The advent of DNA technologies for field-based application promises to provide rapid intelligence to aid investigations. Their validation and adoption by enforcement groups have demonstrated utility in sample screening and prioritisation, but field application in some areas of forensic science, such as human remains identification, is little evidenced. Assessing the ability of such approaches to provide meaningful data is critical as decomposition is likely to complicate analysis and limit the effective use of such field-based DNA interventions. This research assessed the ability to collect viable DNA data in the field using the ParaDNA Field Instrument and Intelligence Test chemistry. Different sample collection methods were assessed; direct from skin surface; direct from exposed tissue; indirect from muscle swab transferred to FTA card; and from larvae on the donors. Samples were collected and processed on-site at the Anthropology Research Facility, University of Tennessee. The data show that the muscle tissue provided the most effective sample template and, using this approach, it was possible to generate STR profiles from human remains in under two hours from the time of sample collection. STR profile data were collected up to four days from donor placement (114 Accumulated Degree Days). After this time there was a rapid decrease in the quality of the profiles collected due to the onset of decomposition. The data also show that effective sample recovery was not possible from the surface of the skin, exposed tissue or from carrion larvae. Inhibition studies in the laboratory suggest that by-products of the decomposition process are the primary mode of failure. Together these data suggests a possible application for screening and prioritisation in criminal casework but highlights issues that may affect the success of the approach.

Historically, forensic STR panels have been unsuccessful for population assignment due to the limited ancestry information that can be derived from the non-coding STR loci and the low number of loci included in the panel. However, given the recent adoption of expanded (16+ loci) and 'mega-plex' (23+ loci) STR panels, the ability to identify source population groups may be improved. This study assessed the impact of increasing locus number on population assignment under different analysis conditions using a published US population dataset comprised of individuals from the African American, Caucasian, Hispanic and Asian populations. The Bayesian clustering programme STRUCTURE was used to assess first, whether increasing the number of loci and the inclusion of known sample population data enabled greater resolution between the four populations in the dataset, and second, the utility for population assignment using criteria based on inferred ancestry scores. Results suggest that increasing the number of loci and including population of origin data allowed the identification of more distinct populations, with three primary populations being observed; African American, Asian, and Caucasian/Hispanic. The close grouping of the Caucasian and Hispanic populations is supported by their recently common ancestry from Western Europe. The ability of the programme to support population assignment to each of the four existing populations was assessed through the application of population and panel specific assignment thresholds based on the inferred ancestry scores obtained from the analysis programme. Predictive accuracy based on a training dataset of 984 individuals suggest that assignment accuracy is > 96% across the four populations and can reach 100% under some test conditions. The accuracy was > 90% when blind testing was performed on 40 'unknown' individuals. As such, the approach described is considered within the acceptable range for a presumptive test and can be performed using data already collected as part of routine forensic investigations.