Analysis of Volatile Organic Compounds Produced During the Decomposition of Human Analogues

The study of volatile organic compounds (VOCs) associated with decomposing remains is a field of growing interest in forensic science. Over the past decade, there has been an increasing demand to explicate key signatures of decomposition odour in an attempt to improve upon current training kits, aid in the development of an implement which could be used to detect clandestine graves, and to determine the post-mortem interval of a corpse. At present, current research seems to validate the view that the decomposition odour profile is ambiguous because of disparities in the environment in which decomposition occurs, methods of chemical analysis and sample collection techniques. The purpose of this research was to investigate the odour profile surrounding buried and exposed human analogues in controlled environments, in order to understand how VOCs partition between different mediums. To this effect, a bespoke decomposition chamber was developed and characterised in the Real-time Air Fingerprinting Technology laboratory at the University of Leicester, and decomposition VOCs released into the headspace within chamber were monitored online via the use of Chemical Ion Reaction Mass spectrometry (CIR-MS) and Solid Phase Micro Extraction coupled with Gas Chromatography-Mass Spectrometry (SPME-GC-MS). Overall, there was an abundance of polysulphides, specifically dimethyl disulphide detected by both CIR-MS and SPME-GC-MS in the buried and exposed experiments. Seven VOCs argued to be key markers of decomposition in the exposed trials comprised of 2-butanone, dimethyl disulphide, methanethiol, trimethylamine, 1-propanol, 1- butanol, and acetone. In effect, findings from these trials demonstrated that there are distinctive subsets of VOCs released from decomposing carcasses, and soil acts as a sink for majority of these compounds. The use of CIR-MS in the study of decomposition odour was successful, as was the application a novel approach in extracting patterns within the CIR-MS data.