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[Classification of oils by attenuated total reflectance-Fourier transform infrared spectrometry combined with pattern recognition techniques].

In the present work, the combination of attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR) and pattern recognition, including principal components analysis (PCA) and hierarchical cluster analysis (HCA), is used as a fast and convenient analytical tool to classify oil samples. Twenty five samples including crude oils and fuel oils with different total contents of n-alkanes were analyzed. It was found that multiplicative scatter correction (MSC) and continuous wavelet transform (CWT) as a pretreatment method could improve the classification results of pattern recognition. The classification results were proved to be in agreement with the origin of the oil samples. The oils with high content of n-alkanes and those with low content were classified clearly by this developed method, but it still had some constraint to differentiating oils with little difference. The present work provides a feasible method for quick classification of oils, which can be used for the initial identification of spill oils and afford useful information for the further identification of the oils.