Bee pollen is a complex biological matrix whose functional quality results from the interaction between botanical origin, phenolic composition and antioxidant activity. The aim of this study was to integrate palynological, chemical and antioxidant data through composite functional indices and multivariate analysis to characterize the functional quality of 24 Spanish bee pollen samples. Palynological analysis, phenolic profiling and antioxidant assays (DPPH, ABTS+• and FRAP) were combined with biodiversity metrics to construct a Phenolic Index (PI), an Antioxidant Index (AI) and a Global Functional Index (GFI). Spearman correlation analysis, principal component analysis (PCA) and one-way ANOVA were applied for index validation and interpretation. Strong correlations were observed between AI, GFI, total phenolic content, and antioxidant assays, confirming the robustness of the composite indices. PCA revealed a dominant functional–antioxidant gradient primarily driven by the dominant botanical origin. Samples dominated by Castanea and Rubus showed higher functional indices, whereas those dominated by Cistaceae exhibited lower functional performance. ANOVA confirmed that dominant pollen type significantly affected most physicochemical, antioxidant and functional variables, while palynological diversity indices showed no significant influence. The integrative multivariate approach provides a robust framework for functional quality assessment of bee pollen, supporting authentication, quality control and the development of functional products.
Rodríguez-Flores et al. (Sat,) studied this question.