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

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Latest update: 23 Jul 2024
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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.