Personal Information from Latent Fingerprints Using Desorption Electrospray Ionization Mass Spectrometry and Machine Learning.

Abstract

Desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) was applied to latent fingerprints to obtain not only spatial patterns but also chemical maps. Samples with similar lipid compositions as those of the fingerprints were collected by swiping a glass slide across the forehead of consenting adults. A machine learning model called gradient boosting tree ensemble (GDBT) was applied to the samples that allowed us to distinguish between different genders, ethnicities, and ages (within 10 years). The results from 194 samples showed accuracies of 89.2%, 82.4%, and 84.3%, respectively. Specific chemical species that were determined by the feature selection of GDBT were identified by tandem mass spectrometry. As a proof-of-concept, the machine learning model trained on the sample data was applied to overlaid latent fingerprints from different individuals, giving accurate gender and ethnicity information from those fingerprints. The results suggest that DESI-MSI imaging of fingerprints with GDBT analysis might offer a significant advance in forensic science.