温 健,潘师翰,郑鑫,等.产纤溶酶海洋枯草芽孢菌的液体发酵条件优化[J].中国海洋药物,2015,34(3):35-45.
产纤溶酶海洋枯草芽孢菌的液体发酵条件优化
Optimization of liquid fermentationconditions of marine Bacillus subtils for producing the fibrinolytic enzyme
投稿时间:2014-09-22  修订日期:2014-11-18
DOI:
中文关键词:  枯草芽孢杆菌  纤溶酶  液体发酵  plackett-Burman(PB)设计  响应面法
English Keywords:Bacillus subtilis  fibrinolytic enzyme  Liquid-state fermentation  Plakett-Burman (PB) design  response surface methodology
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作者单位E-mail
温 健 广西大学生命科学与技术学院 heimaweijian@163.com 
潘师翰 广西大学生命科学与技术学院  
郑鑫 广西大学生命科学与技术学院  
焉凯舟 广西大学生命科学与技术学院  
梁智群* 广西大学生命科学与技术学院 zqliang@gxu.edu.cn 
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中文摘要:
      为进一步增加高产纤溶酶海洋菌株Y-6-A的产酶量,采用响应面法对其液体发酵条件进行优化。先采用单因素实验考查各因素对菌株产酶的影响,并在此基础上利用Plackett-Burman设计筛选到影响菌株产酶的四个主要因素分别为:可溶性淀粉、黄豆粕粉、温度和转速,接着通过最陡爬坡实验逼近酶活最高区域,最后根据Box-Behnken中心组合实验设计对各显著因子进行优化。最终得到最佳发酵条件为:可溶性淀粉4.61g/100mL,黄豆粕粉2.33g/100mL,温度31.2℃,转速184r/min,在此发酵条件下得到的酶活为3606.23IU/mL,经三次验证实验测得的酶活稳定在3580.32±14.82IU/mL,较优化前提高了22.4%,此外,实验值与预测值之间的相对误差为0.63%。表明该优化结果比较理想。
English Summary:
      To enhance the enzyme production of high- yield marine stain Y-6-A futher,Response surface analysis was applied to optimize the liquid fermentation conditions ,Fristly, the impact of single factors on fibrinolytic enzyme production was studied and through the Plakett-Burman design, the most significant effect on fibrinolytic enzyme production was obstained which include soluble starch , soybean meal, temperature and speed, then, the highest fibrinolytic activity area was investigated by steepest climbing experiments , finally, different levels of the most four significant factors were oprtimized through central composite designs by Box-Behnken. As aresult , the optimal fermentation conditions of strain Y-6-A was determined for 4.61% soluble starch 2.33% soybean meal, temperature at 31.2℃, rotate at 184 r/min, under the optimal conditions, the enzyme activity was 3606.06IU/mL, three times validation experiments showed that the activity of fibrinolytic enzyme was stable at 3580±14.82IU/mL , the optimized enzyme activity was 22.4% higher than before .In addition, relative error betwen the actual value and predictive value was 0.7%, therefore,the optimization results was resonable.
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