Title of article :
Characterisation of heavy oils using near-infrared spectroscopy: Optimisation of pre-processing methods and variable selection Original Research Article
Author/Authors :
Jérémy Laxalde، نويسنده , , Cyril Ruckebusch، نويسنده , , Olivier Devos، نويسنده , , Noémie Caillol، نويسنده , , François Wahl، نويسنده , , Ludovic Duponchel، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
Pages :
8
From page :
227
To page :
234
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.
Keywords :
Spectral pre-processing , Genetic Algorithm , Heavy oils , Partial Least Squares regression , variable selection , Near infrared spectrocopy
Journal title :
Analytica Chimica Acta
Serial Year :
2011
Journal title :
Analytica Chimica Acta
Record number :
1026714
Link To Document :
بازگشت