Computational Antioxidant Capacity Simulation (CAOCS): A Novel Framework of Antioxidant Capacity Profiling

Abstract

Abstract Inconsistent ranking is a well-known drawback of antioxidant capacity (AOC) profiling methodologies that use free-radical species as oxidant. This problem leads to assay results that are not biorelevant. Linear free energy relationships (LFER) theory predicts proton transfer (PT) kinetics as a surrogate for biorelevant hydrogen atom transfer (HAT) kinetics. Computational antioxidant capacity simulation (CAOCS), based on real-time proton transfer kinetics modeling (PTKM) of polyphenols and phenol-like small molecules, inspired a novel AOC profiling methodology. Kinetic data acquired by incremental addition of resorcinol to an oxidized probe (phenol red), was fitted to mono-exponential decay equation (MED). Absorbance decay data from strongly antioxidant phenol-like molecules (e.g. ascorbic acid) and a new chromogenic probe (phenolphthalein) was fitted to MED and bi-exponential decay equation. The preferred model and corresponding best-fit rate constant (Kptt) was identified by comparison of fits, using Akaike’s Information Criterion (AICc). Photometric phenolphthalein assay (PPA)-derived metric was normalized with photometric phenol red assay (PPRA) results by using a function developed from proton concentration differential between phenolphthalein and phenol red, with respect to decay threshold to plateau (assay endpoint) interval. pKa dependence of the CAOCS’ metric is a signature of structure–function relationships, and hence, biorelevance. It is shown, unambiguously, that a combination of two phenolic probe molecules, an analytical system devoid of free radicals, and statistical identification of preferred exponential decay fit to PT kinetics data, constitutes a novel algorithm for AOC profiling of polyphenols and phenol-like molecules. This methodology holds a promise of utility in quality assurance of dietary supplements.