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Target screening of 105 veterinary drug residues in milk using UHPLC/ESI Q-Orbitrap multiplexing data independent acquisition

机译:Target screening of 105 veterinary drug residues in milk using UHPLC/ESI Q-Orbitrap multiplexing data independent acquisition

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This paper presents a multi-class target screening method for the detection of 105 veterinary drug residues from 11 classes in milk using ultra-high performance liquid chromatography electrospray ionization quadrupole Orbitrap mass spectrometry (UHPLC/ESI Q-Orbitrap). The method is based on a non-target approach of full mass scan and multiplexing data-independent acquisition (Full MS/mDIA). The veterinary drugs include endectocides, fluoroquinolones, ionophores, macrolides, nitroimidazole, NSAIDs, beta-lactams, penicillins, phenicols, sulfonamides, and tetracyclines. Veterinary drug residues were extracted from milk using a salting-out and solid-phase extraction (SOSPE) procedure, which entailed the precipitation of milk proteins by an extraction buffer (oxalic acid and EDTA, pH 3) and acetonitrile, a salting-out acetonitrile/water phase separation using ammonium sulfate, and solid-phase extraction for clean-up using polymeric reversed-phase sorbent cartridges. The Q-Orbitrap Full MS/dd-MS2 (data-dependent acquisition) was used to acquire product-ion spectra of individual veterinary drugs to build a compound database and a mass spectral library, whereas its Full MS/mDIA was utilized to acquire sample data from milk for target screening of veterinary drugs fortified at 1.0 or 10.0 mu g/kg. The in-spectrum mass correction or solvent background lock-mass correction was used to minimize mass error when building the compound database from experimental dd-MS2 accurate mass data. Retention time alignment and response threshold adjustment were used to eliminate or reduce false negatives and/or false positive rates. The validated method was capable of screening 58 and 96 of 105 veterinary drugs at 1.0 and 10.0 mu g/kg, respectively, without manually evaluating every compound during data processing, which will reduce the workload in routine practice.

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