Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6640252 | Fuel | 2013 | 7 Pages |
Abstract
A chemometric approach has been developed for characterization of gasoline samples regarding their quality. Attenuated total reflectance - infrared spectrometric data were processed by genetic algorithm (GA) and successive projection algorithm (SPA) feature selection techniques, being employed as an initial step prior to apply a discriminative tool. It was aimed to classify the fuel samples according to their quality passed/failed data. Chemometric predictive procedures were developed using quadratic discriminant analysis (QDA) combined with GA and SPA as a feature subset and feature selection strategy. Results showed 93.3% and 95.6% accuracy for SPA-QDA and GA-QDA models respectively.
Keywords
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Chemical Engineering (General)
Authors
Mohammadreza Khanmohammadi, Amir Bagheri Garmarudi, Keyvan Ghasemi, Miguel de la Guardia,