisture, starch, and α-amylase content of sweet potatoes were measured for eight weeks after harvest. Using nondestructive near-infrared analyses and chemometric models, the moisture and starch contents were predicted. The partial least squares (PLS) method was used for prediction, while linear discriminant analysis (LDA) was used for discrimination. To increase the accuracy of the model, the near-infrared spectrum was preprocessed using the Savitzky–Golay derivative (S–G), standard normal variate (SNV), and multiplicative scattering correction methods. When applying PLS to the moisture content, the best calibration model accuracy was obtained using the S–G preprocessed spectrum. Furthermore, the best calibration model accuracy for starch content was obtained using the SNV preprocessed spectrum. The moisture and starch contents were categorized into five classes for LDA, with results indicating that the internal quality of sweet potatoes can be predicted and classified using chemometric models through nondestructive detection.