| As a means of quality control of traditional Chinese medicinc (TCM), TCM fingcrprinting aims to conduct a comprehensive research on TCM to fully reflect the complex relationship of the complicated components in medicines. High performance liquid chromatography (HPLC) and near infrared spectroscopy (NIR) combined with chemometrics methods are applied in this article to establish the TCM fingerprints.In the first chapter, the significance of quality control of TCMs and the development of TCMs fingerprinting are reviewed. The developing situation and existing problems of the TCM industry are described. Besides, the methods for establishing TCMs fingerprints and the applications of chemometrics in fingerprints are introduced.In the second chapter, HPLC was used to establish the fingerprints of a Chines patent medicine Ganmao Kesou Granules (GKG).11 GKG samples from three manufactures and six crude materials were analyzed by HPLC. Similarity analysis and principal component analysis (PCA) were conducted to evaluate the quality of GKG samples. It was found that there were differences in the quality of samples from difference manufacturers by comparing their chromatograms. By using PCA and similarity analysis it is able to discriminate them. Compared the crude materials'chromatograms with the patent medicines', the differences of specified constituent could be obtained combining the reference literatures. The results showed that the proposed method could be used to discriminate GKG samples from difference manufacturers and be set as a reference method for the quality control of Chinese patent medicies.In the third chapter, three kinds of processed Rhizoma atractylodis macrocephalae (RAM) samples and RAM's substitute Rhizhoma atractylodis (RA) were studied with HPLC-DAD-FLD. Two-dimensional fingerprints were esbalished by combing the UV fingerprints and fluorescence fingerprints, Unsupervised pattern recognition methods PCA and cluster analysis results showed that two-dimensional fingerprints provided better classification ability as compared with one dimensional fingerprints method. Multiple criteria decision making methods PROMETHEE and GAIA ranked and classified the samples, and the results reflected that the RAM samples ranked in order according to the processing degree. Supervised pattern recognition methods, K-nearest neighbors (KNN), partial least squares (PLS) and artifical neural networks (ANN), were used to build the prediction models with two dimensional and one dimensional fingerprints data. It is proved that two dimensional data sets exhibit better recognition and prediction abilities.In the fourth chapter, HPLC-DAD was used to establish the three-dimensional fingerprints of Wuweizi. Nan-wuweizi and Bei-wuweizi samples were collected, which have similar appearances but different in the constituents. Parallel factor analysis (PARAFAC) was used to resolve the three-way fingerprints, and three data sets that were related to the constituent content, the chromatographic elution profiles and spectral profiles of the constituents were obtained.K-nearest neighbors (KNN), partial least squares (PLS) and least squares - support vector machine (LS-SVM), were used to build the training and prediction models for Wuweizi samples, and performed well in the classification results.In the fifth chapter, NIR fingerprints of Nan-wuweizi and Bei-wuweizi samples were established. Different pre-processing methods were applied to pre-treat the original spectrums and effective wavelengths were selected with the use of variable selecting methods. The prediction results of training models showed that the pre-processed spectrums proved to be more reliable. And the models based on effective wavelengths performed better results than that of the whole spectrum. |
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