Optimization of medium composition for two-step fermentation of vitamin C based on artificial neural network-genetic algorithm techniques - Yang, Yu; Gao, Ming; Yu, Xiaodan; Zhang, Yunhe; Lyu, Shuxia (Biotechnology, biotechnological equipment, 2015)
The production of 2-keto-L-gulonic acid (2-KGA) during the conversion from L-sorbose to 2-KGA in the two-step fermentation of vitamin C can be improved by using an efficient companion strain Bacillus subtilis A9 to facilitate the growth of Ketogulonicigenium vulgare and the production of 2-KGA. Two optimization models, namely response surface methodology (RSM) and artificial neural network (ANN), were built to optimize the medium components for mixed-culture fermentation of 2-KGA. The root mean square error, R 2 and the standard error of prediction given by the ANN model were 0.13%, 0.99% and 0.21%, respectively, while the RSM model gave 1.89%, 0.84% and 2.9%, respectively. This indicated that the fitness and the prediction accuracy of the ANN model were higher than those of the RSM model. Furthermore, using genetic algorithm (GA), the input space of the ANN model was optimized, predicting that the maximum 2-KGA production of 72.54 g·L −1 would be obtained at the GA-optimized concentrations of the medium components (L-sorbose, 92.5 g·L −1 ; urea, 10.2 g·L −1 ; corn steep liquor, 16 g·L −1 ; CaCO 3 , 3.96 g·L −1 ; MgSO 4 , 0.28 g·L −1 ). The 2-KGA production experimentally obtained using the ANN-GA-designed medium was 71.21 ± 1.53 g·L −1 , which was in good agreement with the predicted value. The same optimization process may be used to improve the production during bacterial mixed-cultures fermentation by changing the fermentation parameters.