Summary: A new study reveals that the composition of scent compounds on a person’s hand can accurately determine their sex.
The analysis, using mass spectrometry, successfully predicted an individual’s sex with an impressive accuracy rate of 96.67%. In criminal investigations, this could provide valuable trace evidence where other discriminative evidence like DNA is lacking.
Further validation of these techniques could even reveal other individual characteristics such as age and racial or ethnic group.
The scent compounds on a person’s hand can be analyzed to predict their sex, with a 96.67% accuracy rate.
The study suggests that scent evidence could be a valuable resource in criminal investigations, particularly when other forms of evidence are lacking.
Further development of these techniques could potentially identify other characteristics of an individual, such as age and racial or ethnic group.
The profile of scent compounds from a person’s hand can be used to predict their sex, according to a new study led by Kenneth Furton of Florida International University, publishing July 5 in the open-access journal PLOS ONE.
In criminal investigations, dogs have long been used to reliably identify and track people based on their odor.
But while human scent evidence from the field is well established, researchers have made little progress in analyzing human scent profiles in the lab.
In the new study, researchers used an analysis technique called mass spectrometry to analyze the volatile scent compounds present on the palms of 60 individuals – half male and half female.
After identifying the compounds in each sample, the team performed a statistical analysis to see if they could determine the individual’s sex based on their profile of scents. The analysis successfully predicted a person’s sex with a 96.67% accuracy rate.
Robberies, assaults and rape are all crimes that are often executed with a perpetrator’s hands, and thus have the potential to leave behind valuable trace evidence at a crime scene.
The new study shows that it is possible to predict a person’s sex based on hand scents, and existing human odor research indicates scent compounds can also reveal a person’s age and racial or ethnic group.
With further validation, the chemical and statistical analyses presented in this paper could be used to uncover many details about a potential perpetrator solely through their hand scent profiles.
The authors add: “This approach to analyzing hand odor volatiles can be applied when other discriminatory evidence such as DNA is lacking and allow for differentiation or class characterization such as sex, race and age.”
About this olfaction research news
Author: Hanna Abdallah Source: PLOS Contact: Hanna Abdallah – PLOS Image: The image is credited to Neuroscience News
Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS
The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking.
Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited.
This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender.
The volatile organic compound (VOC) signatures from subjects’ hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects.
The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model.
The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects.
The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.