Using GC × GC-FID profiles to estimate the age of weathered gasoline samples

Abstract

Predicting the amount of time that a petroleum mixture has been exposed to weathering effects has applications in areas of environmental and other forensic investigations, such as aiding in determining the cause and intent of a fire. Historically, research on the evaporation rates of hydrocarbon mixtures has focused on forensic oil spill identification and predicting if a fresh sample could be weathered to give an observed composition in an aged sample. Relatively little attention has focused on approaching the problem from the other direction: estimating exposure time based on the observed composition of a weathered sample at a given time and assuming a prior composition. Here, we build upon our previous research into the weathering of model mixtures by extending our work to gasoline. Samples of gasoline with varying octane ratings and from several vendors were weathered under controlled conditions and their composition monitored over time by two-dimensional gas chromatography (GC × GC). A variety of chemometric models were explored, including partial least squares (PLS), nonlinear PLS (PolyPLS) and locally weighted regression (LWR). A hierarchical application of multivariate techniques was able to predict the time for which a sample had been exposed to evaporative weathering. Partial least squares discriminant analysis could predict whether a sample was relatively fresh (<12 h exposure time) or highly weathered (>20 h exposure time). Subsequent regression models for these classes were evaluated for accuracy using the root mean square error of prediction. LWR was the most successful, whereby fresh and highly weathered samples were predicted to within 30 min and 5 h of exposure, respectively.