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Biosensor for Seven Sulphonamides in Drinking, Ground, and Surface Water with Difficult Matrices

Abstract Environmental monitoring of antibiotics and other pharmaceuticals in real water samples with difficult matrices places high demands on chemical analysis. Biosensors have suitable characteristics like their efficiency in a fast, sensitive, and cost‐effective detection of pollutants. In this article, we present a recently developed immunoassay for seven sulphonamides (sulphadiazine, sulphamethoxazole, sulphadimidine, sulphamethizole, sulphadimethoxine, sulphathiazole, and sulphamethoxypyridazine) which can only be detected separately. For the simultaneous determination of multiple sulphonamides in the future we performed measurements with different combinations of binary mixtures. The results of the immunosensor were compared to a mathematical model which was developed in our group. Using an automated biosensor system it was possible for the first time to achieve limits of detection (LOD) below 10 ng L−1 and limits of quantification (LOQ) below 100 ng L−1 without sample pre‐concentration for these sulphonamides. Sulphonamide calibrations with different immobilised analyte derivatives were made in Milli‐Q water. Unstrained spiked and un‐spiked real water samples with complex matrices (drinking, ground, and surface water) were measured. In compliance with the Association of Analytical Communities (AOAC) International most recovery rates obtained were between 70% and 120%. The reproducibility was checked by measuring replica of each sample within independent repetitions. Robustness could be demonstrated by long‐term stability tests of the biosensor surface. These studies show that the biosensor used offers the necessary reproducibility, precision, and robustness required for an analytical method. The measuring data of the binary mixtures show a systematic error compared to the mathematical model at high concentrations of both sulphonamides, because the approximation uses only the standard calibration curves (data of the logistic fit function) as input data. It is also hard to adequately describe the cross‐reactivity and the behaviour of a mixture of polyclonal antibodies.