食品科学

• 生物工程 • 上一篇    下一篇

适于解淀粉芽孢杆菌BGP20菌体生长的培养基响应面优化

赵延存,李鹏霞,黄开红,王毓宁,孙 娅,胡花丽   

  1. 江苏省农业科学院农产品加工研究所,江苏 南京 210014
  • 出版日期:2014-02-13 发布日期:2014-03-17

Response Surface Optimization of Medium Components for Cell Growth of Bacillus amyloliquefaciens BGP20

ZHAO Yan-cun, LI Peng-xia, HUANG Kai-hong, WANG Yu-ning, SUN Ya, HU Hua-li   

  1. Institute of Agro-product Processing, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
  • Online:2014-02-13 Published:2014-03-17

摘要:

为获得解淀粉芽孢杆菌BGP20的低成本高效菌体生长培养基配方,利用响应面试验设计和摇瓶发酵对培养基各组分进行优化。根据单因素试验和Plackett-Burman设计试验结果确定了豆粕和玉米淀粉为主要因素。以发酵培养液菌体密度为响应值,利用Design-Expert软件的中心组合试验设计进行优化,并对预测值进行验证。结果表明:回归方程具有很好的拟合性,培养基最优配比为豆粕16.03 g/L、玉米淀粉6.30 g/L、MgSO4·7H2O 0.75 g/L、NaCl1.25 g/L、KH2PO4 0.75 g/L、痕量元素 15 mL/L,获得BGP20发酵液菌体密度为1.86×109 CFU/mL,模型预测最高值为1.985×109 CFU/mL,验证实验结果为预测值的93.72%,说明该模型对实际生产具有指导意义。

关键词: 解淀粉芽孢杆菌, 发酵培养基, 菌体密度, 响应面设计, 回归模型

Abstract:

A cost-effective culture medium for shake flask fermentation of Bacillus amyloliquefaciens subsp. plantarum BGP20
was developed by optimizing the medium components using response surface methodology. First, based on the results of
univariate analysis and Plackett-Burman design experiments, defatted soy flour and cornstarch were identified as two key medium
components. Then a central composite design was applied to fit the relationship between these two components and optimal levels
of BGP20 cell growth. The results showed that the regression model had a high fitting degree. The optimal medium components
were 16.03 g/L defatted soy flour, 6.30 g/L cornstarch, 0.75 g/L MgSO4·7H2O, 1.25 g/L NaCl, 0.75 g/L KH2PO4 and 15 mL/L trace
elements. The maximum predicted value from the regression model was 1.985 × 109 CFU/mL, and the actual value observed in
validation experiments was 93.72% as compared to the predicted value. These results demonstrate that the regression model is of
great guiding significance for commercial fermentation of BGP20.

Key words: Bacillus amyloliquefaciens subsp. plantarum, fermentation medium, cell concentration, response surface methodology, regression model