Metabolomics is an area of increased interest in many scientific fields including chemical food safety and toxicology in the scope of investigating and identifying (bio)markers of exposure, effect and/or susceptibility. Biological samples contain hundreds or even thousands of metabolites with different physicochemical properties, limiting the characterisation of the entire metabolome using a single analytical technique. Therefore, the use of a multi-analytical platform in combination with appropriated sample preparation is required to enlarge the number of metabolites detected. However, one of the most important and challenging steps of a metabolomics study is the unequivocal identification of the detected metabolites [1]. Different public databases are available for that purpose, but the most straightforward approach for unambiguous confirmation of the identity of metabolites consists in generating reference data from a commercially available standard compound using the same analytical system and conditions applied during the characterization of the samples to be profiled. In that context, an in house-database containing information about chromatographic retention time, elemental composition and fragmentation pattern of around 300 chemical compounds in the current state of incrementation has been developed to facilitate metabolite identification [2]. In addition, an automatic Excel© macro was developed to help filtering and allowing a fast and direct annotation of compounds. Criteria as retention time, accurate mass and isotopic pattern have established as a primary identification. Nevertheless, these criteria are not always sufficient for unequivocal identification and the use of tandem mass spectrometry and/or other analytical techniques are necessary. As a result a multi-analytical platform based on LC-HRMS using the complementarities of reversed phase and HILIC, and GC-MS has been developed for a more comprehensive and semi-automated annotation of metabolomic profiles generated from biological matrices [3]. [1] Creek, D.J., et al., Metabolomics, 2014, 350-353. [2] Courant, F., et al., Analyst, 2012, 4958–4967. [3] C. Jacob, C., et al. Metabolomics, 2015, 184-197.