Chemical Discrimination Of Lubricant Marketing Types Using Direct Analysis In Real Time Time-Of-Flight Mass Spectrometry

Abstract

Rationale: In comparison to other violent crimes, sexual assaults suffer from very low prosecution and conviction rates especially in the absence of DNA evidence. As a result, the forensic community needs to utilize other forms of trace contact evidence, like lubricant evidence, in order to provide a link between the victim and the assailant. Methods: In this study, 90 personal bottled and condom lubricants from the three main marketing types, silicone-based, water-based and condoms, were characterized by direct analysis in real time time of flight mass spectrometry (DART-TOFMS). The instrumental data was analyzed by multivariate statistics including hierarchal cluster analysis, principal component analysis, and linear discriminant analysis. Results: By interpreting the mass spectral data with multivariate statistics, 12 discrete groupings were identified, indicating inherent chemical diversity not only between but within the three main marketing groups. A number of unique chemical markers, both major and minor, were identified, other than the three main chemical components (i.e. PEG, PDMS and nonoxynol-9) currently used for lubricant classification. The data was validated by a stratified 20% withheld cross-validation which demonstrated that there was minimal overlap between the groupings. Conclusions: Based on the groupings identified and unique features of each group, a highly discriminating statistical model was then developed that aims to provide the foundation for the development of a forensic lubricant database that may eventually be applied to casework. Copyright © 2017 John Wiley & Sons, Ltd.

Publication Date

6-30-2017

Publication Title

Rapid Communications in Mass Spectrometry

Volume

31

Issue

12

Number of Pages

1014-1022

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1002/rcm.7876

Socpus ID

85019833819 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85019833819

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