Application of artificial neural networks to describe the combined effect of pH, time, NaCl and ethanol concentrations on the biofilm formation of Staphylococcus aureus. (April 2020) - Vaezi, Sayedeh Saleheh; Poorazizi, Elahe; Tahmourespour, Arezoo; Aminsharei, Farham
Abstract: Biofilms are organized communities, adherent to the surface and resistant to adverse environmental and antimicrobial agents. So, its control is very important. Staphylococcus aureus is an opportunistic pathogen with the biofilm-forming ability that causes numerous problems in the medicine and food industry. Therefore, this study aimed to investigate the effect of pH, ethanol and NaCl concentrations after 24 and 48 h incubation times at 37 ° C, also modeling the results with artificial neural network (ANN). For this purpose, after both incubation times, the effect of each parameter was studied, separately and also in combination at the levels in which the highest biofilm was formed. All results were modeled using multiple ANN and compared in terms of R-value and MSE. The highest biofilm formation ability was in neutral pH. Adding the ethanol and NaCl stimulated biofilm formation, but the inhibitory effect was observed at high concentrations of ethanol and NaCl and very acidic or highly alkaline pH levels. The more incubation time also led to an increase in biofilm formation. Eventually, the Feed-Forward, Back-Propagation Neural Network model with the Levenberg–Marquardt training algorithm and 4-12-1 topology was chosen (R-value = 0.995 and validation MSE = 0.011467). This ANN had high modeling ability because there was a high correlation between experimental data and modeling data. Therefore, it was concluded that pH, ethanol, NaCl, and time are effective parameters in the biofilm formation and there is a nonlinear relationship between these factors that the ANN is capable of modeling them. Highlights: Staphylococcus aureus biofilm is very important in the food and medical industry. The effect of time, pH, ethanol & NaCl concentration on biofilm formation was investigated individually and in combination. Due to the nonlinearity relationships of parameters and biofilm formation, artificial intelligence was used for modeling. The Feed-forward backpropagation NN performed modeling with a high correlation between experimental and computational data. A FFBPNN with tansig transfer function and Levenberg–Marquardt training algorithm reduced network computing time.