| 光谱学与光谱分析 |
|
|
|
|
|
| Feature Selection and Interpretation in Infrared Quantitative Models of Liquiritin and Glycyrrhizin in Radix Glycyrrhizae |
| ZHAN Xue-yan1, LIN Zhao-zhou1, SUN Yang1, YUAN Rui-juan1, YANG Zhan-lan2, DUAN Tian-xuan1* |
1. School of Chinese Materia Medica, Beijing University of Chinese Medicine，Beijing 100102, China
2. College of Chemistry and Molecular Engineering, Peking University，Beijing 100871, China |
|
|
|
|
Abstract Feature selection can improve the interpretation of the modeling variables to a certain extent by selecting variables from the complex spectra backgrounds. However, the improvement of models interpretation does not mean that the modeling variables have the exact physical or chemical significance. In this paper, We explore the relation between the chemical characteristics of target components and the spectrum variables selected with 3 kinds of variables selection methods which are moving window partial least squares regression(mwPLS), synergy interval partial least squares regression(siPLS) and competitive adaptive reweighted sampling(CARS), and compare the interpretation difference of the variables selected with the above variables selection methods. The results show that the variables selected with mwPLS accord with ν(φ)CC of liquiritin and δCH3 or δCH2 of glycyrrhizin, which are the obvious spectra differences between the flavonoids and saponins in Radix Glycyrrhizae, and the variables selected with siPLS are the characteristic intervals combinations of the flavonoids or saponins in Radix Glycyrrhizae, which is the combination of ν()CC, ν()C—O, ν()C—H of flavonoids or the combination of νC—O, νC—H, νO—H of saponins while the variables selected with CARS can better accord with most of the characteristic peaks from 1 000 to 4 000 cm-1 of liquiritin or glycyrrhizin in Radix Glycyrrhizae, and the predict performance of the infrared quantitative model established on the spectroscopic variables selected with CARS can be improved. Therefore, most of the variables selected with CARS can be interpreted by the characteristic peaks in the infrared characteristic region of the target components, which is beneficial to improve the interpretation of the quantitative model.
|
|
Received: 2014-06-20
Accepted: 2014-09-25
|
|
|
|
Corresponding Authors:
DUAN Tian-xuan
E-mail: duantx@sina.com
|
|
[1] The State Pharmacopoeia Commission(国家药典委员会). The People’s Republic of China Pharmacopoeia (2010 Version A) (中华人民共和国药典2010版一部). Beijing：Chinese Medicine Science and Technology Press(北京：中国医药科技出版社)，2010.
[2] Nrgaard A S L, Wagner J, Nielsen J P, et al. Applied Spectroscopy, 2000, 54(3)：413.
[3] Kasemsumran S, Du Y P, Maruo K, et al. Chemometrics and Intelligent Laboratory Systems , 2006, 82：97.
[4] Zou X, Zhao J, Malcolm J W P, et al. Analytica Chimica Acta 2010, 667：14.
[5] Ferrao M F, Viera M D, Pazos R E P, et al. Fuel, 2011, 90(2)：701.
[6] Shariati-Rad M, HasaniM. Journal of Chemometrics, 2010, 24(1-2)：45.
[7] Li H, Liang Y, Xu Q, et al. Analytica Chimica Acta, 2009, 648(1)：77.
[8] Yi Q X, Jiapaer G, Chen J M, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 91：72.
[9] Centner V, Massart D L. Analytical Chemistry, 1996, 68：3851.
[10] Kirkpatrick S, Vecchi M P. Science, 1983, 220：671.
[11] Lucasius C B, Beckers M L M, Kateman G. Analytica Chimica Acta, 1994, 286：135.
[12] Kagarise R E. J. Phys. Chem., 1955, 59(3)：271. |
| [1] |
WANG Yan1, HUANG Yi1, 2*, YANG Fan1, 2*, WU Zhong-wei2, 3, GUAN Yao4, XUE Fei1. The Origin and Geochemical Characteristics of the Hydrothermal Sediments From the 49.2°E—50.5°E Hydrothermal Fields of the Southwest Indian Ocean Ultra-Slow Spreading Ridge[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2868-2875. |
| [2] |
HU Qiao1, YANG Ming-xing1, 2*, LIU Yue1, LIU Ji-fu1, DAI Lu-lu1. Study on the Material and Mineral Source Characteristics of Jade Excavated From Longwangshan Tomb in Jingmen[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3736-3744. |
| [3] |
LI Yan-kun1*, DONG Ru-nan1, ZHANG Jin2, HUANG Ke-nan3, MAO Zhi-yi4. Variable Selection Methods in Spectral Data Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3331-3338. |
| [4] |
SUN Zong-bao, LI Jun-kui, LIANG Li-ming, ZOU Xiao-bo*, LIU Xiao-yu, NIU Zeng, GAO Yun-long. Prediction and Distribution Visualization of Salmon Quality Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2591-2597. |
| [5] |
WANG Dong1, 3, WU Jing-zhu2*, HAN Ping1, 3*, WANG Kun2. Application of Spectral Key Variable Selection in Non-Destructive Detection of the Qualities of Agricultural Products and Food[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1593-1601. |
| [6] |
AN Si-yu1, 2, ZHANG Lei1, SHANG Xian-zhao1, YUE Hong-shui1, LIU Wen-yuan2*, JU Ai-chun1*. Variable Selection Method in the NIR Quantitative Analysis Model of Total Saponins in Red Ginseng Extract[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 206-209. |
| [7] |
ZHANG Li-juan1, XIA Qi-le2*, CHEN Jian-bing2, CAO Yan2, GUAN Rong-fa1*, HUANG Hai-zhi1. Prediction of Anthocyanin Content in Three Types of Blueberry Pomace by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2246-2252. |
| [8] |
LIU Ning1, XING Zi-zheng1, QIAO Lang1, LI Min-zan1, SUN Hong1*, Qin Zhang2. Discussion on Spectral Variables Selection of Potato Chlorophyll Using Model Population Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2259-2266. |
| [9] |
YAN Fang, LIU Cheng-hao*, WANG Zhi-chun, LI Wei. Quantum Chemical Calculation of Terahertz Vibration Mode of Glycyrrhizic Acid[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1780-1784. |
| [10] |
GUO Jin-ke, LU Ji-long*, YIN Ye-chang, ZHAO Yu-yan, TANG Xiao-dan, FAN Yu-chao, LIU Yang. Application of Portable XRF in Core Geochemical Characteristics of Qujia Gold Deposit in Jiaodong Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1461-1466. |
| [11] |
ZHEN Huan-yi, MA Rui-jun*, CHEN Yu*, SUN Xiao-peng, MA Chuang-li. Study on Prediction Model of Malathion Pesticide Concentration Absorption Spectra Based on CARS and K-S[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1601-1606. |
| [12] |
HU Li1, ZHAO Nan-jing2*, LI Da-chuang1，TANG Lei1, FANG Li2. Study of Variable Selection Method Based on PLS for Quantitatively Measuring Heavy Metal in Water with LIBS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(08): 2585-2589. |
| [13] |
OUYANG Ai-guo, WU Ming-ming, WANG Hai-yang, LIU Yan-de*. Establishment and Optimization of Online Model for Detecting Soluble Solids Content in Hybrid “Skiranui Tangerine” Citrus[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(05): 1497-1501. |
| [14] |
LI Jiang-bo, GUO Zhi-ming, HUANG Wen-qian, ZHANG Bao-hua, ZHAO Chun-jiang* . Near-Infrared Spectra Combining with CARS and SPA Algorithms to Screen the Variables and Samples for Quantitatively Determining the Soluble Solids Content in Strawberry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(02): 372-378. |
| [15] |
FAN Shu-xiang1, 2, HUANG Wen-qian2, LI Jiang-bo2，GUO Zhi-ming2, ZHAO Chun-jiang1, 2* . Application of Characteristic NIR Variables Selection in Portable Detection of Soluble Solids Content of Apple by Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(10): 2707-2712. |
|
|
|
|
