Articles | Volume 13, issue 6
https://doi.org/10.5194/amt-13-2995-2020
https://doi.org/10.5194/amt-13-2995-2020
Research article
 | 
09 Jun 2020
Research article |  | 09 Jun 2020

Comparison of dimension reduction techniques in the analysis of mass spectrometry data

Sini Isokääntä, Eetu Kari, Angela Buchholz, Liqing Hao, Siegfried Schobesberger, Annele Virtanen, and Santtu Mikkonen

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Cited articles

Äijälä, M., Heikkinen, L., Frohlich, R., Canonaco, F., Prevot, A. S. H., Junninen, H., Petaja, T., Kulmala, M., Worsnop, D., and Ehn, M.: Resolving anthropogenic aerosol pollution types – deconvolution and exploratory classification of pollution events, Atmos. Chem. Phys., 17, 3165–3197, https://doi.org/10.5194/acp-17-3165-2017, 2017. 
Allan, J. D., Jimenez, J. L., Williams, P. I., Alfarra, M. R., Bower, K. N., Jayne, J. T., Coe, H., and Worsnop, D. R.: Quantitative sampling using an Aerodyne aerosol mass spectrometer: 1. Techniques of data interpretation and error analysis, J. Geophys. Res.-Atmos., 108, 4090, doi:10.1029/2002JD002358, 2003. 
Brunet, J. P., Tamayo, P., Golub, T. R., and Mesirov, J. P.: Metagenes and molecular pattern discovery using matrix factorization, P. Natl. Acad. Sci. USA, 101, 4164–4169, https://doi.org/10.1073/pnas.0308531101, 2004. 
Cattel, R. B.: The scree test for the number of factors. Multivariate behavioral research, Multivar. Behav. Res., 1, 245–276, 1966. 
Chakraborty, A., Bhattu, D., Gupta, T., Tripathi, S. N., and Canagaratna, M. R.: Real-time measurements of ambient aerosols in a polluted Indian city: Sources, characteristics, and processing of organic aerosols during foggy and nonfoggy periods, J. Geophys. Res.-Atmos., 120, 9006–9019, https://doi.org/10.1002/2015JD023419, 2015. 
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Short summary
Online mass spectrometry produces large amounts of data. These data can be interpreted with statistical methods, enabling scientists to more easily understand the underlying processes. We compared these techniques on car exhaust measurements. We show differences and similarities between the methods and give recommendations on applicability of the methods on certain types of data. We show that applying multiple methods leads to more robust results, thus increasing reliability of the findings.