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Classification of highly similar crude oils using data sets from comprehensive two-dimensional gas chromatography and multivariate techniques

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Author: Mispelaar, V.G. van · Smilde, A.K. · Noord, O.E. de · Blomberg, J. · Schoenmakers, P.J.
Institution: TNO Kwaliteit van Leven
Source:Journal of Chromatography A, 1-2, 1096, 156-164
Identifier: 238821
doi: doi:10.1016/j.chroma.2005.09.063
Keywords: Nutrition · Analytical research · Clustering · Crude oil characterization · Discrimination · GC×GC · Validation · Crude petroleum · Data processing · Mathematical models · Mixtures · Separation · Volatile organic compounds · Clustering · Crude oil characterization · Discrimination · GC×GC · Validation · Gas chromatography · petroleum · volatile agent · article · chemical analysis · cluster analysis · gas chromatography · mathematical analysis · multivariate analysis · priority journal · separation technique · Chromatography, Gas · Multivariate Analysis · Petroleum · Principal Component Analysis


Comprehensive two-dimensional gas chromatography (GC × GC) has proven to be an extremely powerful separation technique for the analysis of complex volatile mixtures. This separation power can be used to discriminate between highly similar samples. In this article we will describe the use of GC × GC for the discrimination of crude oils from different reservoirs within one oil field. These highly complex chromatograms contain about 6000 individual, quantified components. Unfortunately, small differences in most of these 6000 components characterize the difference between these reservoirs. For this reason, multivariate-analysis (MVA) techniques are required for finding chemical profiles describing the differences between the reservoirs. Unfortunately, such methods cannot discern between 'informative variables', or peaks describing differences between samples, and 'uninformative variables', or peaks not describing relevant differences. For this reason, variable selection techniques are required. A selection based on information between duplicate measurements was used. With this information, 292 peaks were used for building a discrimination model. Validation was performed using the ratio of the sum of distances between groups and the sum of distances within groups. This step resulted in the detection of an outlier, which could be traced to a production problem, which could be explained retrospectively. © 2005 Elsevier B.V. All rights reserved.