Abstract
This study describes a real-time classification methodology for petroleum products based upon the near-infrared (NIR) spectra. The proposed real-time classifier (RTC) is designed based on the combination of principal component analysis (PCA) and a Bayesian classifier. Principal component analysis is employed to extract essential features that are selected considering both classification power and easiness of implementation for classification of the spectra. Bayesian classifier minimizes classification error. The RTC based on NIR spectra offers the faster and more accurate identification capacity of products on-line than the conventional analyzers. The proposed RTC has been applied to classify six petroleum products: diesel, gasoline, kerosene, light gas oil, light straight-run, and naphtha. It has shown good classification power for industrial petroleum products.
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