A forensic international market survey of condom lubricants and personal hygiene products using ATR-FTIR coupled to chemometrics. Issue 3 (May 2021)

• Record Type: Journal Article
• Authors: Burnier, Céline; Coulson, Sally; Massonnet, Geneviève; Pitts, Kari; Sauzier, Georgina; Lewis, Simon W.
• Is Part Of: Science & justice. Volume 61:Issue 3(2021)
• Page Start: 235
• Page End: 248
• Publication Date: 2021-05
• Subjects: Silicone -- Lubricants -- Glycerine -- International market -- Chemometrics -- ATR-FTIR; Forensic sciences -- Periodicals; Criminal investigation -- Periodicals; Forensic Medicine -- Periodicals; Jurisprudence -- Periodicals; Criminalistique -- Périodiques; Enquêtes criminelles -- Périodiques; Criminal investigation; Forensic sciences; Electronic journals; Periodicals; 363.2505
• DOI: 10.1016/j.scijus.2021.01.005
• Languages: English
• ISSNs: 1355-0306

Abstract

Highlights: Condom lubricants and personal hygiene products may be relevant in forensic cases. Population of condoms and personal hygiene products from 3 international markets analysed. 90% Accuracy was observed for discrimination of spectral profiles. Two-step approach was developed to reach 100% discrimination accuracy. Model robustness was confirmed by blind validation. Abstract: Condom residues may be encountered in forensic investigations as traces in sexual assault or rape cases. Casework studies have shown the value of distinguishing condom residues from other types of personal products used by women. However, up to now, there has been no investigation of their chemical variability within an international context. This work employed attenuated total reflectance Fourier transform infrared spectroscopy with chemometrics to provide objective characterisation of condom lubricants and personal hygiene products from the international market. 166 samples were obtained covering five major classes of products likely to be used by women. Principal component analysis distinguished most major classes based on their spectral profiles, with subsequent support vector machine models yielding discrimination accuracies over 90%. A two-step approach was subsequently developed and enabled both classification and a discrimination accuracy of 100%. This could provide greater confidence in chemical discrimination of residues from these products when conducting investigations and help assess the origin of the chemical profile obtained. Further testing using three validation sets produced an identification accuracy of 100% for generic classes, which may allow investigative leads to be more readily obtained from recovered evidence.