Application of chemical attribution in matching OPNAs-exposed biological samples with exposure sources- based on the...

Chemical attribution is a vital tool to attribute chemicals or related materials to their origins in chemical forensics via various chemometric methods. Current progress related to organophosphorus nerve agents (OPNAs) has mainly focused on the attribution of chemical sources and synthetic pathways. It has not yet been applied in matching exposed biological samples to their sources. This work used chemical attribution to explore organic impurity profiles in biological samples exposed to various OPNAs. Chemical attribution was first used to identify the exposure source of biological samples based on the full-scan data via comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometer (GC × GC-TOFMS). Taking peak area as the only variable, it can quickly match exposed samples to their sources by applying unsupervised or supervised models, screen difference compounds via one-way ANOVA or t-tests, and then identify valuable impurities that can distinguish different types of exposed samples. To further obtain the impurity profile only applicable to a certain weapon' samples, the irrelevant components were removed via conventional methods. The findings showed there were 53 impurities that can promote distinguishing six groups of OPNA exposed samples, as well as 42 components that can be used as valuable impurities to distinguish class G and class V samples. These were all unique impurities that appear in a certain weapon' samples. The outcomes can be a reference for tracing the source for OPNA-exposed samples, which was beneficial to the further development in source matching of forensic samples. Moreover, the chemical attribution for impurity profiles in biological samples after weapons exposure may inspire research into the characteristics of impurity profile in biological samples as well as practical applications of chemical attribution for OPNA-exposed samples, that may expand potential biomarkers and break the limits of existing markers in the future.