Tianjin University of Traditional Chinese Medicine, School of Chinese Materia Medica, Tianjin 301617, China.
Tianjin University of Traditional Chinese Medicine, Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin 300193, China.
J AOAC Int. 2023 Sep 1;106(5):1313-1322. doi: 10.1093/jaoacint/qsad064.
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.
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.
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.
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.
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.
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.
升麻，又名“升麻”，是一种常见的中药材，主要用于治疗风热头痛、咽喉肿痛、子宫脱垂等疾病。
采用超高效液相色谱（UPLC）、MS 和多元化学计量学方法相结合的方法，评估升麻的质量。
将所有材料粉碎成粉末，将粉末样品溶解在 70%的甲醇水溶液中进行超声处理。采用层次聚类分析（HCA）、主成分分析（PCA）和正交偏最小二乘判别分析（OPLS-DA）等化学计量学方法对升麻进行分类和综合可视化研究。HCA 和 PCA 的无监督识别模型进行了初步分类，并为分类提供了依据。此外，我们构建了一个有监督的 OPLS-DA 模型，并建立了一个预测集，以进一步验证模型对变量和未知样本的解释能力。
探索性研究发现，样品分为两组，差异与外观特征有关。预测集的正确分类也证明了模型对新样本的强大预测能力。随后，采用 UPLC-Q-Orbitrap-MS/MS 对 6 种化学标志物进行了表征，并测定了 4 种成分的含量。含量测定结果揭示了两类样品中代表性化学标志物咖啡酸、阿魏酸、异阿魏酸和升麻苷的分布。
该策略可为评估升麻的质量提供参考，对升麻的临床应用和 QC 具有重要意义。
HCA、PCA 和 OPLS-DA 模型通过外观特征直观地对升麻进行分类，并获得影响分类的化学标志物。建立了训练集和预测集，以证明分类的准确性。先进的 UPLC-Q-Orbitrap-MS/MS 技术为关键化学标志物的鉴定提供了强大的手段。
