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The types of crude oil for producing asphalt have a decisive influence on various performance measures (including aging resistance and durability) of asphalt. To discriminate and predict the crude oil source of different asphalt samples, a discrimination model was established using 12 greatly different infrared (IR) characteristic absorption peaks (CAPs) as predictive variables. The model was established based on diverse fingerprint recognition technologies (such as principal component analysis (PCA) and multivariate logistic regression analysis) by using attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR). In this way, the crude oil source of different asphalt samples can be effectively discriminated. At first, by using PCA, the 12 CAPs in the IR spectra of asphalt samples were subjected to dimension reduction processing to control the variables of key factors. Moreover, the scores of various principal components in asphalt samples were calculated. Afterwards, the scores of principal components were analysed through modelling based on multivariate logistic regression analysis to discriminate and predict the crude oil source of different asphalt samples. The result showed that the logistic regression model shows a favourable goodness of fit, with the prediction accuracy reaching 93.9% for the crude oil source of asphalt samples. The method exhibits some outstanding advantages (including ease of operation and high accuracy), which is important when controlling the source and quality and improving the performance of asphalt.

Crude Oil Source Identification of Asphalt via ATR-FTIR Approach Combined with Multivariate Statistical Analysis