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Predictive modeling of rice milling degree for three typical Chinese rice varieties using interpretative machine learning methods

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成果类型:
期刊论文
作者:
Yang, Liu;Xu, Zilong;Xiao, Xuan;Cui, Bo;Luo, Yang;...
通讯作者:
Yang, L
作者机构:
[Song, Shaoyun; Zhang, Yonglin; Xu, Zilong; Yang, Liu; Xiao, Xuan; Fan, Yuchao; Pei, Houchang; Luo, Yang; Cui, Bo; Fan, Lanlan] Wuhan Polytech Univ, Coll Mech Engn, Wuhan 430048, Hubei, Peoples R China.
[Song, Shaoyun; Zhang, Yonglin] Hubei Cereals & Oils Machinery Engn Ctr, Wuhan, Peoples R China.
通讯机构:
[Yang, L ] W
Wuhan Polytech Univ, Coll Mech Engn, Wuhan 430048, Hubei, Peoples R China.
语种:
英文
关键词:
brown rice;DOM prediction;features extraction;machine learning;SHAP interpretation
期刊:
Journal of Food Science
ISSN:
0022-1147
年:
2024
卷:
89
期:
10
页码:
6553-6574
基金类别:
Jiangsu Key Laboratory Advanced Food Manufacturing Equipment and Technology Project [FM-202103]; Hubei Provincial Grain Bureau Project [2023HBLSKJ004]; Science Foundation of Wuhan Polytechnic University [2020J06]; Natural Science Foundation of Hubei Province [2022CFB944]
机构署名:
本校为第一且通讯机构
院系归属:
机械工程学院
摘要:
Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) detection and prediction remain a challenge for moderate processing. In this study, a self-established grain image acquisition platform was built. Degree of bran layer remaining (DOR) datasets is established with image capturing and processing (grain color, texture, and shape features extraction). The mapping relationship between DOR and the DOM is in-depth analyzed. Rice grain DOR typical machine learning and deep learning prediction models are established. The results indicate that the optimized ...

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