Simultaneous quantitative chiral analysis of four isomers by ultraviolet photodissociation mass spectrometry and artificial neural network
Although mass spectrometry (MS) has its unique advantages in speed, specificity and sensitivity, its application in quantitative chiral analysis aimed to determine the proportions of multiple chiral isomers is still a challenge. Herein, we present an artificial neural network (ANN) based approach fo...
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Published in | Frontiers in chemistry Vol. 11; p. 1129671 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
09.03.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Although mass spectrometry (MS) has its unique advantages in speed, specificity and sensitivity, its application in quantitative chiral analysis aimed to determine the proportions of multiple chiral isomers is still a challenge. Herein, we present an artificial neural network (ANN) based approach for quantitatively analyzing multiple chiral isomers from their ultraviolet photodissociation mass spectra. Tripeptide of GYG and iodo-L-tyrosine have been applied as chiral references to fulfill the relative quantitative analysis of four chiral isomers of two dipeptides of
His
Ala and
Asp
Phe, respectively. The results show that the network can be well-trained with limited sets, and have a good performance in testing sets. This study shows the potential of the new method in rapid quantitative chiral analysis aimed at practical applications, with much room for improvement in the near future, including selecting better chiral references and improving machine learning methods. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Iker León, University of Valladolid, Spain Chi-Yu Lu, Kaohsiung Medical University, Taiwan Edited by: Wei-Lung Tseng, National Sun Yat-Sen University, Taiwan This article was submitted to Analytical Chemistry, a section of the journal Frontiers in Chemistry |
ISSN: | 2296-2646 2296-2646 |
DOI: | 10.3389/fchem.2023.1129671 |