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当前位置: 首页 > 文章 > BP人工神经网络用于食品防腐剂构效关系建模的研究 安徽农业大学学报 2010,37 (2) 382-386
Position: Home > Articles > Modeling the relationship of structure and activity of food preservatives based on back-propagation artificial neural network Journal of Anhui Agricultural University 2010,37 (2) 382-386

BP人工神经网络用于食品防腐剂构效关系建模的研究

作  者：
于辉;陈海光;陈悦娇;宁正祥
单  位：
华南理工大学轻工与食品学院;仲恺农业工程学院轻工食品学院
关键词：
BP人工神经网络;食品防腐剂;构效关系
摘  要：
采用摇床培养,测试了20种食品防腐剂在实验条件下对大肠杆菌的最低抑菌浓度;计算了所有供试防腐剂的10种结构、电子、理化性质参数,随机抽取其中17种防腐剂作为训练样本,另外3种防腐剂作为预测样本,构造并训练得到能较好预测"未知"食品防腐剂在实验条件下对大肠杆菌的最低抑菌浓度的BP人工神经网络,建立了能较准确预测食品防腐剂抗菌活性的QSAR模型,该模型对防腐剂抗菌活性的预测值和实测值相对误差不超过±5%。
译  名：
Modeling the relationship of structure and activity of food preservatives based on back-propagation artificial neural network
作  者：
YU Hui1,CHEN Hai-guang1,CHEN Yue-jiao1,NING Zheng-xiang2(1.College of Light Industry and Food Science,Zhongkai University of Agriculture and Engineering,Guangzhou 510225;2.College of Light Industry and Food Science,South China University of Technology,Guangzhou 510640)
关键词：
BP artificial neural network;food preservatives;structure-activity relationship
摘  要：
The minimum inhibition concentrations(MICs) of twenty food preservatives were obtained by inhibition experiments on Escherichia coli under experimental conditions;ten types of different parameters about structure,electron and physical-chemical characteristics of all food preservatives were figured out.Seventeen preservatives were taken out to act as training samples in random,and other three preservatives were used for predictive samples,and then a QSAR model based on back-propagation artificial neural network which had good prediction results of the MICs for "unknown" food preservatives was obtained.The predicted values and the measured values of the relative errors were less than ±5%.

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