基于人工神经网络-遗传算法的樟芝发酵培养基优化

Medium optimization for mycelia production of Antrodia camphorata based on artificial neural network-genetic algorithm

摘要:

采用优化模型对药用丝状真菌樟芝的复杂发酵过程进行建模，并获得最优发酵培养基组成。对樟芝发酵过程中的形态变化过程进行了观察，并分别采用人工神经网络 (ANN) 和响应面法 (RSM) 对樟芝发酵过程进行建模，同时采用遗传算法 (GA) 优化了发酵培养基组成。结果表明，ANN模型比RSM模型具有更好的实验数据拟合能力和预测能力，GA计算得到樟芝生物量理论最大值为6.2 g/L，并获得发酵最佳接种量及培养基组成：孢子浓度1.76×105个/mL，葡萄糖29.1 g/L，蛋白胨9.4 g/L，黄豆粉2.8 g/L。在最佳培养条件下，樟芝生物量为 (6.1±0.2) g/L。基于ANN-GA的优化方法可用于优化其他丝状真菌的复杂发酵过程，从而获得生物量或活性代谢产物。

Abstract:

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.