|
|
|
|
|
|
Research on Flavonoids Based on Terahertz Time-Domain Spectroscopy |
YIN Ming1, WANG Jian-lin1, HUANG Hao-liang1, HUANG Qiu-ping2, YANG Meng-meng1, FU Zheng-ping3, LU Ya-lin2* |
1. National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230026, China
2. Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
3. School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China |
|
|
Abstract Flavonoids are a large class of polyphenols widely distributed in plants in the form of free or glycoside, which has anti-oxidation, anti-bacterial, anti-viral, anti-tumor growth and other pharmacological effects. As an important active component of traditional Chinese medicine, they have high medicinal value and development prospect. In this paper, the biomolecular properties of eight common flavonoids, including baicalein, quercetin, naringenin, daidzein, baicalin, puerarin, genistein and gastrodin, were studied by terahertz time-domain spectroscopy (THz-TDS) in the 0.2~2.5 THz band. The results showed that these flavonoids have different characteristic absorption peaks in the terahertz band. The terahertz absorption characteristics with temperature variation in the range of 78~320 K were studied. The results showed that the characteristic absorption peaks gradually increased with the decrease of temperature, and the frequency position of absorption peak was blue-shifted. In addition, Qualitative identification and quantitative analysis of flavonoids were carried out by chemometrics combined with terahertz absorption spectra. First, the spectral characteristic variables were extracted by principal component analysis (PCA), then the first five principal components were used as input variables of support vector machine (SVM) to establish a classification model, and the optimal parameters were selected through the optimization model, and finally, the classification accuracy of 100% was obtained. In addition, the partial least squares regression (PLSR) model and the artificial neural network (ANN) model were used to analyze the flavonoids with different concentrations in starch quantitatively. By comparing the two methods, the ANN model obtained the highest prediction accuracy. The correlation coefficients of naringenin and daidzein in the prediction set were R2=0.994 4, R2=0.996 4, and the root means square error was RMSE=1.932 5 and RMSE=1.544 1, respectively. In summary, the biomolecular properties of flavonoids were studied by THz-TDS technology, and a rapid, effective and non-destructive qualitative identification and quantitative analysis of flavonoids were provided. This method has potential application value in the detection of Chinese herbal medicine, and has better reference significance for the study of other biomolecules.
|
Received: 2019-09-12
Accepted: 2020-01-18
|
|
Corresponding Authors:
LU Ya-lin
E-mail: yllu@ustc.edu.cn
|
|
[1] SUN Heng, JIN Hang, HU Qiang, et al(孙 恒, 金 航, 胡 强, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(6): 1702.
[2] CAI Shuang, RUAN Cheng-jiang, DU Wei, et al(蔡 爽, 阮成江, 杜 维, 等). Journal of Analytical Science(分析科学学报), 2019, 35(3): 311.
[3] YANG Li-xin, LIU Dai, FENG Xue-feng, et al(杨立新, 刘 岱, 冯学锋, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2002, (3): 166.
[4] ZHANG Wei-bing, WANG Zhi-cong, ZHANG Ling-yi(张维冰, 王智聪, 张凌怡). Chinese Journal of Analytical Chemistry(分析化学), 2013, 41(12): 1851.
[5] ZHANG Wei-bing, WANG Zhi-cong, ZHANG Ling-yi(张维冰, 王智聪, 张凌怡). Chinese Journal of Analytical Chemistry(分析化学), 2014, 42(3): 415.
[6] LI Tian-ying, JIANG Ling, ZHANG Long, et al(李天莹, 蒋 玲, 章 龙, 等). Science and Technology of Food Industry(食品工业科技), 2019,(12): 359.
[7] Zhou Lu,Chen Ligang,Ren Guanhua. Physical Chemistry Chemical Physics, 2018, 20, 27205.
[8] Shen Y C, Upadhya P C, Linfield E H, et al. Appl. Phys. Lett., 2003, 82:2350.
[9] Takahashi M, Okamura N, Fan X, et al. J. Phys. Chem. A, 2017, 121:2558.
[10] Walther M, Fischer B W,Jepsen P U. Chem. Phys., 2003, 288: 261. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[3] |
WAN Mei, ZHANG Jia-le, FANG Ji-yuan, LIU Jian-jun, HONG Zhi, DU Yong*. Terahertz Spectroscopy and DFT Calculations of Isonicotinamide-Glutaric Acid-Pyrazinamide Ternary Cocrystal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3781-3787. |
[4] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[5] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[6] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
[7] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
[8] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
[9] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[10] |
LI Yang1, LI Xiao-qi1, YANG Jia-ying1, SUN Li-juan2, CHEN Yuan-yuan1, YU Le1, WU Jing-zhu1*. Visualisation of Starch Distribution in Corn Seeds Based on Terahertz Time-Domain Spectral Reflection Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2722-2728. |
[11] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
[12] |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3. Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2012-2018. |
[13] |
CHENG Xiao-xiang1, WU Na2, LIU Wei2*, WANG Ke-qing2, LI Chen-yuan1, CHEN Kun-long1, LI Yan-xiang1*. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2166-2173. |
[14] |
ZHENG Zhi-jie1, LIN Zhen-heng1, 2*, XIE Hai-he2, NIE Yong-zhong3. The Method of Terahertz Spectral Classification and Identification for Engineering Plastics Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1387-1393. |
[15] |
CHEN Rui1, WANG Xue1, 2*, WANG Zi-wen1, QU Hao1, MA Tie-min1, CHEN Zheng-guang1, GAO Rui3. Wavelength Selection Method of Near-Infrared Spectrum Based on
Random Forest Feature Importance and Interval Partial
Least Square Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1043-1050. |
|
|
|
|