您好，欢迎访问云南省农业科学院 机构知识库！

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.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：云南省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台