Literature DB >> 26100574

Travelling-wave ion mobility time-of-flight mass spectrometry as an alternative strategy for screening of multi-class pesticides in fruits and vegetables.

Séverine Goscinny1, Laure Joly2, Edwin De Pauw2, Vincent Hanot3, Gauthier Eppe4.   

Abstract

This paper reports a novel approach to screening multi-class pesticides by ion mobility time-of-flight mass spectrometry detection. Nitrogen was selected as mobility gas. After optimization of the different ion mobility parameters, determination of matrix effect on the drift times was conducted using different matrix extracts. The results showed that drift time values are not influenced by the matrix and also are independent of the concentration within the working range for 100 pesticides tested, making drift time a powerful additional identification tool. Based on statistics, 2% variation criteria provides a good fit for all the pesticides targeted, and could be considered as a maximum acceptable criteria associated with the drift time parameter for identification purpose. This 2% value is in agreement with already reported criteria, for instance, for GC or LC retention time in European documents. Finally, the well-known feature of mobility to separate complex mixtures was also tested to obtain purified extracted mass spectra of pesticides present in fruit extract.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Drift time; Identification power; Ion-mobility mass spectrometry; Pesticide analysis; Screening

Mesh:

Substances:

Year:  2015        PMID: 26100574     DOI: 10.1016/j.chroma.2015.05.057

Source DB:  PubMed          Journal:  J Chromatogr A        ISSN: 0021-9673            Impact factor:   4.759


  6 in total

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Authors:  Christopher Kune; Cédric Delvaux; Jean R N Haler; Loïc Quinton; Gauthier Eppe; Edwin De Pauw; Johann Far
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Review 2.  Recent Advances and Future Challenges in Modified Mycotoxin Analysis: Why HRMS Has Become a Key Instrument in Food Contaminant Research.

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Journal:  Mar Drugs       Date:  2022-08-23       Impact factor: 6.085

4.  Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning.

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Journal:  Molecules       Date:  2022-09-29       Impact factor: 4.927

5.  Travelling Wave Ion Mobility-Derived Collision Cross Section for Mycotoxins: Investigating Interlaboratory and Interplatform Reproducibility.

Authors:  Laura Righetti; Nicola Dreolin; Alberto Celma; Mike McCullagh; Gitte Barknowitz; Juan V Sancho; Chiara Dall'Asta
Journal:  J Agric Food Chem       Date:  2020-09-10       Impact factor: 5.279

6.  Target, suspect and non-target screening analysis from wastewater treatment plant effluents to drinking water using collision cross section values as additional identification criterion.

Authors:  Vanessa Hinnenkamp; Peter Balsaa; Torsten C Schmidt
Journal:  Anal Bioanal Chem       Date:  2021-03-25       Impact factor: 4.142

  6 in total

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