Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice
文献类型： 外文期刊
作者： Deng, Fei 1 ; Lu, Hui 1 ; Yuan, Yujie 1 ; Chen, Hong 2 ; Li, Qiuping 1 ; Wang, Li 1 ; Tao, Youfeng 1 ; Zhou, Wei 1 ; Cheng, Hong 1 ; Chen, Yong 1 ; Lei, Xiaolong 3 ; Li, Guiyong 4 ; Li, Min 5 ; Ren, Wanjun 1 ;
作者机构： 1.Sichuan Agr Univ, Coll Agron, State Key Lab Crop Gene Explorat & Utilizat Southw, Key Lab Crop Ecophysiol & Farming Syst Southwest C, Chengdu 611130, Peoples R China
2.Southwest Univ Sci & Technol, Coll Life Sci & Engn, Mianyang 621010, Sichuan, Peoples R China
3.Sichuan Agr Univ, Coll Mech & Elect Engn, Yaan 625014, Peoples R China
4.Yunnan Acad Agr Sci, Food Crops Res Inst, Kunming 650221, Peoples R China
5.Rice Res Inst Guizhou Prov, Guiyang 550025, Peoples R China
6.Huimin Rd 211,Gongping Town, Chengdu, Sichuan Prov, Peoples R China
关键词： Artificial neural networks; Eating and cooking quality; Prediction model; Rice; Texture properties
期刊名称：FOOD CHEMISTRY （ 影响因子：9.231； 五年影响因子：8.795 ）
ISSN： 0308-8146
年卷期： 2023 年 407 卷
页码：
收录情况： SCI
摘要： Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and ma-chine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted de-cision tree had determination coefficients (R2) of 0.156-0.452, 0.357, 0.160-0.460, 0.192-0.746, 0.453-0.708, and 0.469-0.880, respectively, which were improved to 0.675-0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574-1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice.
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