LC–HRMS fingerprinting as an efficient approach to highlight fine differences in cheese metabolome during ripening
Résumé
New approaches, mainly based on mass spectrometry techniques, are being developed and appear as a
must in the modern food science and microbiology research to investigate food quality and safety. To date, the
investigation of cheese ripening mechanisms has mostly used targeted approaches. The aims of the present project were to assess the use of untargeted metabolomics as an approach to investigate the influence of altering one ripening parameter to generate fine differences in the microbial metabolism within cheese. Two cheeses were made which varied with respect to the spatial distribution of bacterial colonies, leading to cheeses with only big or only small colonies. Liquid chromatography high resolution mass spectrometry metabolic fingerprints were acquired on cheese extracts collected after 2, 13 and 27 days of ripening using two different extraction methods (water or acetonitrile) and analyzed using two different simultaneous ionization modes (positive and negative electrospray). Data processing involving XCMS and multivariate statistical analysis highlighted significant discriminant profiles of the cheese metabolomes according to the two different spatial
distributions compared. The different fractions investigated (water and acetonitrile extractions in two ionization modes) were complementary and resulted in a view as global as possible of the cheese metabolome which had been modulated by the spatial distribution of bacterial colonies. Some of the metabolites were then identified using an inhouse database. These results show the relevance of cheese LC–HRMS fingerprinting to understand the influence of a ripening parameter generating fine differences on microbial metabolism within cheese.