A Monoclonal Antibody-Based Time-Resolved Fluorescence Microsphere Lateral Flow Immunoassay for Dinotefuran and Clothianidin Detection

Author:

Qin Lehong1,Chen Haojie1,Nie Yingxiang1,Zhou Mengxin1,Huang Junjun1,Xiao Zhili1

Affiliation:

1. Guangdong Provincial Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China

Abstract

Dinotefuran and clothianidin belong to the third generation of nicotinic insecticides and are widely used in crop pest control. It is necessary to detect their residues in food. The time-resolved fluorescent microspheres lateral flow immunoassay (TRFMs-LFIA) has the advantages of high sensitivity, short duration, and simple operation and is suitable for rapid field testing. In this study, two haptens (FCA-1, FCA-2) were synthesized in three steps and conjugated to the carrier proteins to obtain artificial antigens, which were subsequently used for monoclonal antibody preparation. A TRFMs-LFIA based on monoclonal antibodies was established to detect dinotefuran and clothianidin residues in food. The limit of detection (LOD) for dinotefuran was 0.045 ng/mL, with an IC50 of 0.61 ng/mL and a linear range (IC20~IC80) of 0.12~3.11 ng/mL. The LOD for clothianidin was 0.11 ng/mL, with an IC50 of 0.94 ng/mL and a linear range (IC20~IC80) of 0.24~3.65 ng/mL. Cross-reactivity rates with seven tested structural analogs were less than 1.5%. The pretreatment method was optimized for wheat, cucumber, and cabbage samples, which was time-saving (20 min) and easy to operate. The average recovery rates ranged from 88.0% to 114.8%, with the corresponding coefficients of variation appearing (CV) between 1.9% and 13.5%. The results of actual wheat, cucumber, and cabbage samples detected by the established TRFMs-LFIA were consistent with those of Ultra-Performance Liquid Chromatography coupled with Tandem Mass Spectrometry (UPLC-MS/MS). These results demonstrate that the established TRFMs-LFIA is sensitive, accurate, rapid, and suitable for real sample detection.

Funder

National Natural Science Foundation General Program of China

Publisher

MDPI AG

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