Identification of Two Types of Safflower and Bezoar by Terahertz Spectroscopy
YANG Yu-ping1, ZHANG Cheng1, LIU Hai-shun2, ZHANG Zhen-wei2*
1. School of Science, Minzu University of China, Beijing 100081, China
2. Department of Physics, Beijing Advanced Innovation Center for Imaging Technology, Beijing Key Laboratory for Terahertz Spectroscopy and Imaging, Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Capital Normal University, Beijing 100048, China
Abstract：Saffron and natural bezoar are two traditional Chinese medicines widely used in clinical practice. Due to their lower yields, high medicinal value and price, market demand and other factors, more and more adulteration and counterfeit goods not only seriously damage the health of patients but also hinder market normal operation. However, the empirical methods based on observation, smell and soak have become increasingly difficult to distinguish high imitation counterfeits. In addition, the traditional physical and chemical detection techniques through chemical extraction, chromatography and mass spectrometry are cumbersome and time-consuming, and have high requirements and reliance on testing environments, professional ability and equipment. They cannot meet the actual needs of on-site, rapid and simple identification. Thus, it is urgent to explore new and effective detection methods and identification techniques. Terahertz radiation has very low energy and terahertz time-domain spectroscopy (THz-TDS) possesses not only the high specificity of pure compounds but also the “macroscopic fingerprint characteristics” of the hybrid system to identify the diversity and complexity of the chemical composition in the mixture. In addition, as a common statistical analysis method, principal component analysis (PCA) mainly replaces the original variables with a few comprehensive variables that can explain the variance of the original data to the greatest extent and can perform pattern recognition on different kinds of samples. In this work, 18 pieces of saffron and safflower samples as well as 20 groups of natural and artificial bezoar were respectively compressed by using pellet compression. The absorption spectra of two kinds of valuable Chinese medicinal materials and their counterfeit products, saffron and safflower as well as natural and artificial bezoar, were measured using THz time-domain spectroscopy in the range of 0.3~2.5 THz. Finally, the principal component analysis (PCA) was used to identify the obtained data set. In order to improve the identification ability of PCA, on one hand, the data set was mapped to a set of bases (feature vectors) for simplification, and larger eigenvalues were selected instead of describing the original main spectral information; on the other hand, in order to eliminate the impact of noise on the classification process, we adopted Savitzky-Golay(S-G)smoothing before PCA to remove the redundant and irrelevant spectral features; the discriminant analysis was then performed using Fisher’s diagnostic line. Comparing the principal component scores with and without S-G smoothing, classification results with S-G smoothing were obviously distinguished and the first two principal components could basically reflect the differences between spectra. It could be clearly seen that in the unprocessed score plots, the overlapping of the two types of samples is severe, whereas only a relatively small number of sample points overlap in the smoothed score plots, indicating the role of SG smoothing in spectral identification. The classification results showed that the saffron and safflower had obvious clustering trends, and the accuracy of classification identification of saffron and safflower were both 100%; while there was a slight overlap of artificial bezoar and natural bezoar even though the intra-class samples basically gathered together, and the classification accuracy was 100% and 90%, respectively. Furthermore, the principal component score of the sample can also reflect the internal characteristics of the sample and the clustering information. Among them, the saffron sample contains higher compounds of crocin, crocetin and other content, so that better degree of polymerization has been obtained and the distribution is relatively concentrated; on the other hand, the compounds contained in the natural bezoar are more complex. Consequently, the clustering effect is poor and the distribution range is wide. The reliable results based on the THz-TDS and PCA not only distinguish between saffron and safflower as well as natural and artificial bezoar, but also provide the means and theoretical basis for enriching the quality standard of Chinese herbal medicine.
Key words：Terahertz time-domain spectroscopy; Principal component analysis; Saffron; Bezoar
杨玉平，张 成，刘海顺，张振伟. 两类红花和牛黄的太赫兹光谱法真伪鉴别分析[J]. 光谱学与光谱分析, 2019, 39(01): 45-49.
YANG Yu-ping, ZHANG Cheng, LIU Hai-shun, ZHANG Zhen-wei. Identification of Two Types of Safflower and Bezoar by Terahertz Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(01): 45-49.
[1] YAO Jian-biao, JIN Hui-hui, HE Hou-hong, et al(姚建标, 金辉辉, 何厚洪，等). Chinese Traditional and Herbal Drugs(中草药)，2015, 46(9): 1378.
[2] ZOU Qin-wen, SHI Yan, LIU Wei, et al(邹秦文, 石 岩, 刘 薇, 等). Chinese Journal of Pharmaceutical Analysis(药物分析杂志), 2015, 35(1): 8.
[3] Xia Jing, Xia Li, Wang Weiyi, et al. Chinese Hertal Medicines, 2013, 5(3): 224.
[4] Liu Haishun, Zhang Zhenwei, Zhang Xin, et al. IEEE Trans. Terahertz Science and Technology, 2018, 8(3): 271.
[5] WANG Jing-rong, ZHANG Zhuo-yong, YANG Yu-ping, et al(汪景荣，张卓勇，杨玉平，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(5): 1606.
[6] Zhang Huo, Li Zhi, Chen Tao, et al. Optik, 2017, 138: 95.
[7] MA Pin, YANG Yu-ping(马 品，杨玉平). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(10): 3016.
[8] MA Pin, YANG Yu-ping(马 品，杨玉平). Journal of Terahertz Science and Electronic Information Technology(太赫兹科学与电子信息学报), 2017, 15(1): 26.
[9] Yang Yuping, Lei Xiangyun, Yue Ai, et al. SCIENCE CHINA: Physics, Mechanics & Astronomy, 2013, 56(4): 713.
[10] Yang Y P, Sree Harsha S, Shutler A J, et al. Journal of Pharmaceutical and Biomedical Analysis, 2012, 62： 177.
[11] HE Xiao-qun(何晓群). Multivariate Statistical Analysis(多元统计分析). Beijing: China Renmin University Press(北京： 中国人民大学出版社)，2008. 152.
