Fast Determination of Amylose Content in Lotus Seeds Based on Hyperspectral Imaging

Abstract

Different varieties of fresh lotus seeds have varying levels of amylose content. It has a direct impact on the following processing and final product quality, so the non-destructive detection of amylose content is meaningful before lotus seed production. This study proposed a non-destructive method to detect the amylose content of fresh lotus seeds. Hyperspectral images of 120 fresh lotus seeds of three different varieties were obtained, and different pretreatments were applied to the average spectra obtained from the region of interest (ROI). The calibration and prediction set were divided by the sample set joint x–y distances algorithm (SPXY). Then, the partial lease square regression (PLSR) method was established for modeling, with Savitzky–Golay pretreatment-based PLSR showing the best results. To further improve the stability of the predictive model, different methods of feature variables selection were compared. The results showed that the best PLSR model was established with the inputs of 15 feature bands selected from 472 bands by the successive projection algorithm (SPA). The correlation coefficient of the prediction set (Rp), root mean square error of the prediction set (RMSEP), and residual predictive deviation (RPD) were 0.890, 15.154 mg g−1, and 2.193, respectively. Meanwhile, this study visualized the amylose content distribution maps from which it could estimate the content level directly. This study could provide a reference for further development of portable detection equipment for the amylose content of fresh lotus seeds.