Chemometric evaluation of the CUPRAC assay for propolis quality control: a decision-making framework for deciphering...

The antioxidant capacity of propolis is traditionally inferred from single compositional indicators such as total phenolic (TPC) or flavonoid content (TFC). However, these reductionist approaches often overlook complex inter-compound dynamics, leading to 'antioxidant paradoxes' and inconsistent quality assessments. To address this gap, our study introduces a structured analytical workflow that transitions from descriptive profiling to a decision-oriented quality control model. By examining the reliability of the Cupric Reducing Antioxidant Capacity (CUPRAC) assay within an integrated chemometric framework, a diverse library of 81 propolis samples was analyzed to challenge the interpretative robustness of univariate measurements. An integrated multivariate strategy, incorporating Z-score normalization, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA), was employed to decouple mass-quantification from functional performance. Despite strong global correlations (r > 0.92), multivariate modeling revealed significant analytical discrepancies at the sample level. Standardized Z-score profiles and 95% confidence ellipses effectively exposed 'paradoxical' cases where samples with near-identical TPC levels exhibited different functional outcomes. Based on these findings, we propose concrete decision rules-including a Z-score deviation threshold of ±2.0-to serve as a diagnostic trigger for industrial quality control. This research provides a robust methodological framework for data-driven quality assurance, offering a clear regulatory template for the assessment of propolis and other bioactive-rich matrices.