摘要
以出芽短梗霉(Aureobasidium pullulans)为出发菌株,利用紫外诱变技术筛选高产普鲁兰多糖菌株,进一步通过正交试验偶联人工神经网络对高产普鲁兰多糖菌株的发酵条件进行优化。结果表明,通过紫外诱变筛选获得了一株高产普鲁兰多糖菌株,命名为UV013,普鲁兰多糖产量为(55.72±3.47)g/L,较出发菌株提高了39.3%。最佳发酵条件为葡萄糖174.50 g/L,酵母浸粉7.96 g/L,MgSO_(4)·7H_(2)O 1.64 g/L,K_(2)HPO_(4)5.08 g/L,NaCl 6.39 g/L,味精0.53 g/L,初始pH值6.08。在此优化条件下,普鲁兰多糖产量为(87.50±2.40)g/L,较基础培养基优化前提升了57.1%,其重均分子质量达到1.26×10^(5)Da,该研究结果可为普鲁兰多糖工业化生产提供参考。
Taking Aureobasidium pullulans as a starting strain,high-yield pullulan strains were screened by ultraviolet mutagenesis technology.Fur-ther,the fermentation conditions of high-yield pullulan strains were optimized through orthogonal experiments coupled with artificial neural net-works.The results showed that a high-yield pullulan strain was obtained through ultraviolet mutagenesis screening and named as UV013.The pullu-lan yield was(55.72±3.47)g/L,which was 39.3%higher than that of the starting strain.The optimal fermentation conditions were as follows:glucose 174.50 g/L,yeast extract 7.96 g/L,MgSO_(4)·7H_(2)O 1.64 g/L,K_(2)HPO_(4)5.08 g/L,NaCl 6.39 g/L,monosodium glutamate 0.53 g/L,and initial pH 6.08.Under these optimized conditions,the pullulan yield was(87.50±2.40)g/L,which was 57.1%higher than that before the basic medium optimization.Its weight-average molecular mass reached 1.26×10^(5)Da.The research results could provide reference for the industrial production of pullulan.
作者
李文敏
孙营营
赵晓雯
李淼鑫
乔长晟
赵廷彬
LI Wenmin;SUN Yingying;ZHAO Xiaowen;LI Miaoxin;QIAO Changsheng;ZHAO Tingbin(College of Bioengineering,Tianjin University of Science and Technology,Tianjin 300457,China;State Key Laboratory of Bio-based Fiber Materials,Tianjin University of Science&Technology,Tianjin 300457,China;Tianjin Engineering Center of Microbial Metabolism and Fermentation Process Control,Tianjin 300457,China;Tianjin Huizhi Baichuan Bioengineering Co.,Ltd.,Tianjin 300457,China)
出处
《中国酿造》
北大核心
2026年第2期257-265,共9页
China Brewing
基金
宁夏回族自治区重点研发计划项目(2022BEG02006)。
关键词
出芽短梗霉
普鲁兰多糖
紫外诱变
人工神经网络
正交试验
培养基优化
Aureobasidium pullulans
pullulan
ultraviolet mutagenesis
artificial neural network
orthogonal experiment
medium optimization
