Suppr超能文献

黄柏药材质量评价的指纹⁻化学模式识别研究。

Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint⁻Chemical Pattern Recognition.

机构信息

School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.

Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.

出版信息

Molecules. 2018 Sep 10;23(9):2307. doi: 10.3390/molecules23092307.

Abstract

Phellodendri Chinensis Cortex (PCC) and Phellodendri Amurensis Cortex (PAC) are increasingly being used as traditional herbal medicines, but they are often mistaken for each other. In this study, the fingerprints of PCC from six different geographical sources were obtained by high-performance liquid chromatography, and multivariate chemometric methods were used for comprehensive analysis. Two unsupervised pattern recognition models (principal component analysis and hierarchical cluster analysis) and a supervised pattern recognition model (partial least squares discriminant analysis) were established on the basis of the chemical composition and physical traits of PCC and PAC. PCC and PAC were found to be distinguishable by these methods. The PCC category was divisible into two categories, one with more crude cork and a maximum thickness of ~1.5 mm, and the other with less net crude cork and a maximum thickness of 0.5 mm. According to the model established by partial least squares discriminant analysis (PLS-DA), the important chemical marker berberine hydrochloride was obtained and analyzed quantitatively. From these results combined with chemometric and content analyses, the preliminary classification standards for phellodendron were established as three grades: superior, first-order and mixed. Compared with the traditional identification methods of thin layer chromatography identification and microscopic identification, our method for quality evaluation is relatively simple. It provides a basis and reference for identification of PCC and enables establishment of grade standards. It also could be applied in quality control for compound preparations containing PCC.

摘要

黄柏和关黄柏作为传统的药用植物,应用日益广泛,但两者常被混淆。本研究采用高效液相色谱法建立了 6 个不同产地黄柏的指纹图谱,并运用多元化学计量学方法进行综合分析。基于黄柏和关黄柏的化学成分和物理特性,建立了 2 种无监督模式识别模型(主成分分析和层次聚类分析)和 1 种有监督模式识别模型(偏最小二乘判别分析)。这些方法可以区分黄柏和关黄柏。黄柏可分为 2 类,一类粗栓皮较多,最大厚度约 1.5mm;另一类粗栓皮较少,最大厚度 0.5mm。根据偏最小二乘判别分析(PLS-DA)建立的模型,获得了盐酸小檗碱这一重要的化学标记物,并进行了定量分析。综合化学计量学和含量分析结果,初步制定了黄柏的分级标准,分为优等品、一等品和混等品。与传统的薄层色谱鉴别和显微镜鉴别鉴定方法相比,该质量评价方法较为简单,为黄柏的鉴别提供了依据和参考,建立了等级标准,也可应用于含有黄柏的复方制剂的质量控制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d315/6225206/4929fb10e115/molecules-23-02307-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验