| National medicine is a part of the medical system with Chinese characteristics. The biggest obstruction of the development in national medicine is the weakness of theoretical foundation and the defect of quality control system. The emergence of the traditional Chinese medicine(TCM) fingerprint offers a new method for the quality control of national drugs. The fingerprint technologies such as high performance liquid chromatography(HPLC), infrared spectroscopy(IR) and ultraviolet spectroscopy(UV), etc are widely used in the identification and quality evaluation of TCM. Based on the homology of the natural medicine, we could choose TCM fingerprint as the quality control method of the national medicine. In this paper, by using modern analysis technology combined with chemical fingerprint identification in Swertia davidi which collected from different regions, and classification and evaluation of three kinds medicinal Swertia plants.The first chapter of the thesis discusses the link between ethnic medicine and traditional Chinese medicine(TCM), fingerprint method to play a huge role in the identification and evaluation of national drug, the value of Swertia davidi and the current research field of the national medicine. In the current research, the report of Swertia davidi by chemistry fingerprint was less.The second chapter, Fourier transform infrared spectroscopy(FTIR) were used to rapidly discriminate the Swertia davidi which collected from different origins. The original infrared spectra data of different parts of all the 70 samples which collected from four different regions were preprocessed by automatic calibration, automatic smoothing, the first derivative and the second derivative. Then the processed data were imported into OMNIC 8.2 and the absorption peaks were compared; PLS-DA was performed by SIMCA-P+ 10.0 and the effect of discrimination of different origins was compared by 3D score plot of the first three principal components; the infrared spectral data were imported into SPSS 19.0 for HCA to compare classification results of different parts by the dendrogram. FTIR combined with PLS-DA and HCA can rapidly and accurately differentiate S. davidi that collected from different origins, the origin discrimination effect of different parts was clearly different that the classification of roots is the best, the second derivative could enhance the specificity of the samples, the classification in 3D score plot could be visualized and obvious.The third chapter, discriminate three species Swertia medicinal plant(Swertia davidi, Swertia angustifolia and Swertia punicea) by FTIR. We compared the original spectrum of samples. Discriminant analysis(DA) and hierarchical cluster analysis(HCA) were used to analyze the data. The effect of classification was different in different parts, stems and leaves were better than roots.The forth chapter, Ultra-high performance liquid chromatography(UPLC) combined with similarity analysis, cluster analysis(HCA) and principal component analysis(PCA) were used to evaluate and discriminate different regions of Swertia davidi. The article compares 5 kinds of standard compounds content in stems and leaves of different origins samples, content difference to be markedly. The result of HCA showed that the effect of identification was not good by only stem or leaf data matrix, excellent classification would get if we combined with the matrix of stem and leaf.The fifth chapter, collected three kinds of Swertia total 27 samples chromatograms. The chromatograms imported Similarity Evaluation System for Chromatographic Fingerprint of Chinese Materia Medica 2004 A to obtain samples of data retention time and peak area. Three sets of samples of similarity were analyzed. The peak area data imported SPSS and SIMCA-P+ software for cluster analysis and principal component analysis, obtained dendrogram and principal component scores, observe the clustering effect. Three kinds of Swertia medicinal plant were differences in the content of index compound, the content of index compound in same species was different. The accuracy of dendrogram classification was 85.2%. The content of index compound of three false anomalies was less than the remaining nine samples of S. punicea. The effect of discrimination by PCA scores plot of three Swertia plants was good, compared S. davidi and S. angustifolia, the effect of clustering of S. punicea uncomfortable.The sixth chapter, The UV spectra of different parts of samples obtained from four regions were collected and all of the spectra had the characteristic absorption peaks in the wavelength of 240, 276 and 324 nm. In the previous study, swertiamarin and gentiopicroside had maximum absorption band in 240 and 276 nm, respectively. It could be inferred that the contents of these two chemical components in leaves were the highest by different concentrations of gradients and Lambert-Berr’s law. The spectra data were imported into UV Probe 2.34 software to compare the same part of S. davidi. Raw and pre-processed data(8 point smoothing, the first derivative and the second derivative) were imported into SIMCA-P 11.5 and the effect of discrimination of origins was compared by 3D score plot of PCA. PCA indicated that the raw and 8 point smoothing data of leaves showed the best classification and the cumulative contribution rate of the first three factors was 98.8%. The other pre-processed methods could not obtain better identification and it may be related to the cumulative contribution value(the cumulative contribution rate of the data processed by the first derivative was 83.9% while the second derivative was 47.3%). Samples from Chongqing and Hubei could be distinguished with that of Hunan by the data of roots, but the samples of Chongqing and Hubei could not be separated. The model of PLS-DA may provide the basis of discrimination of more origins. The validation set was imported into the model developed by the training set and it proved that the model was feasible and effective. In PLS-DA, the correlation index of predictive value and true value in the training set was 0.985 and the RMSEE was 0.159. The correlation index of predictive value and true value after importing the validation set in the training set was 0.972 and RMSEP was 0.327. Both RMSEE and RMSEP were similar and less than 0.500. So the model had high reliability. |
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