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Estimation of wheat protein content and wet gluten content based on fusion of hyperspectral and RGB sensors using machine learning algorithms

文献类型： 外文期刊

作者： Zhang, Shaohua 1 ; Qi, Xinghui 1 ; Gao, Mengyuan 1 ; Dai, Changjun 3 ; Yin, Guihong 1 ; Ma, Dongyun 1 ; Feng, Wei 1 ; Guo, Tiancai 1 ; He, Li 1 ;

作者机构： 1.Henan Agr Univ, Agron Coll, State Key Lab Wheat & Maize Crop Sci, Zhengzhou 450046, Henan, Peoples R China

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

3.Heilongjiang Acad Agr Sci, Harbin 150000, Heilongjiang, Peoples R China

4.Henan Agr Univ, Agron Coll, 15 Longzihu Coll Dist, Zhengzhou 450046, Henan, Peoples R China

关键词： Wheat; Protein content; Wet gluten content; Multi-modal data; Pearson-CARs-VIF; Machine learning

期刊名称：FOOD CHEMISTRY （ 影响因子：8.8； 五年影响因子：8.6 ）

ISSN： 0308-8146

年卷期： 2024 年 448 卷

页码：

收录情况： SCI

摘要： The protein content (PC) and wet gluten content (WGC) are crucial indicators determining the quality of wheat, playing a pivotal role in evaluating processing and baking performance. Original reflectance (OR), wavelet feature (WF), and color index (CI) were extracted from hyperspectral and RGB sensors. Combining Pearsoncompetitive adaptive reweighted sampling (CARs)-variance inflation factor (VIF) with four machine learning (ML) algorithms were used to model accuracy of PC and WGC. As a result, three CIs, six ORs, and twelve WFs were selected for PC and WGC datasets. For single-modal data, the back-propagation neural network exhibited superior accuracy, with estimation accuracies (WF > OR > CI). For multi-modal data, the random forest regression paired with OR + WF + CI showed the highest validation accuracy. Utilizing the Gini impurity, WF outweighed OR and CI in the PC and WGC models. The amalgamation of MLs with multimodal data harnessed the synergies among various remote sensing sources, substantially augmenting model precision and stability.

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