Exploring the analysis and differentiation of plastic explosives by comprehensive multidimensional gas chromatography-mass spectrometry (GC × GC–MS) with a statistical approach

Abstract Plastic explosives (PE) are a relatively insensitive form of explosives that are composed of several different types of chemicals. Differentiation of such a complex mixture is of interest to law enforcement agencies. In this work, we explored the possibility of utilizing a mass spectrometric method with statistical processing for PE analysis. Comprehensive two-dimensional gas chromatography coupled with a time-of-flight mass spectrometer (GC × GC–MS) was implemented for the analysis of PE extracts. The resulting chromatographic peaks were aligned using mass spectra and retention time data. The aligned peaks were then subjected to statistical analysis using scores generated via principal component analysis (PCA), followed by a k nearest neighbor classification with Euclidean distance. Using appropriate data treatment steps, a 0% error rate and a 0% unidentified rate in the classification of PE to production lots was achieved in cross validation classification of training samples and classification of test samples. This demonstrates accurate classification of PE samples into production lots using these data treatment steps.