Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration.

A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.

[1]  M. C. Ortiz,et al.  Qualitative and quantitative aspects of the application of genetic algorithm-based variable selection in polarography and stripping voltammetry , 1999 .

[2]  L. Bokobza.  Near Infrared Spectroscopy , 1998 .

[3]  R. Jones.  Determination of Sulfur in Gasoline by X-Ray Emission Spectrography , 1961 .

[4]  R. Leardi,et al.  Variable selection for multivariate calibration using a genetic algorithm: prediction of additive concentrations in polymer films from Fourier transform-infrared spectral data , 2002 .

[5]  Kus Hidajat,et al.  Quality Characterisation of Crude Oils by Partial Least Square Calibration of NIR Spectral Profiles , 2000 .

[6]  Jeffrey J. Kelly,et al.  Nondestructive analytical procedure for simultaneous estimation of the major classes of hydrocarbon constituents of finished gasolines , 1990 .

[7]  R. Wormsbecher,et al.  Quantitative determination of sulfur compounds in FCC gasolines by AED. A study of the effect of catalyst type and catalytic conditions on sulfur distribution , 1993 .

[8]  M. J. Navas,et al.  Chemiluminescent Methods in Petroleum Products Analysis , 2000 .

[9]  R. Jungers,et al.  Determination of total sulfur in gasoline by gas chromatography with a flame photometric detector , 1977 .

[10]  John B. Cooper,et al.  Comparison of Near-IR, Raman, and Mid-IR Spectroscopies for the Determination of BTEX in Petroleum Fuels , 1997 .

[11]  Israel Schechter,et al.  On-line remote prediction of gasoline properties by combined optical methods , 1997 .

[12]  V. Bellon-Maurel,et al.  Using Genetic Algorithms to Select Wavelengths in Near-Infrared Spectra: Application to Sugar Content Prediction in Cherries , 2000 .

[13]  M. C. U. Araújo,et al.  The successive projections algorithm for variable selection in spectroscopic multicomponent analysis , 2001 .

[14]  A. Salgó,et al.  Prediction of Gasoline Properties with near Infrared Spectroscopy , 1998 .

[15]  Maria Fernanda Pimentel,et al.  Aspects of the successive projections algorithm for variable selection in multivariate calibration applied to plasma emission spectrometry , 2001 .