Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan

Response surface methodology (RSM) is the most preferred method for fermentation media optimization so far. In last two decades, artificial neural network-genetic algorithm (ANN-GA) has come up as one of the most efficient method for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between ANN-GA and RSM in fermentation media optimization. Fermentative production of biopolymer scleroglucan has been chosen as case study. The yield of scleroglucan was modeled and optimized as a function of four independent variables (media components) using ANN-GA and RSM. The optimized media produced 16.22 ± 0.44 g/l scleroglucan as compared to 7.8 ± 0.54 g/l with unoptimized medium. Two methodologies were compared for their modeling, sensitivity analysis and optimization abilities. The predictive and generalization ability of both ANN and RSM were compared using separate dataset of 17 experiments from earlier published work. The average % error for ANN and RSM models were 6.5 and 20 and the CC was 0.89 and 0.99, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. The sensitivity analysis performed by both methods has given comparative results. The prediction error in optimum yield by hybrid ANN-GA and RSM were 2% and 8%, respectively.