Abstract
This work describes a new approach to predict the properties of crude oil by using near infrared spectrum with a reflective fiber-optic probe. The absorbance spectra of crude oil samples were pre-processed by the first derivative. The pretreated near-infrared spectrum data were analyzed with principal component analysis to detect eventual outliers in the data set. The spectral data were correlated with crude oil property parameters by partial least squares regression. The comparison results of predicted and measured values for API gravity and sulfur content revealed a better performance for the model.
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