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关于Lacticaseibacillus casei TCS发酵动力学模型和高密度培养策略的研究
Chen Chen1, Tianyu Guo1, Di Wu1
1School of Perfume and Aroma Technology, Shanghai Institute of Technology , Shanghai, Shanghai, China.
使用人工神经网络和遗传算法优化 Lacticaseibacillus casei TCS 生产,实现高密度培养. 这一强有力的战略提高了乳制品和功能性食品的益生菌产量.
科学领域:
- 微生物学 微生物学
- 生物技术是生物技术.
- 食品科学 食品科学 食品科学
背景情况:
- Lacticaseibacillus casei TCS对于发酵乳制品至关重要,因为它具有芳香特性.
- 高密度种植L. casei TCS的目的是提高产量,克服发酵的限制.
- 挑战包括酸抑制,盐应激和基质限制,阻碍高效的工业规模生产.
研究的目的:
- 为了优化中介组成和发酵条件,为高密度培养Lacticaseibacillus casei TCS.
- 为工业益生菌生产制定一个具有成本效益和强大的战略.
- 为了提高L. casei TCS的生产效率和产量,用于芳香启动应用.
主要方法:
- 采用单因素,直角实验设计和响应表面方法来优化介质.
- 利用人工神经网络 (ANN) 和遗传算法 (GA) 优化,以提高细菌数量.
- 应用后勤增长和Luedeking-Piret模型用于发酵动力学,加上离子交换 (Amberlite IRA 67) 和料批发发酵.
主要成果:
- 最佳的介质组成和发酵条件显著增加了可活性的细菌数量.
- ANN-GA优化方法导致细菌密度大幅增加.
- 最大细胞密度达到10.01毫克CFU/毫升,通过减轻酸抑制和压力因素增加了9.3倍.
结论:
- 为工业L. casei TCS生产制定了强大且具有成本效益的战略.
- 成功优化了益生菌生产过程,克服了关键的发酵挑战.
- 这项研究为乳制品和功能性食品行业高产量的芳香启动剂生产提供了基础.
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