Harvesting information from chromatographic data

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In all walks of life there are complex samples which must be studied. These samples can comprise hundreds (if not thousands) of different chemicals and are found in situations that range from identifying and measuring unsaturated fatty acids in fish oils and foods, to tracking changes in the composition of petrochemical samples during refining processes, to screening environmental samples for known and/or emerging pollutants, or profiling metabolites in the body to identify a disease state. Powerful tools such as gas chromatography/mass spectrometry (GC/MS) and comprehensive two-dimensional gas chromatography (GCxGC) are used to perform many of these measurements. With these techniques, researchers can separate and analyze incredibly complex samples. Two challenges with these techniques are ascertaining the identity of the compounds that have been separated, and efficiently transforming the incredibly detailed data the instruments provide into usable information from which a decision can be made. The proposed research program addresses both of these issues. First through study of the chemistry governing the separation process, and second through the development of mathematical and computational tools that efficiently extract information from the data. Through modeling the properties that govern the separation process and linking those back to the structures of molecules, we will develop a new tool to aid in the identification of unknown molecules. Our new mathematical tools work with these methods to rapidly interpret the data and generate useful information about a set of samples.