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Updated: Jul 20, 2025

Phosphopeptide Analysis of Rodent Epididymal Spermatozoa
Published on: December 30, 2014
LMJSSP for analysis of prophylactic lubricants, spermicides and residues
Jessie F Deng1, Haidy Metwally1, Rachel L Theriault2
1Department of Chemistry, Queen's University, Kingston Ontario, K7L 3N6, Canada.
Abstract:
DNA evidence in sexual assault cases have proven increasingly difficult to obtain and analyse due to increased condom use. With more interest in alternatives to DNA evidence, prophylactic lubricants, spermicides and residues may be interesting prospects. Current interest in the analysis of prophylactic residues focuses on the evaluation and identification of lubricants and constituents, primarily through gas chromatography or Fourier transform infrared spectroscopy. Though cost-effective methods, extensive sample preparation and destructive modes of analysis remain an area for improvement. As a result, the focus has since shifted to ambient ionization methods that offer adequate sensitivity and reduced sample preparation. The Liquid Microjunction Surface Sampling Probe (LMJSSP) is a versatile ambient ionization source that employs a probe that supports a continuously flushing droplet that extracts analytes when placed in contact with a surface. The analytes are aspirated into the mass spectrometer with a Venturi pressure. In this work we use the LMJSSP to analyse the trace transfer of condom lubricant to different types of fabric (cotton, cotton-spandex, and denim). Furthermore, we examine the sensitivity and storage conditions for the direct analysis method on different swab types (cotton, silicone, and foam). Additionally, Principal Component Analysis (PCA) and Maximally Collapsing Metric Learning (MCML) are utilized for visualization of differentiability of commercially available condom brands including Durex™ and Trojan™, and product subtypes. The results present an interesting multi-disciplinary approach of using a direct liquid extraction ambient ionization technique and machine learning to improve the overall workflow for the analysis of lubricants, swabs and fabrics. Machine learning algorithms were able to differentiate between inherent differences of Durex™ and Trojan™ condoms.
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