Jiang Jiashun, Yang Jingan, Zhu Tong, Hu Yongjin, Li Hong, Liu Lijing
Yunnan Coffee Modern Industry College, Yunnan Agricultural University, Fengyun Road 452, Kunming 650201, China.
College of Food Science and Technology, Yunnan Agricultural University, Fengyuan Road 452, Kunming 650201, China.
Foods. 2025 Mar 17;14(6):1014. doi: 10.3390/foods14061014.
To investigate the metabolic differences and mechanisms during the fermentation process of coffee-grounds craft beer, HS-SPME-GC/MS untargeted metabolomics technology was used to study the metabolic differences during the fermentation process of coffee-grounds craft beer. Multivariate statistical analysis and pathway analysis were combined to screen for significantly different metabolites with variable weight values of VIP ≥ 1 and < 0.05. The results indicate that at time points T7, T14, T21, and T28, a total of 183 differential metabolites were detected during the four fermentation days, with 86 metabolites showing significant differences. Its content composition is mainly composed of lipids and lipid-like molecules, organic oxygen compounds, and benzoids, accounting for 63.64% of the total differential metabolites. KEGG enrichment analysis of differentially expressed metabolites showed a total of 35 metabolic pathways. The top 20 metabolic pathways were screened based on the corrected -value, and the significantly differentially expressed metabolites were mainly enriched in pathways such as protein digestion and absorption, glycosaminoglycan biosynthesis heparan sulfate/heparin, and benzoxazinoid biosynthesis. The different metabolic mechanisms during the fermentation process of coffee-grounds craft beer reveal the quality changes during the fermentation process, providing theoretical basis for improving the quality of coffee-grounds craft beer and having important theoretical and practical significance for improving the quality evaluation system of coffee-grounds craft beer.
为探究咖啡渣精酿啤酒发酵过程中的代谢差异及机制,采用顶空固相微萃取-气相色谱/质谱非靶向代谢组学技术研究咖啡渣精酿啤酒发酵过程中的代谢差异。结合多元统计分析和通路分析,筛选出变量重要性投影(VIP)值≥1且校正P值<0.05的显著差异代谢物。结果表明,在T7、T14、T21和T28时间点,四个发酵日共检测到183种差异代谢物,其中86种代谢物存在显著差异。其含量组成主要为脂质及类脂分子、有机氧化合物和苯类化合物,占差异代谢物总数的63.64%。对差异表达代谢物进行KEGG富集分析,共得到35条代谢通路。基于校正P值筛选出前20条代谢通路,显著差异表达的代谢物主要富集在蛋白质消化与吸收、硫酸乙酰肝素/肝素糖胺聚糖生物合成、苯并恶嗪生物合成等通路中。咖啡渣精酿啤酒发酵过程中不同的代谢机制揭示了发酵过程中的品质变化,为提高咖啡渣精酿啤酒品质提供了理论依据,对完善咖啡渣精酿啤酒品质评价体系具有重要的理论和现实意义。