Huang Yang, Wang Tiejie, Yin Guo, Wang Jue, Jiang Kun, Tu Jiasheng
Shenzhen Institute for Drug Control, Shenzhen, P. R. China.
State Key Laboratory of Natural Medicines, Department of Pharmaceutics, China Pharmaceutical University, Nanjing, P. R. China.
J Sep Sci. 2020 Sep;43(18):3625-3635. doi: 10.1002/jssc.201901219. Epub 2020 Aug 9.
A simple and efficient high-performance liquid chromatography method combined with chemical pattern recognition was established for quality evaluation of Mahonia bealei (Fort.) Carr. A common pattern of 30 characteristic peaks was applied for similarity analysis, hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis in the 37 batches of M. bealei (Fort.) Carr. to discriminate wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes. The results showed that partial least squares discriminant analysis was the most effective method for discrimination. Eight characteristics peaks with higher variable importance in projection values were selected for pattern recognition model. A permutation test and 26 batches of testing set samples were performed to validate the model that was successfully established. All of the training and testing set samples were correctly classified into three clusters (wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes) based on the selected chemical markers. Moreover, 26 batches of unknown samples were used to predict the accuracy of the established model with a discrimination accuracy of 100%. The obtained results indicated that the method showed great potential application for accurate evaluation and prediction of the quality of M. bealei (Fort.) Carr.
建立了一种简单高效的高效液相色谱法结合化学模式识别方法，用于十大功劳药材的质量评价。采用30个特征峰的共有模式，对37批十大功劳药材进行相似度分析、聚类分析、主成分分析和偏最小二乘判别分析，以鉴别野生十大功劳、栽培十大功劳及其替代品。结果表明，偏最小二乘判别分析是最有效的鉴别方法。选择8个投影变量重要性较高的特征峰建立模式识别模型。通过排列检验和26批测试集样本对成功建立的模型进行验证。基于所选化学标志物，所有训练集和测试集样本均被正确分为三类（野生十大功劳、栽培十大功劳及其替代品）。此外，用26批未知样品对所建立模型的准确性进行预测，判别准确率为100%。所得结果表明，该方法在十大功劳药材质量的准确评价和预测方面具有很大的潜在应用价值。
