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Software launched for discovery-based analysis of complex samples

Submitted by Diana Knight on April 6, 2021 - 3:57pm
ChromaTOF Tile example

A commercial version of software developed by the Synovec research group has been licensed and launched by LECO Corporation, a world leader in GC×GC-TOFMS technology, through CoMotion, UW's collaborative innovation hub.

Professor Robert Synovec’s research group pioneered the development of experimental design driven, discovery-based data analysis software for comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). The software implements a tile-based Fisher ratio algorithm that readily overcomes chromatographic retention time shifting problems and is highly effective at uncovering statistically significant trace analytes. This enables the user community to discover chemical class-distinguishing analytes in complex datasets for various applications such as metabolomics, petroleum-based fuels, biofuels, forensics, environmental, food quality and safety, and industrial feed stocks. Key contributors to the project were Luke Marney, PhD ‘13, Brendon Parsons, PhD ‘16, and Sarah Prebihalo, PhD ’20.ChromaTOF Tile Analytical Software. GCxGC-TOFMS Data Analysis: Turn Your Data into Chemistry

The Synovec group worked with LECO Corporation in the development of a commercial version of this novel data analysis software platform known as ChromaTOF Tile. After several years of development, the Synovec group is proud that their software was licensed and launched by LECO, the leading manufacturer of the instrumentation the group uses.

ChromaTOF Tile logo

For more information about Prof. Synovec and his research, visit his faculty page or research group website.

Professor Synovec says, "We are delighted to see our research come to fruition in the release of ChromaTOF Tile. We anticipate that the GCxGC users community will benefit greatly from this discovery-based data analysis tool to address the many challenges stemming from the supervised analysis of complex data sets."

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