菌株特定的代谢终点和预测阶段分类在 gnotobiotic kimchi 发酵过程中
Yujin Kim1, Se Hee Lee2, Mi-Ja Jung2
1Kimchi Functionality Research Group, World Institute of Kimchi, Gwangju 61755, Republic of Korea; Division of Food and Nutrition, Chonnam National University, Youngbong-ro, Buk-gu, Gwangju 61186, Republic of Korea.
Food chemistry
|January 11, 2026
概括
我们使用定义的乳酸细菌 (LAB) 联盟开发了一种受控的金奇发酵模型. 机器学习识别了用于预测发酵阶段的关键代谢物,使LAB驱动的金生产能够进行预测控制.
科学领域:
- 食品微生物学 食品微生物学
- 发酵科学 发酵科学
- 系统生物学 系统生物学
背景情况:
- 发酵依赖于复杂的微生物相互作用,阻碍了预测性控制.
- 像金这样的食品中的天然微生物联盟很难以预测的方式管理.
研究的目的:
- 为了创建一个 gnotobiotic kimchi 模型,用于可预测的发酵控制.
- 在金发酵过程中确定关键的微生物和代谢物动态.
- 为乳酸细菌 (LAB) 驱动的金生产建立一个预测框架.
主要方法:
- 在受控温度下,用定义的LAB联盟和单个菌株对kimchi进行注射.
- 在发酵过程中监测微生物群落结构和代谢物概况.
- 阶段规范化,机器学习和网络分析的应用.
主要成果:
- 联盟发酵显示了温度依赖的生长和酸化,社区转移在15°C.
- 一个九个代谢物签名准确地分类了数据集中的发酵阶段.
- 网络分析确定了Leuconostoc mesenteroides和Lactococcus lactis作为关键物种.
结论:
- 为LAB驱动的金发酵建立了一个预测框架.
- 定义的LAB联盟使控制的发酵具有可预测的结果.
- 代谢物分析和机器学习是了解发酵动态的强大工具.
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