The use of ANN-GA and Neuro fuzzy for modeling the population dynamics of bacterial (Escherichia coli ATTC 29998) in the frankfurter sausage containing of Redcurrant extract

The purpose of this study was to evaluate the effect of time, kind of extract, extract concentration and temperature on the dynamically the population of Escherichia coli (infectious agents) in the complex food system (frankfurter sausage) and Genetic Algorithm - Artificial neural network and neuro fuzzy system (CANFIS) were used for dynamic modeling of population of E.coli. At this research laboratory, Minimum Inhibitory Concentration (MIC (and Minimum Bactericidal Concentration (MBC) of aqueous and ethanolic extracts were studied using the micro broth dilution method. GA- ANN, neuro fuzzy (CANFIS) were fed with four inputs: concentration at the five level (0, 2000, 4000, 6000, 8000 ppm), kind of extract (watery, ethanol), temperature at the three level (5,15, 25 ̊С) and time (1-20). The results showed that the ANN with 1 hidden layer comprising 10 neurons, Tangent hyperbolic function, momentum training rule and percent of used data 30/30/40 for training/cross validation/testing respectively gives the best fitting with the experimental data, which made it possible to predict with high determination coefficient (R2 equal to 0.995). Also the correlation between CANFIS predictions and experimental data was very good (R2 equal to 0.96). It's worth to mention that in this research GA- ANN was better approach to simulation dynamically the population of E.coli.