Miao Zijian, Bai Yu, Wang Xinlei, Han Chao, Wang Bowen, Li Zexia, Sun Jinyuan, Zheng Fuping, Zhang Yuhang, Sun Baoguo
Key Laboratory of Geriatric Nutrition and Health (Beijing Technology and Business University), Ministry of Education, Beijing Technology and Business University, Beijing 100048, China.
Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Laboratory for Food Quality and Safety, School of Light Industry, Beijing Technology and Business University, Beijing 100048, China.
Foods. 2023 Sep 14;12(18):3425. doi: 10.3390/foods12183425.
Fermentation vessels affect the characteristics of food fermentation; however, we lack an approach to identify the biomarkers indicating fermentation. In this study, we applied metabolomics and high-throughput sequencing analysis to reveal the dynamic of metabolites and microbial communities in age-gradient fermentation vessels for baijiu production. Furthermore, we identified 64 metabolites during fermentation, and 19 metabolites significantly varied among the three vessels ( < 0.05). Moreover, the formation of these 19 metabolites were positively correlated with the core microbiota (including , , , and ). In addition, ethyl lactate or ethyl acetate were identified as the biomarkers for indicating the metabolism among age-gradient fermentation vessels by BP-ANN (R > 0.40). Therefore, this study combined the biological analysis and predictive model to identify the biomarkers indicating metabolism in different fermentation vessels, and it also provides a potential approach to assess the profiling of food fermentations.
发酵容器会影响食品发酵的特性；然而，我们缺乏一种识别指示发酵的生物标志物的方法。在本研究中，我们应用代谢组学和高通量测序分析来揭示白酒生产中不同年份梯度发酵容器中代谢物和微生物群落的动态变化。此外，我们在发酵过程中鉴定出64种代谢物，其中19种代谢物在三个容器之间存在显著差异（<0.05）。而且，这19种代谢物的形成与核心微生物群（包括、、、和）呈正相关。此外，通过BP人工神经网络（R>0.40）将乳酸乙酯或乙酸乙酯鉴定为指示不同年份梯度发酵容器间代谢情况的生物标志物。因此，本研究结合生物学分析和预测模型来识别指示不同发酵容器中代谢情况的生物标志物，同时也提供了一种评估食品发酵概况的潜在方法。
