The efficacy of human scent volatiles as a form of forensic evidence has been well established for missing persons and tracking/trailing of a suspect. However, most crimes are committed with use of a perpetrator’s hands and as a result, the volatiles from hands have become a focal point in criminal investigations. Skin microbiota and volatile organic compounds (VOCs) are closely related due to specific bacteria breaking down non-volatile organic compounds to volatile organic compounds that are characteristically present in human scent. Microbiota and VOCs are unique to each person, and this ‘uniqueness’ can be exploited and applied to forensic identification. By targeting specific microbiota and VOCs that can be deposited on everyday objects through touch interactions, an association can be made between a suspect and the scene of a crime. However, analyses of microbiota and human volatiles from touch interactions have proven to be difficult due to the low levels of genomic DNA (gDNA) that can be collected and the limited benchtop evaluation of human volatile profiles. The study addressed this, with the main objective being to analyze samples collected from subject’s palms to determine the relationship between the bacterial microbiota profile and the VOC profile as it relates to the classification and discrimination of individuals.
VOCs were evaluated with an analytical technique and linear regression models for prediction of gender. Headspace Solid Phase Microextraction Gas Chromatography Mass Spectrometry (HS-SPME-GCMS) was used to detect the VOCs of each subject. Supervised linear regression models (PLS-DA, OPLS-DA, and LDA) were executed with detected VOCs as a tool for the prediction and discrimination of subject gender. This evaluation indicated potential biomarkers that could distinguish male and female subjects and demonstrated a 96.67% correct prediction rate for female and male subjects.
Skin microbiota was analyzed through amplified DNA targeting the V1