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文章摘要
基于反向传播人工神经网络的酱牛肉中金黄色葡萄球菌的生长模型
Growth Model of Staphylococcus aureus in Sauced Beef Based on the Back Propagation Artificial Neural Network
  
DOI:10.3969/j.issn.1673-1689.2020.07.012
中文关键词: 人工神经网络  金黄色葡萄球菌  预测模型
英文关键词: artificial neural network, Staphylococcus aureus, prediction mode
基金项目:
作者单位
范志文 食品科学与技术国家重点实验室江南大学江苏 无锡 214122 
光翠娥 食品科学与技术国家重点实验室江南大学江苏 无锡 214122 
干建平 黄冈师范学院 经济林木种质改良与资源综合利用湖北省重点实验室/大别山特色资源开发湖北省协同创新中心湖北 黄冈 438000 
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中文摘要:
      探讨了反向传播神经网络模型预测酱牛肉中金黄色葡萄球菌生长情况的准确性。收集不同温度(15、25、36℃)和不同初始接菌量(102、10 3、10 4 CFU/mL)组合条件下金黄色葡萄球菌在酱牛肉中的生长数据,借助Python和Matlab软件,筛选出拟合效果最佳的网络结构,构建起反向传播神经网络模型,同时建立起修正的Gompertz模型作对比。通过平方根误差、偏差因子和准确因子分别检验两个模型的准确性。结果显示:网络结构为2-35-30的模型拟合效果最佳,反向传播神经网络模型与修正的Gompertz模型误差均在可接受范围内;与修正的Gompertz模型相比,反向传播神经网络误差更小,能更加准确预测金黄色葡萄球菌在酱牛肉中的生长情况。
英文摘要:
      This study investigated the accuracy of back propagation artificial neural network model for prediction of the growth of Staphylococcus aureus in sauced beef. The data of Staphylococcus aureus growing in sauced beef were collected at different temperatures (15, 25, 36 ℃) with different initial inoculations (10< sup> 2< /sup>,10< sup> 3,10< sup> 4< /sup> CFU/mL). With the saftwares of Python and MATLAB, the network structure with the best fitting effect was screened out and the back propagation neural network model was constructed. At the same time, the modified Gompertz model was established for the comparison. The accuracy of the two models was tested using square root error, bias and accuracy factors. The results showed that the model with the network structure of 2-35-30 had the best fitting. The errors generated in the back propagation neural network model and the modified Gompertz model were within an acceptable range. Compared with that of the modified Gompertz model, the error of back propagation neural network was smaller and it could be used to predict the growth of Staphylococcus aureus in sauced beef with higher accuracy.
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