Comprehensive two-dimensional gas chromatography (GC Â GC) presents information technology challenges in data handling, visualization, processing, analysis and reporting. With its significantly greater separation capacity, GC Â GC generates data sets that are two to three orders-of-magnitude larger than for conventional GC and that exhibit an order-of-magnitude or more distinct peaks in complex multidimensional patterns. GC Â GC is a much richer source of data for chemical analyses, but extracting relevant information from the large, complex data sets requires advanced information technologies. This paper reviews a range of state-of-the-art information technologies for GC Â GC, including graphical user-interfaces (GUIs), computer graphics for data visualization, data file formats for storage and interchange, digital image processing, image analysis and pattern recognition, data and information formats, and software architecture and engineering. D 2004 Published by Elsevier B.V.

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This paper describes a language for expressing criteria for chemical identification with comprehensive two-dimensional gas chromatography paired with mass spectrometry (GC×GC-MS) and presents computer-based tools implementing the language. The Computer Language for Indentifying Chemicals (CLIC) allows expressions that describe rules (or constraints) for selecting chemical peaks or data points based on multi-dimensional chromatographic properties and mass spectral characteristics. CLIC offers chromatographic functions of retention times, functions of mass spectra, numbers for quantitative and relational evaluation, and logical and arithmetic operators. The language is demonstrated with the compound-class selection rules described by Welthagen et al. [W. Welthagen, J. Schnelle-Kreis, R. Zimmermann, J. Chromatogr. A 1019 (2003) 233-249]. A software implementation of CLIC provides a calculator-like graphical user-interface (GUI) for building and applying selection expressions. From the selection calculator, expressions can be used to select chromatographic peaks that meet the criteria or create selection chromatograms that mask data points inconsistent with the criteria. Selection expressions can be combined with graphical, geometric constraints in the retention-time plane as a powerful component for chemical identification with template matching or used to speed and improve mass spectrum library searches.

Identifying compounds of interest for peaks in data generated by comprehensive two-dimensional gas chromatography (GC × GC) is a critical analytical task. Manually identifying compounds is tedious and time-consuming. An alternative is to use pattern matching. Pattern matching identifies compounds by matching previously observed patterns with known peaks to newly observed patterns with unidentified peaks. The fundamental difficulty of pattern matching comes from peak pattern distortions that are caused by differences in data acquisition conditions. This paper investigates peak pattern variations related to varying oven temperature ramp rate and inlet gas pressure and evaluates two types of affine transformations for matching peak patterns. The experimental results suggest that, over the experimental ranges, the changes in temperature ramp rate generate non-linear pattern variations and changes in gas pressure generate nearly linear pattern variations. The results indicate the affine transformations can largely remove the pattern variations and can be used for applications such as pattern matching and normalizing retention times to retention indices.

Comprehensive two-dimensional gas chromatography (GCxGC) is an emerging technology for chemical separation that provides an order-of-magnitude increase in separation capacity over traditional gas chromatography. GCxGC separates chemical species with two capillary columns interfaced by two-stage thermal desorption. Because GCxGC is comprehensive and has high separation capacity, it can perform multiple traditional analytical methods with a single analysis. GCxGC has great potential for a wide variety of environmental sensing applications, including detection of chemical warfare agents (CWA) and other harmful chemicals.This paper demonstrates separation of nerve agents sarin and soman from a matrix of gasoline and diesel fuel. Using a combination of an initial column separating on the basis of boiling point and a second column separating on the basis of polarity, GCxGC clearly separates the nerve agents from the thousands of other chemicals in the sample. The GCxGC data is visualized, processed, and analyzed as a two-dimensional digital image using a software system for GCxGC image processing developed at the University of Nebraska -Lincoln.