Untargeted metabolomic analysis using liquid chromatography quadrupole time-of-flight mass spectrometry for non-volatile profiling of wines

Wine science in the metabolomics era

“…While profiling involves the analysis of a group of preselected metabolites, which are in most cases identified and quantified, fingerprinting is based on the determination of as many metabolites as possible without necessarily identifying or quantifying the compounds present. While targeted analysis of specific metabolites misses a large part of the molecular information regarding the metabolome of wine, untargeted metabolomics can be a powerful tool for the molecular fingerprinting of a complex beverage, such as wine [14]. The goal is to obtain qualitative and (semi)-quantitative information to compare patterns or fingerprints of changes in metabolites.…”

“…Additionally, the accurate mass MS/MS capability of quadrupole and collision cell together with TOF were used for elucidation of unknown marker compounds to provide a high level of confidence in the identification process. Recently, ESI-LC-QTOF was used in a non-targeted study in order to characterize the nonvolatile profile of Graciano wines [14]. Around 1770 features were detected and PCA pointed out 15 compounds as differentiators between Graciano and Tempranillo wines.…”

Discovery of characteristic chemical markers for classification of aconite herbs by chromatographic profile and probabilistic neural network

“…Recently, the supervised machine learning methods have been a powerful technique for data classification and estimation of variable contributions in various fields, such as species classification [11], pharmaceutical research [12], credit rating analysis [13] and food science [14]. The elucidation of relationships between variables, often complicated and highly non-linear, are more objective by mathematical modeling methods rather than by subjective analysis.…”

Metabolomics approaches for resolving and harnessing chemical diversity in grapes, yeast and wine

“…While untargeted metabolomics experiments produce a list of features with their corresponding P-values and fold-changes as measures of differences in relative intensity and likely significance, the identity of a feature and unknown metabolite is initially assessed by searching the accurate mass of the molecule against a range of databases. Assignment of the 2109 wine compounds covered by WinMet relies on availability of accurate mass data, for example, from QTOF mass spectrometry as reported by Arbulu et al (2015) for the analysis and identification of the nonvolatile and semi-volatile metabolome of Graciano wine. Key databases for metabolite identification include METLIN (https://metlin.scripps.edu/index.php), KEGG (http://www .genome.jp/kegg/), the Human Metabolome database (http:// www.hmdb.ca/), BioCyc (http://biocyc.org/), MetaCyc (http:// metacyc.org/), Pathway Interaction database (http://pid.nci .nih.gov/), Pathcase (http://nashua.cwru.edu/pathwaysweb/) and Phenol-Explorer (http://www.phenol-explorer.eu).…”