Characterization of Gasoline by 1H Nuclear Magnetic Resonance and Chemometrics. (24th July 2017)

• Record Type: Journal Article
• Authors: Ruschel, Carla F. C.; Marcelo, Marcelo C. A.; dos Santos, Francisco P.; Ferrão, Marco F.
• Is Part Of: Analytical letters. Volume 50:Number 11(2017)
• Page Start: 1767
• Page End: 1777
• Publication Date: 2017-07-24
• Subjects: Exploratory multivariate analysis -- fuel adulteration -- hydrogen-1 nuclear magnetic resonance spectroscopy -- partial least square discriminant analysis -- partial least squares; Chemistry, Analytic -- Periodicals; Chemistry, Analytic -- Abstracts; 543
• DOI: 10.1080/00032719.2016.1249877
• Languages: English
• ISSNs: 0003-2719

Abstract

ABSTRACT: Automotive fuel adulteration is an old and significant problem. One common type of fuel adulteration is the addition of diesel to gasoline. Unsupervised models were developed through hierarchical cluster and principal component analysis models. Supervised models through partial least square discriminant analysis using 1 H nuclear magnetic resonance spectra as the input were used to classify samples as adulterated or unadulterated. Quantitative models were developed using partial least squares to determine the gasoline and diesel concentrations in the samples. This set contained samples composed of pure gasoline and anhydrous ethanol reproducing commercial gasoline and other samples treated with diesel. Hierarchical cluster and principal component analysis did not distinguish between adulterated and unadulterated samples except for the most adulterated materials. However, partial least square discriminant analysis classified 100% of the samples correctly. The partial least square algorithm provided excellent regression models for the gasoline and diesel content. The determination coefficient was 0.9920 for both models, whereas the root mean square error of cross-validation and root mean square error of prediction for the diesel model were 2.32 and 1.42%, respectively, and 2.40 and 1.38% for the gasoline model.