基于枸杞红外光谱人工神经网络的产地鉴别

The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network

应用红外光谱技术,快速鉴别枸杞药材产地。利用傅里叶变换红外光谱,测定了45个来自青海省不同产地的枸杞样品的红外光谱。以常规预处理方法和小波变换对红外光谱原始数据进行了预处理。对比常用的窗口移动平滑预处理、标准正态变换以及多元散射校正,小波变换是一种有效实用的光谱预处理方法。为了提高神经网络的训练速度,在利用人工神经网络建立模型之前,通过小波变换的方法对光谱变量进行了压缩,同时对建立的模型的相关参数进行了详细的讨论。结果表明,红外光谱数据压缩到原来的1/8,其分析精度与原始光谱数据基本相当。以压缩了的光谱数据作为反向传播(BP)网络的输入变量,产地类别作为神经网络的输出变量,建立3层人工神经网络。其中隐含层神经元个数为5个,输出层神经元个数为1个。隐层的传递函数是tansig,输出层传递函数是purelin,网络训练函数trainlm,权阈值的学习函数是learngdm。net.trainParam.epochs=1 000,net.trainParam.goal=0.001。对10个未知枸杞产地类别进行了预测,预测结果准确率达100%。实验表明,建立的模型能够正确地对枸杞样品快速地进行产地鉴别。红外光谱法结合人工神经网络可作为中药材产地分类鉴别的一种新的现代化方法。

The Fourier Transform Infrared Spectroscopy（FTIR）is established to find the geographic origins of Chinese wolfberry quickly.In the paper,the 45 samples of Chinese wolfberry from different places of Qinghai Province are to be surveyed by FTIR.The original data matrix of FTIR is pretreated with common preprocessing and wavelet transform.Compared with common windows shifting smoothing preprocessing,standard normal variation correction and multiplicative scatter correction,wavelet transform is an effective spectrum data preprocessing method.Before establishing model through the artificial neural networks,the spectra variables are compressed by means of the wavelet transformation so as to enhance the training speed of the artificial neural networks,,and at the same time the related parameters of the artificial neural networks model are also discussed in detail.The survey shows even if the infrared spectroscopy data is compressed to 1/8of its original data,the spectral information and analytical accuracy are not deteriorated.The compressed spectra variables are used for modeling parameters of the backpropagation artificial neural network（BP-ANN）model and the geographic origins of Chinese wolfberry are used for parameters of export.Three layers of neural network model are built to predict the 10 unknown samples by using the MATLAB neural network toolbox design error back propagation network.The number of hidden layer neurons is 5,and the number of output layer neuron is 1.The transfer function of hidden layer is tansig,while the transfer function of output layer is purelin.Network training function is trainl and the learning function of weights and thresholds is learngdm.net.trainParam.epochs=1 000,while net.trainParam.goal=0.001.The recognition rate of 100%is to be achieved.It can be concluded that the method is quite suitable for the quick discrimination of producing areas of Chinese wolfberry.The infrared spectral analysis technology combined with the artificial neural networks is proved to be a reliable and new method for the identification of the original place of Traditional Chinese Medicine.