Table of Contents
Separations, Volume 5, Issue 4 (December 2018)
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Cover Story (view full-size image) Forensic fire debris analysis presents data interpretation challenges due to the partial [...] Read more. Forensic fire debris analysis presents data interpretation challenges due to the partial evaporation of ignitable liquids (IL) and highly varied contributions by compounds originating from the thermal breakdown of building materials and furnishings (SUB). Current data interpretation methods rely on visual chromatographic pattern recognition. Unsupervised machine learning techniques can assist fire debris analysts in identifying groups of IL and SUB with similar chromatographic or mass spectral patterns. This report details the use of a Kohonen self-organizing map (SOM) coupled with extracted ion mass spectra to organize IL and SUB pyrolysis samples on a two-dimensional grid. Groupings corresponding to the ASTM E1618-defined IL classes and SUB pyrolysis are observed. The SOM component planes give important information regarding the ions that contribute to the different groups. View this paper.