Food and Fermentation Industries >

2023 , Vol. 49 >Issue 8: 60 - 66

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.032594

Optimization of glucose fermentation conditions for pullulan polysaccharide production by artificial neural network based on genetic algorithm

  • CHEN Shiwei ,
  • LUO Jiawei ,
  • WANG Genan ,
  • ZHAO Tingbin ,
  • YIN Haisong ,
  • ZHENG Zhiqiang ,
  • ZHENG Guobao ,
  • QIAO Changsheng
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  • 1(College of Bioengineering, Tianjin University of Science and Technology, Tianjin 300457, China)
    2(Tianjin Huizhi Baichuan Bioengineering Co. Ltd., Tianjin 300457, China)
    3(College of Bioengineering, Tianjin Modern Vocational Technology College, Tianjin 300457, China)
    4(Institute of Systems Engineering, Academy of Military Sciences, Beijing 101300, China)
    5(Agricultural Biotechnology Research Center, Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan 750002, China)
    6(Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education and Tianjin Key Laboratory of Industrial Microbiology, Tianjin 300457, China)
    7(Tianjin Engineering Center of Microbial Metabolism and Fermentation Process Control, Tianjin 300457, China)

Received date: 2022-06-07

  Revised date: 2022-07-04

  Online published: 2023-05-16

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Abstract

Using glucose as raw material, the fermentation conditions of pullulan polysaccharide produced by Aureobasidium pullulans were optimized by artificial neural network based on genetic algorithm (GA-ANN). Firstly, single factor test and Plackett-Burman (PB) experiment were used to screen significant factors, then Box-Behnken (BBD) experiment was conducted to establish data samples, and finally Matlab was used to establish model optimization to find the optimal solution. Results showed that glucose and yeast extracts had a significant positive effect on the synthesis of pullulan polysaccharide, while K2HPO4 had a significant negative effect on the synthesis of pullulan polysaccharide. The coefficient and relative error of GA-ANN were 0.998 8 and 1.67% respectively. Finally, the optimal fermentation conditions of pullulan polysaccharide were 150 g/L of glucose, 7.1 g/L of yeast extract, 1.4 g/L of MgSO4·7H2O, 7 g/L of K2HPO4, 7 g/L of NaCl, and natural pH. Under these conditions, the yield of pullulan polysaccharide was 83.25 g/L, 79.73 % higher than that before optimization. The cost of optimized medium was reduced by about 70% compared with that before optimization. The results provide data support for the industrial production of pullulan polysaccharide and help to improve the competitiveness of pullulan polysaccharide in the industry.

Key words： pullulan; genetic algorithm; artificial neural network; nonlinear relation; model

Cite this article

CHEN Shiwei , LUO Jiawei , WANG Genan , ZHAO Tingbin , YIN Haisong , ZHENG Zhiqiang , ZHENG Guobao , QIAO Changsheng . Optimization of glucose fermentation conditions for pullulan polysaccharide production by artificial neural network based on genetic algorithm[J]. Food and Fermentation Industries, 2023 , 49(8) : 60 -66 . DOI: 10.13995/j.cnki.11-1802/ts.032594

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