Study of Brazilian Gasoline Quality Using Hydrogen Nuclear Magnetic Resonance (1H NMR) Spectroscopy and Chemometrics (original) (raw)
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This work describes the application of low-field and medium-resolution 1 H NMR (LF-1 H NMR) combined with PLS and SVM multivariate regression techniques for fast prediction of five fundamental quality parameters of commercial Brazilian gasoline (specific gravity, distillation temperatures of 50 and 90 percent of recovered and olefinic and aromatic content). All these five parameters are of paramount importance to predict the fuel quality and the application of the developed PLS and SVM models showed prediction errors compared to the reference methodologies applied for these essays. Nevertheless, it is important to highlight that in a different way of the reference methodologies, such as distillation or chromatographic methods, the application of LF-1 H NMR combined with PLS or SVM models enables the fast determination of those five parameters directly from a single LF-1 H NMR spectrum, which is acquired without any sample pre-treatment nor dilution in deuterated solvents, and this whole process takes no more than 15 s.
Talanta, 2010
The combination of ASTM D6733 gas chromatographic fingerprinting data with patternrecognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a monitoring program for quality control of automotive fuels. SIMCA was performed on chromatographic fingerprints to classify the quality of the gasoline samples. Using SIMCA, it was possible to correctly classify 94.0% of commercial gasoline samples, which is considered acceptable. The method is recommended for quality-control monitoring. Quality control and police laboratories could employ this method for rapid monitoring.
Journal of Chemometrics, 2011
Samples of commercial gasoline, from the National Program of Fuel Quality Monitoring of the National Petroleum Agency, were collected from gas stations located in the Midwestern state of Sao Paulo, Brazil, and analyzed by several physicochemical standard methods established by ANP Resolution no. 309. Also, important information related to tampering was analyzed with the marker solvent. Statistical analysis and exploratory chemometric were employed to discriminate the presence of markers of solvents in commercial gasoline. The results showed that statistical and chemometric parameters such as atmospheric distillation temperatures T10 and T90, RON, benzene and saturated and aromatic hydrocarbons satisfactorily describe the presence of marker solvent, usually with a probability exceeding 70%. Furthermore, after optimizing the SIMCA algorithm, sensitivity in the training set with cross-validation leave-one-out (83.8%) and the set of prediction (77.1%) were revealed. The proposed method will become indispensable and recommended for discriminating samples of fuels for commercial applications in routine monitoring programs and quality control.