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TCS发酵动力学模型及高密度培养策略研究

Study on TCS fermentation kinetic models and high-density culture strategy.

作者信息

Chen Chen, Guo Tianyu, Wu Di, Shu Jingyan, Huang Ningwei, Tian Huaixiang, Yu Haiyan, Ge Chang

机构信息

School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai, Shanghai, China.

出版信息

Microbiol Spectr. 2025 Jul;13(7):e0259024. doi: 10.1128/spectrum.02590-24. Epub 2025 May 19.

Abstract

This study enhanced the production efficiency of Lacticaseibacillus casei TCS by optimizing medium composition and fermentation conditions for high-density culture. Initially, single-factor and orthogonal experimental designs and the response surface methodology were used to determine the optimal concentrations of medium. Subsequently, we applied the artificial neural network-genetic algorithm optimization method, which significantly increased the viable bacterial count. Fermentation kinetics were modeled using logistic growth and Luedeking-Piret models, which accurately predicted cell growth. Amberlite IRA 67, an anion exchange resin, effectively adsorbed lactic acid and maintained pH levels. Furthermore, the combined use of fed-batch fermentation and ion exchange alleviated the effects of acid inhibition, salt stress, and substrate limitation, resulting in a maximum cell density of 10.01 lg CFU/mL, a 9.3-fold increase over the basal medium. This study develops a robust and cost-effective strategy for the industrial production of TCS, significantly optimizing probiotic production processes.IMPORTANCE TCS possesses outstanding aromatic characteristics, making it suitable for producing fermented dairy products. The goal of cultivating TCS at high density is to increase production yields, overcome challenges related to acid inhibition, and optimize fermentation processes. This study employed an artificial neural network (ANN) and genetic algorithms (GA) to determine the ideal composition of the proliferation medium for TCS. It constructed a dynamic model to track bacterial growth, product formation, and substrate consumption during fermentation, analyzing the process's dynamic patterns. Furthermore, by utilizing resin adsorption and fed-batch cultivation techniques, the production of lactic acid as a by-product was effectively minimized. This approach enabled to multiply rapidly to high concentrations, laying a foundation for the industrial production of high-yield aroma starters. This advancement supports the bacterium's application in various sectors, including dairy processing and functional food production.

摘要

本研究通过优化培养基组成和高密度培养的发酵条件,提高了干酪乳杆菌TCS的生产效率。首先,采用单因素和正交实验设计以及响应面法来确定培养基的最佳浓度。随后,应用人工神经网络 - 遗传算法优化方法,显著提高了活菌数。使用逻辑生长模型和Luedeking - Piret模型对发酵动力学进行建模,准确预测了细胞生长。阴离子交换树脂Amberlite IRA 67有效地吸附了乳酸并维持了pH值。此外,补料分批发酵和离子交换的联合使用减轻了酸抑制、盐胁迫和底物限制的影响,使最大细胞密度达到10.01 lg CFU/mL,比基础培养基提高了9.3倍。本研究为TCS的工业化生产制定了一种稳健且经济高效的策略,显著优化了益生菌生产工艺。重要性TCS具有出色的芳香特性,适用于生产发酵乳制品。高密度培养TCS的目标是提高产量、克服与酸抑制相关的挑战并优化发酵过程。本研究采用人工神经网络(ANN)和遗传算法(GA)来确定TCS增殖培养基的理想组成。构建了一个动态模型来跟踪发酵过程中细菌生长、产物形成和底物消耗,分析该过程的动态模式。此外,通过利用树脂吸附和补料分批培养技术,有效地减少了副产物乳酸的产生。这种方法使TCS能够迅速繁殖至高浓度,为高产香气发酵剂的工业化生产奠定了基础。这一进展支持了该细菌在包括乳制品加工和功能性食品生产在内的各个领域的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba2d/12210951/dfe95e14a566/spectrum.02590-24.f001.jpg

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