Determination of methanol and methyl tert-butyl ether in gasoline by infrared spectroscopy using the CIRCLE cell and multivariate calibration (original) (raw)
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Applied Spectroscopy, 1993
The feasibility of predicting concentrations of methanol and methyl tertbutyl ether (MTBE) in gasolines from the mid-infrared absorption spectra was investigated. The C-O bond stretching band region was related to these oxygenated compounds' levels with the aid of multivariate statistics. The sample spectra were taken with the use of a circular internal reflectance (CIRCLE ®) cell. With the use of partial least-squares (PLS) regression a model with 18 samples was built and used to predict a set of 10 gasolines with good agreement. Two additional samples prepared with other brands of gasoline were analyzed with the same model and no matrix effects were found.
Journal of Near Infrared Spectroscopy, 1998
This paper describes a near infrared spectroscopic method developed for determination of ethanol and methyl tert-butyl ether (MTBE) as additives in gasoline. The methodology employs data collected from a near infrared spectrophotometer whose monochromator is an Acousto-Optic Tunable Filter (AOTF) operating in the 1500–2400 nm range. Genetic Algorithm variable selection was used in the multiple linear regression (MLR) modelling. Seven wavelengths were selected by the algorithm and the results obtained by MLR revealed that the method produces improved results, when compared with the PLS regression method, as confirmed by the lower RMSEP obtained for ethanol and MTBE determination. Besides the improvement achieved in the analytical results, the variable selection allows a reduction in the time necessary for data acquisition. This fact has special importance when AOTFs are being used as the monochromator element. The AOTF's capability of random access to the selected wavelengths can...
Fuel, 2008
In the present work, Fourier transform infrared spectroscopy (FTIR) in association with multivariate chemometrics classification techniques was employed to identify gasoline samples adulterated with diesel oil, kerosene, turpentine spirit or thinner. Results indicated that partial least squares (PLS) models based on infrared spectra were proven suitable as practical analytical methods for predicting adulterant content in gasoline in the volume fraction range from 0% to 50%. The results obtained by PLS provided prediction errors lower than 2% (v/v) for all adulterant determined. Additionally, Soft Independent Modeling of Class Analogy (SIMCA) was performed using all spectral data (650-3700 cm À1) for sample classification into adulterant classes defined by training set and the results indicated that undoubted adulteration detection was possible but identification of the adulterant was subject to misclassification errors, specially for kerosene and turpentine adulterated samples, and must be carefully examined. Quality control and police laboratories for gasoline analysis should employ the proposed methods for rapid screening analysis for qualitative monitoring purposes.
The criminal act of fuel (gasoline) adulteration still remains a global worry due to its environmental, health and economic effect. Current methods for the detection of fuel adulteration have not been effective in most developing countries due to the associated cost of implementation. Therefore, there is the need for a fast, reliable and cheaper approach for screening of adulterants in fuel. This study combined FTIR analyses with Chemometric (multivariate) techniques for qualitative and quantitative determination of four possible adulterants: kerosene, diesel, naphtha and premix in gasoline. Synthetic admixtures prepared by mixing the gasoline with varying proportions of the adulterants were obtained and used for the model calibration. Soft Independent Modeling Class Analogy (SIMCA) classification and Partial Least Square (PLS) regression methods were the Chemometric techniques employed. The SIMCA classification model developed predicted the type of adulterant present at an error rate of 6.25% for Kerosene and naphtha, and 12.5% for premix. However, no prediction error was recorded for classifying samples contaminated with diesel. The PLS regression model was able to predict the concentrations of adulterant with prediction errors lower than 5% for all adulterants ABOUT THE AUTHORS
Analytica Chimica Acta, 2006
A new method is proposed that enables the identification of five refinery fractions present in commercial gasoline mixtures using infrared spectroscopic analysis. The data analysis and interpretation was carried out based on independent component analysis (ICA) and spectral similarity techniques. The FT-IR spectra of the gasoline constituents were determined using the ICA method, exclusively based on the spectra of their mixtures as a blind separation procedure, i.e. assuming unknown the spectra of the constituents. The identity of the constituents was subsequently determined using similarity measures commonly employed in spectra library searches against the spectra of the constituent components. The high correlation scores that were obtained in the identification of the constituents indicates that the developed method can be employed as a rapid and effective tool in quality control, fingerprinting or forensic applications, where gasoline constituents are suspected.
Energy & Fuels, 2017
Ethanol, due to its high octane rating of 108, is often added as adulterant to premium 91 gasoline fuels to boost up their octane ratings to 96 or more but it does not provide the same power to engine as that of super-premium 96 gasoline fuels. In this study, a sensitive near infrared spectroscopy (NIRS) coupled with chemometrics was proposed for analysis of ethanol content in Premium 91 gasoline fuels. Standard samples of Premium 91 octane gasoline were collected from Oman's national refining and Petrochemicals Company commonly known as ORPIC. The Premium 91 samples were then intentionally spiked with ethanol at various levels. The nearinfrared spectroscopy was employed in the absorption mode to obtain the spectra of all samples scanning from 700 to 2500 nm. Then, partial least-squares (PLS) regression, partial least-squares discriminant analysis (PLS-DA) and principal component analysis (PCA), and were applied to model and interpret the near-infrared spectra. A PLS-DA model was developed to discriminate between the pristine gasoline samples and those intentionally mixed with ethanol, with excellent results (R 2 = 98% and RMSE = 0.049) by random cross validation. A PLS regression model was established to determine the ethanol content in Premium 91 gasoline samples, with values of R 2 = 99% and RMSECV = 1.88 and R 2 = 99% and RMSEP = 1.58 for cross-validation and test-set validation results, respectively. This newly developed method, is simple, rapid, and can quantify less than 2 % of ethanol adulteration in premium 91 gasolines.
Talanta, 2008
Near infrared (NIR) spectroscopy was employed for simultaneous determination of methanol and ethanol contents in gasoline. Spectra were collected in the range from 714 to 2500 nm and were used to construct quantitative models based on partial least squares (PLS) regression. Samples were prepared in the laboratory and the PLS regression models were developed using the spectral range from 1105 to 1682 nm, showing a root mean square error of prediction (RMSEP) of 0.28% (v/v) for ethanol for both PLS-1 and PLS-2 models and of 0.31 and 0.32% (v/v) for methanol for the PLS-1 and PLS-2 models, respectively. A RMSEP of 0.83% (v/v) was obtained for commercial samples. The effect of the gasoline composition was investigated, it being verified that some solvents, such as toluene and o-xylene, interfere in ethanol content prediction, while isooctane, o-xylene, m-xylene and p-xylene interfere in the methanol content prediction. Other spectral ranges were investigated and the range 1449-1611 nm showed the best results.
2012
Due to dependence on the use of gasoline, traders see an opportunity to increase their profit by adding solvents such as ethanol, kerosene, turpentine and diesel. Attenuated total reflectance with Fourier transform infrared spectroscopy (ATR-FTIR) is a fast and nondestructive technique and requires little sample. In this work, the applicability of ATR-FTIR for detection of adulteration of Brazilian gasolines using liquid-liquid aqueous separation and chemometric tools was evaluated. Gasolines from 15 gas stations from the cities of Tietê, Ibaté and São Carlos (State of São Paulo) were acquired. Three data sets were generated: pure gasolines, polar phase and aqueous phase. The partition procedure aids in the identification of possible adulterants, since ethanol, present in Brazilian gasoline, presents strong peaks in the IR spectra. By IR spectra analysis methanol was found in the polar fraction of a sample, a novelty in the "gasoline tampering procedure". The same partition procedure was applied on samples spiked with methanol (MTH), ethanol (ETH), turpentine spirit (TUS) and toluene (TOL) from 0 to 25% yielding twelve datasets. It was found that identification of adulterated gasolines can be performed using spectra without partition but the quality of PCA separation is increased when pure, nonpolar and polar fraction spectra are concatenated, due the increase of chemical information. PLS models presented standard errors of cross-validation of 1.36%, 4.10%, 1.92% and 2.34% for MTH, ETH, TUS and TOL, respectively. The tampered sample presented 8.93% of methanol, using the developed model.
Fuel, 2008
A set of 160 gasoline samples was collected from commercial stations in five Brazilian states and analyzed by ASTM methods for 13 properties. Principal component analysis (PCA) was employed to investigate the effect of infrared spectral region (near or middle), calibration algorithm (principal component regression, partial least squares or multiple linear regression) and pre-processing procedure (derivative, smoothing and variable selection) in the resulting root-mean-square error of prediction (RMSEP). The PCA score plots revealed that all properties can be satisfactorily predicted by multiple linear regression in the 1600-2500 nm region, with variables selected by a genetic algorithm, using any pre-processing technique.
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018
Methanol gasoline, known as a new energy, has a certain degree of damage to automobile. The aim of this work was to identify and quantify the methanol in methanol gasoline using three-dimensional fluorescence spectroscopy technique combined with second order chemometric methods. Parallel factor analysis (PARAFAC) and selfweighted alternating trilinear decomposition (SWATLD) methods were used to analyse artificial samples. However, the obtained results by PARAFAC were not satisfactory. On the other hand, excellent prediction results were obtained when SWATLD model was applied, with recovery rate between 98.7 and 102.8%, and between 97.4 and 101.9% for two and three factor respectively. In order to verify the accuracy of the method, four real samples were predicted using SWATLD model with RMSEP between 0.1 μg/mL and 0.23 μg/mL.