High-Resolution Mass Spectrometry Associated with Data Mining Tools for the Detection of Pollutants and Chemical Characterization of Honey Samples

Analytical
methods for food control are mainly focused on restricted
lists of well-known contaminants. This paper shows that liquid chromatography–high-resolution
mass spectrometry (LC/ESI-HRMS) associated with the data mining tools
developed for metabolomics can address this issue by enabling (i)
targeted analyses of pollutants, (ii) detection of untargeted and
unknown xenobiotics, and (iii) detection of metabolites useful for
the characterization of food matrices. A proof-of-concept study was
performed on 76 honey samples. Targeted analysis indicated that 35
of 83 targeted molecules were detected in the 76 honey samples at
concentrations below regulatory limits. Furthermore, untargeted metabolomic-like
analyses highlighted 12 chlorinated xenobiotics, 1 of which was detected
in lavender honey samples and identified as 2,6-dichlorobenzamide,
a metabolite of dichlobenil, a pesticide banned in France since 2010.
Lastly, multivariate statistical analyses discriminated honey samples
according to their floral origin, and six discriminating metabolites
were characterized thanks to the MS/MS experiments.