This study is based on ultra-high-performance liquid chromatography(UPLC), gas chromatography-mass spectrometry(GC-MS), and network pharmacology methods to analyze and predict potential quality markers(Q-markers) of Artemisiae Argyi Folium. First, UPLC and GC-MS techniques were used to analyze the content of 12 non-volatile components and 8 volatile components in the leaves of 33 Artemisia argyi germplasm resources as candidate Q-markers. Subsequently, network pharmacology was employed to construct a "component-target-pathway-efficacy" network to screen out core Q-markers, and the biological activity of the markers was validated using molecular docking. Finally, cluster analysis and principal component analysis were performed on the content of Q-markers in the 33 A. argyi germplasm resources. The results showed that 18 candidate components, 60 targets, and 185 relationships were identified, which were associated with 72 pathways related to the treatment of 11 diseases and exhibited 5 other effects. Based on the combination of freedom and component specificity, six components, including eupatilin, cineole, β-caryophyllene, dinatin, jaceosidin, and caryophyllene oxide were selected as potential Q-markers for Artemisiae Argyi Folium. According to the content of these six markers, cluster analysis divided the 33 A. argyi germplasm resources into three groups, and principal component analysis identified S14 as having the highest overall quality. This study provides a reference for exploring Q-markers of Artemisiae Argyi Folium, establishing a quality evaluation system, further studying its pharmacological mechanisms, and breeding new varieties.
Keywords: Artemisiae Argyi Folium; GC-MS; UPLC; molecular docking; principal component analysis; quality markers.