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Title: Synthesis route attribution of sulfur mustard by multivariate data analysis of chemical signatures

Abstract

A multivariate model was developed here to attribute samples to a synthetic method used in the production of sulfur mustard (HD). Eleven synthetic methods were used to produce 66 samples for model construction. Three chemists working in both participating laboratories took part in the production, with the aim to introduce variability while reducing the influence of laboratory or chemist specific impurities in multivariate analysis. A gas chromatographic/mass spectrometric data set of peak areas for 103 compounds was subjected to orthogonal partial least squares - discriminant analysis to extract chemical attribution signature profiles and to construct multivariate models for classification of samples. For one- and two-step routes, model quality allowed the classification of an external test set (16/16 samples) according to synthesis conditions in the reaction yielding sulfur mustard. Classification of samples according to first-step methodology was considerably more difficult, given the high purity and uniform quality of the intermediate thiodiglycol produced in the study. Model performance in classification of aged samples was also investigated.

Authors:
 [1];  [2];  [1];  [1];  [1];  [2];  [2];  [2];  [2];  [2];  [1];  [1]
  1. Swedish Defence Research Agency, Umeå (Sweden)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Forensic Science Center
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Swedish Defence Research Agency, Umeå (Sweden)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); Dept. of Homeland Security (DHS) (United States); Swedish Civil Contingencies Agency
OSTI Identifier:
1512629
Report Number(s):
LLNL-JRNL-738170
Journal ID: ISSN 0039-9140; 887649
Grant/Contract Number:  
AC52-07NA27344; HSHQPM-16-X-00102; 2014–5170
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Talanta
Additional Journal Information:
Journal Volume: 186; Journal ID: ISSN 0039-9140
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
chemical forensics; multivariate data analysis; sulfur mustard; synthesis method attribution

Citation Formats

Höjer Holmgren, Karin, Hok, Saphon, Magnusson, Roger, Larsson, Andreas, Åstot, Crister, Koester, Carolyn, Mew, Daniel, Vu, Alexander K., Alcaraz, Armando, Williams, Audrey M., Norlin, Rikard, and Wiktelius, Daniel. Synthesis route attribution of sulfur mustard by multivariate data analysis of chemical signatures. United States: N. p., 2018. Web. doi:10.1016/j.talanta.2018.02.100.
Höjer Holmgren, Karin, Hok, Saphon, Magnusson, Roger, Larsson, Andreas, Åstot, Crister, Koester, Carolyn, Mew, Daniel, Vu, Alexander K., Alcaraz, Armando, Williams, Audrey M., Norlin, Rikard, & Wiktelius, Daniel. Synthesis route attribution of sulfur mustard by multivariate data analysis of chemical signatures. United States. doi:10.1016/j.talanta.2018.02.100.
Höjer Holmgren, Karin, Hok, Saphon, Magnusson, Roger, Larsson, Andreas, Åstot, Crister, Koester, Carolyn, Mew, Daniel, Vu, Alexander K., Alcaraz, Armando, Williams, Audrey M., Norlin, Rikard, and Wiktelius, Daniel. Thu . "Synthesis route attribution of sulfur mustard by multivariate data analysis of chemical signatures". United States. doi:10.1016/j.talanta.2018.02.100. https://www.osti.gov/servlets/purl/1512629.
@article{osti_1512629,
title = {Synthesis route attribution of sulfur mustard by multivariate data analysis of chemical signatures},
author = {Höjer Holmgren, Karin and Hok, Saphon and Magnusson, Roger and Larsson, Andreas and Åstot, Crister and Koester, Carolyn and Mew, Daniel and Vu, Alexander K. and Alcaraz, Armando and Williams, Audrey M. and Norlin, Rikard and Wiktelius, Daniel},
abstractNote = {A multivariate model was developed here to attribute samples to a synthetic method used in the production of sulfur mustard (HD). Eleven synthetic methods were used to produce 66 samples for model construction. Three chemists working in both participating laboratories took part in the production, with the aim to introduce variability while reducing the influence of laboratory or chemist specific impurities in multivariate analysis. A gas chromatographic/mass spectrometric data set of peak areas for 103 compounds was subjected to orthogonal partial least squares - discriminant analysis to extract chemical attribution signature profiles and to construct multivariate models for classification of samples. For one- and two-step routes, model quality allowed the classification of an external test set (16/16 samples) according to synthesis conditions in the reaction yielding sulfur mustard. Classification of samples according to first-step methodology was considerably more difficult, given the high purity and uniform quality of the intermediate thiodiglycol produced in the study. Model performance in classification of aged samples was also investigated.},
doi = {10.1016/j.talanta.2018.02.100},
journal = {Talanta},
issn = {0039-9140},
number = ,
volume = 186,
place = {United States},
year = {2018},
month = {3}
}

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