Issue 66, 2019

Rapid screening and quantification of multi-class antibiotic pollutants in water using a planar waveguide immunosensor

Abstract

Antibiotics are commonly used in livestock-related agriculture and aquaculture, but they also remain in water and potentially threaten human health. Immunosensors are attractive tools for the rapid detection of antibiotics in water due to their high sensitivity and low costs. However, the simultaneous detection of multi-class antibiotics remains a challenge due to the limited number of detection sites on the immunochip. Also, matrix effects hinder the practical application of these sensors. This paper presents a method for multi-class antibiotic detection in real water using a planar waveguide immunosensor (PWI). We integrate the screening and quantitive detection sites on the same immunochip, and a single screening detection site could detect multi-class antibiotics from the same family, increasing the detection types of analytes. In addition, to eliminate the matrix effects, we develop a testing buffer for real water detection, so that complex pretreatments of the samples can be omitted. Using our sensor and testing buffer, we detect 14 different antibiotics in real water. Lincomycin can be detected with a detection limit of 0.01 μg L−1, and 13 quinolones can be screened in a single assay. These results demonstrate that this planar waveguide immunosensor is capable of simultaneous screening and quantification of multi-class antibiotic pollutants and is expected to be applied for practical environmental monitoring.

Graphical abstract: Rapid screening and quantification of multi-class antibiotic pollutants in water using a planar waveguide immunosensor

Article information

Article type
Paper
Submitted
28 Aug 2019
Accepted
15 Nov 2019
First published
25 Nov 2019
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2019,9, 38422-38429

Rapid screening and quantification of multi-class antibiotic pollutants in water using a planar waveguide immunosensor

T. Zhang, Y. Li, C. Chen, X. Liu, Y. Tian, S. Zeng and M. He, RSC Adv., 2019, 9, 38422 DOI: 10.1039/C9RA06796E

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