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Updated: Aug 5, 2026

Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water
Published on: June 9, 2014
A Colorimetric Aptasensor for Rapid Detection of Sulfadimethoxine in Aquaculture
Hong Liang1,2, Jiahao Tan1, Tingyu Wang1
1School of Geography and Oceanography, College of Materials and Chemistry Engineering, Minjiang University, Fuzhou 350108, China.
Abstract:
Sulfadimethoxine (SDM) is a sulfonamide antibiotic widely used in the aquaculture of aquatic organisms. Its excessive residues in animal-derived food products can cause irreversible harm to human health and the environment. Current primary detection methods for SDM, such as instrumental methods and Immunoassay techniques, demonstrate high sensitivity and accuracy. However, their industrial application is impeded by laborious sample pretreatment, reliance on specific equipment, and dependence on specially trained personnel. Therefore, there is an urgent need to develop a simple and rapid method for detecting SDM residues. In this study, we constructed a novel colorimetric sensing platform based on functional nucleic acids for SDM detection. This sensor incorporates a nucleic acid aptamer capable of specifically recognizing SDM, a G-quadruplex/Hemin complex with peroxidase-like catalytic activity, and a shielding sequence that suppresses catalytic activity while undergoing SDM-induced conformational changes. The colorimetric signal was generated using a 3,3',5,5'-Tetramethylbenzidine (TMB) chromogenic substrate, and the sensor's performance was evaluated via absorbance measurements with a microplate reader. After optimizing detection conditions, the sensor exhibited a linear response to SDM concentrations ranging from 0.155 to 3.10 ng/mL, with a detection limit of 0.0796 ng/mL. Furthermore, the sensor demonstrated excellent selectivity and achieved recoveries of 83.0% to 107% in spiked aquaculture water and fish samples, with coefficients of variation below 10.4%, confirming its superior practicality for real-world sample analysis.
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