应用太赫兹光谱技术快速无损鉴别中草药品种

确定中药品种是确保中药材质量的第一关。为探索中草药品种的快速鉴别方法，本文应用太赫兹光谱技术结合模式识别方法对6种中草药进行分类鉴别。采集了白附片、大黄、党参、陈皮、麦冬、天麻等6种常用中草药，共得到420组太赫兹光谱数据，在0.2~1.5 THz波段分别采用支持向量机(SVM)、主成分分析(PCA)和支持向量机相结合、线性判别分析(LDA)结合支持向量机等方法对6种中药材进行了定性鉴别分析。结果表明，太赫兹光谱数据结合线性判别分析和支持向量机建立的LDA-SVM中草药品种识别模型最优，模型准确率达100%，对未知样本的鉴别准确率达98.41%。本文的LDA-SVM模型具有较好的鉴别能力，能快速准确地鉴别出中药材的品种，为中草药的质量控制提供了又一鉴别手段。

Rapid and nondestructive identification of Chinese herbal medicine varieties by terahertz spectroscopy

Determining the variety of traditional Chinese medicine is the first step to ensure the quality of traditional Chinese medicine. In order to explore the rapid identification method of Chinese herbal medicine varieties, the classification and identification of six kinds of Chinese herbal medicine varieties are studied by terahertz spectroscopy combined with pattern recognition. Six kinds of commonly used Chinese herbal medicines such as Baifupian, Rhubarb, Dangshen, Tangerine Peel, Ophiopogon Japonicus and Gastrodia Elata are collected. A total of 420 groups of terahertz spectral data are obtained. Support Vector Machine(SVM), Principal Component Analysis(PCA) combined with SVM, Linear Discriminant Analysis(LDA) combined with SVM are employed in the 0.2-1.5 THz band to qualitatively identify six kinds of traditional Chinese medicine. The results show that the LDA-SVM Chinese herbal medicine variety recognition model based on terahertz spectral data combined with linear discriminant analysis and SVM is the best, with the accuracy of 100% and 98.41% for unknown samples. The LDA-SVM model in this paper bears good identification ability, can quickly and accurately identify the varieties of traditional Chinese medicine, and provides another identification means for the quality control of traditional Chinese medicine.