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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.

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There is an urgent need to implement holistic and untargeted doping control protocols with improved discriminatory power, compared to conventional methods that only target doping agents. Metabolomics, which aims to characterize all metabolites present in biological matrices, could fulfill this need. In this context, the aim of this study was to evaluate the impact of environmental factors on the ability to obtain a metabolic signature of stanozolol administration in horse doping situation. Urine samples from 16 horses breeded in two different places were collected over a one-year period, before, during and seven months after the administration of stanozolol, a horse doping agent. Metabolomic analysis was performed using ultra-high pressure reverse phase liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (MS). Results showed a major impact of the nutritional regimen, drug administration (for de-worming purpose) and breeding place on the metabolite profiles of horse urines, which hampered the detection of metabolic perturbations induced by stanozolol administration. After having used MS/MS experiments to characterize some MS features related to these environmental factors, we showed that highlighting and then removing the features impacted by these confounding factors before performing supervised multivariate statistical analyses could address this issue. In conclusion, adequate consideration should be given to environmental and physiological factors; otherwise, they can emerge as confounding factors and conceal doping administration.

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