title: Characterisation of heavy oils using near-infrared spectroscopy: optimisation of pre-processing methods and variable selection.
abstract: In this study, chemometric predictive models were developed from near infrared (NIR) spectra for the quantitative determination of saturates, aromatics, resins and asphaltens (SARA) in heavy petroleum products. Model optimisation was based on adequate pre-processing and/or variable selection. In addition to classical methods, the potential of a genetic algorithm (GA) optimisation, which allows the co-optimisation of pre-processing methods and variable selection, was evaluated. The prediction results obtained with the different models were compared and decision regarding their statistical significance was taken applying a randomization t-test. Finally, the results obtained for the root mean square errors of prediction (and the corresponding concentration range) expressed in %(w/w), are 1.51 (14.1-99.1) for saturates, 1.59 (0.7-61.1) for aromatics, 0.77 (0-34.5) for resins and 1.26 (0-14.7) for asphaltens. In addition, the usefulness of the proposed optimisation method for global interpretation is shown, in accordance with the known chemical composition of SARA fractions.
authors.affiliation: LASIR, CNRS-Université Lille1 Sciences et technologies, bât. C5, 59655 Villeneuve d'Ascq Cedex, France.
authors.forename: Jérémy
authors.initials: J
authors.lastname: Laxalde
authors.forename: Cyril
authors.initials: C
authors.lastname: Ruckebusch
authors.forename: Olivier
authors.initials: O
authors.lastname: Devos
authors.forename: Noémie
authors.initials: N
authors.lastname: Caillol
authors.forename: François
authors.initials: F
authors.lastname: Wahl
authors.forename: Ludovic
authors.initials: L
authors.lastname: Duponchel