Background Cimicifugae Rhizoma, known in Chinese as Shengma, is a common medicinal material in traditional Chinese medicine (TCM), mainly used for treating wind-heat headaches, sore throat, uterine prolapse, and other diseases. Objectives An approach using a combination of ultra-performance liquid chromatography (UPLC), mass spectrometry (MS), and multivariate chemometric methods was designed to assess the quality of Cimicifugae Rhizoma. Materials and methods All materials were crushed into powder and the powdered sample was dissolved in 70% aqueous methanol for sonicating. Chemometric methods, including hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA), were adopted to classify and perform a comprehensive visualization study of Cimicifugae Rhizoma. The unsupervised recognition models of HCA and PCA obtained a preliminary classification and provided a basis for classification. In addition, we constructed a supervised OPLS-DA model and established a prediction set to further validate the explanatory power of the model for the variables and unknown samples. Results Exploratory work research found that the samples were divided into two groups, and the differences were related to appearance traits. The correct classification of the prediction set also demonstrates a strong predictive ability of the models for new samples. Subsequently, six chemical makers were characterized by UPLC-Q-Orbitrap-MS/MS, and the content of four components was determined. The results of the content determination revealed the distribution of representative chemical markers caffeic acid, ferulic acid, isoferulic acid, and cimifugin in two classes of samples. Conclusions This strategy can provide a reference for assessing the quality of Cimicifugae Rhizoma, which is significant for the clinical practice and quality control of Cimicifugae Rhizoma.
Background: Panax japonicus (PJ) is a widely used Chinese herbal medicine, functional food and tonic. However, its origin has a great influence on the quality of PJ, and with the increasing demand for PJ, fake and inferior products, such as Panax stipuleanatus (PS), often appear. The identification of the origin and authenticity of PJ is critical for ensuring the quality, safety and effectiveness of drugs. Objective: Proposing a strategy to identify the origin, authenticity, and quality of PJ using HPLC fingerprints, chemometrics, and network pharmacology. Method: The chromatographic fingerprint method was established to analyze the origin and authenticity of PJ. Multiple chemometric methods were performed to analyze the fingerprints, including a hierarchical cluster analysis (HCA), principal component analysis (PCA), and counter propagation artificial neural network (CP-ANN). Finally, the network pharmacology method was used to construct the "active ingredient-target" network, predict and assist in analyzing potential Q-markers in PJ. Result: Ward’s method was used for the HCA. The results showed that PJ samples from different origins had significant regional differences and could be accurately distinguished from PS. The PCA classification results are consistent with the HCA classification results, further illustrating the model's accuracy. The CP-ANN model can analyze and predict PJ and PS and accurately obtain PJ and PS chemical markers to identify PJ and PS correctly. The network pharmacology of PJ was constructed, and three PJ Q-markers, namely, ginsenoside Ro, ginsenoside Rb1, and chikusetsu saponin Ⅳa, were identified, which lays a foundation for the establishment of PJ quality standards. Conclusion: This research provides a feasible platform for the quality evaluation of PJ in the future.
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