布拉酵母高密度发酵培养基及发酵工艺优化

model the relationship between dry biomass production and medium composition. The optimization of medium composition
was carried out using genetic algorithm (GA). The results showed that the ANN model, more suitable for complex and
nonlinear modeling, had better goodness of fit and prediction performance. The optimal medium composition was obtained
as follows (g/L): glucose 40.52, peptone 36.8, corn steep liquor 17.32, KNO3 14, yeast nutrients 1.5, KH2PO4 0.6 and MgSO4 0.8 shake flask cultivation using the optimized medium gave a dry biomass yield of 8.21 g/L, which was 2.39 folds higher than that obtained from the original medium. Based on these results, we determined the optimal high cell density culture conditions for S. boulardii cultivated in a 1-L fermentor as follows: temperature 30 ℃, 10% inoculum, pH 5.0 and
Optimization of High Cell Density Fermentation of Saccharomyces boulardii for Enhanced Biomass Production
LIU Kaifang, XI Zhiwen, HUANG Linna, HUI Fengli*
(College of Life Science and Technology, Nanyang Normal University, Nanyang 473061, China)
Abstract: For high cell density cultivation of Saccharomyces boulardii, we attempted to optimize medium composition
DOI:10.7506/spkx1002-6630-20180424-323 中图分类号：Q815 引文格式：
40% dissolved oxygen. Furthermore, we scaled up the culture process in a 50-L fermentor with the addition of glucose and
peptone to maintain the glucose concentration at 3 g/L and the ammonia nitrogen concentration at 0.06 g/L. The dry biomass
and culture conditions. Plackett-Burman design was used to recognize the significant medium components. Subsequently,
response surface methodology and artificial neural network (ANN) based on central composite design (CCD) were applied to
yield of S. boulardii reached 51.21 g/L in t来自百度文库e large scale experiment.
Keywords: Saccharomyces boulardii; neural network; genetic algorithm; enrichment medium; high cell density fermentation
56 2019, Vol.40, No.08
食品科学
※生物工程
布拉酵母高密度发酵培养基及发酵工艺优化
刘开放，席志文，黄林娜，惠丰立*
（南阳师范学院生命科学与技术学院，河南 南阳 473061）
摘 要：为实现布拉酵母高密度培养，对其高密度发酵培养基和发酵工艺进行优化。采用Plackett-Burman试验筛选 培养基中的显著因素，并进行中心组合设计。通过人工神经网络（artificial neural network，ANN）和响应面试验 建立菌体布拉酵母产量与培养基之间的关系模型，利用遗传算法（genetic algorithm，GA）进行全局寻优。结果表 明，ANN模型有较好的数据拟合能力和预测能力，更适合处理复杂的非线性问题。GA优化获得最佳培养基组合： 葡萄糖40.52 g/L、蛋白胨36.8 g/L、玉米浆17.32 g/L、硝酸钾14 g/L、酵母营养盐1.5 g/L、磷酸二氢钾0.6 g/L、硫酸 镁0.8 g/L。利用该培养基进行摇瓶培养，菌体布拉酵母产量可达到8.21 g/L，比优化前提高1.39 倍。在此基础上利 用1 L发酵罐培养确定最佳发酵工艺：温度30 ℃、接种量10%、pH 5.0、溶氧40%。利用50 L发酵罐进行扩大培养， 流加葡萄糖和蛋白胨控制发酵液中葡萄糖3 g/L、氨氮0.06 g/L，菌体布拉酵母产量达到51.21 g/L。 关键词：布拉酵母；神经网络；遗传算法；增殖培养基；高密度培养