Biofilm Formation Of Staphylococcus Aureus In Presence Of Sodium Chloride, Ethanol And Ph Different Levels And Application Of Artificial Neural Networks To Describe The Combined Effect Of Them

Abstract Background: Some microorganisms have the ability to connect to surfaces and produce biofilms. Bacterial biofilms are a major problem in the food and medical industry. Bacteria in biofilms have higher resistance to environmental adverse conditions and antibiotics than planktonic bacteria. Staphylococcus aureus is a food borne pathogen can form biofilms. Method: In this research, the main objective of the study was to investigate the effect of three parameters, pH, sodium chloride concentration and ethanol concentration on S.aureus ATCC ۳۳۵۹۱ biofilm formation after ۲۴ and ۴۸ hours' incubation times (۳۷ °C) by microtiter plate method, furthermore modeling the results with artificial neural network (ANN). For this intention, after both incubation times, the effect of all parameters (separately), and the combined effect of all parameters, it was deliberated. Results: Results were modeled using ANN. several ANN were compared in terms of MSE and R value. The results showed the strongest biofilm was formed in neutral pH. Increasing the Sodium chloride and ethanol stimulated the biofilm formation, but high concentrations of Sodium chloride and ethanol and highly alkaline or very acidic pH levels had the inhibitory effects. In addition, the biofilm formation increased in more incubation time. Eventually, a kind of multilayer ANN (Feed-Forward Back-Propagation) model with Levenberg–Marquardt (LM) training algorithm was chosen. The topology of this ANN was ۴-۱۲-۱ with validation MSE=۰.۰۱۰۲ and R value=۰.۹۸۹. There was a very high correlation between modeling data and experimental data. Conclusion: The biofilm formation of S.aureus is affected by Sodium chloride, ethanol, pH and time and the ANN was able to model these parameters with nonlinear relationships.

کلیدواژه ها:

Biofilm ، sodium chloride concentration ، Ethanol concentration ، pH ، Artificial Neural Network.