| Title: | Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS |
| Author(s): | Frazier CJG; Gokool VA; Holness HK; Mills DK; Furton KG; |
| Address: | "Department of Chemistry and Biochemistry, Global Forensic and Justice Center, Florida International University, Miami, FL, United States of America. Department of Chemistry and Food Science, Currently at Framingham State University, Framingham, Massachusetts, United States of America. Currently at Lawrence Livermore National Laboratory, Physical and Life Sciences Directorate, Nuclear and Chemical Sciences Division, Livermore, CA, United States of America. Department of Biological Sciences, Global Forensic and Justice Center, Global Forensic and Justice Center, Florida International University, Miami, FL, United States of America" |
| DOI: | 10.1371/journal.pone.0286452 |
| ISSN/ISBN: | 1932-6203 (Electronic) 1932-6203 (Linking) |
| 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" |
| Keywords: | Humans Male Female Animals Dogs *Volatile Organic Compounds/analysis Gas Chromatography-Mass Spectrometry/methods Solid Phase Microextraction/methods Odorants/analysis Discriminant Analysis; |
| Notes: | "MedlineFrazier, Chantrell J G Gokool, Vidia A Holness, Howard K Mills, DeEtta K Furton, Kenneth G eng Research Support, U.S. Gov't, Non-P.H.S. 2023/07/05 PLoS One. 2023 Jul 5; 18(7):e0286452. doi: 10.1371/journal.pone.0286452. eCollection 2023" |
