CLASSIFICATION OF CRUDE-OIL GAS CHROMATOGRAMS BY PATTERN-RECOGNITION TECHNIQUES

Pattern recognition methods have been employed to classify crude oils based on their gas chromatograms. Four oil types were represented by gas chromatograms taken before and after artificial weathering. The chromatograms were hand digitized and coded with 13 descriptors each—peak areas for the normal alkanes for C16 through C25 plus pristane and phytane and also one descriptor characterizing the unresolved background. A statistical algorithm was used to find the best subsets of descriptors to use with Bayesian discriminant analysis. A variety of different partitions of the data set showed the similarities of some classes of oils and some dissimilarities for others. The nonparametric linear learning machine method of discriminant training was also applied to various partitions of the data. Predictive abilities as high as 100% were obtained for some of the partitions of the data set. © 1979, American Chemical Society. All rights reserved.