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), MS, and multivariate chemometric methods was designed to assess the quality of Cimicifugae Rhizoma.
Methods: All materials were crushed into powder and the powdered sample was dissolved in 70% aqueous methanol for sonication. 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 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 demonstrated 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 QC of Cimicifugae Rhizoma.
Highlights: The HCA, PCA and OPLS-DA models visually classify Cimicifugae Rhizoma by appearance traits and obtain the chemical markers that influence the classification. The training and prediction sets were built to demonstrate the accuracy of the classification. Advanced UPLC-Q-Orbitrap-MS/MS technology provides powerful elucidation of critical chemical markers.
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