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

M C Breitkreitz, I M Raimundo, J J R Rohwedder, C Pasquini, H A Dantas, G E Jose, M C U Araujo

Research output: Contribution to journalArticle

107 Citations (Scopus)

Abstract

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.

LanguageEnglish
Pages1204-1207
Number of pages4
JournalAnalyst
Volume128
Issue number9
DOIs
Publication statusPublished - 2003

Fingerprint

Gasoline
Near-Infrared Spectroscopy
Diesel fuels
Sulfur
Calibration
genetic algorithm
spectroscopy
sulfur
Spectroscopy
calibration
Linear regression
Genetic algorithms
Least-Squares Analysis
Linear Models
Mean square error
diesel
Sulfur determination
wavelength
Wavelength
Near infrared spectroscopy

Keywords

  • successive projections algorithm
  • variable selection
  • genetic algorithms
  • gasoline

Cite this

Breitkreitz, M. C., Raimundo, I. M., Rohwedder, J. J. R., Pasquini, C., Dantas, H. A., Jose, G. E., & Araujo, M. C. U. (2003). Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration. Analyst, 128(9), 1204-1207. https://doi.org/10.1039/b305265f
Breitkreitz, M C ; Raimundo, I M ; Rohwedder, J J R ; Pasquini, C ; Dantas, H A ; Jose, G E ; Araujo, M C U . / Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration. In: Analyst. 2003 ; Vol. 128, No. 9. pp. 1204-1207.
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Breitkreitz, MC, Raimundo, IM, Rohwedder, JJR, Pasquini, C, Dantas, HA, Jose, GE & Araujo, MCU 2003, 'Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration' Analyst, vol. 128, no. 9, pp. 1204-1207. https://doi.org/10.1039/b305265f

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration. / Breitkreitz, M C ; Raimundo, I M ; Rohwedder, J J R ; Pasquini, C ; Dantas, H A ; Jose, G E ; Araujo, M C U .

In: Analyst, Vol. 128, No. 9, 2003, p. 1204-1207.

Research output: Contribution to journalArticle

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T1 - Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration

AU - Breitkreitz, M C

AU - Raimundo, I M

AU - Rohwedder, J J R

AU - Pasquini, C

AU - Dantas, H A

AU - Jose, G E

AU - Araujo, M C U

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N2 - 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.

AB - 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.

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KW - genetic algorithms

KW - gasoline

U2 - 10.1039/b305265f

DO - 10.1039/b305265f

M3 - Article

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JO - Analyst

T2 - Analyst

JF - Analyst

SN - 0003-2654

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Breitkreitz MC, Raimundo IM, Rohwedder JJR, Pasquini C, Dantas HA, Jose GE et al. Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration. Analyst. 2003;128(9):1204-1207. https://doi.org/10.1039/b305265f