径向基函数网络-红外光谱法用于中药大黄样品的真伪分类

将径向基函数网络与红外光谱分析技术相结合，对正品和非正品大黄样品进行分类判别。采用小波变换对原始数据进行压缩，将原来的775个数据点压缩到49个变量，既保持了原来的特征谱峰又提高了网络的训练速度。对45种样品进行了测定和鉴别，正确率可以达到97.78%。并对影响分类结果的网络参数，目标误差和分布函数对样品分类的影响做了讨论。红外光谱法与神经网络相结合用于中草药的分类鉴定更加快速和方便。

Radial Basis Function Networks and IR Spectrometry Applied for Identification of Official Rhubarb Samples

The Fourier transform infrared spectrometry (FTIRS) and radial basis function ne ural network (RBF-NN) have been applied to develop classification models fo r id entifying official and unofficial rhubarb samples. The original data were compre ssed from 775 variables to 49 variables by using wavelet transformation method. The compressed spectra with reduced variables maintain the characteristics of t he IR spectra and speed up the network training process. The effects of network parameters including error goal and spread constant, were investigated. The rate of correct classification is up to 97.78% at optimized conditions. Results sho w that the combination of IRS and ANN can be used as fast and convenient tool fo r identification of Chinese herbal samples.