Ultrasound-assisted sample preparation for simultaneous extraction of anionic, cationic and non-ionic surfactants in sediment

Despite their very wide use in various fields, knowledge concerning surfactants in environmental solid matrices is generally poor. One of the difficulties encountered in the analysis of surfactants is their very diverse physicochemical properties which require different extraction techniques. The objective of this work was therefore to develop an extraction method in sediments that allows the simultaneous analysis of anionic, cationic and non-ionic surfactants. Different extraction techniques (salting-out, ultrasound), solvents and additives were compared. The optimized method, followed by analysis by coupling liquid chromatography with tandem mass spectrometry, was then validated and applied to real samples in which the analytes were quantified by matrix matched calibration. Optimization of the extraction parameters showed different trends depending on the surfactant family. However, ultrasound assisted extraction with a 90/10 acetonitrile/water mixture at 1% acetic acid and 0.1 M EDTA showed the best results overall. The quantification limits obtained, between 6.4 mu g/kg for linear alkyl benzene sulfonate (LAS) C10 and 158 mu g/kg for 1-laureth sulfate, allow the analysis of traces in sediments. Eighteen of the 27 targeted surfactants were thus detected. The highest concentrations were found for LAS and quaternary ammoniums. Strong correlations between concentrations of different homologues of the same families of surfactants were observed.

Automated summary

Despite their wide use in various fields, the knowledge about surfactants in environmental solid matrices is generally limited. Aiming to develop an extraction method in sediments for the simultaneous analysis of anionic, cationic, and non-ionic surfactants, this study compared different extraction techniques, solvents, and additives. The optimized method, coupled with liquid chromatography and tandem mass spectrometry, was validated and applied to real samples, allowing the detection of 18 out of 27 targeted surfactants.