Robust, automated data tools for multidimensional separations in exposure assessment.

点击翻译按钮获取中文摘要

Gas chromatography (GC), and more recently comprehensive two-dimensional gas chromatography (GCxGC) are technologies that support a myriad of scientific inquiries. These tools are generally applicable to the separation, quantification, and identification of small organic molecules (<30-40 carbons) in a mixture. In the case of GCxGC, we routinely perform separations of mixtures comprising 2000-8000 different compounds. These analytical tools are critical for the analysis of petroleum, environmental contaminants, forensic science, and health research. Indeed GC was the technology that allowed (for better or for worse) the proliferation of the entire artificial flavouring industry in the 1950s and 60s. Overarching goals for the research program include the development of data handling tools to improve our ability to interpret chemical data, in particular that arising from GC and GCxGC separations and to provide these tools to the community. As a node in The Metabolomics Innovation Centre, Canada's national metabolomics platform, we provide access to expertise and GCxGC-MS instrumentation to the Canadian and international research communities through our service operations. Undergrad, grad, and even high school students are integrated throughout the highly collaborative, multidisciplinary program, gaining valuable skills in analytical chemistry, computer programming, project management, designing multidisciplinary research projects, and communicating scientific results. Specific projects include the development of an online portal for our automated tools that identify important features in highly complex chemical data and developing a robust, automated platform for handling data sets from GCxGC studies with hundreds or thousands of samples. Other students will continue to develop our benchmark-setting thermodynamic models of chromatographic retention time, improving our ability to identify unknown compounds in complex samples such as environmental samples containing emerging contaminants of concern. These new data tools require data to test them. We will perform pilot studies in several areas to develop sets of data for algorithm testing that also have some meaningful purpose, and which will serve as the foundation for future research projects. Pilot studies include new approaches to the analysis of biofluids (urine, blood, etc) to support future health diagnostics; studies of the relationship between methods of combustion/vaporization and the composition of mainstream cannabis smoke; and development of non-target environmental analysis tools by performing surveys of contamination around orphaned oil and gas wells and legacy pollutants potentially affecting soil and water on First Nations and Métis lands.