外文摘要：BACKGROUNDWhole-grain rice noodles are a kind of healthy food with rich nutritional value, and their product quality has a notable impact on consumer acceptability. The quality evaluation model is of great significance to the optimization of product quality. However, there are few methods that can establish a product quality prediction model with multiple preparation conditions as inputs and various quality evaluation indexes as outputs. In this study, an artificial neural network (ANN) model based on a backpropagation (BP) algorithm was used to predict the comprehensive quality changes of whole-grain rice noodles under different preparation conditions, which provided a new way to improve the quality of extrusion rice products.RESULTSThe results showed that the BP-ANN using the Levenberg-Marquardt algorithm and the optimal topology (4-11-8) gave the best performance. The correlation coefficients (R2) for the training, validation, testing, and global data sets of the BP neural network were 0.927, 0.873, 0.817, and 0.903, respectively. In the validation test, the percentage error in the quality prediction of whole-grain rice noodles was within 10%, indicating that the BP-ANN could accurately predict the quality of whole-grain rice noodles prepared under different conditions.CONCLUSIONThis study showed that the quality prediction model of whole-grain rice noodles based on the BP-ANN algorithm was effective, and suitable for predicting the quality of whole-grain rice noodles prepared under different conditions.
外文关键词：BP-ANN;extrusion parameters;whole-grain mixtures;quality prediction model
作者：Zhao, Siming;Wang, Chujun;Shi, Xin;Xue, Jianyi;Jia, Caihua;Niu, Meng;Zhang, Binjia;Xu, Yan
作者单位：Huazhong Agr Univ
期刊名称：JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：104（7）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台