Fingerprinting of complex mixtures with the use of high performance liquid chromatography, inductively coupled plasma atomic emission spectroscopy and chemometrics.
Fingerprinting of complex mixtures with the use of high performance liquid chromatography, inductively coupled plasma atomic emission spectroscopy and chemometrics.
复制标题
DOI：
10.1016/j.aca.2008.04.015
复制
发表时间：
2008-05
影响因子：
6.2
通讯作者：
Y. Ni;Yunyan Peng;S. Kokot
中科院分区：
文献类型：
--
作者：
Y. Ni;Yunyan Peng;S. Kokot
采用高效液相色谱(HPLC)和电感耦合等离子体原子发射光谱(ICPAES)技术，对中药苍术进行了分子和金属指纹图谱分析。在这项工作中，这种物质被用作一种复杂的生物材料，它已被用作中药。这类中药样本传统上采用麸皮、切块、油炸和煎煮等方法加工，采集自中国所在的五个省份。两类分析得到的数据矩阵产生了两个主成分双线图，这表明高效液相指纹图谱数据是根据生药的加工方法进行区分的，而金属分析是根据产地进行分组的。当两个数据矩阵组合成一个单双向矩阵时，根据高效液相指纹图谱，所产生的双向图显示出明显的分离。重要的是，在每个不同的分组中，对象根据它们的地理来源分开，并且它们在每个组中以大致相同的顺序排序。这一结果表明，通过使用这种方法，可以在这两种分析数据的基础上得出更好的复杂中药材的表征。此外，两种有监督的模式识别方法--K-近邻(KNNS)方法和线性判别分析(LDA)--被成功地应用于单个数据矩阵，从而支持主成分分析方法。
The molecular and metal profile fingerprints were obtained from a complex substance, Atractylis chinensis DC—a traditional Chinese medicine (TCM), with the use of the high performance liquid chromatography (HPLC) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) techniques. This substance was used in this work as an example of a complex biological material, which has found application as a TCM. Such TCM samples are traditionally processed by the Bran, Cut, Fried and Swill methods, and were collected from five provinces in China. The data matrices obtained from the two types of analysis produced two principal component biplots, which showed that the HPLC fingerprint data were discriminated on the basis of the methods for processing the raw TCM, while the metal analysis grouped according to the geographical origin. When the two data matrices were combined into a one two-way matrix, the resulting biplot showed a clear separation on the basis of the HPLC fingerprints. Importantly, within each different grouping the objects separated according to their geographical origin, and they ranked approximately in the same order in each group. This result suggested that by using such an approach, it is possible to derive improved characterisation of the complex TCM materials on the basis of the two kinds of analytical data. In addition, two supervised pattern recognition methods, K-nearest neighbors (KNNs) method, and linear discriminant analysis (LDA), were successfully applied to the individual data matrices—thus, supporting the PCA approach.