摘要
大黄是我国最常用的中草药之一。对正品和非正品大黄的快速、准确鉴别对于大黄及其中草药产品的质量控制具有重要的意义。将近红外漫反射光谱分析技术与人工神经网络方法相结合,对52种大黄样品进行了测定和鉴别,正确率可达96%。并对神经网络的隐含层个数和动量因子的影响做了讨论。由于近红外光谱法具有样品前处理少,测定快速和非破坏性等特点,因而特别适合于中草药的鉴别。
Rhubarb is one of the most widely used Chinese medicinal herbs in China. Fast and accurate identification of official and unofficial rhubarb samples is most critical for quality control of Chinese medicine production. In the present paper near-infrared reflectance spectrometry (NIRS) and artificial neural network (ANN) were combined to develop classification models for identifying 52 official and unofficial rhubarb samples. The measured spectra were compressed by wavelet transformation (WT) and then the ANN classification models were trained with the reduced-variables spectral data. The rate of correct classification was over 96%. The effects of neurons in hidden layer and the momentum were also discussed. Owing to its fast and nondestructive properties, NIRS is a promising approach to identifying Chinese medicinal herbs.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2004年第11期1348-1351,共4页
Spectroscopy and Spectral Analysis
基金
北京市教育委员会科技发展项目资助
