Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data

• DOI: 10.1007/s11306-022-01923-6
• Publication Date: 2022-08-02T09:07:20Z
• Authors: Jasen P. Finch; Thomas Wilson; Laura Lyons; Helen Phillips; Manfred Beckmann; John Draper
• Keywords: Metabolome

Abstract Introduction Flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, dimensional data sets which require specialist in-silico software tools to process the prior analysis. Objectives Present spectral binning as a pragmatic approach post-acquisition procession of FIE-HRMS metabolome data. Methods A was developed that included elimination single scan m/z events, spectra and averaging across profile. The modal accurate then extracted for each bin. This assessed using four different biological matrices mix 31 known chemical standards analysed by an Exactive Orbitrap. Bin purity centrality metrics were objectively assess distribution position within individual bin respectively. Results optimal width found be 0.01 amu. 80.8% matched predicted ionisation products have error below 3 ppm. open-source R package binneR user friendly implementation approach. able 100 files 4 Central Processing Units (CPU) workers in only 55 seconds with maximum memory usage 1.36 GB. Conclusion Spectral is fast robust method processing allows users efficiently from experiments resources available on standard desktop computer.