Mushrooms are known all over the world both due to the remarkable gastronomic value of some species and for severe intoxications mediated by other species that are frequently difficult to distinguish from the edible ones, by the common user. Therefore, it is important to develop strategies to discover molecules that can identify mushroom species. In the present work, two GC-MS methodologies were applied in the chemical characterization of 22 mushroom species (12 edible, 3 toxic and 7 potentially toxic) - a multi-target procedure to simultaneously determine amino acids (AA), fatty acids (FA) and sterols by previous derivatization procedure with MSTFA, and a Head Space-Solid Phase Microextraction method to determine volatiles. For both methods, two approaches to data analysis were used: (I) targeted analysis, to identify and quantify AA, FA sterols and volatiles; (II) untargeted analysis, including Principal Component Analysis and Partial Least Square Discriminant Analysis, in order to identify metabolites/metabolite pattern with potential species identification and/or differentiation. Multi-target experiment allowed the identification and quantification of twenty one primary metabolites (9 AA, 11 FA and 1 sterol). Furthermore, through untargeted data analysis, it was possible to identify a 5-carbon sugar alcohol structure molecule, which was tentatively identified as xylitol or adonitol, with potential to be a species-marker of the edible Suillus bovinus mushrooms. Volatile profiling studies resulted in the identification of the main volatiles in mushrooms. Untargeted analysis allowed the identification of 6 molecules that can be species- or genus-specific: one secondary metabolite specific to the edible species Lycoperdon perlatum, an ester of hexanoic acid, tentatively identified as allyl or vinyl caproate; and five other secondary metabolites, whose identification was not achieved, which were only detected in Lactarius aurantiacus specimens (edibility/toxicity unknown).
- Edible and toxic wild mushrooms
- HS-SPME GC-IT/MS
- Partial Least Square Discriminant Analysis (PLS-DA)
- Principal Component Analysis (PCA)
- Species discrimination
- Targeted and non-targeted analysis