Prediction of Collision Cross-Section Values for Small Molecules: Application to Pesticide Residue Analysis
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Other documents of the author: Bijlsma, Lubertus; Bade, Richard; Celma, Alberto; Mullin, Lauren; Cleland, Gareth; Stead, Sara; Hernandez, Felix; Sancho, Juan V
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comunitat-uji-handle2:10234/33596
comunitat-uji-handle3:10234/33597
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https://doi.org/10.1021/acs.analchem.7b00741 |
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Title
Prediction of Collision Cross-Section Values for Small Molecules: Application to Pesticide Residue AnalysisAuthor (s)
Date
2017ISSN
0003-2700; 1520-6882Bibliographic citation
BIJLSMA, Lubertus, et al. Prediction of collision cross-section values for small molecules: application to pesticide residue analysis. Analytical chemistry, 2017, vol. 89, no 12, p. 6583-6589.Type
info:eu-repo/semantics/articlePublisher version
https://pubs.acs.org/doi/full/10.1021/acs.analchem.7b00741Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
The use of collision cross-section (CCS) values obtained by ion mobility high-resolution mass spectrometry has added a third dimension (alongside retention time and exact mass) to aid in the identification of compounds. ... [+]
The use of collision cross-section (CCS) values obtained by ion mobility high-resolution mass spectrometry has added a third dimension (alongside retention time and exact mass) to aid in the identification of compounds. However, its utility is limited by the number of experimental CCS values currently available. This work demonstrates the potential of artificial neural networks (ANNs) for the prediction of CCS values of pesticides. The predictor, based on eight software-chosen molecular descriptors, was optimized using CCS values of 205 small molecules and validated using a set of 131 pesticides. The relative error was within 6% for 95% of all CCS values for protonated molecules, resulting in a median relative error less than 2%. In order to demonstrate the potential of CCS prediction, the strategy was applied to spinach samples. It notably improved the confidence in the tentative identification of suspect and nontarget pesticides. [-]
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Analytical chemistry, 2017, vol. 89, no 12Investigation project
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