2134/26205 Kayleigh L. Arthur Kayleigh L. Arthur Matthew Turner Matthew Turner Alan D. Brailsford Alan D. Brailsford Andrew T. Kicman Andrew T. Kicman David A. Cowan David A. Cowan Jim Reynolds Jim Reynolds Colin Creaser Colin Creaser Rapid analysis of anabolic steroid metabolites in urine by combining field asymmetric waveform ion mobility spectrometry with liquid chromatography and mass spectrometry Loughborough University 2017 untagged Chemical Sciences not elsewhere classified 2017-08-24 11:08:02 Journal contribution https://repository.lboro.ac.uk/articles/journal_contribution/Rapid_analysis_of_anabolic_steroid_metabolites_in_urine_by_combining_field_asymmetric_waveform_ion_mobility_spectrometry_with_liquid_chromatography_and_mass_spectrometry/9394073 © 2017 American Chemical Society. The combination of field asymmetric waveform ion mobility spectrometry with liquid chromatography-mass spectrometry (LC-FAIMS-MS) has been developed for the analysis of glucuronide and sulfate metabolites of seven anabolic-androgenic steroids in urine. Separation by FAIMS-MS was investigated in positive ion mode for selected cationic adducts (H + , NH 4 + , Na + , K + , and Cs + ). LC-FAIMS-MS analysis of the doubly sodiated adducts ([M + 2Na - H] + ) of isobaric and coeluting steroid metabolites allowed their rapid (8 min) qualitative and quantitative determination in spiked urine using hydrophilic interaction liquid chromatography prior to FAIMS-MS separation, with discrimination > 95% achieved between the steroids investigated. A quantitative evaluation of the LC-FAIMS-MS method was performed giving limits of detection in the range 1-6 ng mL -1 , limits of quantification in the range 3-20 ng mL -1 , with reproducibility (%RSD < 10%; n = 6) and linearity (R 2 > 0.99). The LC-FAIMS-MS method demonstrates increases in signal-to-noise ratios for the doubly sodiated steroid metabolites in unspiked urine ( > 250%) by the reduction of isobaric interferences from the matrix. An alternative or additional tool for identification of the steroid metabolites is based on the observations of different patterns of sodium acetate clusters that are characteristic for each metabolite.