ScienceDirect® Home Skip Main Navigation Links
You have guest access to ScienceDirect. Find out more.
 
Home
Browse
My Settings
Alerts
Help
 Quick Search
 Search tips (Opens new window)
    Clear all fields    
advertisementadvertisement
Journal of Chromatography A
Volume 1096, Issues 1-2, 25 November 2005, Pages 156-164
Chemical Separations and Chemometrics
 
Font Size: Decrease Font Size  Increase Font Size
 Abstract - selected
Article
Purchase PDF (712 K)

 
 
 
Related Articles in ScienceDirect
View More Related Articles
 
Special issue
View Record in Scopus
 
doi:10.1016/j.chroma.2005.09.063    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2005 Elsevier B.V. All rights reserved.

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

V.G. van Mispelaara, Corresponding Author Contact Information, E-mail The Corresponding Author, A.K. Smildeb, c, O.E. de Noorda, J. Blomberga and P.J. Schoenmakersc

aShell Global Solutions International B.V., Analytical Problem Solving Amsterdam, P.O. Box 38000, 1030 BN Amsterdam, The Netherlands bTNO Quality of Life, Zeist, The Netherlands cUniversity of Amsterdam, Amsterdam, The Netherlands

Available online 19 October 2005.

Purchase the full-text article



References and further reading may be available for this article. To view references and further reading you must purchase this article.

Abstract

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.

Keywords: GC×GC; Crude oil characterization; Discrimination; Clustering; Validation

Article Outline

1. Introduction
2. Theory
2.1. GC × GC
2.2. Data analysis
2.2.1. Exploratory methods
2.2.1.1. PCA
2.2.1.2. Projection pursuit
2.2.2. Supervised techniques
2.2.2.1. PCDA
2.2.3. Validation
3. Experimental
3.1. Instrumentation
3.2. Instrument control and data processing
3.3. Samples
4. Results and discussion
4.1. Pre-processing
4.1.1. Alignment
4.1.2. Variable selection
4.1.3. Manual selection
5. Conclusions
Acknowledgements
References












Journal of Chromatography A
Volume 1096, Issues 1-2, 25 November 2005, Pages 156-164
Chemical Separations and Chemometrics
 
Home
Browse
My Settings
Alerts
Help
Elsevier.com (Opens new window)
About ScienceDirect  |  Contact Us  |  Information for Advertisers  |  Terms & Conditions  |  Privacy Policy
Copyright © 2008 Elsevier B.V. All rights reserved. ScienceDirect® is a registered trademark of Elsevier B.V.