SBIR Phase I: Automated Pattern Recognition in Images Produced by Comprehensive Two-Dimensional Gas Chromatography
Small Business Information
PO Box 57403, Lincoln, NE, 68505
Name: Stephen Reichenbach
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AbstractThis Small Business Innovation Research Phase I project initiates rigorous investigation of automated pattern recognition in images produced by comprehensive two-dimensional gas chromatography. Comprehensive two-dimensional gas chromatography (GCxGC) is an emerging technology for chemical separation that provides a multiplicative increase in separation capacity over traditional GC. With this greatly increased performance, GCxGC generates data in significantly larger quantity and with significantly greater complexity. The quantity and complexity of GCxGC data makes human analyses of GCxGC images difficult and time-consuming and motivates the need for automated processing. This Phase I project undertakes both experimental and theoretical investigations into automating the process of matching observed patterns of chemical separations against previously recorded patterns annotated by human experts. The goals are to determine promising statistical models for GCxGC pattern matching and to demonstrate the feasibility of automated recognition. In this Phase I work, important anticipated results include statistical characterization of pattern variations and warping in GCxGC images, a catalog of useful annotations of previously observed pattern templates, and development of a prototype algorithm for automated pattern recognition. Phase I results, characterizing GCxGC patterns, cataloging annotations, and demonstrating the feasibility of automated processing, will provide a foundation for Phase II research aimed at developing commercially sign cant GCxGC methods. This research has high potential impact for a variety of applications. Commercial applications of GC include analyses of petroleum, environmental samples, foods and beverages, fragrances, and toxins (e.g., chemical warfare agents). The availability of software for automated recognition of chemical components from GCxGC images will facilitate adoption of GCxGC technology in laboratories using traditional GC and will contribute to the development of new markets, which require superior separation performance.
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