Integrated Metabolomics and Machine Learning Reveal Drying-Dependent Phenolic Markers in Asteraceae Edible Plants From Ulleungdo Island. (PubMed, Food Sci Nutr) - Sep 4, 2026 - "Random Forest analysis combined with SHAP interpretation identified caffeoylquinic acids, including chlorogenic acid and dicaffeoylquinic acid isomers, as processing-stable chemotaxonomic markers, while glycosylated flavonoids served as sensitive indicators of processing intensity. Targeted quantification confirmed these trends and revealed species-specific phenolic allocation patterns. Overall, this study demonstrates that integrating metabolomics with explainable machine learning enables robust identification of processing-dependent marker compounds and provides a biochemical foundation for traditional drying practices and marker-based quality control strategies for Ulleungdo wild vegetables."
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Doo-Hee Lee; In Young Lee; Ju Hong Park; Nami Joo
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