Rapid and nondestructive prediction of amylose and amylopectin contents in sorghum based on hyperspectral imaging. (15th October 2021) - Huang, Haoping; Hu, Xinjun; Tian, Jianping; Jiang, Xinna; Sun, Ting; Luo, Huibo; Huang, Dan
Highlights: CF was used to predict the contents of amylose and amylopectin in sorghum. PCA-SPA was used to extract characteristic wavelengths corresponding to amylose and amylopectin. The average RPD values of the CF models were both greater than 4.7. Accurate recognition for the varieties of sorghum grains was realized. This study provides a new method for the starch content detection of cereals. Abstract: The contents of amylose and amylopectin in sorghum directly affects the quality and yield of liquor. Hyperspectral imaging (HSI) is an emerging technology widely applied in the content analysis of food ingredients. In this study, the effects of different preprocessing methods on visible-light and near-infrared spectral data were analyzed, and the prediction accuracies of these spectral data were compared. Principal components analysis (PCA) and successive projections algorithm (SPA) were combined to extract the characteristic wavelengths. Using both the full and characteristic wavelengths, partial least square regression (PLSR) and cascade forest (CF) models were developed to predict the contents of amylose and amylopectin in different varieties of sorghum. The average RPD values of the CF models established by the characteristic wavelengths were 4.7622 and 5.5889, respectively. These results corroborated the utility of HSI in achieving the rapid and nondestructive prediction of amylose and amylopectin contents in different varieties of sorghum.