An approach based on virtual samples for gasoline discrimination using physicochemical properties or distillation curves

Forensic cases such as fuel theft may require comparison of gasoline samples to decide whether or not gasolines seized at different locations are from the same origin. For example, a police investigation may need to identify if a gasoline sample seized from a tanker truck is the same as a sample from a filling station tank. This work proposes a novel methodology based on gasoline physicochemical properties or distillation curves enabling comparison of gasoline samples in pairs. A pair-wise comparison approach generally requires repeated measurements. Aiming to overcome the need for numerous repeated measurements, a computational approach to generate virtual samples from real gasoline measurements was developed. Measurement uncertainties of the methods were considered in this approach. The real and virtual samples were then used to compose the data set evaluated by pattern recognition techniques. Principal component analysis (PCA) was applied for exploratory studies and soft independent modeling of class analogy (SIMCA) was used for classification. Fifty-four common gasoline samples containing anhydrous ethanol fuel (27–30%v/v) collected from fuel stations located in the state of Pernambuco (Brazil) were used in this study. The 1431 possible pairs of samples were evaluated by SIMCA, with between 96% and 99% of them completely distinguishable when physicochemical assays and distillation curves were employed, respectively. These results suggest that the proposed methodology could be useful for helping in investigations requiring this type of gasoline comparison as fast as possible. Moreover, the strategy developed could also be extended to other applications that require discrimination of samples in pairs. •Forensic cases may require discrimination of gasoline samples in pairs.•Virtual samples based on measurement uncertainties were generated.•Physicochemical properties and distillation curves used for discrimination.•Pattern recognition methods applied for pair-wise comparison.•Approach useful for helping in investigations and extendable to other applications.