Investigating sex determination through MALDI MS analysis of peptides and proteins in natural fingermarks through comprehensive statistical modelling

Cameron Heaton, Charles S. Bury, Ekta Patel, Robert Bradshaw, Florian Wulfert, Ron M. Heeren, Laura Cole, Leeanna Marchant, Neil Denison, Richard McColm, Simona Francese*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

In the last decade, Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI MS) has proven to be a valuable analytical tool in forensic research as it can detect and map molecular information of forensic relevance in trace evidence such as fingermarks and hair. The first published proof of concept demonstrating that it was possible to differentiate males and females from the peptide and protein content of their fingermarks was published in 2012. In that work, MALDI MS was used in Profiling mode (MALDI MSP) to quickly obtain spectral profiles of ungroomed marks. These were submitted to Partial Least Square Discriminant Analysis (PLS-DA) yielding sex discrimination with an accuracy between 67.5% and 74.4%, if harsh classification criteria were applied. Since then, this research has progressed to investigate the opportunity to increase the accuracy of prediction in natural marks (obtained with no preparation of the fingertip prior to deposition) either unenhanced or enhanced prior to matrix application and MALDI analysis. Extensive statistical modelling has been employed to determine the model with the highest sex predictive accuracy. Results show that in natural marks the presence of polymers (as external contaminants) in fingermarks affects the peptide/protein signals to various degrees and, only, by using one type of scoring system, a method has been identified to provide up to 86.1% predictive power in discriminating female from male marks.

Original languageEnglish
Article number100271
Number of pages12
JournalForensic Chemistry
Volume20
DOIs
Publication statusPublished - Aug 2020

Keywords

  • Fingermark
  • Natural
  • MALDI MS
  • Sex
  • Machine learning
  • LASER-DESORPTION IONIZATION
  • MASS-SPECTROMETRY
  • LATENT FINGERMARKS
  • MOLECULES

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