Articles | Volume 20, issue 2
Atmos. Chem. Phys., 20, 1021–1041, 2020
https://doi.org/10.5194/acp-20-1021-2020
Atmos. Chem. Phys., 20, 1021–1041, 2020
https://doi.org/10.5194/acp-20-1021-2020

Research article 27 Jan 2020

Research article | 27 Jan 2020

Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments

Abigail R. Koss et al.

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Cited articles

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Bar-Joseph, Z., Gifford, D. K., and Jaakkola, T. S.: Fast optimal leaf ordering for hierarchical clustering, Bioinformatics, 17, S22–S29, https://doi.org/10.1093/bioinformatics/17.suppl_1.S22, 2001. 
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Bloss, C., Wagner, V., Bonzanini, A., Jenkin, M. E., Wirtz, K., Martin-Reviejo, M., and Pilling, M. J.: Evaluation of detailed aromatic mechanisms (MCMv3 and MCMv3.1) against environmental chamber data, Atmos. Chem. Phys., 5, 623–639, https://doi.org/10.5194/acp-5-623-2005, 2005b. 
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Oxidation chemistry of organic compounds in the atmosphere produces a diverse spectrum of products. This diversity is difficult to represent in air quality and climate models, and in laboratory experiments it results in large and complex datasets. This work evaluates several methods to simplify the chemistry of oxidation systems in environmental chambers, including positive matrix factorization, hierarchical clustering analysis, and gamma kinetics parameterization.
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