Flavonol aglycones as chemical markers for pollen quality assessment: a validated HS – US / UHPLC ‐ PDA study with chemometric differentiation of bee‐collected and Pinus pollens

Abstract BACKGROUND Pollen represents a pivotal resource at the agriculture–food nexus, valued for its dual functionality as a nutraceutical and food ingredient. However, the lack of standardized quality markers impedes its reliable characterization and broader industrial application. This study aimed to establish a validated analytical framework for the comparative profiling of flavonol aglycones in pollen, addressing the critical need for objective indicators to ensure standardization and traceability within agricultural supply chains. RESULTS Thirty‐one pollen samples, comprising 19 bee‐collected and 12 Pinus varieties from 15 Chinese provinces, were analyzed via high‐speed shear homogenization coupled with ultrasonication (HS–US) extraction, acid hydrolysis, and ultrahigh‐performance liquid chromatography‐photodiode array (UHPLC‐PDA) quantification. The HS–US method yielded a 2.9‐fold increase in flavonol aglycone recovery compared to conventional protocols, satisfying rigorous validation criteria (linearity R ≥ 0.999; accuracy 90–106%; precision relative standard deviation < 5%). Quercetin and kaempferol were identified as robust quality indices. Bee‐collected pollen exhibited significantly higher total flavonol aglycone content (mean: 0.825 g kg −1 ) compared to Pinus pollen (mean: 0.156 g kg −1 ; P < 0.001). Furthermore, chemometric analysis revealed biogeographic clustering, supporting the utility of these markers for quality assessment. CONCLUSION This study presents a robust analytical approach that effectively surmounts the sporopollenin extraction barrier, enabling reliable quantification of flavonol aglycones. The validated markers provide objective tools for quality control, establishing a chemical foundation for linking standardized pollen products to their functional applications. Consequently, the developed HS–US/UHPLC‐PDA platform is readily adaptable for routine quality assessment in food and agricultural settings. © 2026 Society of Chemical Industry.