Simultaneous quantitative chiral analysis of four isomers by ultraviolet photodissociation mass spectrometry and artificial neural network

Abstract

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/DHisL/DAla and L/DAspL/DPhe, 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.