Articles | Volume 15, issue 17
https://doi.org/10.5194/amt-15-5061-2022
https://doi.org/10.5194/amt-15-5061-2022
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

Viewed

Total article views: 1,605 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,067 457 81 1,605 160 65 50
  • HTML: 1,067
  • PDF: 457
  • XML: 81
  • Total: 1,605
  • Supplement: 160
  • BibTeX: 65
  • EndNote: 50
Views and downloads (calculated since 11 Feb 2022)
Cumulative views and downloads (calculated since 11 Feb 2022)

Viewed (geographical distribution)

Total article views: 1,605 (including HTML, PDF, and XML) Thereof 1,602 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Nov 2024
Download
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.