黑曲霉产β-葡萄糖苷酶工艺的神经网络优化

Optimization of Conditions for Producing β-Glucosidase by Aspergillus niger Based on Neural Network

在单因素试验研究的基础上,采用Box-Behnken中心组合设计,利用JMP 7.0中的神经网络平台,在接种量、初始pH、发酵时间和装液量等4个方面对黑曲霉CICC 2475发酵产β-葡萄糖苷酶工艺条件进行优化,以期获得高酶活力的β-葡萄糖苷酶。结果表明:接种量约11.0%、初始pH值5.6、发酵时间130 h以及装液量70.0 mL条件下,黑曲霉发酵产β-葡萄糖苷酶的酶活力达到最大值118.73 U/mL,与单因素优化前61.01 U/mL相比,酶活力提高了48.61%。

Based on the single factor experiments,this research obtained high enzyme activity ofβ-glucosidase from Aspergillus niger CICC 2475 by using Box-Behnken center-united design and JMP7.0 neural network,the conditions of β-glucosidase were optimized by the control of inoculationamount,fermentation pH,fermentation time and broth content. The results showed that: the inoculationamount was about 11.0 %,the initial pH of 5.6,fermentation time was 130 hours and broth content 70.0mL,the enzyme activity of Aspergillus niger fermentation β-glucosidase reached the maximum(118.73 U/mL),compared with the previous optimization(61.01 U/mL),the enzyme activityincreased by 48.61%.