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J Chromatogr A. 2018 Mar 16;1541:57-62. doi: 10.1016/j.chroma.2018.02.016. Epub 2018 Feb 7.

Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples.

Author information

1
Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA.
2
Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA; Department of Chemistry, Pomona College, Claremont, CA, 91711, USA.
3
Graduate School of Public Health, San Diego State University, San Diego, CA, 92182, USA.
4
Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA. Electronic address: paulc@science.oregonstate.edu.
5
Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA; Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, 97331, USA. Electronic address: staci.simonich@oregonstate.edu.

Abstract

Non-targeted analysis of environmental samples, using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × 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™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO® ChromaTOF® 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® ChromaTOF® 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.

KEYWORDS:

GC × GC/ToF-MS; LECO(®) ChromaTOF(®); Non-targeted analysis; Python™; Statistical compare

PMID:
29448996
PMCID:
PMC5909067
[Available on 2019-03-16]
DOI:
10.1016/j.chroma.2018.02.016
[Indexed for MEDLINE]

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