Articles | Volume 13, issue 6
https://doi.org/10.5194/amt-13-2995-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-2995-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jun 2020
Research article |
| 09 Jun 2020
| 09 Jun 2020
Comparison of dimension reduction techniques in the analysis of mass spectrometry data
Department of Applied Physics, University of Eastern Finland,
Kuopio, 70210, Finland
Eetu Kari
Department of Applied Physics, University of Eastern Finland,
Kuopio, 70210, Finland
currently at: Neste Oyj, Espoo, 02150, Finland
Angela Buchholz
Department of Applied Physics, University of Eastern Finland,
Kuopio, 70210, Finland
Liqing Hao
Department of Applied Physics, University of Eastern Finland,
Kuopio, 70210, Finland
Siegfried Schobesberger
Department of Applied Physics, University of Eastern Finland,
Kuopio, 70210, Finland
Annele Virtanen
Department of Applied Physics, University of Eastern Finland,
Kuopio, 70210, Finland
Santtu Mikkonen
Department of Applied Physics, University of Eastern Finland,
Kuopio, 70210, Finland
Department of Environmental and Biological Sciences, University of
Eastern Finland, Kuopio, 70210, Finland
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- A Coupled Volatility and Molecular Composition Based Source Apportionment of Atmospheric Organic Aerosol P. Rund et al. 10.1021/acsearthspacechem.3c00034
- A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization A. Rusanen et al. 10.5194/amt-17-1251-2024
- Simplicity within biological complexity N. Pržulj et al. 10.1093/bioadv/vbae164
- 10 years long-term assessment on characterizing spatiotemporal trend and source apportionment of metal(loid)s in terrestrial soils along the west coast of South Korea S. Yoon et al. 10.1016/j.scitotenv.2022.154214
- Heterogeneous data integration methods for patient similarity networks J. Gliozzo et al. 10.1093/bib/bbac207
Latest update: 08 May 2025
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.
Online mass spectrometry produces large amounts of data. These data can be interpreted with...
