Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21543
Title: PYQUAN: A rapid workflow around the AMDIS deconvolution software for high throughput analysis of pyrolysis GC/MS data
Authors: SMITS, Mark 
CARLEER, Robert 
COLPAERT, Jan 
Issue Date: 2016
Publisher: ELSEVIER SCIENCE BV
Source: JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS, 118, p. 335-342
Abstract: Pyrolysis-GC/MS is a powerful technique to characterize soil organic matter. Most samples produce a high number of peaks in chromatograms, many of them overlapping, which complicates data analysis. In order to facilitate the speed and ease of analysis, we have developed an automated workflow that enables us to rapidly identify and quantify peaks from (pyrolysis-)GC/MS data. It is based on identification by the software program AMDIS, followed by peak deconvolution and quantification using our own developed, user-friendly, Python script. Visual inspection of each peak and the possibility to re-quantify individual peaks with adjusted parameters are included in the Python script as well. We applied this workflow to a series of dissolved organic matter samples with increasing glucose concentrations. More than 97% of the peaks were automatically correctly identified and quantified, including compounds with nearly similar spectra and RTs less than 10 s apart. (C) 2016 Elsevier B.V. All rights reserved.
Notes: [Smits, Mark M.; Colpaert, Jan V.] Hasselt Univ, Environm Biol Grp, Ctr Environm Sci, Agoralaan Bldg D, B-3590 Diepenbeek, Belgium. [Carleer, Robert] Hasselt Univ, Ctr Environm Sci Appl & Analyt Chem, Agoralaan Bldg D, B-3590 Diepenbeek, Belgium.
Keywords: Pyrolysis-GC/MS; software; deconvolution; alignment; soil organic matter;Pyrolysis-GC/MS; Software; Deconvolution; Alignment; Soil organic matter
Document URI: http://hdl.handle.net/1942/21543
ISSN: 0165-2370
e-ISSN: 1873-250X
DOI: 10.1016/j.jaap.2016.01.006
ISI #: 000373541400034
Rights: © 2016 Elsevier B.V. All rights reserved.
Category: A1
Type: Journal Contribution
Validations: ecoom 2017
Appears in Collections:Research publications

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