Abstract
Partial least squares regression (PLSR) modeling was performed to predict the moisture content in steamed, dried purple sweet potato based on spectral data obtained from hyperspectral imaging analysis. The PLSR model with a combination of multiplicative scatter correction, Savitzky-Golay, and first derivative exhibited the highest accuracy (RP 2 = 0.9754). The wavelengths found that strongly affected the PLSR model were 961.12, 1065.50, 1083.93, 1173.23, and 1233.89 nm. These wavelengths were associated with the O-H second overtone and the second overtone of C-H, C-H2, and C-H3. When PLSR modeling was performed using these selected wavelengths, the prediction accuracy of the PLSR model exhibited high accuracy (RP 2 = 0.9521). Therefore, the moisture content could be predicted with high accuracy using only five wavelengths rather than the full spectrum.