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Classification of different gluten wheat varieties based on hyperspectral preprocessing, feature screening, and machine learning

文献类型： 外文期刊

作者： Qi, Xinghui 1 ; Zhang, Shaohua 1 ; Wang, Liyang 1 ; Hu, Xuexu 3 ; Zhang, Haiyan 1 ; Feng, Wei 1 ; Wang, Chenyang 1 ; Guo, Tiancai 1 ; He, Li 1 ;

作者机构： 1.Henan Agr Univ, Agron Coll, State Key Lab High Efficiency Prod Wheat Maize Dou, Zhengzhou 450046, Henan, Peoples R China

2.Natl Wheat Technol Innovat Ctr, Zhengzhou 450046, Henan, Peoples R China

3.Chinese Acad Agr Sci, Inst Crop Sci, Beijing 100081, Peoples R China

关键词： Wheat; Gluten type; Feature selection; Preprocessing; Machine learning

期刊名称：FOOD CHEMISTRY-X （ 影响因子：8.2； 五年影响因子：8.2 ）

ISSN： 2590-1575

年卷期： 2025 年 26 卷

页码：

收录情况： SCI

摘要： Classifying different gluten wheat varieties can meet diversified food needs. To rapidly classify wheat gluten types using hyperspectral data, four preprocessing methods combined with two feature screening methods and four machine learning algorithms were used in this study. Our findings indicated that the feature wavelengths extracted by ReliefF showed better classification model accuracy than that of the full wavelength classification model and the minimum redundancy maximum relevance (mRMR) classification model. The classification accuracy of continuous wavelet transform (CWT) was higher than that of original reflectance, continuous removal, and first derivative. The performance of the four classifiers were in the order support vector machine (SVM) > convolutional neural network (CNN) > random forest (RF) >K-nearest neighbor (KNN). ReliefF-CWT-SVM was identified as the optimal classification model (overall accuracy = 94.5 %). The developed combination method supplies theoretical and technical support to classify wheat varieties with different types of gluten.

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