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中国精品科技期刊2020
张曦予,李锐定,莫明规,等. 基于人工神经网络耦联遗传算法(BP-GA)优化干酪乳杆菌LTL1361冻干保护剂配方[J]. 食品工业科技,2022,43(21):175−184. doi: 10.13386/j.issn1002-0306.2022010143.
引用本文: 张曦予,李锐定,莫明规,等. 基于人工神经网络耦联遗传算法(BP-GA)优化干酪乳杆菌LTL1361冻干保护剂配方[J]. 食品工业科技,2022,43(21):175−184. doi: 10.13386/j.issn1002-0306.2022010143.
ZHANG Xiyu, LI Ruiding, MO Minggui, et al. Optimization of Lyophilized Protective Agent Formulation of Lactobacillus casei LTL1361 Based on Artificial Neural Network Coupled Genetic Algorithm (BP-GA)[J]. Science and Technology of Food Industry, 2022, 43(21): 175−184. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022010143.
Citation: ZHANG Xiyu, LI Ruiding, MO Minggui, et al. Optimization of Lyophilized Protective Agent Formulation of Lactobacillus casei LTL1361 Based on Artificial Neural Network Coupled Genetic Algorithm (BP-GA)[J]. Science and Technology of Food Industry, 2022, 43(21): 175−184. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022010143.

基于人工神经网络耦联遗传算法(BP-GA)优化干酪乳杆菌LTL1361冻干保护剂配方

Optimization of Lyophilized Protective Agent Formulation of Lactobacillus casei LTL1361 Based on Artificial Neural Network Coupled Genetic Algorithm (BP-GA)

  • 摘要: 为提高干酪乳杆菌LTL1361在真空冷冻干燥过程中的冻干存活率,以脱脂乳为基础保护剂,通过单因素实验以及Plackett-Burman试验确定影响干酪乳杆菌LTL1361冻干存活率的主要因素,基于上述试验结果进行Box-Behnke响应面试验设计构建数据集,并构建人工网络耦联遗传算法(BP-GA)模型对干酪乳杆菌LTL1361的冻干保护剂配方进行深层模拟预测。结果表明:采用单因素及Plackett-Burman试验筛选出主要影响菌株冻干存活率的三个因素为:海藻糖、谷氨酸及甘露醇,并确定将上述三种因素与基础脱脂乳为优化条件开展后续试验。通过构建BP-GA模型进行全局寻优,得到干酪乳杆菌LTL1361的最佳保护剂浓度配比为脱脂乳10.3%、谷氨酸0.8%、海藻糖6.7%和甘露醇4.0%,此时菌株的最高冻干存活率达到89.56%,经过与响应面模型结果比较,发现BP-GA模型具有更好的预测性能。利用BP-GA模型,本研究探索出了一种较高冻干存活率的益生菌冻干保护剂配方,并对制备高活性菌株冻干制剂以及商业化直投式发酵剂研发提供参考。

     

    Abstract: To improve the freeze-drying survival rate of Lactobacillus casei LTL1361 in the vacuum freeze-drying process, the single factor and Plackett-Burman designs were first conducted to verify the main factors affecting the freeze-drying survival rate of Lactobacillus casei LTL1361. According to the experimental results, the artificial network coupling genetic algorithm was constructed using the Box Behnken design. The artificial network coupled genetic algorithm (BP-GA) model was constructed to simulate and predict the lyophilized protective agent formulation of Lactobacillus casei LTL1361. The results showed that the three main factors affecting the lyophilisation survival rate of the strain were: Alginate, glutamic acid and mannitol, which were selected by single-factor and Plackett-Burman tests, and the three factors and the base skim milk were identified as the optimisation conditions for subsequent optimisation tests. The BP-GA model was used to find the optimum concentration of protectant for Lactobacillus casei LTL1361, which was 10.3% skim milk, 0.8% glutamic acid, 6.7% alginate and 4.0% mannitol, and the maximum lyophilisation survival rate of the strain was 89.56%. Using the BP-GA model, this study explored a probiotic lyophilisation protectant formulation with a high lyophilisation survival rate, and provided a reference for the preparation of lyophilised formulations of highly active strains and the development of commercial direct-injection ferments.

     

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