Error rate and similarity determination of latent fingerprint chemistry via 1D GC and GC × GC–MS

Abstract

In response to the National Academy of Science’s report on Strengthening Forensic Science, many researchers have investigated ways of improving and supplementing pattern-based visual methods for latent fingerprint (LF) analysis. Gas chromatography (GC) has been used to study the initial and aged chemistry of latent fingerprints, along with attempting to statistically asses same source association with donor characteristics. Since LFs are composed of a complex mixture of oil, sweat, and other components, sections within the chromatographic profile that have poor resolution could benefit from better separation of components, thus yielding more associations to the correct source. This study investigates the potential of increased source association using comprehensive two-dimensional (GC × GC) and one-dimensional (1D) gas chromatography-mass spectrometry (GC–MS). Three LFs from a sample of thirteen volunteers were collected, derivatized with boron trifluoride–methanol (BF₃-MeOH), and analyzed via 1D GC and GC × GC–MS. The statistical strength of source association was performed by evaluating the log likelihood ratios (LLRs) obtained from the intra- and inter-personal pairwise Pearson correlation comparisons (PCCs) and using the normal and kernel density comparison R functions. The 1D GC method provided stronger same-source association due to high intra-personal PCC values; however, lower inter-personal PCCs were acquired from GC × GC. Higher area under the curves (AUCs) and LLR calibration were computed for the 1D GC PCCs. Conversely, poor statistical calibration and discriminating power were obtained for both 1D GC and GC × GC methods when assessing the LLRs despite having high AUCs.