Constructing a mass accuracy surface to improve automatic annotation in LC-MS based metabolomics

Accurate mass measurement is essential for metabolite identification in the field of mass spectrometry (MS) based metabolomics, since many unidentified peak signals are being resolved by mass-to-mass matching to reference databases. Contrary to studies focused on the mass accuracy of MS instruments done in the past, which had a limited scope, the method presented here uses a much larger amount of data to build a model which predicts MS mass accuracy as a function of the two most influential parameters, namely: the mass value and the peak intensity. The widely used Synapt qTOF-MS instrument was chosen to demonstrate the method. The output model gives the analytical chemist an option to estimate the mass measurement accuracy on a peak-by-peak basis. We demonstrate that this can lead to a better performance in untargeted metabolite annotation scenario.