光谱学与光谱分析 |
|
|
|
|
|
Research on the Background Correction in the Non-Invasive Sensing of Glucose by Near-Infrared Spectroscopy |
LIU Rong,GU Xiao-yu,XU Ke-xin* |
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China |
|
|
Abstract For the non-invasive blood glucose concentration sensing by the near-infrared spectroscopy, the signal to noise ratio of the optical measurement system is very low.Both the content of glucose in body and the absorption coefficient of glucose in the near-infrared region are quite weak.More over, the structure of spectral noise is complicated and the variation of noise intensity is very large.The background correction is one of the most effective pre-processing methods to improve the signal to noise ratio of the near-infrared optical detection system.In this paper, the theory expression formula of traditional background correction method was induced firstly.Then in order to avoid the influence from the variation of optical characteristics in the sample and the drift in the optical system, the similar background correction method was proposed, that is, the background which has the similar optical characteristics with the sample was chosen as the reference.The in vitro experiments of pure absorption media and scattering media were conducted to validate the effect.The results showed that, for the glucose in the blood plasma solution and Intralipid-2% solution, after the correction of the background which has the similar optical characteristics with the sample, the prediction precision of multivariate model for glucose concentration has been improved by 25.9% and 40.1%, respectively.
|
Received: 2007-08-02
Accepted: 2007-11-06
|
|
Corresponding Authors:
XU Ke-xin
E-mail: Kexin@tju.edu.cn
|
|
[1] Khalil O S.Clin.Chem., 1999, 45:165. [2] Khalil O S.Diabetes Technology Therapeutics., 2004, 6:660. [3] Kohl M, Essenpreis M, Cope M.Phys.Med.Boil., 1995, 40:1267. [4] Sun J.J.Chemometrics, 1997, 11:525. [5] XU Guang-tong, YUAN Hong-fu, LU Wan-zhen(徐广通,袁洪福,陆婉珍).Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2001, 21(4):459. [6] Amerov A K, Chen J, Arnold M A.Appl.Spectrosc., 2004, 58:1195. [7] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍, 袁洪福, 徐广通, 等).Modern Near Infrared Spectroscopy Analytical Technology(Second Edition)(现代近红外光谱分析技术, 第2版).Beijing:China Petrochemical Press(北京:中国石化出版社),2007. [8] Xu Ke-xin, Qiu Qing-jun, Jiang Jing-ying, et al.Opt.& Lasers Eng., 2005, 43:1096. [9] Kohl M, Essenpreis M, Bocker D, et al.Proc.SPIE., 1995, 2389:780. [10] Qu J, Wilson B C.J.Biomed.Opt., 1997, 2:319. [11] Pickering J W, Posthumus P, van Gemert M J C.Lasers Surg.Med., 1994,15(2):200. [12] Prahl S A, van Gemert M J C, Welch A J.Appl.Opt., 1993, 32(4):559. [13] Maruo K, Tsurugi M, Tamura M, et al.Appl.Spectrosc., 2003, 57(10):1236. [14] LUO Yun-han, GU Xiao-yu, XU Ke-xin(罗云瀚, 谷筱玉, 徐可欣).Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(8):1416. |
[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] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
[3] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[4] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[5] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
[6] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
[7] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[8] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[9] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[10] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[11] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[12] |
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. |
[13] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[14] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[15] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
|
|
|
|