Multivariate optimization of a dispersive liquid-liquid microextraction method for the determination of six antiparasite drugs in kennel effluent waters by using second-order chromatographic data

Abstract

Six veterinary active ingredients (imidacloprid, albendazole, fenbendazole, praziquantel, fipronil and permethrin) were extracted and quantified by liquid chromatography with diode array detection in water samples from a wetland system used for the treatment of waste from a dog breeding plant. Response surface methodology, based on least-squares and artificial neural networks modelling, was applied for the optimization of a dispersive liquid-liquid microextraction (DLLME) procedure. Firstly, two experimental designs were built for screening and optimization, respectively. Then, the desirability function was implemented for the simultaneous optimization of the six recoveries (chromatographic areas of the six compounds). The optimum conditions were: 600 μL of acetone (dispersive solvent), 670 μL of dichloromethane (extractant solvent) and 0.6 min of vortex mixing. The preconcentration factor was 37.5. Then, in order to identify and quantify the six drugs, second-order calibration with MCR-ALS modeling of HPLC-DAD data was implemented attaining successful results. The limits of quantification were 4 ng mL−1 for imidaclopril, albendazole and fenbendazole; 8 ng mL−1 for praziquantel and fipronil; and 26 ng mL−1 for permethrin. The developed method allowed the quantitation of the target analytes, even in the presence of unexpected compounds from dirty water samples. The following maximum levels of veterinary drugs were found (in ng mL−1): imidaclopril, 7; albendazole, 46; fenbendazole, 21; praziquantel, 29; fipronil, 29 and permethrin, 217.