应用代谢组学技术预测啤酒发酵过程中的腐败菌

doi:10.16088/j.issn.1001-6600.2022102004

摘要/Abstract

摘要： 通过超高效液相四级杆飞行时间串联质谱(UPLC-Q-TOF-MS)的非靶向质谱分析结合机器学习的方法能够预测啤酒是否感染腐败菌。在啤酒发酵过程中,分别添加短乳杆菌Lactobacillus brevis、芽孢杆菌Bacillus、植物乳杆菌Lactobacillus parabuchneri、类布氏乳杆菌Lactobacillus plantarum和葡萄球菌Staphylococcus saprophyticu等啤酒腐败菌的样品并进行代谢组学分析,随后,采用随机森林模型分析代谢组学数据,选取模型的前30%重要特征物质进行通路富集分析。最终发现,发酵初期,胆碱、甘油磷酸胆碱、三乙醇胺、磷脂酰乙醇胺和2-(3-羧基丙酰基)-6-羟基-环六-2,4-二烯羧酸(SHCHC)这些代谢物在腐败啤酒样品比正常啤酒样品中的含量增加,其中:36~48 h腐败啤酒中胆碱的含量高于正常啤酒50%~130%;甘油磷酸胆碱在发酵24 h腐败啤酒中比正常啤酒的含量低10%~30%,随着发酵时间延长,逐渐增加到高于正常啤酒9%以上;三乙醇胺在24 h正常啤酒中的含量最高,比腐败啤酒中的含量高出2~3倍;磷脂酰乙醇胺在96 h时的腐败啤酒中含量比正常啤酒高出6%~13%;SHCHC仅在36 h时的腐败啤酒中检测到。这些物质可以作为啤酒腐败的生物标记物。

关键词: 机器学习, 啤酒腐败菌, 代谢组学

Abstract: Non-targeted mass spectrometry analysis based on ultra high performance liquid chromatography-quadrupole-time of flight-tandem mass spectrometry (UPLC-Q-TOF-MS) combined with machine learning can predict whether beer is infected by spoilage bacteria. The metabonomics was used to analyze beer spoilage bacteria such as Lactobacillus brevis, Bacillus, Lactobacillus parabuchneri, Lactobacillus plantarum and Staphylococcus saprophyticus during beer fermentation. The metabonomics data was then analyzed by the multivariate analysis method of random forest. The first 30% of the important characteristic substances of the model were selected for pathway enrichment analysis. Finally, it was found that in the initial stage of fermentation, the contents of choline, choline glycerophosphate, triethanolamine, phosphatidylethanolamine and 2-(3-carboxylpropionyl)-6-hydroxy-cyclohexa-2,4-dicarboxylic acid (SHCHC) in spoiled beer samples were higher than those in normal beer samples. The contents of choline in spoiled beer with 36-48 h fermentation were 50% - 130% higher than those in normal beer samples; The content of glycerophosphate choline in spoiled beer with 24 h fermentation was 10% - 30% lower than that in normal beer. As the fermentation goes on, the content of glycerophosphate choline in spoiled beer gradually increased to more than 9% higher than that in normal beer; The content of triethanolamine in normal beer with 24 h fermentation was the highest, which was 2-3 times higher than that in spoiled beer; Phosphatidylethanolamine in spoiled beer was 6% - 13% higher than that in normal beer with 96 h fermentation; SHCHC was also only detected in spoiled beer with 36 h fermentation. These substances could be used as biomarkers of beer spoilage.

Key words: machine learning, beer spoilage bacteria, metabonomics

中图分类号: TS207.4

郭永昊, 刘储睿, 孙珍. 应用代谢组学技术预测啤酒发酵过程中的腐败菌[J]. 广西师范大学学报（自然科学版）, 2023, 41(5): 161-170.

GUO Yonghao, LIU Churui, SUN Zhen. Applying Metabonomics to Predict the Spoilage Bacteria in Beer Fermentation[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 161-170.

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