AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Comprehensive Detection of All Analytes in a Large Chromatographic Dataset of Complex Environmental Samples
SUNGWOO KIM, Gabriel Isaacman-VanWertz, Virginia Polytechnic Institute and State University
Abstract Number: 427 Working Group: Instrumentation and Methods
Abstract Gas chromatography/mass spectrometry is a common analytical method used in environmental analysis to separate and identify individual compounds within a complex mixture. However, in nearly all cases, data reduction requires manual inspection of representative chromatograms to catalog potential chromatographic peaks of interest for more in-depth analysis. This approach represents unique challenges in the analysis of complex environmental mixtures. Ambient aerosols may contain hundreds or thousands of unique organic compounds, and even minor components may provide valuable insight into particle sources and formation chemistry. Furthermore, due to the highly dynamic nature of aerosol composition so many chromatograms may need to be inspected to fully catalog all constituents. These difficulties have limited comprehensive chromatographic analysis of atmospheric aerosols, with typically only a few dozen analytes reported for a dataset and the discarding of a large amount of potentially useful data. We present here an automated approach of cataloging and potentially identifying all analytes in a large chromatographic dataset of ambient aerosols. We use Positive Matrix Factorization (PMF) of small sub-sections of multiple chromatograms to extract factors that describe individual or small numbers of analytes. Potential chromatographic peaks in these factors are evaluated based on features such as peak shape, noise, and retention time. With our approach, all analytes within the small section of the chromatogram are cataloged, and the process is repeated for overlapping sections across the chromatogram, generating a complete list of the retention times and estimated mass spectra of all peaks in a dataset. As a case study, we demonstrate that in real-world chromatograms of ambient aerosols, even a small (e.g. 10 second) section of a chromatogram may contain several dozen chromatographic peaks. We demonstrate that this method extracts peaks that appear in only a fraction of chromatograms, so provides an automated and means to build a complete list of analytes in a dataset with minimal user interaction.