[Recognition of three classes of skullcaps by FTIR spectroscopy combined with artificial neural networks]

In order to recognition of three classes of skullcaps (cultivated, wild Scutellaria baicalensis Georgi and Scutellaria viscidula Bge) three kinds of models of artificial neural networks (ANN), nonlinear-linear, linear-linear and nonlinear-nonlinear model, were used combined with their infrared spectra. Skullcaps samples were collected by Fourier Transform Infrared (FTIR) spectra. 42 samples were gathered as a train set, and 34 samples as a test set, then their supervision trains were performed using three models each. When the summation of error square of train target was selected as 0.01, the correct rate for recognition of three classes of skullcaps using each ANN was 100% for the train set, but was different for the test set, which depended on the number of node 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 non-linearity of the networks, so that the models of networks were not fit for the train of this kind of sample set. When using linear-linear model of ANN varied with S1 in some extent, the correct rate was generally about 85%. Recognizability obtained using nonlinear-linear model of ANN was the best. Its correct rate of recognition was > 97% when S1 = 3, and so this method can be used to recognize three of skullcaps simply, rapidly, and accurately.