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A multivariate model was developed 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.

Chemical attribution signatures indicative of O-isobutyl S-(2-diethylaminoethyl) methylphosphonothioate (Russian VX) synthetic routes were investigated in spiked food samples. Attribution signatures were identified using a multifaceted approach: Russian VX was synthesized using six synthetic routes and the chemical attribution signatures identified by GC-MS and LC-MS. Three synthetic routes were then down selected and spiked into complex matrices: bottled water, baby food, milk, liquid eggs, and hot dogs. Sampling and extraction methodologies were developed for these materials and used to isolate the attribution signatures and Russian VX from each matrix. Recoveries greater than 60% were achieved for most signatures in all matrices; some signatures provided recoveries greater than 100%, indicating some degradation during sample preparation. A chemometric model was then developed and validated with the concatenated data from GC-MS and LC-MS analyses of the signatures; the classification results of the model were > 75% for all samples. This work is part three of a three-part series in this issue of the United States-Sweden collaborative efforts towards the understanding of the chemical attribution signatures of Russian VX in crude materials and in food matrices.

This work is part two of a three-part series in this issue of a Sweden-United States collaborative effort towards the understanding of the chemical attribution signatures of Russian VX (VR) in synthesized samples and complex food matrices. In this study, we describe the sourcing of VR present in food based on chemical analysis of attribution signatures by liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with multivariate data analysis. Analytical data was acquired from seven different foods spiked with VR batches that were synthesized via six different routes in two separate laboratories. The synthesis products were spiked at a lethal dose into seven food matrices: water, orange juice, apple puree, baby food, pea puree, liquid eggs and hot dog. After acetonitrile sample extraction, the samples were analyzed by LC-MS/MS operated in MRM mode. A multivariate statistical calibration model was built on the chemical attribution profiles from 118 VR spiked food samples. Using the model, an external test-set of the six synthesis routes employed for VR production was correctly identified with no observable major impact of the food matrices to the classification. The overall performance of the statistical models was found to be exceptional (94%) for the test set samples retrospectively classified to their synthesis routes.