Collaborative Research: SEI: Information Modeling for Comparative Visualizations and Analyses - Informatics for Comprehensive Two-Dimensional Gas Chromatography

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This project is investigating and developing information modeling for comparative visualizations and analyses of complex chemical separations produced by comprehensive two-dimensional gas chromatography (GCxGC). GCxGC is a powerful new technology that provides an order-of-magnitude increase in separation capacity over traditional GC. A few advanced laboratories are pioneering GCxGC for a variety of applications, such as environmental monitoring and health-care, but researchers lack advanced information technologies for analyzing and interpreting the large, complex data generated by GCxGC.The models being developed in this project abstract essential analytical information for comparative visualization and analyses. Analytical methods are being developed to extract model components from the structural characteristics of both GCxGC intensity data and data from GCxGC with mass spectrometry (GCxGC-MS). The models support calibration for varying chemical concentrations and measurements of the quality of fit to the data. Comparative visualizations of the GCxGC information models employ multiple viewing strategies with colorization and animation to provide users with multiple avenues for interactive analyses. Interactive analyses support comparisons with view navigation and controls, queries and edits of model-based information and metadata, and structured reporting.The model-based information technologies facilitate advanced chemical analyses in a broad range of problems. The project is demonstrating their value for two applications of GCxGC. One application analyzes changes of complex mixtures of petroleum hydrocarbons in the environment following an oil spill. The other application uses GCxGC to fingerprint fragrances for classification and identification.