| The aim of this work was to optimize the biomass production by Bifidobacterium bifidum 255 using the response surface methodology (RSM) and artificial neural network (ANN) both coupled with GA. To develop the empirical model for the yield of probiotic bacteria, additional carbon and nitrogen content, inoculum size, age, temperature and pH were selected as the parameters. Models were developed using ¼ fractional factorial design (FFD) of the experiments with the selected parameters. The normalized percentage mean squared error obtained from the ANN and RSM models were 0.05 and 0.1%, respectively. Regression coefficient (R²) of the ANN model showed higher prediction accuracy compared to that of the RSM model. The empirical yield model (for both ANN and RSM)... |
