食品科学
化学
代谢组学
苹果酸
主成分分析
感官的
葡萄酒
棕榈酸
色谱法
脂肪酸
生物化学
数学
柠檬酸
统计
作者
Yue Miao,Qingfei Zou,Qiuping Wang,Jiashun Gong,Chao Tan,Chunxiu Peng,Chunyan Zhao,Zelin Li
出处
期刊:Food bioscience
[Elsevier BV]
日期:2022-01-14
卷期号:46: 101561-101561
被引量:18
标识
DOI:10.1016/j.fbio.2022.101561
摘要
Coffee is one of the most important agricultural commodities and has the unique organoleptic characteristics such as strong but not bitter taste, fragrant, oily, and fruity. In this study, an untargeted metabolomics approach based on UHPLC-QE-MS was used to investigate the differences in terms of components of precursor metabolites in coffee beans from 18 producing regions worldwide. Fingerprint analysis, principal component analysis and hierarchical clustering analysis revealed a neat separation among coffee beans. Compounds with high relevance to variance in the projection values in supervised multivariate analysis were selected as important metabolites for the discrimination of coffee samples. In total, 10 different families of compounds were considered as potential markers of the coffee beans: 3-hydroxycoumarin, 4,5-di-O-caffeoylquinic, cryptochlorogenic acid, palmitic amide, linoleamide, arachidic acid, petroselinic acid, trehalose, l-glutamic acid, l-malic acid. The findings presented herein serve as a suitable framework for the design of novel discrimination strategies in food origin tracing.
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