Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS

• NCJ Number: 307446
• Journal: PLoS ONE Volume: 18 Issue: 7 Dated: July 2023 Pages: e0286452
• Author(s): Chantrell J. G. Frazier; Vidia A. Gokool; Howard K. Holness; DeEtta K. Mills; Kenneth G. Furton
• Date Published: July 2023
• Grant Number(s): DJO-NIJ-21-RO-0002; 2014-DNR-4918
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)
• Award: Interagency Collaboration to Support Forensic Science Research; NIJ Co-funding for the NSF Center for Advanced Research in Forensic Science (CARFS)

Annotation

This study establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.

Abstract

This study, which 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), establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.  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. 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. (Published Abstract Provided)