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中国精品科技期刊2020
龚敏慧,单成俊,李双健,等. 基于响应面法和人工神经网络优化复合乳酸菌发酵蓝莓汁产胞外多糖工艺[J]. 食品工业科技,2023,44(17):242−250. doi: 10.13386/j.issn1002-0306.2022110110.
引用本文: 龚敏慧,单成俊,李双健,等. 基于响应面法和人工神经网络优化复合乳酸菌发酵蓝莓汁产胞外多糖工艺[J]. 食品工业科技,2023,44(17):242−250. doi: 10.13386/j.issn1002-0306.2022110110.
GONG Minhui, SHAN Chengjun, LI Shuangjian, et al. Optimization of Exocytopolysaccharide Production from Fermented Blueberry Juice by Complex Lactic Acid Bacteria Based on Response Surface Method and Artificial Neural Network[J]. Science and Technology of Food Industry, 2023, 44(17): 242−250. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022110110.
Citation: GONG Minhui, SHAN Chengjun, LI Shuangjian, et al. Optimization of Exocytopolysaccharide Production from Fermented Blueberry Juice by Complex Lactic Acid Bacteria Based on Response Surface Method and Artificial Neural Network[J]. Science and Technology of Food Industry, 2023, 44(17): 242−250. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022110110.

基于响应面法和人工神经网络优化复合乳酸菌发酵蓝莓汁产胞外多糖工艺

Optimization of Exocytopolysaccharide Production from Fermented Blueberry Juice by Complex Lactic Acid Bacteria Based on Response Surface Method and Artificial Neural Network

  • 摘要: 为提高发酵蓝莓汁中的胞外多糖(exocytopolysaccharide,EPS)含量,以蓝莓为原料,选择三株高产胞外多糖的乳酸菌,采用单因素法、响应面法(RSM)对发酵条件进行优化,筛选出影响最大的四个因素:初始pH、接种量、发酵温度、发酵时间。在此基础上,采用人工神经网络(ANN)和遗传算法(GA)求解得到最佳发酵工艺条件为植物乳杆菌9sh、发酵乳杆菌SR2-6、柠檬明串珠菌GM11的菌种比例2:1:1,乳糖6%,大豆肽0.6%,蓝莓汁初始pH4.5,接种量8%,发酵温度30 ℃,发酵时间60 h,此时EPS含量为3.537 g/L。研究表明RSM和ANN可用于优化发酵蓝莓汁产EPS工艺。

     

    Abstract: To improve the content of extracellular polysaccharide (EPS) in fermented blueberry juice, three strains of lactic acid bacteria with high EPS production were selected in this study, and the fermentation conditions of blueberry juice were optimized by single-factor method and response surface methodology (RSM), and the four most influential factors were screened out, namely, initial pH, inoculum amount, fermentation temperature and fermentation time. Based on this, the optimal fermentation process conditions were obtained by artificial neural network (ANN) and genetic algorithm (GA). The optimized process conditions were 2:1:1 ratio of Lactobacillus plantarum 9sh, Lactobacillus fermentum SR2-6 and Citrobacter cepacia GM11, lactose was 6%, soybean peptide was 0.6%, the initial pH of blueberry juice was 4.5, the inoculum amount was 8%, fermentation temperature was 30 ℃, and fermentation time was 60 h. Under these conditions, the EPS content was 3.537 g/L. This study shows that RSM and ANN can be used to optimize the EPS production process of blueberry juice by lactic acid bacteria fermentation.

     

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