ANN and RSM based modelling for optimization of cell dry mass of Bacillus sp strain B67 and its antifungal activity against Botrytis cinerea

The present study was conducted to present the comparative modelling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for optimization of fermenting medium. Cell dry mass and inhibition zone of strain B67 against Botrytis cinerea were used as response variables. The response variables were optimized and modelled as a function of five independent variables (pH, gelatine percentage, incubation period, agitation speed, and temperature) using response surface methodology and artificial neural network. The results of both approaches were compared for their modelling abilities in terms of root-mean-squared error (RMSE), mean absolute error (MAD), chi-square, and correlation coefficient, computed from experimental and predicted data. ANN models were proved to be superior to RSM with lower RMSE, MAD, and chi-square and higher values for correlation coefficient, coefficient of determination, and predictive coefficient of determination. The optimum fermenting conditions predicted were pH 6.65, gelatine 3.30%, incubation period 35h, agitation speed 163 rpm, and incubation temperature 33.64 degrees C, with 15.00 g/L and 31.64 mm cell dry mass and inhibition zone, respectively. The predictive models were validated experimentally and were found in agreement with experimentally obtained values.