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.