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中国精品科技期刊2020
王丽,闫子康,杜金,等. 基于MFO优化BP神经网络构建冷鲜肉品质预测模型[J]. 食品工业科技,2024,45(21):310−321. doi: 10.13386/j.issn1002-0306.2023120367.
引用本文: 王丽,闫子康,杜金,等. 基于MFO优化BP神经网络构建冷鲜肉品质预测模型[J]. 食品工业科技,2024,45(21):310−321. doi: 10.13386/j.issn1002-0306.2023120367.
WANG Li, YAN Zikang, DU Jin, et al. Establishment of a Predictive Model for the Quality Assessment of Chilled Meat Using a Moth-Flame Optimization BP Neural Network[J]. Science and Technology of Food Industry, 2024, 45(21): 310−321. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023120367.
Citation: WANG Li, YAN Zikang, DU Jin, et al. Establishment of a Predictive Model for the Quality Assessment of Chilled Meat Using a Moth-Flame Optimization BP Neural Network[J]. Science and Technology of Food Industry, 2024, 45(21): 310−321. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023120367.

基于MFO优化BP神经网络构建冷鲜肉品质预测模型

Establishment of a Predictive Model for the Quality Assessment of Chilled Meat Using a Moth-Flame Optimization BP Neural Network

  • 摘要: 为能准确预测冷鲜肉在贮藏中品质的变化规律及质量安全,本文探究贮藏温度(0、4和25 ℃)对冷鲜肉菌落总数、TVB-N、pH、水分含量、色度和生物胺含量的影响,确定冷鲜肉的特征品质指标。基于反向传播(Backpropagation,BP)神经网络和飞蛾火焰优化(Moth-Flame Optimization,MFO)BP神经网络,利用特征指标作为训练数据,构建不同贮藏温度下冷鲜肉的品质预测模型,快速准确评价和预测食品的质量安全。结果表明,不同贮藏温度下冷鲜肉的菌落总数、pH、TVB-N、色泽和生物胺含量随着贮藏时间的延长均呈上升趋势(P<0.05),且各指标在不同贮藏温度下的变化规律不一致,温度越高,腐败变质的速度越快。通过相关性分析得出菌落总数和TVB-N为冷鲜肉品质特征指标,以特征指标为训练数据构建BP神经网络和MFO优化BP神经网络模型。结果显示,MFO优化BP神经网络优于单一的BP神经网络模型,指标菌落总数和TVB-N通过BP神经网络模型训练后的R值分别为0.95018、0.94283,通过MFO算法优化训练后的R值分别为0.97538、0.98001,更接近于1,且优化后的RMSE、MSE和MAE值相对较小,其模型拟合度更好,在整个贮藏期的预测性能更好,准确率更高。因此,MFO优化BP神经网络可用于预测冷鲜肉在贮藏过程中品质的变化规律。

     

    Abstract: To quickly and accurately evaluate and predict the quality and safety of food products, in this study, storage temperatures (0, 4 and 25 ℃) were used to investigate the effects on the total number of colonies, TVB-N, pH, moisture content, color, and biogenic amine content of chilled meat. It aimed to accurately predict the pattern of change in the quality of chilled meat in storage, as well as its quality and safety, and determined the characteristic quality indexes of chilled meat. Based on Backpropagation (BP) neural network and Moth-Flame Optimization (MFO) BP neural network, the quality prediction model of chilled meat under different storage temperatures was constructed using characteristic indexes as the training data to quickly and accurately evaluate and predict the quality and safety of food. The results showed that the total number of colonies, pH, TVB-N, color and biogenic amine content of chilled meat under different storage temperatures showed an increasing trend with the extension of storage time (P<0.05), and the pattern of change of each index under different storage temperatures was inconsistent, and the higher the temperature, the faster the rate of corruption and deterioration. Based on the results of this part of the experiment, the total number of colonies and TVB-N as feature indicators were used as training data by the Backpropagation (BP) neural network and Moth-Flame Optimization (MFO) BP neural network to construct a quality prediction model for chilled meat under different storage temperatures. The findings demonstrated that the MFO-optimized BP neural network outperforms the standalone BP neural network model. Specifically, the R-values for the colony count and TVB-N indicators achieved through training with the BP neural network model were 0.95018 and 0.94283, respectively. In contrast, the R-values obtained through the optimized MFO algorithm reached 0.97538 and 0.98001, respectively, which closely approached 1. Furthermore, the optimized values of RMSE, MSE, and MAE indicated smaller discrepancies, indicating a better fit of the model. Consequently, the MFO-optimized BP neural network exhibited superior prediction performance throughout the entire storage duration, showcasing heightened accuracy. Hence, this model can effectively forecast the patterns of change in chilled meat quality during storage.

     

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