Multivariate calibrations on H-1 NMR profiles for prediction of physicochemical parameters of Brazilian commercial gasoline

Abstract

Brazilian commercial gasoline follows a rigid quality control, covered by Regulation ANP 309 and following international analytical protocols, such as ASTM and ABNT. Each property is a complicated function of the gasoline chemical composition, which would be represented by diverse types of mathematical correlations. However, these correlations are not adjusted to Brazilian gasoline, whose chemical composition is modified by anhydrous ethanol addition. The purpose of this work is to find correlations using PLS regressions, between H-1 NMR Brazilian gasoline fingerprintings and physicochemical parameters, such as relative density, distillation curve, octane numbers, hydrocarbons compositions (olefins, aromatics and saturated) and anhydrous ethanol and benzene. One hundred and fifty representative gasoline samples, collected randomly from different gas stations, were analyzed following ASTM/ABNT analytical protocols. All H-1 NMR spectroscopic fingerprintings, reported in parts per million (ppm) relative to residual proton signals of CDCl3 at 7.24 ppm, were acquired on a Varian INOVA spectrometer 500 MHz. FIDs were zero filled and Fourier transformed. Data matrix, composed by H-1 NMR chemical shifts and physicochemical parameters, was constructed and imported into Pirouette (R) 3.11 software for PLS regression. H-1 NMR fingerprinting of 100 gasoline samples were employed in the training set and 50 samples formed the prediction set. RMSEC and RMSEP were the parameters considered to select the "best model". H-1 NMR-PLS models results in good prediction capability when compared to repeatability and reproducibility of ASTM/ABNT analytical protocols. H-1 NMR-PLS multivariate regressions supplies an alternative analytical procedure for commercial automotive gasoline quality control. (C) 2012 Elsevier Ltd. All rights reserved.