Temperature-Dependent DART-MS Analysis of Sexual Lubricants to Increase Accurate Associations

  • Candice BridgeEmail author
  • Mark Marić
Focus: Emerging Investigators: Research Article


The analysis of lubricant evidence is a recent development in sexual assault investigations and in the absence of any biological evidence may assist in linking an assailant to the victim or crime scene. An ambient ionization technique, high-resolution direct analysis in real-time mass spectrometry (HR-DART-MS), was employed to characterize a sample set of 33 water-based lubricants. As lubricants are complex multicomponent mixtures, this study investigated if different thermal desorption temperatures could elucidate different additives and provide additional information. A low-temperature, high-temperature, and thermal desorption/pyrolysis DART-MS protocol was used to characterize the water-based lubricant sample set. The strength of the methodologies was evaluated using positive and negative likelihood ratios that were calculated from inter- and intra-pairwise comparisons using Pearson correlation coefficients. The low-temperature DART-MS protocol afforded valuable information pertaining to volatile additives (e.g., flavors and fragrances) and provided positive likelihood ratios that would provide strong support for true positive and negatives than the high-temperature protocol when associating between individual samples and samples to their respective sub-groupings. The thermal desorption/pyrolysis DART analytical protocol provided enhanced differentiation between samples due to the precise temperature control using a thermal gradient. Moreover, the total ion spectra obtained from the thermal desorption/pyrolysis protocol, not only had high positive and negative likelihood ratios, this method also provided the most discrimination as determined by empirical cross entropy plots.

Graphical Abstract


Lubricant analysis Likelihood ratios Pearson correlation HR-DART-MS 



The authors would like to acknowledge Chikako Takei at BioChromato for providing the ionRocket used in this research.

This work was supported by the State of Florida (USA) and by the National Institute of Justice (USA) [Grant No. 2016-DN-BX-0001].

Supplementary material

13361_2019_2158_MOESM1_ESM.docx (1.2 mb)
ESM 1 (DOCX 1.17 mb)


  1. 1.
    Campbell, G.P., Gordon, A.L.: Analysis of condom lubricants for forensic casework. J. Forensic Sci. 52, 630–642 (2007)CrossRefGoogle Scholar
  2. 2.
    Musah, R.A., Vuong, A.L., Henck, C., Shepard, J.R.E.: Detection of the spermicide nonoxynol-9 via GC-MS. J. Am. Soc. Mass Spectrom. 23, 996–999 (2012)CrossRefGoogle Scholar
  3. 3.
    Smith, W.: PGC/MS for condom lubricant analysis. Anal. Chem. 76, 157A (2004)Google Scholar
  4. 4.
    Baumgarten, B., Marić, M., Harvey, L., Bridge, C.M.: Preliminary classification scheme of silicone based lubricants using DART-TOFMS. Forensic Chem. 8, 28–39 (2018)CrossRefGoogle Scholar
  5. 5.
    Marić, M., Bridge, C.: Characterizing and classifying water-based lubricants using direct analysis in real time®-time of flight mass spectrometry. Forensic Sci. Int. 266, 73–79 (2016)CrossRefGoogle Scholar
  6. 6.
    Moustafa, Y., Bridge, C.M.: Distinguishing sexual lubricants from personal hygiene products for sexual assault cases. Forensic Chem. 5, 58–71 (2017)CrossRefGoogle Scholar
  7. 7.
    Musah, R.A., Cody, R.B., Dane, A.J., Vuong, A.L., Shepard, J.R.: Direct analysis in real time mass spectrometry for analysis of sexual assault evidence. Rapid Commun. Mass Spectrom. 26, 1039–1046 (2012)CrossRefGoogle Scholar
  8. 8.
    Proni, G., Cohen, P., Huggins, L.A., Nesnas, N.: Comparative analysis of condom lubricants on pre & post-coital vaginal swabs using AccuTOF-DART. Forensic Sci. Int. 280, 87–94 (2017)CrossRefGoogle Scholar
  9. 9.
    Durex Stops Making Condoms With Nonoxynol-9 Due to Possible Increased Risk of HIV Transmission. Kaiser Health News (2004)Google Scholar
  10. 10.
    Nonoxynol-9 spermicidal lubricant. Accessed Dec 27, (2018)
  11. 11.
    Damme, L.V., Ramjee, G., Alary, M., Vuylsteke, B., Chandeying, V., Rees, H., Sirivongrangson, P., Mukenge-Tshibaka, L., Ettiègne-Traoré, V., Uaheowitchai, C., Abdool Karim, S.S., Mâsse, B., Perriëns, J., Laga, M.: Effectiveness of COL-1492, a nonoxynol-9 vaginal gell, on HIV-1 transmission in femal sex workers: a randomised controlled trial. Lancet. 360, 971–977 (2002)CrossRefGoogle Scholar
  12. 12.
    Mirabelli, M.F., Chramow, A., Cabral, E.C., Ifa, D.R.: Analysis of sexual assault evidence by desorption electrospray ionization mass spectrometry. J. Mass Spectrom. 48, 774–778 (2013)CrossRefGoogle Scholar
  13. 13.
    Marić, M., Harvey, L., Tomcsak, M., Solano, A., Bridge, C.: Chemical discrimination of lubricant marketing types using direct analysis in real time time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom. 31, 1014–1022 (2017)CrossRefGoogle Scholar
  14. 14.
    Pubus, D., Sell, C.: The Chemistry of Fragrances, vol. 276. Royal Society of Chemistry, London (1999)Google Scholar
  15. 15.
    Youden, W.J.: Index for rating diagnostic tests. Cancer. 3, 32–35 (1950)CrossRefGoogle Scholar
  16. 16.
    Kumar, R., Indrayan, A.: Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr. 48, 277–287 (2011)CrossRefGoogle Scholar
  17. 17.
    Aitken, C.G.G., Lucy, D.: Evaluation of trace evidence in the form of multivariate data. Appl. Stat. 53, 109–122 (2004)Google Scholar
  18. 18.
    Koons, R.D., Buscaglia, J.A.: Interpretation of glass composition measurements: the effects of match criteria on discrimination capability. J Forensic Sci. 47, 505 (2002)CrossRefGoogle Scholar
  19. 19.
    Corzo, R., Hoffman, T., Weis, P., Franco-Pedroso, J., Ramos, D., Almirall, J.: The use of LA-ICP-MS databases to calculate likelihood ratios for the forensic analysis of glass evidence. Talanta (2018)Google Scholar
  20. 20.
    Egli, N.M., Champod, C., Margot, P.: Evidence evaluation in fingerprint comparison and automated fingerprint identification systems--modelling within finger variability. Forensic Sci. Int. 167, 189–195 (2007)CrossRefGoogle Scholar
  21. 21.
    Neumann, C., Champod, C., Puch-Solis, R., Egli, N., Anthonioz, A., Bromage-Griffiths, A.: Computation of likelihood ratios in fingerprint identification for configurations of any number of minutiae. J. Forensic Sci. 52, 54–64 (2007)CrossRefGoogle Scholar
  22. 22.
    Dennis, D.M., Williams, M.R., Sigman, M.E.: Assessing the evidentiary value of smokeless powder comparisons. Forensic Sci. Int. 259, 179–187 (2016)CrossRefGoogle Scholar
  23. 23.
    Pierrini, G., Doyle, S., Champod, C., Taroni, F., Wakelin, D., Lock, C.: Evaluation of preliminary isotopic analysis (13C and 15N) of explosives: a likelihood ratio approach to assess the links between semtex samples. Forensic Sci. Int. 167, 43–48 (2007)CrossRefGoogle Scholar
  24. 24.
    Maric, M., Marano, J., Cody, R.B., Bridge, C.: DART-MS: a new analytical technique for forensic paint analysis. Anal. Chem. (2018)Google Scholar
  25. 25.
    Pluskal, T., Castillo, S., Villar-Briones, A., Oresic, M.: MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 11, 395–405 (2010)CrossRefGoogle Scholar
  26. 26.
    van der Helm, H.J., Hische, E.A.H.: Application of Baye’s theorem to results of quantitative clinical chemical determinations. Clin. Chem. 25, 985–988 (1979)Google Scholar
  27. 27.
    Zweig, M.H., Campbell, G.: Receiver-operating characteristic plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39, 561–577 (1993)Google Scholar
  28. 28.
    Perkins, N.J., Schisterman, E.F.: The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am. J. Epidemiol. 163, 670–675 (2006)CrossRefGoogle Scholar
  29. 29.
    Martire, K.A., Kemp, R.I., Sayle, M., Newell, B.R.: On the interpretation of likelihood ratios in forensic science evidence: presentation formats and the weak evidence effect. Forensic Sci. Int. 240, 61–68 (2014)CrossRefGoogle Scholar
  30. 30.
    Gross, J.H.: Direct analysis in real time--a critical review on DART-MS. Anal. Bioanal. Chem. 406, 63–80 (2014)CrossRefGoogle Scholar
  31. 31.
    Ramos, D., Franco-Pedroso, J., Lozano-Diez, A., Gonzalez-Rodriguez, J.: Deconstructing cross-entropy for probabilistic binary classifiers. Entropy. 20, 208–228 (2018)CrossRefGoogle Scholar
  32. 32.
    Ramos, D., Gonzalez-Rodriguez, J.: Reliable support: measuring calibration of likelihood ratios. Forensic Sci. Int. 230, 156–169 (2013)CrossRefGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2019

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of Central FloridaOrlandoUSA
  2. 2.National Center for Forensic ScienceUniversity of Central FloridaOrlandoUSA

Personalised recommendations