Using hyperspectral imaging technology for assessing internal quality parameters of persimmon fruits during the drying process.

The crucial features of persimmon are required to detect real-time moisture, water-soluble tannin, and soluble solids contents during the drying process. This study developed a method based on hyperspectral imaging (HSI) to execute online and non-destructive assaying of persimmon features. A total of 144 samples were collected, and 150 bands were scanned. The spectral data were analyzed by partial least squares regression (PLSR), principal component regression (PCR), least squares support vector regression (LS-SVR), and radial basis function neural network (RBFNN) with seven preprocessing methods. LS-SVR provided excellent performance for moisture content prediction, while PLSR was better in the analysis of water-soluble tannin and soluble solids contents. Successive projection algorithm (SPA) was used to select the optimal wavelengths to simplify the models, and about twenty important variables were chosen. Overall, these results indicate that HSI could be considered a valuable technique to quantify chemical constituents in dried persimmon fruits.