Related Experiment Video
Updated: Sep 20, 2026

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes
Published on: June 10, 2022
Machine learning-assisted tri-mode nanozyme platform for antioxidant identification and total antioxidant capacity
Yanling Zhao1, Yanru Qin1, Mengfan Bi1
1Institute of Optical Materials and Chemical Biology, Guangxi Key Laboratory of Electrochemical Energy Materials, College of Chemistry and Chemical Engineering, Guangxi University, Nanning, Guangxi 530004, PR China.
Abstract:
Antioxidants are essential for maintaining biological redox homeostasis and preserving food quality, which creates a pressing demand for analytical methods capable of rapidly identifying multiple antioxidants and evaluating total antioxidant capacity (TAC). Herein, we developed a multifunctional nanozyme, Mn-TCPP@MnxOy, that integrates fluorescence, colorimetric, and photothermal sensing within a single platform. The MnxOy shell quenches the fluorescence of the porphyrin (TCPP) core through Förster resonance energy transfer (FRET), and this fluorescence is specifically restored by glutathione (GSH), affording a linear response over the range from 0.1 to 20 μM (R2 = 0.9931) with a limit of detection of 0.032 μM and a limit of quantification of 0.11 μM. Meanwhile, Mn-TCPP@MnxOy exhibits oxidase-like activity that catalyzes the oxidation of colorless 3,3',5,5'-tetramethylbenzidine (TMB) to its blue oxidized product (oxTMB), generating characteristic absorption and photothermal signals within 5 min. The colorimetric mode provided detection limits of 0.21, 0.23, 0.25, and 0.27 μM for GSH, ascorbic acid (AA), cysteine (Cys), and hydrogen sulfide (H2S), respectively, whereas the photothermal mode gave corresponding values of 0.28, 0.30, 0.26, and 0.31 μM. By coupling these differential responses with hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA), in which the first discriminant factor accounted for more than 97% of the total variance, the platform discriminated the four antioxidants with 100% accuracy, a result further confirmed by leave-one-out cross-validation. When applied to fruit and vegetable samples, the platform reliably determined TAC and distinguished the constituent antioxidants, and the results agreed closely with those of the classical ferric reducing antioxidant power (FRAP) assay, demonstrating its strong potential for food quality monitoring and offering a versatile strategy for multi-target antioxidant recognition.
Related Concept Videos
Automated Microbial Diagnostics
Rapid Identification of Pathogens
