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Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples
Örebro University, School of Science and Technology. Department of Chemistry, Oregon State University, Corvallis OR, USA.
Department of Chemistry, Oregon State University, Corvallis OR, USA; Department of Chemistry, Pomona College, Claremont CA, USA.
Department of Chemistry, Oregon State University, Corvallis OR, USA.
Graduate School of Public Health, San Diego State University, San Diego CA, USA.
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2018 (English)In: Journal of Chromatography A, ISSN 0021-9673, E-ISSN 1873-3778, Vol. 1541, p. 57-62Article in journal (Refereed) Published
Abstract [en]

Non-targeted analysis of environmental samples, using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC x GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python (TM) script that acts as a data reduction filter to automate GC x GC/ToF-MS data analysis from LECO (R) ChromaTOF (R) software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post-bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post-bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO (R) ChromaTOF (R) software Statistical Compare feature. Based on this list, 63-100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold. (C) 2018 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 1541, p. 57-62
Keywords [en]
GC x GC/ToF-MS, Python (TM), Non-targeted analysis, LECO (R) ChromaTOF (R), Statistical compare
National Category
Biochemistry and Molecular Biology Analytical Chemistry
Identifiers
URN: urn:nbn:se:oru:diva-66387DOI: 10.1016/j.chroma.2018.02.016ISI: 000427667900008PubMedID: 29448996Scopus ID: 2-s2.0-85043721782OAI: oai:DiVA.org:oru-66387DiVA, id: diva2:1196241
Available from: 2018-04-09 Created: 2018-04-09 Last updated: 2018-08-20Bibliographically approved

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Titaley, Ivan

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