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OACSTPCD

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.