Article ID Journal Published Year Pages File Type
6640252 Fuel 2013 7 Pages PDF
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.
Related Topics
Physical Sciences and Engineering Chemical Engineering Chemical Engineering (General)
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