Huang Haoping, Hu Xinjun, Tian Jianping, Jiang Xinna, Sun Ting, Luo Huibo, Huang Dan
School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
Food Chem. 2021 Oct 15;359:129954. doi: 10.1016/j.foodchem.2021.129954. Epub 2021 Apr 27.
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.
高粱中直链淀粉和支链淀粉的含量直接影响白酒的品质和产量。高光谱成像（HSI）是一项新兴技术，广泛应用于食品成分的含量分析。本研究分析了不同预处理方法对可见光和近红外光谱数据的影响，并比较了这些光谱数据的预测精度。结合主成分分析（PCA）和连续投影算法（SPA）提取特征波长。利用全波长和特征波长，建立了偏最小二乘回归（PLSR）和级联森林（CF）模型，以预测不同品种高粱中直链淀粉和支链淀粉的含量。由特征波长建立的CF模型的平均RPD值分别为4.7622和5.5889。这些结果证实了高光谱成像在实现不同品种高粱中直链淀粉和支链淀粉含量的快速无损预测方面的实用性。
