Rapid and nondestructive determination of sorghum purity combined with deep forest and near-infrared hyperspectral imaging. (30th May 2022)

• Record Type: Journal Article
• Authors: Huang, Haoping; Hu, Xinjun; Tian, Jianping; Peng, Xinghui; Luo, Huibo; Huang, Dan; Zheng, Jia; Wang, Hong
• Is Part Of: Food chemistry. Volume 377(2022)
• Publication Date: 2022-05-30
• Subjects: Sorghum -- Purity -- Hyperspectral imaging -- Deep forest -- Characteristic wavelengths -- Textural features; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.131981
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: A robust model was developed and validated for evaluation of sorghum quality. Isolated forest and CARS-SPA were conducive to the optimization of the model. The average CRR of the model in identifying sorghum varieties was greater than 91%. The deviation of the predicted mixing ratio of adulterated samples was less than 4%. Abstract: This study combined hyperspectral imaging (HSI) and deep forest (DF) to develop a reliable model for conducting a rapid and nondestructive determination of sorghum purity. Isolated forest (IF) algorithm and principal component analysis (PCA) were used to remove the abnormal data of sorghum grains. Competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA) were combined and used to extract the characteristic wavelengths. Gray-level co-occurrence matrix (GLCM) was used to extract the textural features. DF models were established based on the different types of data. Specifically, the DF models established using the characteristic spectra produced the best recognition results: the average correct recognition rate (CRR) of the models was greater than 91%. In addition, the average CRR of validation set Ⅰ was 88.89%. These results show that a combination of HSI and DF could be used for the rapid and nondestructive determination of sorghum purity.