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马铃薯淀粉产衣康酸发酵培养基的优化
引用本文:贺小贤,孙福林,刘苗苗,赵会芳.马铃薯淀粉产衣康酸发酵培养基的优化[J].中国酿造,2012,31(1):80-84.
作者姓名:贺小贤  孙福林  刘苗苗  赵会芳
作者单位:陕西科技大学生命科学与工程学院,陕西西安,710021
基金项目:2010年陕西省自然科学基金项目
摘    要:以马铃薯淀粉为原料,利用土曲霉XL-6发酵生产衣康酸,并对发酵培养基组分进行优化。在单因素基础上,采用Plackett-Bur-man设计法从诸多因素中筛选出显著因素,通过响应面分析方法对其进行优化,建立衣康酸产率的二次多项式回归模型。试验结果表明,在最佳发酵培养基:碳源浓度130g/L,玉米浆2.14g/L,MgSO4.7H2O 2.19g/L,KH2PO4 0.14g/L,NH4NO3 3g/L,FeSO4.7H2O 0.10g/L,衣康酸产率达到7.04%。验证试验的实测值与预测值基本一致,说明模型可较好的反映实际情况。

关 键 词:马铃薯淀粉  衣康酸  发酵培养基  响应面方法  优化

Medium optimization by response surface methodology for itaconic acid production using potato starch
HE Xiaoxian , SUN Fulin , LIU Miaomiao , ZHAO Huifang.Medium optimization by response surface methodology for itaconic acid production using potato starch[J].China Brewing,2012,31(1):80-84.
Authors:HE Xiaoxian  SUN Fulin  LIU Miaomiao  ZHAO Huifang
Affiliation:(College of Life Science and Engineering,Shaanxi University of Science and Technology,Xi'an 710021,China)
Abstract:Potato starch was fermented by Aspergillus terreus XL-6 for itaconic acid production,and the culture medium was optimized.On the basis of single-factor experimental design,Plackett-Burman design was used for evaluation of the effects of 9 variables on the yield of itaconic acid.Initial carbon resource concentration,corn steep liquor,MgSO4·7H2O and KH2PO4 were screened as significant factors and their levels were further optimized using the methods of response surface analysis.A second-order polynomial regression model about yield of itaconic acid was established,and the applicability of the model was verified.The optimum medium was composed of initial carbon resource concentration 130g/L,corn steep liquor 2.14g/L,MgSO4·7H2O 2.19g/L,KH2PO4 0.14g/L,NH4NO3 3g/L,FeSO4·7H2O 0.10g/L.And the maximum yield of itaconic acid was 7.04% with the optimized culture medium.The results of model validation showed that the actual measured values had good agreement with the predicted values,which suggested that the model was feasible.
Keywords:potato starch  itaconic acid  culture medium  response surface methodology  optimization
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