Classification and determination of sulfur content in crude oil samples by infrared spectrometry

Determining and classifying the sulfur content of crude oil has long been of great importance because of its adverse economic and environmental effects. In this study, the total sulfur concentration in crude oil samples was determined and classified using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and chemometric methods. The methods designed for the analysis of crude oils are rapid, economical and non-destructive in the production process of the petroleum industry. Two sets of 70 and 31 samples in regression models were considered for the calibration and prediction sets, respectively. The calibration models were developed using the partial least squares regression model (PLS-R) and support vector machine regression model (SVM-R). Different pre-processing methods were also evaluated for the development of models. The preprocessing methods based on baseline correction, standard normal variate (SNV) and the auto scale were selected for regression and classification models. The use of SVM-R as a non-linear regression provided a model with significantly better root mean square error of prediction (RMSEP) values than the PLS-R model as a linear model. The ATR-FTIR spectral data were also applied by supervised classification method using the partial least squares-discriminant analysis (PLS-DA) and support vector machine-discriminant analysis (SVM-DA) for classifying crude oils based on sulfur content. The samples were classified into two classes according to the sulfur content into sweet and sour crude oil. The result of the classification found an accuracy of 96% and a classification error of 0.0384 for the prediction set in the PLS-DA algorithm. The results indicated that ATR-FTIR spectroscopy associated with multivariate calibration and classification models is a rapid and reliable approach for parallel quantification and qualification of the sulfur content present in crude oils.