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 eval...

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Bibliographic Details
Published in:PloS one 2023-07, Vol.18 (7), p.e0286452-e0286452
Main Authors: Frazier, Chantrell J G, Gokool, Vidia A, Holness, Howard K, Mills, DeEtta K, Furton, Kenneth G
Format: Article
Language:English
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
Odor compounds
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
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0286452