A multi-modal spectroscopic approach to forensic discrimination of liquid cosmetic complexion products
Year: 2025
Type: article
Abstract: Foundations and other complexion products such as concealers or beauty balm creams account for a significant proportion of the global cosmetic market. In addition to being widely used across a range of demographics, these products are inherently transferable through contact and have a variety of distinctive formulations, making them potentially valuable as associative evidence. Despite this, the analysis of complexion products for forensic purposes is a relatively under-researched area, with studies to date largely focussed exclusively on foundation products often analysed using a single technique. In this study, 35 liquid complexion products representing 19 international retailers available within Australia were analysed using ATR-FTIR spectroscopy. Initially indistinguishable samples were then analysed using Raman spectroscopy and X-ray diffraction, which have not previously been applied in the open literature for forensic cosmetic analysis, to enhance discrimination. Chemometric pattern recognition based on infrared data resulted in a discrimination accuracy of 98 % between individual products and classification accuracy of 72 % using a blind validation set. Raman spectroscopy and X-ray diffraction each offered enhanced discrimination of similar products primarily based on mineral content, although similar shades of the same product remain challenging. Analysis of eight duplicate samples purchased from the UK retail market revealed no significant compositional differences between batches from global retailers, which has yet to be explicitly investigated in existing studies. This market survey highlights the chemical variability of complexion products that may be exploited for evidential purposes using a multi-modal analytical workflow.
Source: Forensic Chemistry
Authors: Jesse McEwan, Katy A. Bruce, Kari Pitts, Georgina Sauzier
Institutions: Curtin University, University of Kent, ChemCentre
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,530
Award: G5589820410
OpenAlex ID: W4411081117
