BP神经网络结合遗传算法优化羊肉汤中香辛料的添加量
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国家自然科学基金项目(32072343);“十三五”国家重点研发计划项目(2016YFD0400705)


Optimizing of the Amount of Spices in Stewed Mutton Soup Using BP Neural Network and Genetic Algorithm
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    摘要:

    目的:利用BP神经网络结合遗传算法优化羊肉汤中香辛料的添加量,建立预测模型,为羊肉汤风味的改善及膻味的去除提供最优配比的香辛料添加量。方法:通过单因素试验得出相应添加量后,采用 Box-Benhnken设计(BBD)考察3种香辛料添加量对羊肉汤样品感官评分的影响。利用遗传算法在试验水平范围预测全局最优的3种香辛料添加量的复配结果。结果:方差分析(ANOVA)结果表明:百里香添加量、生姜添加量、花椒添加量与百里香添加量的交互作用以及花椒添加量与生姜添加量的交互作用均对羊肉汤感官评分具有显著影响(P<0.05)。BP神经网络的训练、测试和总体数据的相关系数分别为0.9914,1和0.9849,表明BP神经网络预测模型的准确性很好,可用于羊肉汤感官评分结果的预测。结论:利用遗传算法寻优的结果为百里香添加量0.54%、生姜添加量0.48%、花椒添加量0.16%,最终感官评分的试验值为20,与模型预测值的误差仅为2.7%,说明BP神经网络模型结合遗传算法是一种可用于优化香辛料添加量配比的方法。

    Abstract:

    The BP neural network was combined with the genetic algorithm to optimize the amount of spice added, and the prediction model was established to provide the optimal ratio of the flavor of the mutton soup and the removal of the muttony odour. On the basis of single factor experiment, the sensory score of mutton soup was used as the evaluation index to carry out Box-Benhnken design (BBD) to investigate the effect of three kinds of spices on the sensory score of the stewed mutton soup samples. Finally, the results of BBD experiment were taken as the initial population. The addition amount of thyme, ginger and pepper was taken as the input value, and the sensory score of stewed mutton soup was taken as the output value. The BP neural network model was used to debug the function fitness. The BP neural network generalization ability is verified using a data set other than the BBD data. The genetic algorithm was used to predict the global optimal combination of the three flavors added in the experimental level. Analysis of variance(ANOVA) showed that the addition of thyme and ginger had a significant effect on the sensory score of mutton soup (P<0.05). The correlation coefficients of BP neural network training, testing, and verification data were 0.9914, 1 and 0.9849, respectively, indicating that the BP neural network prediction model has good accuracy and can be used to predict the sensory score of stewed mutton soup. The fitness value of the BP neural network generalization ability verification experiment and the predicted value are R2=0.9922, which indicates that the established BP neural network model has a good generalization ability and can accurately predict the data set not in the training set. The results of using genetic algorithm for optimization were 0.54% thyme, 0.48% ginger, 0.16% pepper. The experimental value of the final sensory score was 20, and the error of the model predicted value was only 2.7%. The above results indicate BP neural network combined with genetic algorithm is a method with better accuracy to optimize the proportion of spices added.

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姬云云;田洪磊;詹萍;未志胜;王鹏;张芳. BP神经网络结合遗传算法优化羊肉汤中香辛料的添加量[J].中国食品学报,2021,21(3):128-137

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  • 在线发布日期: 2021-04-19
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