DUGi

Multiresidue trace analysis of pharmaceuticals, their human metabolites and transformation products by fully automated on-line solid-phase extraction-liquid chromatography-tandem mass spectrometry

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A novel, fully automated analytical methodology based on dual column liquid chromatography coupled to tandem mass spectrometry (LC-LC-MS 2 ) has been developed and validated for the analysis of 12 pharmaceuticals and 20 metabolites and transformation products in different types of water (influent and effluent wastewaters and surface water). Two LC columns were used – one for pre-concentration of the sample and the second for separation and analysis – so that water samples were injected directly in the chromatographic system. Besides the many advantages of the methodology, such as minimization of the sample volume required and its manipulation, both compounds ionized in positive and negative mode could be analyzed simultaneously without compromising the sensitivity. A comparative study of different mobile phases, gradients and LC pre-concentration columns was carried out to obtain the best analytical performance. Limits of detection (MLODs) achieved were in the low ng L −1 range for all the compounds. The method was successfully applied to study the presence of the target analytes in different wastewater and surface water samples collected near the city of Girona (Catalonia, Spain). Data on the environmental presence and fate of pharmaceutical metabolites and TPs is still scarce, highlighting the relevance of the developed methodology

This study has been supported by the Generalitat de Catalunya (Consolidated Research Group: Catalan Institute for water Research 2014 SGR 291), by the Spanish Ministry of Economy and Competitiveness through the SCARCE project (Consolider-Ingenio 2010 CSD2009-00065) and by the European Union through the European Regional Development Fund (ERDF). MJ. García acknowledges the Beatriu de Pinos program (2014 BP-A 00245); Sara Rodriguez-Mozaz acknowledges the Ramon y Cajal program (RYC-2014-16707)

Elsevier