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

Model code and software

TAG Explorer and iNtegration (TERN) (v.2.2.20-beta) Gabriel Isaacman-VanWertz, Brian M. Lerner, and Donna T. Sueper /10.5281/zenodo.6940761

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.