Advertisement

LC-MS Untargeted Protocol for the Analysis of Wine

  • Panagiotis Arapitsas
  • Fulvio Mattivi
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1738)

Abstract

This chapter describes a protocol for the analysis of the metabolomic fingerprint of wine by liquid chromatography-mass spectrometry. The straightforward, optimized sample preparation procedure is limited to a single-step dilution with water or acetonitrile. The separation of wine analytes is carried out by two columns with orthogonal selectivity, including both reversed-phase (C18) and hydrophilic interaction (HILIC) chromatography, while the detection is assured by a high-resolution quadrupole time-of-flight mass spectrometer operating in negative and positive electrospray ionization mode, in order to obtain four different chromatograms for each sample. This validated protocol, or parts of it, could be applied in several oenological topic experimental designs, including wine quality and wine authenticity.

Key words

Vitis vinifera Grape Food Holistic Metabolomics Mass spectrometry Liquid chromatography HILIC 

References

  1. 1.
    Nicholson JK, Lindon JC (2008) Systems biology: metabonomics. Nature 455:1054–1056CrossRefGoogle Scholar
  2. 2.
    Gika HG, Theodoridis GA, Vrhovsek U et al (2012) Quantitative profiling of polar primary metabolites using hydrophilic interaction ultrahigh performance liquid chromatography-tandem mass spectrometry. J Chromatogr A 1259:121–127CrossRefGoogle Scholar
  3. 3.
    Theodoridis G, Gika H, Franceschi P et al (2011) LC-MS based global metabolite profiling of grapes: solvent extraction protocol optimisation. Metabolomics 8:175–185CrossRefGoogle Scholar
  4. 4.
    Theodoridis GA, Gika HG, Want EJ et al (2012) Liquid chromatography–mass spectrometry based global metabolite profiling: a review. Anal Chim Acta 711:7–16CrossRefGoogle Scholar
  5. 5.
    Naz S, Vallejo M, García A et al (2014) Method validation strategies involved in non-targeted metabolomics. J Chromatogr A 1353:99–105CrossRefGoogle Scholar
  6. 6.
    Buscher JM, Czernik D, Ewald JC et al (2009) Cross-platform comparison of methods for quantitative metabolomics of primary metabolism. Anal Chem 81:2135–2143CrossRefGoogle Scholar
  7. 7.
    Cajka T, Fiehn O (2016) Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and Lipidomics. Anal Chem 88:524–545CrossRefGoogle Scholar
  8. 8.
    Arapitsas P, Speri G, Angeli A et al (2014) The influence of storage on the “chemical age” of red wines. Metabolomics 10:816–832CrossRefGoogle Scholar
  9. 9.
    Arapitsas P, Ugliano M, Perenzoni D et al (2016) Wine metabolomics reveals new sulfonated products in bottled white wines, promoted by small amounts of oxygen. J Chromatogr A 1429:155–165CrossRefGoogle Scholar
  10. 10.
    Mattivi F, Arapitsas P, Perenzoni D et al (2015) Influence of storage conditions on the composition of red wines - advances in wine research - ACS symposium series. In: ACS symposium series. ACS Publications, Washington, DC, pp 29–49Google Scholar
  11. 11.
    Franceschi P, Mylonas R, Shahaf N et al (2014) MetaDB a data processing workflow in untargeted MS-based metabolomics experiments. Front Bioeng Biotechnol 2:72CrossRefGoogle Scholar
  12. 12.
    Smith CA, Want EJ, O' Maille G et al (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78(3):779–778CrossRefGoogle Scholar
  13. 13.
    Katajamaa M, Miettinen J, Oresic M (2006) MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22:634–636CrossRefGoogle Scholar
  14. 14.
    Lommen A (2009) MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal Chem 81:3079–3086CrossRefGoogle Scholar
  15. 15.
    Xia J, Wishart DS et al (2016) Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinformatics 55:14.10.1–14.10.91.  https://doi.org/10.1002/cpbi.11 CrossRefGoogle Scholar
  16. 16.
    Sumner LW, Amberg A, Barrett D et al (2007) Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics 3:211–221CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Food Quality and Nutrition, Research and Innovation CentreFondazione Edmund Mach (FEM)San Michele all’AdigeItaly
  2. 2.Center Agriculture Food EnvironmentUniversity of TrentoSan Michele all’AdigeItaly

Personalised recommendations