Accurate mass error correction in liquid chromatography time-of-flight mass spectrometry based metabolomics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-02-22

AUTHORS

Velitchka V. Mihaleva, Oscar Vorst, Chris Maliepaard, Harrie A. Verhoeven, Ric C. H. de Vos, Robert D. Hall, Roeland C. H. J. van Ham

ABSTRACT

Compound identification and annotation in (untargeted) metabolomics experiments based on accurate mass require the highest possible accuracy of the mass determination. Experimental LC/TOF-MS platforms equipped with a time-to-digital converter (TDC) give the best mass estimate for those mass signals with an intensity similar to that of the lock-mass used for internal calibration. However, they systematically underestimate the mass obtained at higher signal intensity and overestimate it at low signal intensities compared to that of the lock-mass. To compensate for these effects, specific tools are required for correction and automation of accurate mass calculations from LC/MS signals. Here, we present a computational procedure for the derivation of an intensity-dependent mass correction function. The chromatographic mass signals for a set of known compounds present in a large number of samples were reconstructed over consecutive scans for each sample. It was found that the mass error is a linear function of the logarithm of the signal intensity adjusted to the associated lock-mass intensity. When applied to all mass data points, the correction function reduced the mass error for the majority of the tested compounds to ≤1 ppm over a wide range of signal intensities. The mass correction function has been implemented in a Python 2.4 script, which accepts raw data in NetCDF format as input, corrects the detected masses and returns the corrected NetCDF files for subsequent (automated) processing, such as mass signal alignment and database searching. More... »

PAGES

171-182

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11306-008-0108-4

DOI

http://dx.doi.org/10.1007/s11306-008-0108-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009236181


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