基于长短期记忆神经网络的反应液葡萄糖含量预测  

Content prediction of glucose in reaction solution based on long short term memory neural network

作　　者：庄殿铮 薛飞 关学铭 ZHUANG Dian-Zheng;XUE Fei;GUAN Xue-Ming(School of Chemical Equipment,Shenyang University of Technology,Liaoyang 111003,China;School of Chemical Process Automation,Shenyang University of Technology,Liaoyang 111003,China)

机构地区：[1]沈阳工业大学化工装备学院,辽阳111003 [2]沈阳工业大学化工过程自动化学院,辽阳111003

出　　处：《食品安全质量检测学报》2024年第7期160-166,共7页Journal of Food Safety and Quality

基　　金：辽宁省自然科学基金项目(2021-MS-238);辽宁省教育厅科学研究项目(LJGD2020002);辽阳市科技计划项目([2021]24号-9)。

摘　　要：目的建立皮尔逊相关系数(Pearson correlation coefficient,PCC)和长短期记忆(long short term memory,LSTM)神经网络的反应液葡萄糖含量预测模型用以实时预测葡萄糖酸锌生产过程中反应液葡萄糖含量。方法通过葡萄糖酸锌制备实验,结合PCC理论确定对反应液葡萄糖含量有较大影响的因素,对这些因素进行数据采集并将其作为神经网络的输入变量,采集反应液葡萄糖含量数据并进行处理,将其作为神经网络的输出变量,进而建立反向传播神经网络(backpropagation neural network,BP)和LSTM神经网络的反应液葡萄糖含量预测模型。结果通过100次模型迭代训练,对照BP反应液葡萄糖含量预测模型可以看出LSTM反应液葡萄糖含量预测模型在测试集的误差约为0.45%,误差较小,准确度较高。结论基于LSTM反应液葡萄糖含量预测模型显著提高了预测精度,相比现有检测方法更加智能高效,能够有效辅助生产进行。Objective To establish Pearson correlation coefficient(PCC)and long short term memory(LSTM)neural network model for predicting the glucose content in the reaction solution during the production process of zinc gluconate.Methods Through the preparation experiment of zinc gluconate,combined with the PCC theory,the factors that have a significant impact on the glucose content of the reaction solution were determined.These factors were collected and used as input variables for the neural network.The glucose content data of the reaction solution was collected and processed as output variables of the neural network,and then a prediction model for the glucose content of the reaction solution was established using the backpropagation neural network(BP)and LSTM neural networks.Results Through 100 iterations of model training and comparing with the BP reaction solution glucose content prediction model,it could be seen that the LSTM reaction solution glucose content prediction model had an error of about 0.45%on the test set,which was relatively small and had high accuracy.Conclusion The glucose content prediction model based on LSTM reaction solution significantly improves the prediction accuracy and is more intelligent and efficient compared to existing detection methods,which can effectively assist production.

关 键 词：双酶法 葡萄糖酸锌 反应液葡萄糖含量 皮尔逊相关系数 长短期记忆神经网络 

分 类 号：TS207.3[轻工技术与工程—食品科学] TP183[轻工技术与工程—食品科学与工程]