Development of Analytical Methodologies Based on Advanced Chemometric Tools Coupled with Spectroscopic Techniques for Screening and Quantifying Fossil Fuel Adulteration.
Screening fuel adulteration; MCR-ALS; PARAFAC; FT-MIR and FT-NIR; Fluorescence Spectroscopy.
Fuel adulteration poses a significant global issue as it involves the illicit use of tax-exempt substances that often go undetected, even during the screening of petroleum products. This illegal practice has far-reaching implications, including environmental pollution, engine damage, health risks, and government tax evasion, leading to illicit gains for those engaged in fraudulent activities. Typically, fraudsters employ non-standard substances that exhibit similar properties and miscibility with fuels, such as kerosene, and residual vegetable oils, making their detection challenging using conventional physicochemical methods employed in the market. In light of these challenges, this research aims to develop advanced analytical methodologies utilizing cutting-edge chemometric tools. By leveraging these techniques, we seek to enhance the screening and quantification of fuel adulteration, enabling more accurate and efficient identification of tax-exempt substances in petroleum derivatives. The outcomes of this study will contribute to combating this pervasive issue and safeguarding the environment, engines, public health, and government revenue. For this, 60 samples of diesel and 98 samples of JET-A1 were applied to simulated the adulteration process with kerosene solvent (KS) for the application of chemometric models couped to spectroscopic techniques. The classification algorithms enabled classifying the blends according to the IR absorption bands assigned to oxidation products, such as phenols and carboxylic acid, with high accuracy and 100% sensitivity and specificity for both diesel and JET-A1. MCR-ALS and PLS regression were able to detect and quantify KS in the fuels with high accuracy (RMSEP<1.64%; R2>0.995). MCR-ALS with area constraint and PARAFAC combined with excitation emission matrix (EEM) fluorescence spectroscopy allowed the quantification of JET-A1. Furthermore, PARAFAC algorithm also quantified the content of KS with high accuracy (RMSEP = 5.36%). MCR-ALS model stood out for recovering the spectral profile of the adulterants by segregating it from the fuel spectra. In addition, the developed methodology had an overall performance superior than the traditional physicochemical methods using to screen the adulteration, showing its potential to future application for in loco fuel quality control.