Issue 20, 2022

A DNA tetrahedral nanomaterial-based dual-signal ratiometric electrochemical aptasensor for the detection of ochratoxin A in corn kernel samples

Abstract

Ochratoxin A (OTA) is a highly toxic food contaminant and is harmful to human beings. Herein, a ratiometric electrochemical aptasensor based on a DNA tetrahedral nanomaterial (NTH) was developed in combination with the signal tag of a zirconium metal–organic framework (UiO-66) for the detection of OTA. In the sensor, UiO-66 and a [Fe(CN)6]3−/4− electrolyte solution were used as the signal probe and the internal reference probe, respectively. In the presence of OTA, the OTA aptamer was released from the electrode due to the specific binding of OTA. Thus, signal probe P1 labeled-UiO-66 was captured on the electrode surface by hybridization with DNA NTH. Since signal probe P1 labeled-UiO-66 was close to the electrode, it leads to an increased signal current of UiO-66 at +0.9 V. As the conductivity of the modified electrode decreased, the current signal of [Fe(CN)6]3−/4− at +0.2 V also decreased. The proposed ratiometric electrochemical aptasensor could effectively eliminate external environmental influences and could avoid electrochemical background signals. The aptasensor demonstrated high specificity for OTA, and achieved a good linear range of 1 pg mL−1–100 ng mL−1 with a detection limit of 330 fg mL−1. The developed electrochemical aptamer biosensor effectively detected OTA in corn kernel samples, verifying its practical application for the determination of OTA in actual samples.

Graphical abstract: A DNA tetrahedral nanomaterial-based dual-signal ratiometric electrochemical aptasensor for the detection of ochratoxin A in corn kernel samples

Supplementary files

Article information

Article type
Paper
Submitted
06 Jun 2022
Accepted
17 Jul 2022
First published
07 Sep 2022

Analyst, 2022,147, 4578-4586

A DNA tetrahedral nanomaterial-based dual-signal ratiometric electrochemical aptasensor for the detection of ochratoxin A in corn kernel samples

Y. Li, F. Xie, C. Yao, G. Zhang, Y. Guan, Y. Yang, J. Yang and R. Hu, Analyst, 2022, 147, 4578 DOI: 10.1039/D2AN00934J

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