Rapid ultrasensitive detection of levamisole and mebendazole residues in meat products based on intelligent nanozyme-imprinted fluorescence microfluidic sensor

Abstract

Veterinary drug residues in food pose a significant threat to human health and developing advanced detection tools is important for food safety. In this study, a nanozyme ratiometric fluorescence paper-based microfluidic imprinted sensor assisted with intelligent analysis module was developed, enabling on-site, rapid and ultrasensitive detection of levamisole (LMS) and mebendazole (MBZ) residues in meat products. Molecularly imprinted sensing units based on metal-organic framework fluorescence materials (UiO-66-NH2@MIP and PCN-224@MIP) with peroxidase-like activity were fabricated to establish a dual-channel ratiometric fluorescence detection system based on the H2O2/o-phenylenediamine reaction. Under excitation at 360 nm for LMS and 420 nm for MBZ, by integrating fluorescence image acquisition using a smartphone with MATLAB program-driven image processing technology, the UiO-66-NH2@MIP/PCN-224@MIP paper-based microfluidic sensor achieved rapid quantitative detection within 12 minutes, exhibiting a linear response range of 0.05-2.8 μM for LMS and 0.05-3.2 μM for MBZ. Satisfactory recoveries of 98.2–103.2% and excellent reproducibility (RSD = 1.68% for LMS and 3.17% for MBZ, n = 5) were obtained in spiked meat samples. This microfluidic sensor based on image-processing/smartphone readout achieves on-site, portable, rapid and ultrasensitive detection of LMS and MBZ residues in meat products without sophisticated equipment, providing a novel and practical approach for food quality and safety assurance.

Supplementary files

Article information

Article type
Paper
Submitted
21 Jan 2026
Accepted
10 Mar 2026
First published
11 Mar 2026

Anal. Methods, 2026, Accepted Manuscript

Rapid ultrasensitive detection of levamisole and mebendazole residues in meat products based on intelligent nanozyme-imprinted fluorescence microfluidic sensor

Q. Wu, Z. Liao, Y. Ouyang, A. Ma, Z. Yang and Z. Zhang, Anal. Methods, 2026, Accepted Manuscript , DOI: 10.1039/D6AY00111D

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