Comparative Visualization and Analysis for GCxGC

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$99,457.00
Award Year:
2004
Program:
SBIR
Phase:
Phase I
Contract:
1R43RR020256-01
Agency Tracking Number:
RR020256
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
GC IMAGE, LLC
GC IMAGE, LLC, BOX 57403, LINCOLN, NE, 68505
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
STEPHEN REICHENBACH
(402) 310-4503
REICH@INEBRASKA.COM
Business Contact:
STEPHEN REICHENBACH
(402) 310-4503
reich@inebraska.com
Research Institution:
n/a
Abstract
DESCRIPTION (provided by applicant): This project will investigate and develop methods for computer-based comparative visualization and interactive analysis of complex data generated by comprehensive two-dimensional gas chromatography (GCxGC). GCxGC is an emerging technology that provides an order-of-magnitude increase in separation capacity over traditional GC. Today, a few advanced laboratories around the world are pioneering GCxGC for a variety of applications such as environmental analyses of exposure profiles in air, soil, food, and water, and health-care analyses for the identification and quantification of toxic products in blood, urine, milk, and breath samples. Many analyses in these applications require detailed chemical comparisons of samples, e.g., the comparison of a new sample with a previous sample to detect and monitor changes or the comparison of a test sample with a reference sample to identify and quantify differences in chemical composition. GCxGC is a powerful technology for comparative analyses. The principal challenge for GCxGC is the difficulty of analyzing and interpreting the large, complex data it generates. The quantity and complexity of GCxGC data necessitates the investigation and development of new information technologies. This project will develop and demonstrate methods and tools for comparative visualization and interactive analysis of GCxGC data. Visualizations for comparative analyses of GCxGC scalar fields will be developed using a Multi-View Multi-Layer (MVML) framework in a Model-View-Controller (MVC) architecture. Within the MVML framework, several different strategies for visualization will be developed, utilizing colorization and animation. The expected results will provide a foundation for development of a computer-based system for comparative GCxGC analyses of complex samples and will advance knowledge of frameworks and methods for visualization and interactive analysis of GCxGC data and other two-dimensional scalar fields.

* information listed above is at the time of submission.

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