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Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening

[Image: see text] Route determination of sulfur mustard was accomplished through comprehensive nontargeted screening of chemical attribution signatures. Sulfur mustard samples prepared via 11 different synthetic routes were analyzed using gas chromatography/high-resolution mass spectrometry. A large...

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Autores principales: Höjer Holmgren, Karin, Mörén, Lina, Ahlinder, Linnea, Larsson, Andreas, Wiktelius, Daniel, Norlin, Rikard, Åstot, Crister
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041246/
https://www.ncbi.nlm.nih.gov/pubmed/33709707
http://dx.doi.org/10.1021/acs.analchem.0c04555
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author Höjer Holmgren, Karin
Mörén, Lina
Ahlinder, Linnea
Larsson, Andreas
Wiktelius, Daniel
Norlin, Rikard
Åstot, Crister
author_facet Höjer Holmgren, Karin
Mörén, Lina
Ahlinder, Linnea
Larsson, Andreas
Wiktelius, Daniel
Norlin, Rikard
Åstot, Crister
author_sort Höjer Holmgren, Karin
collection PubMed
description [Image: see text] Route determination of sulfur mustard was accomplished through comprehensive nontargeted screening of chemical attribution signatures. Sulfur mustard samples prepared via 11 different synthetic routes were analyzed using gas chromatography/high-resolution mass spectrometry. A large number of compounds were detected, and multivariate data analysis of the mass spectrometric results enabled the discovery of route-specific signature profiles. The performance of two supervised machine learning algorithms for retrospective synthetic route attribution, orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF), were compared using external test sets. Complete classification accuracy was achieved for test set samples (2/2 and 9/9) by using classification models to resolve the one-step routes starting from ethylene and the thiodiglycol chlorination methods used in the two-step routes. Retrospective determination of initial thiodiglycol synthesis methods in sulfur mustard samples, following chlorination, was more difficult. Nevertheless, the large number of markers detected using the nontargeted methodology enabled correct assignment of 5/9 test set samples using OPLS-DA and 8/9 using RF. RF was also used to construct an 11-class model with a total classification accuracy of 10/11. The developed methods were further evaluated by classifying sulfur mustard spiked into soil and textile matrix samples. Due to matrix effects and the low spiking level (0.05% w/w), route determination was more challenging in these cases. Nevertheless, acceptable classification performance was achieved during external test set validation: chlorination methods were correctly classified for 12/18 and 11/15 in spiked soil and textile samples, respectively.
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spelling pubmed-80412462021-04-13 Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening Höjer Holmgren, Karin Mörén, Lina Ahlinder, Linnea Larsson, Andreas Wiktelius, Daniel Norlin, Rikard Åstot, Crister Anal Chem [Image: see text] Route determination of sulfur mustard was accomplished through comprehensive nontargeted screening of chemical attribution signatures. Sulfur mustard samples prepared via 11 different synthetic routes were analyzed using gas chromatography/high-resolution mass spectrometry. A large number of compounds were detected, and multivariate data analysis of the mass spectrometric results enabled the discovery of route-specific signature profiles. The performance of two supervised machine learning algorithms for retrospective synthetic route attribution, orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF), were compared using external test sets. Complete classification accuracy was achieved for test set samples (2/2 and 9/9) by using classification models to resolve the one-step routes starting from ethylene and the thiodiglycol chlorination methods used in the two-step routes. Retrospective determination of initial thiodiglycol synthesis methods in sulfur mustard samples, following chlorination, was more difficult. Nevertheless, the large number of markers detected using the nontargeted methodology enabled correct assignment of 5/9 test set samples using OPLS-DA and 8/9 using RF. RF was also used to construct an 11-class model with a total classification accuracy of 10/11. The developed methods were further evaluated by classifying sulfur mustard spiked into soil and textile matrix samples. Due to matrix effects and the low spiking level (0.05% w/w), route determination was more challenging in these cases. Nevertheless, acceptable classification performance was achieved during external test set validation: chlorination methods were correctly classified for 12/18 and 11/15 in spiked soil and textile samples, respectively. American Chemical Society 2021-03-12 2021-03-23 /pmc/articles/PMC8041246/ /pubmed/33709707 http://dx.doi.org/10.1021/acs.analchem.0c04555 Text en © 2021 The Authors. Published by American Chemical Society Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Höjer Holmgren, Karin
Mörén, Lina
Ahlinder, Linnea
Larsson, Andreas
Wiktelius, Daniel
Norlin, Rikard
Åstot, Crister
Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening
title Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening
title_full Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening
title_fullStr Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening
title_full_unstemmed Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening
title_short Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening
title_sort route determination of sulfur mustard using nontargeted chemical attribution signature screening
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041246/
https://www.ncbi.nlm.nih.gov/pubmed/33709707
http://dx.doi.org/10.1021/acs.analchem.0c04555
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