中文摘要：

采用水平衰减全反射傅里叶红外光谱法（HATR-FTIR）测定罂粟和虞美人的FTIR,由于两者为同科同属中药材,所含化学成分较为相近,为了更好地突出罂粟和虞美人在FTIR上的差异,并据此进行正确分类识别,利用离散平稳小波变换（DSWT）分别对罂粟和虞美人的种皮和种仁的FTIR进行若干尺度的变换,从中选择2个最具代表性的尺度作为特征提取的尺度空间。根据罂粟和虞美人的FTIR分布情况,确定将DSWT域内2个尺度的FTIR分别划分为2个特征区域并以每个区域内的光谱能量作为特征参数。从而构造一个包含8个特征参数的特征向量,将这个特征向量输入到径向基函数神经网络（RBFNN）进行训练,从而达到正确识别罂粟和虞美人的目的。实验中共取罂粟和虞美人的FTIR数据128对,其中训练样本78对,测试样本50对。实验结果表明利用文章的方法对罂粟和虞美人的正确识别率分别为99.8%和99.9%,从而验证了方法的有效性。

英文摘要：

Infrared spectra of Papaver somniferum L. and Papaver rhoeas were obtained directly, quickly and accurately by Fourier transform infrared spectroscopy （FTIR）with OMNI sampler. Discrete stationary wavelet transform was used to extrude local region of infrared spectra of Papaver somniferum L. and Papaver rhoeas. The difference of infrared spectra between Papaver somniferum L. and Papaver rhoeas was extruded. Accurate identification rate is improved greatly. One dimensional discrete stationary wavelet transform was implemented to the infrared spectra of Papaver somniferum L. and Papaver rhoeas. The difference between Papaver somniferum L. and Papaver rhoeas was observed at all scales in wavelet domain. Two scales, at which the difference between Papaver somniferum L. and Papaver rhoeas is the most obvious, were selected to extract the features of Papaver somniferum L. and Papaver rhoeas. A feature vector including eight feature parameters was constructed. The feature vector was input to RBFNN for training in order to accurately identify Papaver sornniferurn L. and Papaver rhoeas. In experiment, the authors used one hundred and twenty-eight couples of data of Papaver somniferum L. and Papaver rhoeas （including seventy-eight couples of training samples and fifty couples of testing samples）. The experimental results show that it is effective to apply discrete stationary wavelet transform on the basis of FTIR to identify the Papaver somniferum L. and Papavet rhoeas. The accurate identification rate of Papaver somniferum L. and Papaver rhoeas is 99. 8% and 99.9% respectively.