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Performance enhancement and sample throughput increase of a multiresidue pesticides method in fruits and vegetables using Data-Dependent MS acquisition
- Source :
- Food Additives & Contaminants: Part A. 37:110-120
- Publication Year :
- 2019
- Publisher :
- Informa UK Limited, 2019.
-
Abstract
- Due to the growing number of analysed pesticide residues, analytical strategies have evolved for the data processing of 100s of pesticides in a single analysis. We present herein a LC-MS/MS method based on triple quadrupole technology capable of detecting concentrations at 5 ng/g and confirming 381 pesticides in a single injection. Confirmatory analysis is performed using data-dependent acquisition that compares full MS/MS spectra of candidates to a fast library interrogation within the same injection. A comparison on more than 200 samples of fruits and vegetables (representing principal types: normal, pigmented, and fatty) with pre-existing workflow based on single MRM analysis per compound was performed to validate this approach. A fast turnaround time was demonstrated due to more-unambiguous identification suppressing the need for reinjection to confirm candidates. The automated library searching and confirmation only of putative hits also allowed focusing on the manual verification and validation steps just for putative candidates which hence also increased overall throughput and results quality. Superior robustness of the method due partially to a reduced volume injected was also one of the key points achieved using this methodology. An interesting feature is also the capability to enrich the library and the number of pesticides screened with ease.
- Subjects :
- Computer science
Health, Toxicology and Mutagenesis
Sample (material)
Food Contamination
010501 environmental sciences
Toxicology
01 natural sciences
Turnaround time
Tandem Mass Spectrometry
Robustness (computer science)
Vegetables
Software verification and validation
Throughput (business)
0105 earth and related environmental sciences
Pesticide residue
business.industry
010401 analytical chemistry
Pesticide Residues
Public Health, Environmental and Occupational Health
Pattern recognition
General Chemistry
General Medicine
Pesticide
0104 chemical sciences
Triple quadrupole mass spectrometer
Fruit
Artificial intelligence
business
Food Science
Subjects
Details
- ISSN :
- 19440057 and 19440049
- Volume :
- 37
- Database :
- OpenAIRE
- Journal :
- Food Additives & Contaminants: Part A
- Accession number :
- edsair.doi.dedup.....c87d9c56ca690a3fcd1f78a025c03d73
- Full Text :
- https://doi.org/10.1080/19440049.2019.1676920