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

• Published in: PloS one Vol. 18; no. 7; p. e0286452
• Main Authors: Frazier, Chantrell J. G., Gokool, Vidia A., Holness, Howard K., Mills, DeEtta K., Furton, Kenneth G.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.07.2023; Public Library of Science (PLoS)
• Subjects: Analysis; Animals; Aroma compounds; BASIC BIOLOGICAL SCIENCES; Biology and Life Sciences; chemistry; Clustering; Computer and Information Sciences; computer software; Cotton; Discriminant Analysis; Dogs; Engineering and Technology; Ethnicity; Evaluation; Female; Females; Forensic science; forensics; Gas chromatography; gas chromatography-mass spectrometry; Gas Chromatography-Mass Spectrometry - methods; Gender; Hands; Headspace; Humans; Hypotheses; Investigations; linear discriminant analysis; major histocompatibility complex; Male; Males; Mass spectrometry; Mass spectroscopy; Medicine and Health Sciences; Metabolites; Methods; Microbiota; Modelling; Odorants - analysis; Odors; Olfactory discrimination; Olfactory discrimination learning; Organic compounds; Peptides; Physical Sciences; Physiology; Predictions; Research and Analysis Methods; Sex discrimination; Skin; skin physiology; Solid phase methods; Solid Phase Microextraction - methods; Solid phases; Three dimensional models; Two dimensional analysis; Two dimensional models; VOCs; Volatile compounds; Volatile organic compounds; Volatile Organic Compounds - analysis; Volatiles; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0286452

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.