Modeling the relationship of structure and activity of food preservatives based on back-propagation artificial neural network.
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%.