Efficient production of pullulan by Aureobasidium pullulans using a multi-objective optimization strategy with orthogonal experimental design coupling artificial neural network and genetic algorithm
文献类型： 外文期刊
作者： Chen, Shiwei 1 ; Zhao, Tingbin 4 ; Li, Miaoxin 1 ; Zhao, Xiaowen 1 ; Li, Zhenjiang 5 ; Zheng, Guobao 3 ; Cao, Weifeng 1 ; Qiao, Changsheng 1 ;
作者机构： 1.Tianjin Univ Sci & Technol, Minist Educ, Key Lab Ind Fermentat Microbiol, Tianjin 300457, Peoples R China
2.Tianjin Univ Sci & Technol, Sch Biotechnol, Tianjin Engn Res Ctr Microbial Metab & Fermentat P, Tianjin 300457, Peoples R China
3.Ningxia Acad Agr & Forestry Sci, Agr Biotechnol Res Ctr, Inst Forestry Sci, Yinchuan 750002, Peoples R China
4.Tianjin Huizhi Baichuan Bioengn Co Ltd, Tianjin 300457, Peoples R China
5.Sichuan Baichuan Jinkai Biol Engn Co Ltd, Chengdu 611130, Peoples R China
关键词： Pullulan; Multi-objective optimization; NSGAII
期刊名称：INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES （ 影响因子：8.5； 五年影响因子：8.7 ）
ISSN： 0141-8130
年卷期： 2024 年 280 卷
页码：
收录情况： SCI
摘要： Efficient pullulan production has long been a central research focus. This study used maltodextrin as the carbon source for pullulan production by Aureobasidium pullulans fermentation. A hybrid optimization approach, integrating orthogonal experimental design (OED), backpropagation artificial neural network (BP-ANN), and elite strategy non-dominated sequential genetic algorithm-II (NSGA-II), was developed. Range analysis based on OED revealed that MgSO4 & sdot;7H(2)O significantly affects production but less impacts molecular weight, while pH notably influences molecular weight with a lesser effect on production, underscoring the need for multi-objective optimization. The BP-ANN model showed strong predictive capabilities, with goodness-of-fit values of 0.984 and 0.980 for production and molecular weight, respectively. Using this model as the fitness function for the optimization algorithm enhanced efficiency. Taking cost factors into account, the BP-ANN-NSGA-II algorithm identified the optimal fermentation medium conditions, resulting in a 6.89 % increase in production, a 368.97 % increase in molecular weight, and a 42.49 % reduction in cost. The maximum comprehensive optimization efficiency is 63.73 %, and the multi-objective optimization is better than the single objective optimization. This method significantly guides the improvement of pullulan fermentation optimization efficiency.
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