Issue 46, 2021

A direct competitive nanozyme-linked immunosorbent assay based on MnO2 nanosheets as a catalytic label for the determination of fumonisin B1

Abstract

A direct competitive nanozyme-linked immunosorbent assay (dcNLISA) based on MnO2 nanosheets (MnO2 NSs) as a nanozyme label was developed for the highly sensitive determination of fumonisin B1 (FB1). MnO2 NS-labeled fumonisin B1-bovine serum albumin was easily synthesized as a competing antigen for the dcNLISA. And color changes derived from the MnO2–3,3′,5,5′-tetramethylbenzidine (TMB) system were exploited as the output signals of the dcNLISA. Several experimental parameters including the concentrations of the coating antibody, pH values, ionic strength and methanol concentration were optimized. Under the optimal conditions, the proposed method demonstrated a linear range (1.17–20.74 ng mL−1) with a reliable correlation coefficient (R2 = 0.9989), a satisfactory limit of detection (0.63 ng mL−1) and high selectivity for the detection of FB1. The recoveries of FB1 in spiked corn and wheat samples were in the range of 85.31–108.16% with coefficients of variation (CVs) ranging from 6.14% to 9.23%. Meanwhile, the testing results showed good consistency (R2 = 0.9892) between the developed dcNLISA and the reference method, liquid chromatography/mass spectrometry/mass spectrometry (LC-MS/MS) method. The proposed method was proven to be simple, sensitive, cost-effective and reliable for the screening of FB1 in cereals.

Graphical abstract: A direct competitive nanozyme-linked immunosorbent assay based on MnO2 nanosheets as a catalytic label for the determination of fumonisin B1

Supplementary files

Article information

Article type
Paper
Submitted
22 صفر 1443
Accepted
23 ربيع الأول 1443
First published
27 ربيع الأول 1443

Anal. Methods, 2021,13, 5542-5548

A direct competitive nanozyme-linked immunosorbent assay based on MnO2 nanosheets as a catalytic label for the determination of fumonisin B1

X. Cai, M. Liang, F. Ma, S. R. Mohamed, A. A. Goda, D. H. Dawood, L. Yu and P. Li, Anal. Methods, 2021, 13, 5542 DOI: 10.1039/D1AY01654G

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