Determination of flash point and cetane index in diesel using distillation curves and multivariate calibration

AbstractPartial least squares regression (PLS) was used to predict flash point and cetane index of diesel using distillation curves (ASTM-D86). The low RMSEP values obtained, compared with other chemometric models based on spectrometric methods described in literature, and high correlation coefficients between reference and predicted values showed that PLS was efficient to determine flash point and cetane index. The model built contains diesel samples of different compositions, thus revealing the variety of fuel in the Brazilian market. Furthermore, the proposed method has two advantages: low cost and easy implementation, as it applies the results of a routine test to evaluate the quality of diesel

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

AbstractThis 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