Shi Yingying, Zhou Ming, Kou Min, Zhang Kailin, Zhang Xianyi, Kong Xianglei
State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin, China.
School of Physics and Electronic Information, Anhui Normal University, Wuhu, China.
Front Chem. 2023 Mar 9;11:1129671. doi: 10.3389/fchem.2023.1129671. eCollection 2023.
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.
尽管质谱（MS）在速度、特异性和灵敏度方面具有独特优势，但其在旨在确定多种手性异构体比例的定量手性分析中的应用仍然是一项挑战。在此，我们提出了一种基于人工神经网络（ANN）的方法，用于从紫外光解离质谱中定量分析多种手性异构体。分别使用GYG三肽和碘代-L-酪氨酸作为手性参考物，对组氨酸-丙氨酸和天冬氨酸-苯丙氨酸两种二肽的四种手性异构体进行了相对定量分析。结果表明，该网络可以用有限的数据集进行良好训练，并且在测试集中表现良好。本研究展示了该新方法在面向实际应用的快速定量手性分析中的潜力，在不久的将来还有很大的改进空间，包括选择更好的手性参考物和改进机器学习方法。
