发表期刊：Chinese Medicine

发表时间：2025

Abstract:

Background

Dendrobium officinale (D. officinale) leaves, rich in bioactive compounds comparable to those in stems, remain underutilized as agricultural byproducts.

Purpose

This study aims to establish an ML (machine learning)-driven metabolomic framework to evaluate seasonal variations in bioactive compounds within D. officinale leaves, identify germplasm-specific pharmacological activities, and determine core components driving anti-inflammatory and antioxidant effects.

Methods

An integrated approach combining dynamic metabolomic profiling (UHPLC-QTOF-MS, RP-HPLC, and UPLC-QqQ-MS), in vitro bioassays (TNF-α/IL-6 suppression assays and ABTS radical scavenging assay), and ML modeling was employed.

Results

Phenolics, flavonoids, terpenes, and B-vitamins peaked in October–November, while amino acids accumulated until December. Despite this, July-harvested leaves exhibited maximum anti-inflammatory and antioxidant activity. Random Forest Regression (RFR) models identified vanillic acid 4-β-D-glucoside, schaftoside, and rutin as key bioactive contributors, validated experimentally.

Conclusion

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.

https://doi.org/10.1186/s13020-025-01282-z