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Preliminary Classification Scheme of Silicone Based Lubricants Using DART-TOFMS

NCJ Number
253130
Date Published
May 2018
Length
12 pages
Author(s)
Brooke Baumgarten; Mark Maric; Lauren Harvey; Candice M. Bridge
Agencies
NIJ-Sponsored
Publication Type
Research (Applied/Empirical), Report (Study/Research), Report (Grant Sponsored), Program/Project Description
Grant Number(s)
2016-NE-BX-0001
Annotation
This study analyzed 37 silicone-based personal and condom lubricants using a direct analysis in real time-time of flight mass spectrometer.
Abstract
With the increased usage of condoms in sexual assault cases, the potential of collecting DNA evidence in each case becomes reduced. In the absence of biological evidence, the presence of sexual lubricants after a sexual assault can provide an additional link between a suspect, the crime scene, and/or victim. Data obtained from the comparison of known and unknown sexual lubricants may be the only actionable information available to investigators. The analysis of lubricants is a relatively new technique in sexual assault investigation. Current techniques assign lubricants a type based mostly on the major component of the sample (i.e. polydimethylsiloxane, glycerol, nonoxynol-9, etc.). In the current study, the resulting positive and negative ionization spectra detected nearly all major and minor components that are indicative of a sub-class within silicone-based lubricants. Multivariate statistical techniques were used to create a classification scheme for silicone-based lubricants. Eleven classes of silicone-based lubricants were established based on the relative intensities of the major and minor components of the model dataset. The accuracy of the classification scheme was tested by predicting the class of known test samples and true blind samples via linear discriminant analysis. The results indicated that the model developed was accurate at classifying unknown samples. The classification scheme presented herein provides a foundation to the development of a lubricant database. (publisher abstract modified)
Date Created: July 20, 2021