Abstract

Journal of Near Infrared Spectroscopy
Volume 12 Issue 2, Pages 85–91 (2004)
doi: 10.1255/jnirs.411

A new genetic algorithm applied to the near infrared analysis of gasolines

Carlos E. Boschettia and Alejandro C. Olivieri^b,*
aLaboratorio de Certificación de Calidad, Petrobras Energía S.A., Planta PGSM, Avda Presidente Perón 1000, Puerto General San Martín (S2202CQR), Argentina
^bDepartamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, Rosario (S2002LRK), Argentina. E-mail: aolivier@fbioyf.unr.edu.ar

A new genetic algorithm is presented for sensor selection, in order to predict the octane number and the content of benzene, toluene and total aromatics in gasolines, based on partial least squares multivariate calibration of near infrared spectral data. The algorithm separately labels each of the selected wavenumber ranges with a relative inclusion ranking. Relative prediction errors (% of root mean square error with respect to the mean calibration value) achieved after sensor selection are: octane number, 0.5%, benzene, 0.6%, toluene, 0.6%, and total aromatics, 1.1%. The results are compared with those provided by the spark-ignition engine fuel research method for octane number and by gas chromatography for the remaining parameters.

Keywords: near infrared spectroscopy, gasoline analysis, wavenumber selection, genetic algorithm, multivariate calibration, partial least squares

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