Abstract
Compound-specific isotope analysis (CSIA) is fast becoming an indispensable tool to provide chemical evidence in a forensic investigation. Many attempts to trace environmental oil spill were successful where isotopic values were particularly distinct. However, difficulties arise when a large dataset is analysed and the isotopic differences between samples are subtle. In the present study, forensic discrimination of diesel oils involved in a diesel theft case was carried out to infer the relatedness of the samples to potential source samples. This discriminatory analysis used a suite of hydrocarbon diagnostic indices, alkanes, to generate isotopic data of the compositions of the compounds which were then processed using multivariate statistical analyses to infer the relatedness of the data set. The results from this analysis was put into context by comparing the data with the δ13C and δ2H of alkanes in commercial diesel samples obtained from various locations in the South Island of New Zealand. Based on the isotopic character of the alkanes, it is suggested that diesel fuels involved in the diesel theft case were distinguishable. This manuscript shows that CSIA when used in tandem with multivariate statistical methods can be a collection of tools for the source apportionment of hydrocarbons by demonstrating a straightforward approach thus eliminating lengthy analytical processes.