Abstract

In this study, we developed a chemometrics approach for fingerprinting and identifying discriminant metabolites in Mexican mezcals. The approach was based on direct immersion solid-phase micro-extraction (DI-SPME) and gas chromatography–mass spectrometry (GC-MS). We used multivariate statistical analysis (MSA) to discriminate between ancestral and artisanal manufacturing processes, as well as between different geographical origins and agave species. The designed GC-MS-MSA separation method, labelled “Method 1”, enabled the unequivocal identification of twelve marker compounds that were confirmed with Electronic Impact Mass Spectrometry (EI-MS) mass-profile analyses, comprising a pairwise comparison between experimental m/z patterns and data obtained from the National Institute of Standards and Technology (NIST) MS library. Furthermore, to demonstrate the correlation between the reference and experimental mass spectra, Pearson correlation coefficients and their standard deviations were calculated. In all cases, Pearson’s correlation coefficient presented values with ranges between 0.96 and 0.99, indicating a high match between both spectra, whilst standard deviation values from 0.29 to 4.69 provided the data consistency. Supervised MSA of mezcals’ Method 1 GC cohorts (16 samples) are able to distinguish between different geographical origins (San Luis Potosí, Sierra sur Oaxaca and Valles Centrales, Oaxaca, R2[Y] = 0.79/Q2 = 0.79), agave species (Agave potatorum, Agave angustifolia, Agave karwinskii, Agave salmiana, Agave marmorata, and Agave americana ‘Oaxacensis’, R2[Y] = 0.79/Q2 = 0.79), and manufacturing processes (ancestral vs. artisanal, R2[Y] = 0.87/Q2 = 0.77).

IPC Classification

Keywords