Integrating metabolomics and machine learning to forecast anti-inflammatory and antioxidant activities in D. officinale leaves

This ML-enhanced metabolomic strategy advances the quality assessment and germplasm optimization of D. officinale leaves by linking dynamic phytochemical profiles to bioactivity. The identification of July as the optimal harvest period and critical bioactive compounds underscores the approach's utility in nutraceutical and pharmaceutical applications, promoting sustainable utilization of agricultural byproducts.