Issue 35, 2021

Enzyme cascade-amplified immunoassay based on the nanobody–alkaline phosphatase fusion and MnO2 nanosheets for the detection of ochratoxin A in coffee

Abstract

Ochratoxin A (OTA) is a common food contaminant with multiple toxicities and thus rapid and accurate detection of OTA is indispensable to minimize the threat of OTA to public health. Herein a novel enzyme cascade-amplified immunoassay (ECAIA) based on the mutated nanobody–alkaline phosphatase fusion (mNb–AP) and MnO2 nanosheets was established for detecting OTA in coffee. The detection principle is that the dual functional mNb–AP could specifically recognize OTA and dephosphorylate the ascorbic acid-2-phosphate (AAP) into ascorbic acid (AA), and the MnO2 nanosheets mimicking the oxidase could be reduced by AA into Mn2+ and catalyze the 3,3′,5,5′-tetramethyl benzidine into blue oxidized product for quantification. Using the optimal conditions, the ECAIA could be finished within 132.5 min and shows a limit of detection of 3.38 ng mL−1 (IC10) with an IC50 of 7.65 ng mL−1 and a linear range (IC20–IC80) of 4.55–12.85 ng mL−1. The ECAIA is highly selective for OTA. Good recovery rates (84.3–113%) with a relative standard deviation of 1.3–3% were obtained and confirmed by high performance liquid chromatography with a fluorescence detector. The developed ECAIA was demonstrated to be a useful tool for the detection of OTA in coffee which provides a reference for the analysis of other toxic small molecules.

Graphical abstract: Enzyme cascade-amplified immunoassay based on the nanobody–alkaline phosphatase fusion and MnO2 nanosheets for the detection of ochratoxin A in coffee

Supplementary files

Article information

Article type
Paper
Submitted
08 May 2021
Accepted
15 Jun 2021
First published
21 Jun 2021
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2021,11, 21760-21766

Enzyme cascade-amplified immunoassay based on the nanobody–alkaline phosphatase fusion and MnO2 nanosheets for the detection of ochratoxin A in coffee

Z. Zhang, B. Su, H. Xu, Z. He, Y. Zhou, Q. Chen, Z. Sun, H. Cao and X. Liu, RSC Adv., 2021, 11, 21760 DOI: 10.1039/D1RA03615G

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