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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (27 Jun 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (27 Jun 2018) by Francis Pope
RR by Anonymous Referee #2 (13 Jul 2018)
RR by Anonymous Referee #1 (14 Aug 2018)
ED: Publish subject to minor revisions (review by editor) (03 Sep 2018) by Francis Pope
AR by Daniel J. Cziczo on behalf of the Authors (19 Sep 2018)  Author's response    Manuscript
ED: Publish as is (26 Sep 2018) by Francis Pope
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.