[Identification of rhizoma atractylodes based on FTIR spectra and radial basis function network].
In order to recognize the atractylodes macrocephala Koidz. (rhizoma atractylodes) and its confusable varieties, three kinds of models of radial basis function network(RBF), nonlinear-linear, linear-linear, and nonlinear-nonlinear model, were used combined with their Fourier transform infrared spectra (FTIR). Rhizoma atractylodes models were collected by Fourier transform infrared spectra, 36 samples were gathered as a training target, and 27 samples as a test set, then their supervision training was performed using three models each. When the summation of error square of the training target was selected as 0. 01, the correct rate for recognition of Fourier transform infrared spectra using each RBF was 100% for the training set, but was different for the test set, which depended on the number of mode in hidden layer, S1. It was found that with the increase of S1, the correct rate would decrease oppositely. This may be caused by the high degree of the nonlinearity of the networks, so that the models of networks were not fit for the training of this kind of sample set. When using linear-linear model of RBF, the correct rate varied with S1 to some extent, but was generally about 85%. Recognizing ability obtained using nonlinear-linear model of RBF was the best. Its correct rate of recognition was >97%. When S1 =3, and so this method can be used to recognize atractylodes macrocephala Koidz. (rhizoma atractylodes) and its confusable varieties simply, rapidlly and accurately.
Jin
Wen-ying
WY
College of Chemistry and Life Science, Zhejiang Normal University, Jinhua 321004, China.
Cheng
Cun-gui
CG
Wu
Xiao-hua
XH
D031174:Atractylodes / Q000737:chemistry*; D016571:Neural Networks, Computer; D017550:Spectroscopy, Fourier Transform Infrared