Antibody Generation and Rapid Immunochromatography Using Time-Resolved Fluorescence Microspheres for Propiconazole: Fungicide Abused as Growth Regulator in Vegetable.
Bo ChenXing ShenZhaodong LiJin WangXiangmei LiZhenlin XuYu-Dong ShenYi LeiXinan HuangXu WangHong-Tao LeiPublished in: Foods (Basel, Switzerland) (2022)
Propiconazole (PCZ) is a fungicide popularly used to prevent and control wheat and rice bakanae disease, etc. However, it was recently found to be illegally employed as a plant regulator to induce thick stems and dark green leaves of Brassica campestris , a famous vegetable in Guangdong, South China. Due to a lack of available recognition molecules to the target analyte, it is still a big challenge to establish a rapid surveillance screening method. In this study, a novel chiral hapten was rationally designed, and an artificial immunogen was then prepared for the generation of a specific antibody against propiconazole for the first time. Using the obtained antibody, a highly sensitive time-resolved fluorescence microspheres lateral flow immunochromatographic assay (TRFMs-LFIA) was established with a visual limit of detection of 100 ng/mL and a quantitative limit of detection of 1.92 ng/mL for propiconazole. TRFMs-LFIA also exhibited good recoveries ranging from 78.6% to 110.7% with coefficients of variation below 16%. The analysis of blind real-life samples showed a good agreement with results obtained using HPLC-MS/MS. Therefore, the proposed method could be used as an ideal screening surveillance tool for the detection of propiconazole in vegetables.
Keyphrases
- loop mediated isothermal amplification
- ms ms
- public health
- label free
- real time pcr
- sensitive detection
- transcription factor
- single molecule
- molecularly imprinted
- big data
- simultaneous determination
- machine learning
- mass spectrometry
- high performance liquid chromatography
- health risk
- high resolution
- ionic liquid
- deep learning
- energy transfer
- climate change
- living cells
- drinking water
- health risk assessment
- artificial intelligence
- cell wall