[Analysis of infrared spectra of 60 kinds of plant extract of traditional Chinese medicine and study on the identification and evaluation of characteristics of the regional markers associated with cold and heat nature].

By using the Fourier transform infrared spectroscopy and linear discriminant analysis (LDA), logistic discriminant analysis (Logistic-DA), principal component analysis-linear discriminant analysis (PCA-LDA), partial least-squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), infrared spectra of 60 kinds of plant extract of Chinese traditional medicine were analyzed and the identification and evaluation of characteristics of the regional markers associated with cold and heat nature were studied. Results indicated that LDA and SVM are suitable for the recognition model of water extract infrared spectral data, LDA is suitable for the identification model of anhydrous ethanol extract infrared spectral data, SVM is suitable for the identification model of chloroform extract infrared spectral data, while petroleum ether extract group recognition effect is not ideal. According to the suitable characteristic parameters identification model, data were analyzed by infrared spectroscopy, and parameters and resistance characteristics of the traditional Chinese drug composition can be obtained. Regional characteristics of these two parameters can be used to identify drug ingredients, and can also be used to indicate different degrees of resistance characteristics of traditional Chinese medicine. Component parameter is model identification coefficient corresponding to the position of spectrum and infrared, with a value greater than zero it is cold nature marker, while with a value less than zero it is heat nature marker; model identification score is a parameter reflecting the degree of cold and heat nature, the greater the score (positive), the more it is cold, while the smaller the score, the more it is hot. a parameter reflecting the degree of cold and heat,the greater the score (positive) is cold more strong, the score is small (negative) heat stronger.

[Pattern recognition for traditional Chinese medicine nature of 60 kinds of herbal medicine based on high performance capillary electrophoresis fingerprint].

OBJECTIVE: To establish signature pattern recognition model of cold-hot nature of herbal medicine. METHODS: High performance capillary electrophoresis fingerprints of 60 kinds of herbal medicine (30 kinds of cold, 30 of hot) were established, features of wavelength were screened, 6 analysis methods such as linear discriminant analysis (LDA), logistic discriminant analysis (Logistic-DA), principal component and linear discriminant analysis (PCA-LDA), partial least-squares discriminant analysis (PLS-DA), random forest (RF) and support vector machine (SVM) were used to establish and evaluate recognition model of cold-hot nature after data processing. RESULTS: SVM was proved to be a suitable means of recognition model of herbal medicine cold-hot nature based on data of HPCE fingerprints. Characteristic parameters of nature could be screened according to theoretical spectra signature of nature model, the characteristic regions of components of herbs with cold-heat nature could be identified in the HPCE fingerprint. The characteristic parameters of cold-hot nature were the identifying coefficient for specific retention time of the theoretical spectra of recognition model, identification coefficients greater than zero were for the cold marker, while that less than zero for the hot marker. CONCLUSION: The results imply that HPCE is a feasible and effective means for identification of cold-hot nature of Traditional Chinese medicine.