An open-access computational fingerprinting workflow for source classifications of neat gasoline using GC × GC-TOFMS and Machine Learning

Advances in sensitivity and selectivity of multidimensional chromatography have enhanced our ability to better characterize and identify sources of neat gasoline used in arson cases. However, the large and complex chemical datasets generated present a significant challenge for data management and interpretation, requiring robust computational analysis techniques. In this study, we present a novel, open-access computational fingerprinting workflow to develop regional database of gasoline profiles and source tracking of gasoline samples from local gas stations for arson investigations. The computational workflow included data reduction, normalization, clustering analyses, feature selection and supervised machine learning (ML) to explore the differentiation between gasoline sources. Chromatographic features (n = 25,415) from multidimensional gas chromatography-time of flight mass spectrometry (GC × GC-TOFMS) analysis of 69 neat gasoline samples, collected from 10 gas stations in Alberta (Canada), were used in supervised ML for the classification of neat gasoline samples. Fifty chemical features selected using recursive feature addition (RFA), with associated chemistries of n-alkanes, alkenes, cycloalkanes, and aromatics, were found to differentiate local gas stations. Despite overlapping between gas stations in clustering analyses, an average improvement of 18 % in ML accuracy was achieved by using decision tree-based ML classifiers coupled with RFA as compared to using all features. Our open-source computational workflow ensures transparency and reproducibility in creating a method and regional database for the distinction of gasoline sources commonly used in wildfire arson. The workflow enables forensic analysts to integrate additional chemical features into existing target chemical libraries within the ASTM E1618-19 protocol, enhancing ignitable liquid identification without requiring extensive re-training of computational models or programming expertise.