[Infrared spectroscopy of epimedium brevicornum based on artificial neural network].
Regarding raw drugs of the different habitat and the different cultivation condition, its treatment efficacy is different. This is because they contain different chemical composition and different ingredients content proportion, which causes the difference in their infrared spectra. But these differences are extremely slight, and purely differentiating their characteristics from the infrared spectra is extremely difficult. In the present paper, the samples of epimedium brevicornu from different fields of Jilin province were surveyed by Fourier transform infrared (IR) spectra, and the corresponding pretreatment to the spectra data was carried out. Before establishing model through the artificial neural networks, in order to enhance the training speed of the ANN, the spectra variables were compressed through the wavelet transformation, and the parameters of the ANN model were also discussed in detail. The model can distinguish the producing area of the 42 samples of epimedium brevicornum correctly, avoiding the separation and drawing of raw drugs with traditional spectroscopy analysis at the same time, thus offer an effectively and reliable basis for the quality controls and modernized management of Chinese medicine.
Zhang
Yong
Y
Key Laboratory for Terrain-Machine Bionics Engineering, Ministry of Education, Jilin University, Changchun 130022, China.
Jin
Xiang-jun
XJ
Xie
Yun-fei
YF
Zhao
Bing
B
Cong
Qian
Q
D034181:Epimedium / Q000737:chemistry*; D016571:Neural Networks, Computer; D013055:Spectrophotometry, Infrared / Q000379:methods*