Swedish Defence Research Agency, FOI, CBRN Defence and Security, Cementvägen 20, 90182 Umeå, Sweden.
Forensic Science Center, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States.
Talanta. 2018 Aug 15;186:615-621. doi: 10.1016/j.talanta.2018.02.100. Epub 2018 Mar 1.
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.
建立了一个多变量模型，以确定用于生产硫芥（HD）的合成方法。有 11 种合成方法用于生产 66 个样本，以构建模型。在参与的两个实验室中，有 3 位化学家参与了生产，目的是在引入变异性的同时，减少多元分析中实验室或化学家特定杂质的影响。对 103 种化合物的峰面积进行气相色谱/质谱数据的偏最小二乘-判别分析，以提取化学归因特征谱，并构建用于分类样本的多变量模型。对于一步和两步路线，模型质量允许根据产生硫芥的反应条件对外部测试集（16/16 个样本）进行分类。考虑到在研究中产生的中间硫代二甘醇的高纯度和均匀质量，根据第一步方法对样品进行分类要困难得多。还研究了模型在分类老化样品中的性能。
