Articles | Volume 15, issue 17
Research article
05 Sep 2022
Research article |  | 05 Sep 2022

Comprehensive detection of analytes in large chromatographic datasets by coupling factor analysis with a decision tree

Sungwoo Kim, Brian M. Lerner, Donna T. Sueper, and Gabriel Isaacman-VanWertz


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-16', Anonymous Referee #1, 03 Mar 2022
    • AC1: 'Reply on RC1', Sungwoo Kim, 23 May 2022
  • RC2: 'Comment on amt-2022-16', Alexander Vogel, 23 Mar 2022
    • AC2: 'Reply on RC2', Sungwoo Kim, 23 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Sungwoo Kim on behalf of the Authors (23 May 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (25 May 2022) by Yoshiteru Iinuma
Short summary
Atmospheric samples can be complex, and current analysis methods often require substantial human interaction and discard potentially important information. To improve analysis accuracy and computational cost of these large datasets, we developed an automated analysis algorithm that utilizes a factor analysis approach coupled with a decision tree. We demonstrate that this algorithm cataloged approximately 10 times more analytes compared to a manual analysis and in a quarter of the analysis time.