Dispersive liquid–liquid microextraction as a new clean-up procedure for the determination of parabens, perfluorinated compounds, UV filters, biocides, surfactants, and plasticizers in root vegetables

Concepción Abril, Julia Martín, José Luis Malvar, Juan Luis Santos, Irene Aparicio, Esteban Alonso

An analytical method based on ultrasound-assisted extraction and dispersive liquid-liquid microextraction (DLLME) clean-up has been developed and validated for the determination of 31 emerging pollutants in root vegetables. The target compounds were four preservatives, six perfluoroalkyl compounds, six UV filters, two biocides, eight anionic surfactants, three nonionic surfactants, and two plasticizers. The type and volume of the extraction solvent, those of the disperser solvent, the pH and NaCl content of the DLLME aqueous phase, the amount of sample, and the sonication time were optimized. Box-Behnken experimental design was applied to select the best extraction conditions. Matrix-matched calibration curves were used for quantification. Four internal standards were used to compensate for residual matrix effects. Good linearity (R2 > 0.990), accuracies (expressed as the relative recovery) of >82%, and precisions (expressed as the relative standard deviation) of <18% were achieved. Method quantification limits (MQLs), calculated from spiked samples as the concentrations corresponding to signal-to-noise ratios of 10, were in the range 0.1-25 ng g-1 dry weight (d.w.). MQL values for 26 of the 31 target compounds were lower than 5 ng g-1 d.w. The method was successfully applied to determine the target pollutants in carrots, potatoes, and turnips from a local market. To the best of our knowledge, the proposed method constitutes the first application of DLLME as a clean-up procedure for the multiresidue determination of emerging pollutants in vegetables. The method affords similar recoveries and method detection limits to previously reported methods but requires smaller solvent volumes and sample amounts and is less expensive.