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(10~2、10~3、10~4CFU/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。
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(10~2,10~3,10~4 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.
作者
范志文
光翠娥
干建平
FAN Zhiwen;GUNAG Cuie;GAN Jianping(State Key Laboratory of Food Science and Technology,Jiangnan University,Wuxi 214222,China;Hubei Key Laboratory of Economic Forest Germplasm Improvement and Resources Comprehensive Utilization/Hubei Collaborative Innovation Center for the Characteristic Resources Exploitation of Dabie Mountains,Huanggang Normal College,Huanggang 438000,China)
出处
《食品与生物技术学报》
CAS
CSCD
北大核心
2020年第7期83-90,共8页
Journal of Food Science and Biotechnology
关键词
人工神经网络
金黄色葡萄球菌
预测模型
artificial neural network
Staphylococcus aureus
prediction model
作者简介
通信作者:光翠娥(1976-),女,博士,副教授,硕士研究生导师,主要从事食品营养与功能因子研究。E-mail:quang1226@hotmail.com。
