[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.
Peng
P
Wang
Hong-Lei
HL
Zhou
Fu-Zhong
FZ
Xue
Zhen-Guo
ZG
Wang
D016002:Discriminant Analysis; D004365:Drugs, Chinese Herbal / Q000032:analysis*; D016018:Least-Squares Analysis; D010936:Plant Extracts / Q000032:analysis*; D025341:Principal Component Analysis; D017550:Spectroscopy, Fourier Transform Infrared; D060388:Support Vector Machine
D004365:Drugs, Chinese Herbal; D010936:Plant Extracts