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  • Title: Target screening of 105 veterinary drug residues in milk using UHPLC/ESI Q-Orbitrap multiplexing data independent acquisition.
    Author: Wang J, Leung D, Chow W, Chang J, Wong JW.
    Journal: Anal Bioanal Chem; 2018 Sep; 410(22):5373-5389. PubMed ID: 29404665.
    Abstract:
    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, β-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 μg/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 μg/kg, respectively, without manually evaluating every compound during data processing, which will reduce the workload in routine practice.
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