Ligustici Rhizoma et Radix (LReR) is the dried rhizomes and roots of Ligusticum sinese Oliv. (LS) or Ligusticum jeholense Nakai et Kitag. (LJ). However, in the market, LS and LJ are frequently confused with each other. Since the volatile oils are both the main active components and quality control indicators of LReR, a strategy combining gas chromatography-mass spectrometry (GC-MS) and chemical pattern recognition (CPR) was used to compare the volatile components of LJ and LS. Total ion chromatography (TIC) revealed that phthalides (i.e., neocnidilide) and phenylpropanoids (i.e., myristicin) could be thought of as the most critical components in the volatile oils of LJ and LS, respectively. In addition, the chemical components of the volatile oils in LJ and LS were successfully distinguished by hierarchical cluster analysis (HCA) and principal component analysis (PCA). Moreover, two quality markers, including myristicin and neocnidilide, with a very high discriminative value for the classification of LJ and LS, were found by orthogonal partial least squares discriminant analysis (OPLS-DA). The relative contents of myristicin and neocnidilide were 10.86 ± 6.18% and 26.43 ± 19.63% for LJ, and 47.43 ± 12.66% and 2.87 ± 2.31% for LS. In conclusion, this research has developed an effective approach to discriminating LJ and LS based on volatile oils by combining GC-MS with chemical pattern recognition analysis.

Objective. Magnoliae officinalis cortex (MOC) is one of the most frequently used traditional Chinese medicine (TCM) for the treatment of acute pancreatitis (AP). Magnolia volatile oil (MVO) is considered to be one of the main active ingredients in MOC for AP treatment. However, the underlying mechanism of MVO in AP therapy is unknown. Methods. An integrated strategy of gas chromatography-mass spectrum (GC-MS), network pharmacology, and molecular docking simulation was employed to predict underlying mechanism of MVO in AP treatment. First, the compounds of MVO were identified by GC-MS, and the targets of the identified characteristic compounds were collected from several databases, as well as AP-related targets. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were carried out to obtain the mechanism. Moreover, the binding activity between core therapeutic targets and their corresponding compounds was evaluated by molecular docking simulation. Results. GC-MS results showed a total of 35 compounds that appeared in at least 18 out of 20 chromatograms were considered as characteristic compounds of MVO, and 33 compounds of those were identified. Network analysis demonstrated that 33 compounds regulated 142 AP-related targets. Of those, 8 compounds (α-eudesmol, γ-eudesmol, (−)-terpinen-4-ol, terpineol, hinesol, linalool, borneol, and β-eudesmol) and 8 targets (TNF, IL-1β, PPARγ, PPARα, PTGS2, NCOA1, CNR1, and ESR1) have a close relationship with AP treatment and were recognized as the key active compounds and the core therapeutic targets, respectively. The 142 targets were involved in both inflammation and calcium overload-related biological pathways, such as neuroactive ligand-receptor interaction, estrogen, MAPK, and calcium signaling pathway. Moreover, molecular docking simulation indicated that the 8 core therapeutic targets strongly interacted with their corresponding compounds. Conclusions. In summary, the present study elucidated that the efficacy of MVO in AP treatment might be attributed to anti-inflammation and inhibition of calcium overload through multicomponents and multitargets.