Article open access publication

Gas chromatography – mass spectrometry data processing made easy

Journal of Chromatography A, Elsevier, ISSN 1873-3778

Volume 1503, 2017

DOI:10.1016/j.chroma.2017.04.052, Dimensions: pub.1085071077, PMID: 28499599,

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Affiliations

Organisations

  1. (1) MS-Omics, Birkehegnet 40, Ålsgårde, Denmark
  2. (2) University of Copenhagen, grid.5254.6, KU

Countries

Denmark

Continents

Europe

Description

Evaluation of GC-MS data may be challenging due to the high complexity of data including overlapped, embedded, retention time shifted and low S/N ratio peaks. In this work, we demonstrate a new approach, PARAFAC2 based Deconvolution and Identification System (PARADISe), for processing raw GC-MS data. PARADISe is a computer platform independent freely available software incorporating a number of newly developed algorithms in a coherent framework. It offers a solution for analysts dealing with complex chromatographic data. It allows extraction of chemical/metabolite information directly from the raw data. Using PARADISe requires only few inputs from the analyst to process GC-MS data and subsequently converts raw netCDF data files into a compiled peak table. Furthermore, the method is generally robust towards minor variations in the input parameters. The method automatically performs peak identification based on deconvoluted mass spectra using integrated NIST search engine and generates an identification report. In this paper, we compare PARADISe with AMDIS and ChromaTOF in terms of peak quantification and show that PARADISe is more robust to user-defined settings and that these are easier (and much fewer) to set. PARADISe is based on non-proprietary scientifically evaluated approaches and we here show that PARADISe can handle more overlapping signals, lower signal-to-noise peaks and do so in a manner that requires only about an hours worth of work regardless of the number of samples. We also show that there are no non-detects in PARADISe, meaning that all compounds are detected in all samples.

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University of Copenhagen

Danish Open Access Indicator

2017: Realized

Research area: Science & Technology

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2017: Level 1

Research area: Science & Technology

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Times Cited: 53

Field Citation Ratio (FCR): 13.54

Relative Citation ratio (RCR): 2.46

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Hybrid