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

Abstract

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.

Chemical Characterization of Human Body Odor Headspace Components

“…Female chamber odor samples depicted a tighter clustering, except for one female. These observations further corroborate the previous literature delineating differences in volatile odor signatures as a function of gender [23,24,30]. Corroborating the results from Pojmanova et al, it is important to note that quantitative differences in each individual compound are not as important as the comprehensive group of VOCs and their representative amounts [39], especially when discussing sex differentiation.…”

Predicting Chronological Age via the Skin Volatile Profile

“…Studies have shown that the human skin volatilome is influenced by gender. , Our group, along with others have shown a differences in recovered abundances of medium and long-chain FAs between males and females. Increased abundances of aldehydes and ketones in male participants have also been reported compared to females .…”

“…Machine learning (ML) and multivariate statistical approaches are now routinely employed for the analysis of human volatilomics to include skin as well as breath emissions. − Both unsupervised, such as principal component analysis (PCA) , and hierarchical clustering (HCA) which have discriminatory power, and supervised learning, such as multiple linear regression (MLR), partial least-squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA), others, ,, which allow classification have been frequently used. These approaches allow for analysis through reducing dimensionality and aid in discrimination, clustering, classification and correlation of VOCs that may be linked to disease ,, and other physiological factors. , …”