Journal of Hebei University (Natural Science Edition) ›› 2020, Vol. 40 ›› Issue (4): 379-384.DOI: 10.3969/j.issn.1000-1565.2020.04.007

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Identification of three Chinese herbal medicines based on terahertz time-domain spectroscopy

LI Ruikai1, HOU Kaixuan2, ZHANG Lina3, LOU Cunguang2, LIU Xiuling2   

  1. 1. Information Center, Affiliated Hospital of Hebei University, Baoding 071002, China; 2. Hebei Key Laboratory of Digital Medical Engineering, College of Electronic Information Engineering, Hebei University, Baoding 071002, China; 3. College of Traditional Chinese Medicine, Hebei University, Baoding 071002, China
  • Received:2019-12-18 Online:2020-07-25 Published:2020-07-25

Abstract: This paper employs terahertz(THz)time-domain spectroscopy combined with principal component analysis(PCA)and K-means clustering algorithm for the detection of three groups of Chinese herbal medicines with little discrimination in physical form. The results show that the THz time-domain spectrum of the authentic Chinese herbal medicines has a significant delay relative to the reference signal, and the absorption spectrum of three groups of Chinese herbal medicines shows distinct absorption coefficients and refractive index in the range of 0.2—1.5 THz. The obtained spectral absorbance data is subjected to dimensionality reduction processing and is applied to the K-means clustering algorithm. The score map of three sets of authentic Chinese herbal medicines obtained by PCA reaches up to 100%. The- DOI:10.3969/j.issn.1000-1565.2020.04.007基于太赫兹时域光谱的3种中药材真伪鉴别李瑞凯1,侯凯旋2,张丽娜3,娄存广2,刘秀玲2(1. 河北大学附属医院 信息中心,河北 保定 071002;2. 河北大学 电子信息工程学院,河北省数字医疗工程重点实验室,河北 保定 071002;3. 河北大学 中医学院,河北 保定 071002)摘 要:基于太赫兹时域光谱对3组物理形态区分度很小的中药材进行光谱检测,并结合主成分分析(PCA)与K均值聚类算法(K-means)对光谱数据进行差异性对比. 研究结果表明所测真伪品中药的时域信号相对于参考信号有了明显的延迟,真伪品的频域吸收谱差别明显,3组中药的吸收系数及折射率在0.2~1.5 THz内存在明显差异. 对得到的吸收光谱数据进行降维处理,并应用K均值聚类算法得到主成分得分图,3组真伪中药的差别判断率高达100%. 太赫兹时域光谱结合主成分分析在中药真伪的鉴别中具有很好的应用前景,可以直观地得到真伪中草药的差别,具有快速、稳定、准确、无损的优点,对未来相关便携式太赫兹中药品质检测仪器的开发也具有很高的研究价值. 关键词:太赫兹技术;中药真伪鉴别;光谱分析;主成分分析中图分类号:O433.4 文献标志码:A 文章编号:1000-1565(2020)04-0379-06Identification of three Chinese herbal medicines based on terahertz time-domain spectroscopyLI Ruikai1, HOU Kaixuan2, ZHANG Lina3, LOU Cunguang2, LIU Xiuling2(1. Information Center, Affiliated Hospital of Hebei University, Baoding 071002, China;2. Hebei Key Laboratory of Digital Medical Engineering, College of Electronic Information Engineering, Hebei University, Baoding 071002, China;3. College of Traditional Chinese Medicine, Hebei University, Baoding 071002, China)Abstract: This paper employs terahertz(THz)time-domain spectroscopy combined with principal component analysis(PCA)and K-means clustering algorithm for the detection of three groups of Chinese herbal medicines with little discrimination in physical form. The results show that the THz time-domain spectrum of the authentic Chinese herbal medicines has a significant delay relative to the reference signal, and the absorption spectrum of three groups of Chinese herbal medicines shows distinct absorption coefficients and refractive index in the range of 0.2—1.5 THz. The obtained spectral absorbance data is subjected to dimensionality reduction processing and is applied to the K-means clustering algorithm. The score map of three sets of authentic Chinese herbal medicines obtained by PCA reaches up to 100%. The- 收稿日期:2019-12-18 基金项目:国家自然科学基金资助项目(61673158, 11304103, 61473112);河北省高校青年拔尖人才项目(BJ2016005) 第一作者:李瑞凯(1991—),男,河北邢台人,河北大学附属医院工程师,主要从事基于大分子太赫兹光谱的疾病无损检测方向研究. E-mail: 1012193070@qq.com 通信作者:娄存广(1985—),男,山东聊城人,河北大学副教授,主要从事基于大分子太赫兹光谱的疾病无损检测方向研究.E-mail: loucunguang@163.com第4期李瑞凯等:基于太赫兹时域光谱的3种中药材真伪鉴别method of terahertz time-domain spectroscopy combined with PCA has a good application prospect in the identification of Chinese herbal medicine authenticity with the advantages of being fast, stable,accurate and non-destructive. It also has high research value for the development of related portable terahertz instruments for Chinese herbal medicines quality testing.

Key words: terahertz technology, authenticity identification of traditional Chinese herbal medicines, spectral analysis, principal component analysis

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