Optimizing Machine Learning-Based Prediction of Terrestrial Dissolved Organic Matter in the Ocean Using Fluorescence and LC-FTMS Data.

ABSTRACT: Marine dissolved organic matter (DOM) is an extremely complex mixture of organic compounds that plays a crucial role in the global carbon cycle. In the Arctic, climate change accelerates the release of terrestrial organic carbon. Since chemical information is the only way to track DOM sources and fate, it is essential to improve analytical and data science approaches to assess the DOM composition. Here, we compare random forest (RF), support vector machines, and generalized linear models (GLM) to predict a fluorescence-derived proxy for terrestrial DOM based on molecular formula data from liquid chromatography coupled with Fourier transform mass spectrometry (LC-FTMS). We systematically evaluate different data preprocessing, normalization, and ML techniques to optimize prediction accuracy

SUBMITTER: Bareth M 

PROVIDER: S-EPMC12268369 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature