Multivariate pattern recognition of petroleum-based accelerants by solid-phase microextraction gas chromatography with flame ionization detection Original Research Article

A novel method has been developed for the extraction, analysis and identification of petroleum-based fuels using solid-phase microextraction with analysis by GC–FID. Multivariate data analysis is employed to simplify these data allowing for more accurate classification. Principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) are explored for their effectiveness in establishing accelerant groupings based on the current and previous ASTM International guidelines. The SIMCA models developed for the previous and current ASTM system were 98.5% and 97.2% accurate in unknown sample class prediction. SPME in conjunction with multivariate data analysis is a new approach in accelerant sampling and classification.