Quality classification of gasoline samples based on their aliphatic to aromatic ratio and analysis of PONA content using genetic algorithm based multivariate techniques and ATR-FTIR spectroscopy

Abstract

In this study, attenuated total reflectance fourier transform mid-infrared (ATR-FTIR) spectral information was used to classify gasoline samples according to their aliphatic to aromatic ratio using data driven soft independent modeling of class analogy (dd-SIMCA). The ATR-FTIR spectral data were also used to determine the paraffin, olefin, naphthene and aromatic (PONA) contents in gasoline samples using support vector machine regression (SVMR) model. For comparison, partial least squares discriminant analysis (PLS-DA) and partial least square regression (PLSR) were also performed for classification and prediction purposes, respectively. Genetic algorithm (GA) variable selection method was applied to achieve a better separation of the classes and select characteristic variables closely related to composition differences. The results indicated that GA-dd-SIMCA and GA-SVMR can provide better classification and prediction performances by comparison with GA-PLS-DA and GA-PLSR methods, respectively. For supervised dd-SIMCA discrimination method with GA variable selection, 100% correct classification for three different classes of the gasoline was obtained, each of which has different aliphatic/aromatic ratio. In addition, the optimal GA-SVMR model showed the determination coefficient (R2) of 0.985, 0.986, 0.960 and 0.981 and root mean square error of prediction (RMSEP) were 2.050, 1.243, 2.189 and 3.324 for parrafins, olefins, naphthenes and aromatics, correspondingly. FTIR spectroscopy coupled with multivariate data analysis procedures provides rapid, efficient, and cost-effective quality assurance methods to characterize physicochemical properties of gasoline samples.