Quality assessment of Cortex Periplocae remains a challenge, due to its complex chemical profile. This study aims to investigate the chemical components of Cortex Periplocae, including its non-volatile and volatile constituents, via liquid chromatograph–mass spectrometry (LC-MS/MS) and gas chromatography–mass spectrometry (GC-MS) assays. The established strategy manifested that Cortex Periplocae from different producing areas was determined by identifying 27 chemical markers with ultra-high-performance liquid chromatography, coupled with quadrupole tandem time-of-flight mass spectrometry (UPLC-Q-TOF-MS/MS), including four main groups of cardiac glycosides, organic acids, aldehydes, and oligosaccharides. These groups’ variable importance in the projection (VIP) were greater than 1. Simultaneously, the samples were divided into four categories, combined with multivariate statistical analysis. In addition, in order to further understand the difference in the content of samples from different producing areas, nine chemical markers of Cortex Periplocae from 14 different producing areas were determined by high performance liquid chromatography coupled with mass spectrometry (HPLC-MS/MS), and results indicated that the main effective constituents of Cortex Periplocae varied with places of origin. Furthermore, in GC-MS analysis, samples were divided into three groups with multivariate statistical analysis; in addition, 22 differential components whose VIP were greater than 1 were identified, which were principally volatile oils and fatty acids. Finally, the relative contents of seven main volatile constituents were obtained, which varied extremely with the producing areas. The results showed that the LC-MS/MS and GC-MS assays, combined with multivariate statistical analysis for Cortex Periplocae, provided a comprehensive and effective means for its quality evaluation.
Correlation between Quality and Geographical Origins of Cortex Periplocae, Based on the Qualitative and Quantitative Determination of Chemical Markers Combined with Chemical Pattern Recognition
