Rationale: Electrospray ionization mass spectrometry (ESI-MS) in conjunction with liquid chromatography (LC) can provide accurate quantitative data, but it is not well-suited for the rapid screening (RS) of analytes incurred into complex matrices. This study was designed to determine the usefulness of ESI for rapid detection and quantitation of veterinary drugs from complex biological matrices under near real-time conditions.
Methods: Nine veterinary drugs or metabolites, clenbuterol, erythromycin, flunixin, 5-hydroxyflunixin, meloxicam, ractopamine, salbutamol, tylosin and zilpaterol, present in cow urine, sheep urine, sheep tissues (kidney, muscle, liver and lung) or pig kidney, were simultaneously analyzed. A simple sample clean-up procedure, which included dilution with 10% sodium carbonate followed by extraction with ethyl acetate, was used. For tissues, an additional pre-extraction with hexane was performed to remove fat prior to MS analysis. Samples were introduced into the mass spectrometer through the LC autosampler, but no chromatographic separation was employed. A Sciex 5600+ triple time-of-flight mass spectrometer with a dual-spray source interfaced with a Shimadzu Nexera LC system was used. Samples were analyzed in positive ion mode.
Results: Sample extraction times were typically 10-30 min or less and instrumental analysis time was 1 min/sample. Regression coefficients of matrix-matched standard curves across all compounds ranged from 0.9701-0.9999 in urine (cow and sheep) and tissues (sheep kidney, liver, lung, muscle and pig kidney). Limits of detection ranged from 0.11 to 2.03 ng/mL across analytes in urine and 0.11 to 8.86 ng/g across tissues. Correlations between RS-ESI-MS and LC/MS/MS results were 0.956 to 0.998 for incurred residues of flunixin in cow urine, ractopamine in pig kidney and zilpaterol in sheep urine.
Conclusions: RS-ESI-MS provided rapid, sensitive, and accurate analyses of nine veterinary drugs from complex matrices with very little sample preparation and produced quantitative data akin to LC/MS/MS.
Published 2020. This article is a U.S. Government work and is in the public domain in the USA.