Detection and quantification of adulterants in gasoline using distillation curves and multivariate methods

This study has shown that the use of distillation curves combined with PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares Discriminant Analysis) provides a model with enough sensitivity to discriminate adulterated and unadulterated gasoline samples, as well as, the determination of the solvent used in adulteration with minimum percentage of 97% accuracy. PLS-DA provided the prediction of adulterants with low RMSEC (Root Mean Square Error of Calibration) and low RMSEP (Root Mean Square Error of Prediction) when compared to other methods. The great advantage is the possibility to apply the results of the distillation curves to routine analysis (ASTM D86), therefore not requiring various assays, speeding up the analytical process. In addition to its feasibility this method can be quite useful in fuel quality monitoring and inspection procedures whilst having low cost and good reliability.