Multivariate strategy for identifying and quantifying jet fuel contaminants by MCR-ALS/PLS models coupled to combined MIR/NIR spectra

• Published in: Analytical and bioanalytical chemistry Vol. 414; no. 27; pp. 7897 - 7909
• Main Authors: Câmara, Anne B. F., da Silva, Wellington J. O., Moura, Heloise O. M. A., Silva, Natanny K. N., de Lima, Kassio M. G., de Carvalho, Luciene S.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2022; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Analytical Chemistry; Biochemistry; Carboxylic acids; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Chemometrics; Civil aviation; Contaminants; Contamination; Discriminant analysis; Food Science; Fuel and fuel systems; Fuel contamination; Genetic algorithms; Genetic analysis; Infrared spectra; Infrared spectroscopy; Jet engine fuels; Jet planes; Kerosene; Laboratory Medicine; Least squares; Methods; Monitoring/Environmental Analysis; Multivariate analysis; Oxidation; Phenols; Physicochemical analysis; Pollutants; Polymer blends; Principal components analysis; Quality control; Research Paper; Solvents; Brazil; MCR-ALS; MIR/NIR combined spectra; Jet fuel; Multivariate classification; PLS; Physicochemical analysis
• ISSN: 1618-2642; 1618-2650
• DOI: 10.1007/s00216-022-04324-9

The investigation and control of jet fuel contamination for private aircrafts has gained attention due to the softer monitoring in comparison to commercial aviation. The possible contamination with kerosene solvent (KS) makes this investigation more challenging, since it has physicochemical similarities with jet fuel. To help solve this problem, a chemometric methodology was applied in this research combining multivariate curve resolution with alternating least squares (MCR-ALS) and partial least squares (PLS) models coupled to near- and mid-infrared spectroscopies (MIR/NIR) in order to detect and quantify KS in blends with JET-A1 using 23 samples (5–60% v/v). Additionally, 98 samples were stored for 60 days, and principal component analysis, genetic algorithm, and successive projections algorithm were coupled to linear discriminant analysis (PCA-LDA, GA-LDA, and SPA-LDA) in order to classify the blends according to the 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. Physicochemical analysis was not able to detect the presence of KS in contaminated jet fuel samples, even in high concentrations. The use of MIR-NIR combined spectra improved the quantification results, thus decreasing the experimental error from 5.22% (using only NIR) to 1.64%. PLS regression quantified the content of KS with high accuracy (RMSEP < 1.64%, R 2  > 0.995). The MCR-ALS model stood out for recovering the spectral profile of kerosene solvent by segregating it from jet fuel spectra. The development of models using chemometric tools contributed to a fast, low-cost, and efficient process for quality control that can be applied in the fuel industry. Graphical Abstract