Development and Validation of a Qualitative Method for Target Screening of 448 Pesticide Residues in Fruits and Vegetables Using UHPLC/ESI Q-Orbitrap Based on Data-Independent Acquisition and Compound Database.
Jian WangWillis ChowJames ChangJon W WongPublished in: Journal of agricultural and food chemistry (2017)
A semiautomated qualitative method for target screening of 448 pesticide residues in fruits and vegetables was developed and validated using ultrahigh-performance liquid chromatography coupled with electrospray ionization quadrupole Orbitrap high-resolution mass spectrometry (UHPLC/ESI Q-Orbitrap). The Q-Orbitrap Full MS/dd-MS2 (data dependent acquisition) was used to acquire product-ion spectra of individual pesticides to build a compound database or an MS library, while its Full MS/DIA (data independent acquisition) was utilized for sample data acquisition from fruit and vegetable matrices fortified with pesticides at 10 and 100 μg/kg for target screening purpose. Accurate mass, retention time and response threshold were three key parameters in a compound database that were used to detect incurred pesticide residues in samples. The concepts and practical aspects of in-spectrum mass correction or solvent background lock-mass correction, retention time alignment and response threshold adjustment are discussed while building a functional and working compound database for target screening. The validated target screening method is capable of screening at least 94% and 99% of 448 pesticides at 10 and 100 μg/kg, respectively, in fruits and vegetables without having to evaluate every compound manually during data processing, which significantly reduced the workload in routine practice.
Keyphrases
- liquid chromatography
- high resolution mass spectrometry
- mass spectrometry
- gas chromatography
- ms ms
- tandem mass spectrometry
- ultra high performance liquid chromatography
- risk assessment
- high resolution
- electronic health record
- high performance liquid chromatography
- simultaneous determination
- multiple sclerosis
- big data
- solid phase extraction
- healthcare
- human health
- systematic review
- adverse drug
- primary care
- data analysis
- deep learning
- density functional theory