Medium optimization for mycelia production of Antrodia camphorata based on artificial neural network-genetic algorithm
基于人工神经网络-遗传算法的樟芝发酵培养基优化

To illustrate the complex fermentation process of submerged culture of Antrodia camphorata ATCC 200183, we observed the morphology change of this filamentous fungus. Then we used two optimization models namely response surface methodology (RSM) and artificial neural network (ANN) to model the fermentation process of Antrodia camphorata. By genetic algorithm (GA), we optimized the inoculum size and medium components for Antrodia camphorata production. The results show that fitness and prediction accuracy of ANN model was higher when compared to those of RSM model. Using GA, we optimized the input space of ANN model, and obtained maximum biomass of 6.2 g/L at the GA-optimized concentrations of spore (1.76×105 /mL) and medium components (glucose, 29.1 g/L; peptone, 9.3 g/L; and soybean flour, 2.8 g/L). The biomass obtained using the ANN-GA designed medium was (6.1±0.2) g/L which was in good agreement with the predicted value. The same optimization process may be used to improve the production of mycelia and bioactive metabolites from potent medicinal fungi by changing the fermentation parameters.