Issue 30, 2023

Introduction of a multilayered fluorescent nanofilm into lateral flow immunoassay for ultrasensitive detection of Salmonella typhimurium in food samples

Abstract

Fast and sensitive identification of foodborne bacteria in complex samples is the key to the prevention and control of microbial infections. Herein, an ultrasensitive lateral flow assay (LFIA) based on multilayered fluorescent nanofilm (GO/DQD)-guided signal amplification was developed for the rapid and quantitative determination of Salmonella typhimurium (S. typhi). The film-like GO/DQD was prepared through the electrostatic mediated layer-by-layer assembly of numerous carboxylated CdSe/ZnS quantum dots (QDs) onto an ultrathin graphene oxide (GO) nanosheet, which possessed advantages including higher QD loading, larger surface areas, superior luminescence, and better stability, than traditional spherical nanomaterials. The antibody-modified GO/DQD can effectively attach onto a target bacterial cell to form a GO/DQD-bacteria immunocomplex containing almost ten thousand QDs, thus greatly improving the detection sensitivity of LFIA. The constructed GO/DQD-LFIA biosensor achieved the rapid and sensitive detection of S. typhi in 14 min with detection limits of as low as 9 cells/mL. Moreover, compared with traditional LFIA techniques for bacteria detection, the proposed assay exhibited excellent stability and accuracy in real food samples and enormously improved sensitivity (2–3 orders of magnitude), demonstrating its great potential in the field of rapid diagnosis.

Graphical abstract: Introduction of a multilayered fluorescent nanofilm into lateral flow immunoassay for ultrasensitive detection of Salmonella typhimurium in food samples

Supplementary files

Article information

Article type
Paper
Submitted
11 May 2023
Accepted
10 Jul 2023
First published
10 Jul 2023

Anal. Methods, 2023,15, 3631-3641

Introduction of a multilayered fluorescent nanofilm into lateral flow immunoassay for ultrasensitive detection of Salmonella typhimurium in food samples

Q. Yu, X. Xia, C. Xu, W. Wang, S. Zheng, C. Wang, B. Gu and C. Wang, Anal. Methods, 2023, 15, 3631 DOI: 10.1039/D3AY00738C

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