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

Flumignan, Danilo Luiz; Sequinel, Rodrigo; Hatanaka, Rafael Rodrigues; Boralle, Nivaldo; Oliveira, José Eduardo de

doi:10.1016/j.fuel.2012.04.037

Cited by 10 publications

(5 citation statements)

References 38 publications

(52 reference statements)

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“…The predictive ability in the external set is obtained by the Standard Error of Prediction (SEP, also called Root-Mean Square Error of Prediction or RMSEP), which was the main parameter considered to select the “best model”, together with RMSEC. The majority of the RMSEC and RMSEP obtained for all physicochemical parameters using the regression model built and generated by PLS show results having good prediction capability (Table ), when compared with others works, ,,,, thus indicating the ability of the procedure to obtain accurate results. All 13 C NMR-PLS models developed had variability accumulated in their principal components and correlation coefficients above 70% and 0.80, respectively.…”

Section: Resultsmentioning

confidence: 78%

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

et al. 2012

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Brazilian commercial gasoline follows a rigid quality control, regulated by Brazilian Government Petroleum, Natural Gas, and Biofuels Agency, ANP, following international analytical protocols, such as ASTM and ABNT, covered by Regulation ANP No. 309. 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 partial least-squares (PLS) regressions, between 13 C NMR Brazilian gasoline fingerprintings and several physicochemical parameters, such as relative density, distillation curve (temperatures related to 10, 50, and 90% of distilled volume, final boiling point and residue), octane numbers (motor and research octane number and antiknock index), hydrocarbon compositions (olefins, aromatics, and saturated) and anhydrous ethanol and benzene. 150 representative gasoline samples, collected randomly from different gas stations, were analyzed following international analytical protocols. All 13 C NMR spectroscopic fingerprintings, reported in parts per million (ppm), FIDs (free induction decays) were zero filled and Fourier transformed. A data matrix, composed of 13 C NMR chemical shifts and physicochemical parameters, was constructed and used in PLS regression. 13 C NMR fingerprinting of 100 gasoline samples were employed in the training set, and 50 samples formed the prediction set. In 13 C NMR-PLS models, root-mean square error of calibration (RMSEC) and prediction (RMSEP) were the mains parameters considered to select the "best model", which shown results roughly similar in magnitude to the repeatability and reproducibility of ASTM and NBR officials analytical protocols. 13 C NMR-PLS multivariate regression, as an alternative analytical methodology, offers an appealing procedure for commercial automotive gasoline quality control.

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Section: Resultsmentioning

confidence: 78%

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

et al. 2012

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“…94 Flumignan demonstrated the application of partial least squares regression for building empirical models relating 1 H NMR data to physicochemical properties of Brazilian gasolines. 48 Jameel and co-workers developed models for predicting cetane number using data derived through 1 H NMR and MLR. 57 Most recently, Dussan and co-workers developed a model for ignition delay and validated its application to five complex jet fuels across a range of temperatures.…”

Section: ■ Introductionmentioning

confidence: 99%

“…NMR has previously been applied to link chemical structures within fuels to fuel properties. Table S1 in the Supporting Information, − provides a bibliography of key references where properties important to fossil fuel production and performance, such as ignition characteristics, physical properties, distillation temperatures, soot, and several others, are related using mathematical models to resonances observed in one-dimensional (1-D) 1 H, 13 C, or two-dimensional (2-D) 1 H– 13 C HSQC NMR spectra. Establishing these mathematical relationships is possible because 13 C and 1 H NMR provide detailed, molecular-level information about a substance using very small samples.…”

Section: Introductionmentioning

confidence: 99%

“…Meusinger showed how structures revealed by 1 H NMR spectroscopy could be correlated to RONs and MONs from 144 gasoline samples using hierarchical clustering, factor analysis, and MLR . Flumignan demonstrated the application of partial least squares regression for building empirical models relating 1 H NMR data to physicochemical properties of Brazilian gasolines . Jameel and co-workers developed models for predicting cetane number using data derived through 1 H NMR and MLR .…”

Section: Introductionmentioning

confidence: 99%

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Methodology for the Development of Empirical Models Relating ¹³C NMR Spectral Features to Fuel Properties

et al. 2020

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Effective formulation of new gasoline or diesel fuels for internal combustion engines would benefit from the development of reliable models for predicting key fuel properties based on a set of molecular descriptors obtained from a single measurement. This is particularly relevant in the case of renewable fuels, where the available fuel sample quantity may be limited. In this work, we present a statistically based methodology for building empirical models to predict multiple properties from onedimensional 13 C nuclear magnetic resonance (NMR) spectra measured on around 200 μL of a liquid fuel. NMR spectra contain information about the molecular composition of a sample and the carbon types and molecular substructures therein. Our approach uses this information to build sparse, interpretable models, where the predicted properties are linked to specific molecular features. The approach takes into consideration the constrained nature of the features making up the one-dimensional NMR spectrum, which, after standardization, represents a relative fuel composition. We point to the limitations in interpretability that arise when building this type of empirical predictive model and suggest how these limitations may be diminished. Among the many properties important for maximizing engine performance and minimizing emissions, we build models that predict derived cetane number and distillation temperatures, as these are of particular interest because of their links to fuel economy, drivability, and engine-out emissions. The results suggest that the properties of interest may be impacted by only a few of the 27 13 C NMR regions represented in the data, pointing to new directions for further testing in the development of improved fuels.

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“…Recently, the utilization of 1 H NMR spectroscopy and support vector regression for the prediction of the distillation temperatures of crude oil was investigated by Filgueiras et al Duarte et al also proposed a method for establishing crude oil properties using 1 H NMR . Flumignam et al developed multivariate calibrations to predict several parameters of Brazilian commercial gasoline, using hierarchical cluster analysis for representative dataset creation . Nowadays, the gasoline characterization by 1 H NMR and chemometrics has been studied by Ruschel et al, who developed a model to quantify gasoline content in samples that may be adulterated .…”

Section: Introductionmentioning

confidence: 99%

Estimation of gasoline properties by¹H NMR spectroscopy with repeated double cross‐validated partial least squares models

Leal

Albuquerque²,

Silva

et al. 2020

Journal of Chemometrics

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Commercial gasoline must satisfy several product specifications before trading.In the present work, repeated double cross validation using partial least squares regression was applied to create reliable prediction models for 13 physicochemical parameters (eg, density, vapour pressure, evaporate at 70 C, evaporate at 100 C, evaporate at 150 C, final boiling point, research octane number, motor octane number, aromatic content, olefinic content, benzene content, oxygen content, and methyl tert-butyl ether content) of gasoline produced in Matosinhos' refinery. The input variables for the regression are the 1 H NMR spectral intensities of a total of 448 samples, which were recorded using a pico-Spin NMR spectrometer operating at 80 MHz. The output variables are the corresponding property values, which were also measured according to ISO standard methods. A spectral feature elimination before multivariate analysis was done to remove noise and speed up the chemometric analysis. The optimum complexity of each model was achieved by repeated double crossvalidation strategy, consisting of 100 repetitions of two nested cross-validation loops. Quantitative partial least squares yielded accurate predictions of 11 of 13 properties within the reproducibility of ISO standards. The methodology presented in this work has been proven effective in property estimation and enables a significant reduction in the total time of gasoline quality control.

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

Cited by 10 publications

References 38 publications

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Methodology for the Development of Empirical Models Relating ¹³C NMR Spectral Features to Fuel Properties

Estimation of gasoline properties by¹H NMR spectroscopy with repeated double cross‐validated partial least squares models

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

Cited by 10 publications

References 38 publications

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Methodology for the Development of Empirical Models Relating 13C NMR Spectral Features to Fuel Properties

Estimation of gasoline properties by1H NMR spectroscopy with repeated double cross‐validated partial least squares models

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Product

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Methodology for the Development of Empirical Models Relating ¹³C NMR Spectral Features to Fuel Properties

Estimation of gasoline properties by¹H NMR spectroscopy with repeated double cross‐validated partial least squares models