The field of metabolomics, which studies small molecules in biological matrices, is rapidly growing and the number of published studies reporting on molecular biomarkers with e.g. diagnostic or prognostic value continues to increase. While individual metabolomics studies can provide valuable information, the potential of metabolomics could be further exploited if data of different studies could be combined and interpreted beyond their original goals. This is currently challenging due to the usually semi-quantitative nature of metabolomics data, hampering its widespread translation to the clinic and integration with other -omics data. The most widely applied technique in metabolomics is liquid chromatography coupled to mass spectrometry (LC-MS). This technique has a superb sensitivity compared to e.g. NMR, but it is not as quantitative. Absolute quantification using LC-MS requires customized methods using calibration lines with reference standards. Usually, internal standards (ISTDs) are added to all samples, including calibration samples, to correct for (i) variability in analyte recovery during sample preparation and (ii) variability of the effect of other components in the sample matrix on the ionization of the analyte-of-interest (matrix effect). Quantification is then performed using the peak area ratio of the analyte and its ISTD. The latter can be a structural analogue of the analyte-of-interest, of which successful cases have been reported , but the ideal ISTD is a stable isotope-labeled (SIL) analogue because of its near identical chemical and physical characteristics. However, these stable isotope labeled standards are usually expensive and their commercial availability is limited. The costs and poor availability of SIL standards, but also the hassle of adding individual ISTDs per analyte are some of the reasons that increasing the metabolite coverage of a method is often at the expense of the quantitative reliability. This, because in practice methods with high metabolic coverage often only use one or two ISTDs to correct for the recovery and matrix effect of a whole compound class . Although it may be justified that this suffices to correct for variation in recovery, limited ISTDs may not be adequate for the correction of matrix effect during ionization. Matrix effect can be caused by compounds co-eluting with the analyte of interest by e.g. affecting the droplet formation, changing the droplet viscosity or surface tension, competing for charge, or inducing co-precipitation with nonvolatiles . These processes change with the composition of the eluent exiting the LC column, and an ISTD not co-eluting with the analyte of interest can seriously compromise quantification.