Characterizing and classifying water-based lubricants using direct analysis in real time®⿿time of flight mass spectrometry. (September 2016)

• Record Type: Journal Article
• Authors: Maric, Mark; Bridge, Candice
• Is Part Of: Forensic science international. Volume 266(2016)
• Page Start: 73
• Page End: 79
• Publication Date: 2016-09
• Subjects: Lubricants -- Classification -- DART⿿MS -- High resolution mass spectrometry; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2016.04.036
• Languages: English
• ISSNs: 0379-0738

Abstract

Graphical abstract: Highlights: ⿢ The ability to classify water-based lubricants in the sub-groups was established. ⿢ Lubricants were analyzed directly using DART⿿TOFMS without sample preparation. ⿢ Sub-groups were identified by chemical composition using PCA and LDA methods. ⿢ Achieved 100% classification accuracy using the LOOCV method. Abstract: Lubricant analysis is a relatively recent addition to the examination protocol in sexual assault cases by the forensic science community. Currently, lubricants cannot be unequivocally identified, although their presence can be determined based on the detection of a few chemical components, i.e. polydimethylsiloxane, polyethylene glycol, glycerol or nonoxynol-9. Confirmation of their presence typically requires that an authentic reference sample be submitted and compared to the unknown sample to determine if they potentially came from the same source. In this study, 33 individual personal water-based lubricants were characterized by direct analysis in real time⿿time of flight mass spectroscopy (DART⿿TOFMS). The resultant mass spectral data were evaluated using well-established multivariate statistical techniques, such as principal component and linear discriminant analysis. Statistical analysis revealed six different groupings within the data that could be correlated to sub-categories of water-based lubricants that contain additives in the form of anesthetics, sensation enhancers and flavorings. This variability in the personal lubricant sources can be utilized to aid in identifying the specific type of lubricant when only a questioned sample is available.