An innovative metabolomic approach for golden rums classification combining ultra-high performance liquid chromatography-Orbitrap mass spectrometry and chemometric strategies.
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY(2019)
摘要
A comprehensive fingerprinting strategy for golden rum classification considering different categories such as fermentation barrel, raw material, and aging is provided, using a metabolomic fingerprinting approach. A nontarget fingerprinting of 30 different rums using liquid chromatography coupled to high-resolution mass spectrometry (Exactive Orbitrap mass analyzer, LC-HRMS) was applied. Principal component analysis (PCA) was used to assess the overall structure of the data and to identify potential outliers. Different chemometric analyses such as partial least-squares discriminant analysis (PLS-DA) were used. A variable importance in projection (VIP) selection method was applied to identify the most significant markers that allow group separation. Compounds related to aging and fermentation processes such as furfural derivates (e.g., hydroxymethylfurfural) and sugars (e.g., glucose, mannitol) were found as the most discriminant compounds (VIP threshold value >1.5). Suitable separation according to selected categories was achieved, and a classification ability of the models of close to 100% was achieved.
更多查看译文
关键词
rum,metabolomics,liquid chromatography,high-resolution mass spectrometry,fingerprinting,multivariate analysis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要