Discrimination of brands of gasoline by using DART-MS and chemometrics

Abstract

In this study, Direct Analysis in Real Time Mass Spectrometry (DART-MS) was used to analyze four different brands of gasoline collected from five local fuel service stations. Chemometric methods including analysis of variance-principal component analysis (ANOVA-PCA) and partial least squares discriminant analysis (PLS-DA) were applied to classify the gasoline samples based on brand and identify characteristic features for gasoline discrimination. To test the robustness of this method, the gasoline samples were collected once per week for eight weeks and they were analyzed by DART-MS within 24 h of collection and again two weeks after the last sample collection. Both full DART-MS spectra data and selected ion spectra data were used to construct PLS-DA models and the average classification rates of both models were 99.9 ± 0.1% for 100 independent evaluations with bootstrapped Latin partitions and were 100% for a new validation set collected two weeks later with no parametric changes. The major characteristic features corresponded to polymeric compounds in fuel additives which supported our hypothesis that proprietary additives blended in gasoline should be the marker components to distinguish gasoline with different brands. The weathered gasoline samples at different extents, i.e. 30%, 50%, 70%, and 90%, were studied and compared with un-weathered samples. The polymeric compound patterns in gasoline were found to be brand dependent and weathering extent dependent and the patterns for the weathered gasoline with different brands were still significantly different. DART-MS is demonstrated as a promising tool for the discrimination of brands of gasoline.