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基于正交试验的根霉固态转化坎利酮工艺条件优化
引用本文:黄达明,杜卓蓉,张志才,管国强.基于正交试验的根霉固态转化坎利酮工艺条件优化[J].中国酿造,2018,37(1):155.
作者姓名:黄达明  杜卓蓉  张志才  管国强
作者单位:(江苏大学 食品与生物工程学院,江苏 镇江 212013)
基金项目:国家自然科学基金资助项目(31101269)
摘    要:与液态发酵相比,固态转化具有体积小、转化效率高、无基质抑制、便于提取、安全环保等优点。该研究首先通过单因素试验筛选出根霉(Rhizopus sp.)UJS-0602固态转化坎利酮生成11α-羟基坎利酮最适合的碳源、氮源、无机盐、营养因子种类及试验水平,且确定了较为适合的水分含量和底物添加量,再通过L16(45)正交试验研究了葡萄糖、硫酸铵、甘氨酸添加量以及水分含量、底物添加量对坎利酮转化率的影响。结果表明,固态发酵最适的培养条件为葡萄糖1%,硫酸铵0.6%,甘氨酸0.8%,底物添加量12%,料水比为1.0∶1(g∶mL)。在此条件下,坎利酮转化率可达到94.21%。

关 键 词:生物转化  坎利酮  11α-羟基坎利酮  固态发酵  条件优化  

Optimization of solid-state transformation of canrenone by Rhizopus sp. based on orthogonal tests
HUANG Daming,DU Zhuorong,ZHANG Zhicai,GUAN Guoqiang.Optimization of solid-state transformation of canrenone by Rhizopus sp. based on orthogonal tests[J].China Brewing,2018,37(1):155.
Authors:HUANG Daming  DU Zhuorong  ZHANG Zhicai  GUAN Guoqiang
Affiliation:(School of Food and Bioengineering, Jiangsu University, Zhenjiang 212013, China)
Abstract:Compared with liquid-state fermentation, solid-state transformation has the advantages of small size, high conversion efficiency, no substrate inhibition, convenient extraction, safety and environmental protection. By single factor experiments, the optimum types and levels of carbon and nitrogen sources, inorganic salts, nutritional factors of solid-state fermentation of Rhizopus sp. UJS-0602 were screened for biotransformation of canrenone to 11α-hydroxy canrenone, and the appropriate moisture content and substrate addition were determined. Then the effects of glucose, ammonia sulfate and glycine addition, moisture content and substrate addition on the conversion ratio of canrenon were studied by L16(45) orthogonal tests. Results showed that the optimum transformation conditions of solid-state fermentation were glucose 1%, ammonia sulfate 0.6%, glycine 0.8%, substrate addition 12% and material to water ratio 1.0∶1(g∶ml). Under the conditions, the conversion ratio of canrenon could reach 94.21%.
Keywords:biotransformation  canrenone  11α-hydroxy canrenone  solid-state fermentation  condition optimization  
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