Revealing Contamination and Sequence of Overlapping Fingerprints by Unsupervised Treatment of a Hyperspectral Secondary Ion Mass Spectrometry Dataset

Abstract

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been successfully applied for chemical imaging of overlapping fingermarks. The resulting big dataset has been treated by means of an unsupervised machine learning approach based on uniform manifold approximation and projection. The hyperspectral matrix was composed of 49 million pixels associated with 518 peaks. However, the single-pixel spectrum results in a very poor signal intensity, mostly like a barcode. Contrary to what has been reported in the literature recently, we have not applied a crude approach based on binning but a sophisticated machine learning method capable of separating the chemical signals of the two fingerprints from each other and from the substrate in which they were impressed. Moreover, using ToF-SIMS, an extremely surface-sensitive technique, the sequence of deposition of the fingerprints has been determined.