Optimization of flavonoids extraction and elucidation of antioxidant mechanisms in Dendrobium flexicaule using metabolomics and machine learning.

Recent studies have demonstrated that flavonoids constitute a major class of bioactive compounds in Dendrobium species, contributing significantly to their pharmacological properties. However, the underutilization of flavonoids from Dendrobium is largely attributable to two interrelated bottlenecks: (1) the absence of systematic phytochemical screening to identify high-flavonoid germplasm resources, and (2) the lack of robust, scalable extraction protocols optimized for both yield and reproducibility. To address these limitations, this study first employed untargeted metabolomics to comparatively characterize the flavonoid profiles across four representative Dendrobium species. Subsequently, we developed an integrated optimization framework combining single-factor experimental screening, response surface methodology (RSM), and machine learning-based predictive modeling to rationally design and validate an efficient, high-yield flavonoid extraction protocol. Results revealed that Dendrobium flexicaule exhibited the highest total flavonoid content among the four investigated species. Under the optimized extraction conditions, 94 % (v/v) ethanol, 68 min extraction time, a material-to-liquid ratio of 1:50 (w/v), and 72 °C, the flavonoid yield reached 8.90 ± 0.17 mg/g dry weight. Among the machine learning models evaluated, the support vector regression (SVR) model demonstrated the strongest predictive accuracy, achieving an R