Articles | Volume 15, issue 12
Atmos. Meas. Tech., 15, 3779–3803, 2022
https://doi.org/10.5194/amt-15-3779-2022
Atmos. Meas. Tech., 15, 3779–3803, 2022
https://doi.org/10.5194/amt-15-3779-2022
Research article
24 Jun 2022
Research article | 24 Jun 2022

Ch3MS-RF: a random forest model for chemical characterization and improved quantification of unidentified atmospheric organics detected by chromatography–mass spectrometry techniques

Emily B. Franklin et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-99', Anonymous Referee #1, 17 Apr 2022
    • AC1: 'Reply on RC1', Emily B. Franklin, 18 May 2022
  • RC2: 'Comment on amt-2022-99', Anonymous Referee #2, 22 Apr 2022
    • AC2: 'Reply on RC2', Emily B. Franklin, 18 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Emily B. Franklin on behalf of the Authors (18 May 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (02 Jun 2022) by Albert Presto
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Short summary
The composition of atmospheric aerosols are extremely complex, containing hundreds of thousands of estimated individual compounds. The majority of these compounds have never been catalogued in widely used databases, making them extremely difficult for atmospheric chemists to identify and analyze. In this work, we present Ch3MS-RF, a machine-learning-based model to enable characterization of complex mixtures and prediction of structure-specific properties of unidentifiable organic compounds.