基于大黄的红外光谱的人工神经网络鉴别研究

将傅里叶变换红外光谱法和人工神经网络用于鉴别正品和非正品大黄样品。在对神经网络训练前用小波变换对测量的红外光谱进行压缩，将原700个数据点的光谱压缩到44个变量，因此加速了神经网络的训练速度。52个大黄样品被用于网络模型的建立，其中包括25个正品大黄和27个非正品大黄的样品。文章还对隐含层神经元数目和动量参数的影响做了考察。结果表明，在优化的条件下用该方法对大黄样品的鉴别正确率达到98％。这种方法可被用于含大黄中药生产的质量控制。

Identification of Official Rhubarb Samples Based on IR Spectra and Neural Networks

The Fourier transform infrared (IR) spectrometry and neural networks have been used to identification of official and (un-official) rhubarb samples in the present work. The IR spectra were compressed by using wavelet transform and then were normalized prior to (network) training. Spectra with 700 data points were compressed to 44 variables, therefore, the training process of neural (networks) were speed up. 52 rhubarb samples in which 25 official and 27 unofficial rhubarb samples are included have been used to (network) modeling. The effects of neuron number in hidden layer and momentum parameter on classification have been investigated. Results showed that about 98 % rhubarb samples could be identified correctly when optimized parameters were used. This method can be useful for quality control in rhubarb-contained Chinese medicine production.