Comprehensive detection of analytes in large chromatographic datasets by coupling factor analysis with a decision tree

Abstract

Abstract. Environmental samples typically contain hundreds or thousands of unique organic compounds, and even minor components may provide valuable insight into their sources and transformations. To understand atmospheric processes, individual components are frequently identified and quantified using gas chromatography–mass spectrometry. However, due to the complexity and frequently variable nature of such data, data reduction is a significant bottleneck in analysis. Consequently, only a subset of known analytes is often reported for a dataset, and large amounts of potentially useful data are discarded. We present an automated approach of cataloging and potentially identifying all analytes in a large chromatographic dataset and demonstrate the utility of our approach in an analysis of ambient aerosols. We use a coupled factor analysis–decision tree approach to deconvolute peaks and comprehensively catalog nearly all analytes in a dataset. Positive matrix factorization (PMF) of small subsections of multiple chromatograms is applied to extract factors that represent chromatographic profiles and mass spectra of potential analytes, in which peaks are detected. A decision tree based on peak parameters (e.g., location, width, and height), relative ratios of those parameters, peak shape, noise, retention time, and mass spectrum is applied to discard erroneous peaks and combine peaks determined to represent the same analyte. 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. We validate this approach using samples of known compounds and demonstrate the separation of poorly resolved peaks with similar mass spectra and the resolution of peaks that appear in only a fraction of chromatograms. As a case study, this method is applied to a complex real-world dataset of the composition of atmospheric particles, in which more than 1100 unique chromatographic peaks are resolved, and the corresponding peak information along with mass spectra are cataloged.

Chemical Signatures of Seasonally Unique Anthropogenic Influences on Organic Aerosol Composition in the Central Amazon

“…Despite the compositional differences previously described, this observation was highly consistent between seasons: 91% of compounds traced during the wet season were not identifiable, while 90% of compounds traced in the dry season were not identifiable. This is consistent with findings from an analysis of on-line GC–MS data from the GoAmazon campaign, which found that approximately 90% of compounds observed could not be matched to entries in the NIST/NIH/EPA mass spectral database . Of the three influence categories described in this work, urban influenced organics were by numbers the least well known, with 95% of dry season and 96% of wet season urban influenced compounds not identifiable by database match.…”

“…This is consistent with findings from an analysis of on-line GC−MS data from the GoAmazon campaign, which found that approximately 90% of compounds observed could not be matched to entries in the NIST/NIH/ EPA mass spectral database. 50 Of the three influence categories described in this work, urban influenced organics were by numbers the least well known, with 95% of dry season and 96% of wet season urban influenced compounds not identifiable by database match. Burning-associated compounds were consistently better known, with 85% of dry season and 83% of wet season biomass burning influenced species not available as standards or present in mass spectral databases, while the background/biogenic compounds fell in a middle ground, with 90% of wet season and 89% of dry season compounds not identifiable.…”