食品与发酵工业2024，Vol.50Issue(1)：133-140,中插4-中插6,11.DOI:10.13995/j.cnki.11-1802/ts.034871
基于反向传播神经网络和遗传算法的新鲜Halloumi奶酪生产工艺优化
Production process optimization of fresh Halloumi cheese based on BP neural network and genetic algorithm
摘要
Abstract
To improve the quality of Halloumi cheese,BP neural network and genetic algorithm were used to optimize the multi-process parameters of Halloumi cheese production process.The addition amount of CaCl2,heating temperature,and pressing pressure were used as the optimization variables and two neural network models were established with the yield and sensory score of finished cheese as optimization objectives.The accuracies of the models reached 98.936％and 98.255％,respectively.After that,genetic algorithm was used to search for optimization.The results indicated that under the premise of yield rate higher than 10％and sensory score greater than 85,the optimal production conditions with cheese yield as target were determined as follows:CaCl2 addition of 0.014 4％,heating tem-perature of 83.5 ℃,pressing pressure of 5.12 kPa,and the maximum yield rate reached 12.01％.The optimal production conditions for sensory quality were as follows:CaCl2 addition of 0.017 1％,heating temperature of 83.7 ℃,pressing pressure of 10.38 kPa,and the highest sensory score reached 94.5.This method can effectively realize the rapid optimization of Halloumi cheese production process and provide a theoretical basis for promoting the industrialization of Halloumi cheese.关键词
新鲜奶酪/Halloumi奶酪/神经网络/遗传算法/工艺优化Key words
fresh cheese/Halloumi cheese/neural network/genetic algorithm/process optimization引用本文复制引用
孙嘉,郑远荣,刘振民,张娟,徐杏敏,贾向飞..基于反向传播神经网络和遗传算法的新鲜Halloumi奶酪生产工艺优化[J].食品与发酵工业,2024,50(1):133-140,中插4-中插6,11.基金项目
上海乳业生物工程技术研究中心能力提升项目(19DZ2281400) （19DZ2281400）
