Articles | Volume 17, issue 6
Research article
28 Mar 2024
Research article |  | 28 Mar 2024

Application of fuzzy c-means clustering for analysis of chemical ionization mass spectra: insights into the gas phase chemistry of NO3-initiated oxidation of isoprene

Rongrong Wu, Sören R. Zorn, Sungah Kang, Astrid Kiendler-Scharr, Andreas Wahner, and Thomas F. Mentel


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1896', Anonymous Referee #1, 16 Nov 2023
    • AC2: 'Reply on RC1', Thomas Mentel, 08 Jan 2024
  • RC2: 'Comment on egusphere-2023-1896', Anonymous Referee #2, 17 Nov 2023
    • AC1: 'Reply on RC2', Thomas Mentel, 08 Jan 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Thomas Mentel on behalf of the Authors (08 Jan 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (16 Jan 2024) by Haichao Wang
RR by Anonymous Referee #2 (29 Jan 2024)
ED: Publish as is (29 Jan 2024) by Haichao Wang
AR by Thomas Mentel on behalf of the Authors (04 Feb 2024)  Author's response   Manuscript 
Short summary
Recent advances in high-resolution time-of-flight chemical ionization mass spectrometry (CIMS) enable the detection of highly oxygenated organic molecules, which efficiently contribute to secondary organic aerosol. Here we present an application of fuzzy c-means (FCM) clustering  to deconvolve CIMS data. FCM not only reduces the complexity of mass spectrometric data but also the chemical and kinetic information retrieved by clustering gives insights into the chemical processes involved.