Optimization of medium composition for two-step fermentation of vitamin C based on artificial neural network-genetic algorithm techniques

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 mixedculture 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 center dot L- 1 would be obtained at the GA- optimized concentrations of the medium components ( L- sorbose, 92.5 g center dot L- 1; urea, 10.2 g center dot L- 1; corn steep liquor, 16 g center dot L- 1; CaCO3, 3.96 g center dot L- 1; MgSO4, 0.28 g center dot L- 1). The 2- KGA production experimentally obtained using the ANN - GA- designed medium was 71.21 +/- 1.53 g center dot 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.