Development of Analytical Methodologies Based on Advanced Chemometric Tools for Screening and Quantifying Fossil Fuel Adulteration.
Screening fuel adulteration; MCR-ALS; PARAFAC; FT-MIR and FT-NIR; Fluorescence Spectroscopy
Fuel adulteration is a global problem related to the use of tax-exempt substances, which are often not identified in these products, even in the screening of petroleum derivatives. This is a worldwide illegal practice associated with environmental pollution, engine damaging, health hazards and government tax evasion, thus generating illicit enrichment for those involved in this fraud. Generally, fraudsters use irregular substances that have good miscibility similar properties with fuels, such as kerosene and residual vegetable oils, which makes it difficult to detect this process via the physicochemical methods applied in the market. Therefore, it is necessary to development new methods in order to detect and identify fuel adulteration. This work aims to develop chemometric methodologies combined with infrared (IR) and molecular fluorescence spectroscopies as rapid, non-destructive and highly sensitive method to identify diesel and jet fuel contamination, in order to contribute to the process of eradicating this criminal practice. Principal component analysis, genetic algorithm and successive projections algorithm were associated with linear discriminant analysis (PCA-LDA, GA-LDA, and SPA-LDA) for classifying the blends according to the IR absorption bands assigned to oxidation products, such as phenols and carboxylic acids. GA-LDA and SPA-LDA models were accurate and reached 100% sensitivity and specificity for both diesel and JET-A1 contaminated with kerosene solvent (KS). Multivariate curve resolution with alternating least squares (MCR-ALS) and partial least squares (PLS) regression coupled to MIR/NIR spectroscopy were able to detect and quantify KS in JET-A1 and diesel fuel with high accuracy (RMSEP<1.64%; R2>0.995). MCR-ALS with area constraint and parallel factor analysis (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.