Gas chromatography – mass spectrometry data processing made easy

• Published in: Journal of Chromatography A Vol. 1503; pp. 57 - 64
• Main Authors: Johnsen, Lea G., Skou, Peter B., Khakimov, Bekzod, Bro, Rasmus
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 23-06-2017
• Subjects: Algorithms; Automatic Data Processing - methods; Automatic Data Processing - standards; Chromatography; Data processing; Deconvolution; Gas Chromatography-Mass Spectrometry; GC–MS; PARAFAC2; Software; Data processing; Deconvolution; GC–MS; Chromatography; PARAFAC2
• ISSN: 0021-9673; 1873-3778
• DOI: 10.1016/j.chroma.2017.04.052

•An integrated stand-alone approach for processing GC–MS data is proposed.•The approach enables non-mathematicians to use the powerful curve resolution methods.•Overlapped, embedded, shifted and low S/N ratio peaks are readily handled.•Processes raw CDF files into peak tables using very few steps.•The approach performs just as well or better than other software and is less user-dependent.
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.