Isolation of a New Cyclopeptide (Lyciumin H) and Identification of Lyciumin C as the Novel Marker for Lycii Cortex Discrimination

There have been no efficient tools for Lycii Cortex (LyC) discrimination between Lycium chinense and Lycium barbarum origins. The study aimed to develop methods for LyC discrimination both with total chemical characteristics and with a single marker. In this work, a pattern-oriented approach (tool 2) was studied for marker selection, using principal component analysis and machine learning (ML) algorithms on liquid chromatography coupled with mass spectrometry data. Lyciums (cyclopeptides) were isolated from LyC. Their chemical structures were elucidated on the basis of spectroscopic data and fluorodinitrophenyl-5-L-alanine amide derivatization. The species-specific marker (tool 1) was confirmed by analysing content determination in 63 batches of species-authenticated LyC samples. Upon analysis, a predictor based on the support vector machines' classifier was proven to be valid for the species identification of LyC with an accuracy of 100% in verification. The top-performing features, including lyciumins (cyclopeptides), were identified from the dataset during the ML model training. A new cyclononapeptide, lyciumin H (1), and five known cyclooctapeptides, lyciumins A (2), B (3), C (4), D (5), and lyciumin B methyl ester (6), were isolated from LyC of L. chinense. Lyciumin C (4), specific to the species of L. chinense root barks, was successfully selected as an identity marker to discriminate LyC, originating from L. chinense. Given the above, our study proposed two tools for LyC discrimination. A pattern-oriented approach can be successfully applied to the identification of botanical origin, while also providing directions for a compound-oriented approach and a rational marker selection.