Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening.

Karin Höjer Holmgren,Daniel Wiktelius,Lina Mörén,Andreas Larsson,Rikard Norlin,Linnea Ahlinder,Crister Åstot

doi:10.1021/acs.analchem.0c04555

Karin Höjer Holmgren, Daniel Wiktelius + Show 5 more

Open Access

https://doi.org/10.1021/acs.analchem.0c04555

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Journal: Analytical Chemistry	Publication Date: Mar 12, 2021
Citations: 9	License type: CC BY 4.0

Affiliation: Swedish Defence Research Agency

Abstract

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.

Highlights

Route determination of sulfur mustard was accomplished through comprehensive nontargeted screening of chemical attribution signatures
Attribution of chemical samples to the production method is based on Chemical attribution signatures (CAS) profiling, i.e., identification and analysis of trace components that are diagnostic for specific production conditions or starting materials used
Because chemical forensic investigations of incidents involving chemical warfare agents may require analysis of samples taken from the environment, we investigated the extraction of HD-associated CAS from spiked matrix samples

Summary

Introduction

Route determination of sulfur mustard was accomplished through comprehensive nontargeted screening of chemical attribution signatures. A similar approach was previously used to classify production routes of the nerve agent Russian VX, based on data for spiked food samples.[28] The hierarchic decision tree method (Figure 2) first uses a classification model, M1, which distinguishes between the TDG routes (R1−R9) and the ethylene routes (R10 and R11). A second classification model, M2, was constructed to classify HD samples attributed to the TDG two-step routes (by M1) to the method of chlorination used in their synthesis, i.e., to attribute samples to one of the groups R(1, 4, 7), R(2, 5, 8), or R(3, 6, 9).

Results

Conclusion