Analysis of gasoline quality by ATR-FTIR spectroscopy with multivariate techniques

Abstract

In this paper, chemometric methods were used for exploratory analysis, categorization, and quantification of gasoline fuel using Fourier Transform Infrared Spectroscopy (FTIR) data. During exploratory analysis, Principal Component Analysis (PCA) was employed, and to categorise the gasoline samples, Support Vector Machine Classification (SVMC), Linear Discrimination Analysis (LDA), and Partial Least Squares Discriminant Analysis (PLS-DA) were used. The concentration of Benzene, Methyl Tert-butyl Ether (MTBE), and Public Distribution System (PDS) Kerosene were determined using the Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Support Vector Machine Regression (SVMR). All of the chemometric models had 100% accuracy and high R-square and RMSEC significance values. The SVM classification techniques were identified as a suitable choice for both classifying oxygenated and adulterated samples among all approaches. Both PLSR and PCR can also be suitable choices for quantification when dealing with high dimensional data. As a result, the research reported in this study has the potential to become a significant tool for improving gasoline sample discrimination and as a resource for quality control in the petroleum industry.