A machine learning approach to aerosol classification for single-particle mass spectrometry

Christopoulos, Costa D.; Garimella, Sarvesh; Zawadowicz, Maria A.; Möhler, Ottmar; Cziczo, Daniel J.

doi:https://doi.org/10.5194/amt-11-5687-2018

Articles | Volume 11, issue 10

https://doi.org/10.5194/amt-11-5687-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-11-5687-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 10

Research article

|

18 Oct 2018

Research article |

| 18 Oct 2018

A machine learning approach to aerosol classification for single-particle mass spectrometry

Costa D. Christopoulos, Sarvesh Garimella, Maria A. Zawadowicz, Ottmar Möhler, and Daniel J. Cziczo

Data sets

A Machine Learning Approach to Aerosol Classification for Single Particle Mass Spectrometry Costa Christopoulos https://doi.org/10.7910/DVN/J1FZYU

Short summary

Compositional analysis of atmospheric and laboratory aerosols is often conducted with mass spectrometry. In this study, machine learning is used to automatically differentiate particles on the basis of chemistry and size. The ability of the machine learning algorithm was then tested on a data set for which the particles were not initially known to judge its ability.