PLS regression on spectroscopic data for the prediction of crude oil quality: API gravity and aliphatic/aromatic ratio
2012
Abbas, Ouissam | Rebufa, Catherine | Dupuy, Nathalie | Permanyer, Albert | Kister, Jacky
This work describes a chemometric approach for predicting quality parameters of crude oils by using the information present in spectroscopic data as Fourier Transform Infrared–Attenuated Total Reflectance (FTIR–ATR) absorption and Synchrounous Ultra Violet Fluorescence (SUVF). Using multivariate analysis such as Partial Least-Square (PLS) analysis, the predictive ability of spectroscopic techniques has been explored to estimate the American Petroleum Industry (API) gravity usually determined using standard physical methods and infrared structural/functional indices characterizing the repartition of aliphatic and aromatic structures present in crude oils. Giving global information on chemical compounds present in oil, FTIR–ATR also appears to be a rapid analytical method for quantifying changes in abundances of aliphatic and aromatic structures with the help of the infrared indices calculated from area ratio of specific bands. Then, a PLS model based on MIR data allows to predict this aliphatic/aromatic ratio for various crude oils and avoid time-consuming step of infrared peaks integration.
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