Optimization of fermentation medium for Acinetobacter sp. DNS32 by response surface methodology and artificial neural network
响应面法和神经网络优化Acinetobacter sp. DNS32发酵基质

The aim of this research was to increase the biomass production of atrazine-degrading Acinetobacter sp. DNS32 by adopting response surface methodology (RSM) and genetic algorithm based on artificial neural network (ANN-GA) to optimize the three important fermentation medium compositions, respectively. According to RSM, these three optimized compositions were composed as follows: corn flour 39.494 g/L, soybean flour 25.638 g/L and K2HPO4 3.265 g/L. The predicted and verifiable values by RSM were 7.079×108CFU/mL and 7.194×108CFU/mL, respectively. The maximum biomass concentration predicted by hybrid ANN-GA was 7.199×108CFU/mL at the optimum level of medium variables as follows: corn flour 39.650 g/L, soybean flour 25.500 g/L and K2HPO4 2.624 g/L, while the experimentally measured value was 7.244×108CFU/mL. Finally, according to the above results, the optimized medium composition was: corn flour 39.650 g/L, soybean flour 25.50 g/L, CaCO3 3.000 g/L, K2HPO4 2.624 g/L, MgSO4·7H2O 0.200 g/L and NaCl 0.200 g/L. After medium optimization, the biomass yeild of atrazine-degrading strain DNS32 increased by 36.6% than that using non-optimized medium. The results showed that RSM and ANN-GA were feasible to optimize the fermentation medium for the production of atrazine-degrading strain DNS32, and ANN-GA had a much better optimizing ability and modeling ability.