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Simultaneous quantitative chiral analysis of four isomers by ultraviolet photodissociation mass spectrometry and artificial neural network.
Shi, Yingying; Zhou, Ming; Kou, Min; Zhang, Kailin; Zhang, Xianyi; Kong, Xianglei.
Afiliación
  • Shi Y; State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin, China.
  • Zhou M; State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin, China.
  • Kou M; School of Physics and Electronic Information, Anhui Normal University, Wuhu, China.
  • Zhang K; State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin, China.
  • Zhang X; Life and Health Intelligent Research Institute, Tianjin University of Technology, Tianjin, China.
  • Kong X; School of Physics and Electronic Information, Anhui Normal University, Wuhu, China.
Front Chem ; 11: 1129671, 2023.
Article en En | MEDLINE | ID: mdl-36970407
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 L/D His L/D Ala and L/D Asp L/D 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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Chem Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Chem Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza