Evaluation of multiblock NIR/MIR PLS predictive models to detect adulteration of diesel/biodiesel blends by vegetal oil

Chemometrics and Intelligent Laboratory Systems

Volumn 106, Issue 2, 2011, Pages 190-197

  Gaydou, V ^a   Kister, J ^a   Dupuy, N ^a  

^a AIX MARSEILLE UNIVERSITÉ   (France)

Author keywords

Diesel biodiesel blends; MIR; Multiblock; NIR; PLS regression; Vegetal oil

Indexed keywords

BIODIESEL; VEGETABLE OIL;

ACCURACY; ANALYTICAL ERROR; ARTICLE; CHEMOMETRIC ANALYSIS; COMBINATORIAL CHEMISTRY; CONTROLLED STUDY; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; NEAR INFRARED SPECTROSCOPY; PREDICTIVE VALUE; PRIORITY JOURNAL; PROCESS DEVELOPMENT; QUANTITATIVE ANALYSIS; SENSITIVITY AND SPECIFICITY; SUBSTITUTION REACTION;

EID: 79955105626     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemolab.2010.05.002     Document Type: Article

References (28)

1. Bastien Philippe, Vinzi Vincenzo Esposito, Tenenhaus Michel PLS generalised linear regression. Computational Statistics & Data Analysis 2005, 48:17-46.
2. Berglund A., Wold S. A serial extension of multiblock PLS. J. Chemom. 1999, 13:461-471.
3. Chunshan S. An overview of new approaches to deep desulfurization for ultraclean gasoline, diesel fuel and jet fuel. Catal. Today 2003, 86:211-263.
4. Breen J.P., Burch R., Hardacre C., Hill C.J., Krutzsch B., Bandl-Konrad B., Jobson E., Cider L., Blakeman P.G., Peace L.J., Twigg M.V., Preis M., Gottschling M. An investigation of the thermal stability and sulphur tolerance of Ag/γ-Al2O3 catalysts for the SCR of NOx with hydrocarbons and hydrogen. Appl. Catal., B Environ. 2007, 70:36-44.
5. Feuille de route pour les sources d'énergie renouvelables, COMMUNICATION DE LA COMMISSION AU CONSEIL ET AU PARLEMENT EUROPÉEN (2006) 848.
6. Oliveira Jefferson S., Montalvão Rafael, Daher Leila, Suarez Paulo A.Z., Rubim Joel C. Determination of methyl ester contents in biodiesel blends by FTIR-ATR and FTNIR spectroscopies. Talanta 2006, 69:1278-1284.
7. Pimentel M.F., Ribeiro G.M.G.S., da Cruz R.S., Stragevitch L., Pacheco J.G.A., Teixeira L.S.G. Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration. Microchem. J. 2006, 82:201-206.
8. Ozdemir D. Determination of octane number of gasoline using near infrared spectroscopy and genetic multivariate calibration methods. Pet. Sci. Technol. 2005, 23:1139-1152.
9. Felicio C., Bras L., Lopes J., Cabrita L., Menezes J. Comparison of PLS algorithms in gasoline and gas oil parameter monitoring with MIR and NIR. Chem. Intell. Lab. Sys. 2005, 78:74-80.
10. Cooper J. Chemometric analysis of Raman spectroscopic data for process control applications. Chem. Intell. Lab. Sys. 1999, 46:231-247.
11. Duewer D.L., Choquette S.J., Green T.L., Chesler S.N. Characterizing the uncertainty in near-infrared spectroscopic prediction of mixed-oxygenate concentrations in gasoline: sample-specific prediction intervals. Anal. Chem. 1998, 70:2972-2982.
12. Brudzewski K., Kesik A., Kolodziejczykb K., Zborowskab U., Ulaczykc J. Gasoline quality prediction using gas chromatography and FTIR spectroscopy: an artificial intelligence approach. Fuel 2006, 85:553-558.
13. Breitkreitz M.C., Raimundo I.M., Rohwedder J.J.R., Heronides C.P., Filho A.D., Jos'e G.E., Ara'ujo M.C.U. Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration. Analyst 2003, 128:1204-1207.
14. Mendes L.S., Oliveira F.C.C., Suarez P.A.Z., Rubim J.C. Determination of ethanol in fuel ethanol and beverages by Fourier transform (FT)-near infrared and FT-Raman spectrometries. Anal. Chim. Acta 2003, 493:219-231.
15. Chung H., Ku M.S., Lee J.S. Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene. Vibr. Spectrosc. 1999, 20:155-163.
16. Höskuldsson A. Prediction methods in science and technology. Basic Theory vol. 1 1996, 245. Thor Publishing, Copenhagen, Denmark.
17. Liang Y.-L., Kvalheim O.M. Heuristic evolving latent projections: resolving two-way multicomponent data. Anal. Chem. 1992, 64:936-946.
18. Stone M. Cross validation choice and assessment of statistical predictions. J. R. Stat. Soc. 1974, 36:111-147.
19. Karoui R., Mouazen A.M., Ramon H., Schoonheydt R., De Baerdemaeker J. Feasibility study of discriminating the manufacturing process and sampling zone in ripened soft cheeses using attenuated total reflectance MIR and fiber optic diffuse reflectance VIS-NIR spectroscopy. Food Res. Int. 2006, 39:588-597.
20. MacGregor J.F., Kourti T. Statistical process control of multivariate processes. Control Eng. Pract. 1995, 3:403-414.
21. Gerlach R.W., Kowalsky B.R., Wold H.O.A. Partial least-squares path modelling with latent variables. Anal. Chim. Acta 1979, 112:417-421.
22. Geladi P. Notes on the history and nature of partial least squares (PLS) modelling. J. Chemom. 1988, 2:231-246.
23. Westerhuis J., Coenegracht P. Multivariate modelling of the pharmaceutical two-step process of wet granulation and tableting with multiblock PLS. J. Chemom. 1997, 11:379-392.
24. Yang Y.K., Chang T.C. Experimental analysis and optimization of a photo resist coating process for photolithography in wafer fabrication. Microelectron. J. 2006, 37:746-751.
25. Wu W., Guo Q., Jouan-Rimbaud D., Massart D.L. Using contrasts as data pretreatment method in pattern recognition of multivariate data. Chem. Intell. Lab. Syst. 1999, 45:39-53.
26. Zhang M., Sheng G., Mu Y., Li W., Yu H., Harada H., Li Y. Rapid and accurate determination of VFAs and ethanol in the effluent of an anaerobic H2-producing bioreactor using near-infrared spectroscopy. Water Res. 2009, 43:1823-1830.
27. Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least-squares procedures. Anal. Chem. 1964, 36:1627-1639.
28. Bylesjö M., Cloarec O., Rantalainen M. Normalization and closure. Comp. Chemometr. 2009, 2.07:109-127.