Colorimetric Detection of Antioxidants in Food Samples Using MnO2/Graphene Quantum Dot Composites with Oxidase-like Activity

Author(s)

Facure, Murilo H. M. ; Andre, Rafaela S. ; Mercante, Luiza A. ; Correa, Daniel S.

• Document type: Journal article
• Source: ACS APPLIED NANO MATERIALS; v. 5, n. 10, p. 9-pg., 2022-09-26.

Abstract

Recently, strategies for developing colorimetric sensors relying on nanozymes for the detection of analytes of health, food, and environmental interest have gained significant importance. Herein, a rapid and sensitive colorimetric assay based on manganese dioxide/graphene quantum dot (MnO2/GQD) composites was developed for the detection of antioxidants in food. The composite, synthesized by a hydrothermal method, served as a nanozyme with outstanding oxidase-like catalytic activity to trigger the 3,3 ',5,5 '-tetramethybenzidine (TMB) chromogenic reaction. Then, an assay was performed to evaluate the reducing ability of antioxidants toward oxTMB by detecting color changes during the procedure. The colorimetric method achieved high selectivity and sensitivity toward the detection of gallic acid, tannic acid, and ascorbic acid with detection limits (S/N = 3) of 0.07, 0.28, and 0.69 mu mol L-1, respectively. Moreover, the assay was used to accurately measure and discriminate the antioxidants in real food samples, revealing its great promise for portable and rapid food analysis. (AU)

• FAPESP's process: 16/23793-4 - Development of nanostructured sensor aiming the detection of volatile compounds for food quality indicators
• Grantee: Rafaela da Silveira Andre
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 17/10582-8 - Production and characterization of graphene quantum dots and their application in chemical sensors
• Grantee: Murilo Henrique Moreira Facure
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis
• Grantee: Osvaldo Novais de Oliveira Junior
• Support Opportunities: Research Projects - Thematic Grants