Characterization of food and beverages has in recent years shown analytical trends increasingly using non-targeted approaches to complement quantitative targeted analyses. Application of these techniques to the analysis of wine have been used to assess previously unknown contents of wine lees1, patterns in glycosilated simple phenols across wine grape hybrid varieties2, and metabolic profiling of pest resistant genotypes against their susceptible ancestor vines3. In addition to high quality HRMS data, it is also important to ensure that the known analytes in these types of experiments can be quantified within relevant concentration ranges. Here, we demonstrate the use of a QTof data independent acquisition (DIA) mode in which the quadrupole is set to scan across the defined mass range using a narrow mass window. Following this, the collision cell alternates between passive and elevated collision energies which generate fragments. The end result is highly selective and specific spectra for both low- and high-collision energy states. Preliminary work thus far on store bought red wines have demonstrated qualitative profiling using this acquisition approach coupled with UPLC and ESI- ionization. Differentiation between metabolites and constituents in the various wines was achieved using the multivariate statistics approaches PCA, OPLS-DA and hierarchical cluster analysis. From the same data set, quantitation of selected native phenols was performed with isotopically-labeled standards of resveratrol, catechin and p-coumaric acid. Data presented will highlight utility of this approach for both targeted and non-targeted assessment of metabolite variation in wines across different grapes as well as years of production from a single vineyard.

Characterization of metabolic variability in red wines using a highly selective non-targeted acquisition approach

255th National Meeting (2018)

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