摘要
为了提高聚-γ-谷氨酸(PGA)的产量,采用正交设计方案对发酵培养基组分中谷氨酸、葡萄糖、柠檬酸、甘油的配比进行试验设计,运用径向基神经网络建立PGA产量与培养基组分浓度之间的预测模型,采用遗传算法对此模型进行全局寻优,得到四种主要组份的最佳配比:谷氨酸21.2g/L、葡萄糖75.4g/L、柠檬酸7.2g/L、甘油10.8g/L,PGA产量达到12.8g/L,采用上述方法优化后的培养基使PGA的产量原始培养基提高了39.1%。
For improving the poly-γ-glutamate （PGA） yield, the orthogonal design was used for the trial design of the formula of medium components： glutamate, glucose, citrate and glycerol, radius basis function neural network （RBFNN） was applied for the predict modeling of the relationships between the PGA yield and the concentration of medium components. Then the genetic algorithm （GA） was used for the global optimization of the model. The optimum combination of the medium was obtained： glutamate 21.2g/L, glucose 75.4g/L, citrate 7.2g/L, glycerol 10.8g/L. The yield of PGA was improved to 12.8g/L, which was increased by 39.1% compared to the original medium.
出处
《食品科学》
EI
CAS
CSCD
北大核心
2006年第10期288-292,共5页
Food Science
基金
湖北省科技攻关课题资助项目(2005AA401C13)
