Differentiation of Gasoline Samples Using Flame Emission Spectroscopy and Partial Least Squares Discriminate Analysis

Abstract

In this work, flame emission spectroscopy (FES) was combined with the partial least squares discriminant analysis (PLS-DA) method aimed at classifying different types of gasoline retailed in gas stations. In Brazil, three different types of gasoline, namely, regular gasoline (RG), gasoline with additives (AG), and premium gasoline (PG), are available for retail. The legislation and literature does not present methods for the discrimination of these types of gasoline and also lacks an agenda with programs that inspect and/or attest to the presence of additives that distinguish these fuels. For each set of samples, spectra were obtained through FES and, subsequently, the results were treated using PLS-DA. The PLS-DA model was built using only three latent variables (LVs) with accumulated variance of 99.98% in X and 51.05% in Y. The model combining FES to PLS-DA provided excellent sensitivity and specificity values for the calibration set and 100% accuracy in predicting. All samples were analyzed as collected at the gas station, and then the results were obtained in a few seconds without any kind of sample preparation.