Metabolomics

, Volume 8, Issue 2, pp 175–185

LC-MS based global metabolite profiling of grapes: solvent extraction protocol optimisation

  • Georgios Theodoridis
  • Helen Gika
  • Pietro Franceschi
  • Lorenzo Caputi
  • Panagiotis Arapitsas
  • Mattias Scholz
  • Domenico Masuero
  • Ron Wehrens
  • Urska Vrhovsek
  • Fulvio Mattivi
Original Article

DOI: 10.1007/s11306-011-0298-z

Cite this article as:
Theodoridis, G., Gika, H., Franceschi, P. et al. Metabolomics (2012) 8: 175. doi:10.1007/s11306-011-0298-z

Abstract

Optimal solvent conditions for grape sample preparation were investigated for the purpose of metabolite profiling studies, with the aim of obtaining as many features as possible with the best analytical repeatability. Mixtures of water, methanol and chloroform in different combinations were studied as solvents for the extraction of ground grapes. The experimental design used a two stage study to find the optimum extraction medium. The extracts obtained were further purified using solid phase extraction and analysed using a UPLC full scan TOF MS with both reversed phase and hydrophilic interaction chromatography. The data obtained were processed using data extraction algorithms and advanced statistical software for data mining. The results obtained indicated that a fairly broad optimal area for solvent composition could be identified, containing approximately equal amounts of methanol and chloroform and up to 20% water. Since the water content of the samples was variable, the robustness of the optimal conditions suggests these are appropriate for large scale profiling studies for characterisation of the grape metabolome.

Keywords

Grape metabolomeLC/MSSample preparationMetabolomicsMetabolite profilingTime of flight mass spectrometer

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Georgios Theodoridis
    • 1
  • Helen Gika
    • 1
  • Pietro Franceschi
    • 1
  • Lorenzo Caputi
    • 1
  • Panagiotis Arapitsas
    • 1
  • Mattias Scholz
    • 1
  • Domenico Masuero
    • 1
  • Ron Wehrens
    • 1
  • Urska Vrhovsek
    • 1
  • Fulvio Mattivi
    • 1
  1. 1.Food Quality and Nutrition DepartmentFondazione Edmund Mach, IASMA Research and Innovation CentreSan Michele all’AdigeItaly