光谱学与光谱分析 |
|
|
|
|
|
Noninvasive Blood Glucose Analysis Based on Near-Infrared Reflectance Spectroscopy |
Lü Xiao-feng, ZHANG Ting-lin, XIAO Feng, LI Guang, WANG You* |
State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, China |
|
|
Abstract Noninvasive glucose detection is highly required for more convenient and less pain glycaemic monitoring. Most of currently used methods are invasive. In this paper, a near-infrared reflectance spectroscopy (NIRS) is proposed to detect blood glucose to protect patient absent of pain. NIRS is a safe, simple and efficient technology applied in many fields. Experiments, based on Oral Glucose Tolerance Test (OGTT), were conducted to collect data modeling with partial least squares (PLS) regression. 42 samples of fingertip blood and palm were measured by commercially available blood glucose meter and NIRS separately at the same time. The glucose concentration range is between 5 and 12 mmol·L-1. With leave-one-out cross-validation, we obtained a result of root mean square error of cross-validation (RMSECV) of 1.16 mmol·L-1 for all the data. With the pre-processing methods of normalization and un-informative variables elimination reducing noise and eliminating some additional effects, we get a better result of 0.79 mmol·L-1. A RMSECV of 0.41 mmol·L-1 for individual modeling is much less than the total modeling. It has a broad application prospect in individual customization.
|
Received: 2015-06-04
Accepted: 2015-10-12
|
|
Corresponding Authors:
WANG You
E-mail: king_wy@zjuem.zju.edu.cn
|
|
[1] Li C, Zhao H, Shi Z, et al. 2012, 8229: 82291H. [2] So C F, Choi K S, Wong T K, et al. Med Devices (Auckl), 2012, 5: 45. [3] Yadav J, Rani A, Singh V, et al. Biomedical Signal Processing and Control, 2015, 18: 214. [4] Siripatrawan U, Makino Y, Kawagoe Y, et al. Sensors and Actuators B-Chemical, 2010, 148(2): 366. [5] Cozzolino D. Planta Medica, 2009, 75(7): 746. [6] Balabin R M, Safieva R Z. Energy & Fuels, 2011, 25(5): 2373. [7] Kim Y J, Yoon G. Journal of Biomedical Optics, 2006, 11(4). [8] Abookasis D, Workman J J. Journal of Biomedical Optics, 2011, 16(2). [9] Alberti K G M M, Zimmet P Z, Consultation W. Diabetic Medicine, 1998, 15(7): 539. [10] Lindgren F, Geladi P, Wold S. Journal of Chemometrics, 1993, 7(1): 45. [11] Wold S, Sjostrom M, Eriksson L. Chemometrics and Intelligent Laboratory Systems, 2001, 58(2): 109. [12] Bastien P, Vinzi V E, Tenenhaus M. Computational Statistics & Data Analysis, 2005, 48(1): 17. [13] Li L N, Zhang G J, Li Q B. Modern Physics Letters B, 2009, 23(7): 925. [14] Centner V, Massart D L, deNoord O E, et al. Analytical Chemistry, 1996, 68(21): 3851. |
[1] |
LIU Yan-de, WANG Shun. Research on Non-Destructive Testing of Navel Orange Shelf Life Imaging Based on Hyperspectral Image and Spectrum Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1792-1797. |
[2] |
SHI Wen-qiang1, XU Xiu-ying1*, ZHANG Wei1, ZHANG Ping2, SUN Hai-tian1, 3, HU Jun1. Prediction Model of Soil Moisture Content in Northern Cold Region Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1704-1710. |
[3] |
WANG Xue-pei1, 2, ZHANG Lu-wei1, 2, BAI Xue-bing3, MO Xian-bin1, ZHANG Xiao-shuan1, 2*. Infrared Spectral Characterization of Ultraviolet Ozone Treatment on Substrate Surface for Flexible Electronics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1867-1873. |
[4] |
WANG Yue1, 3, 4, CHEN Nan1, 2, 3, 4, WANG Bo-yu1, 5, LIU Tao1, 3, 4*, XIA Yang1, 2, 3, 4*. Fourier Transform Near-Infrared Spectral System Based on Laser-Driven Plasma Light Source[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1666-1673. |
[5] |
FENG Rui-jie1, CHEN Zheng-guang1, 2*, YI Shu-juan3. Identification of Corn Varieties Based on Bayesian Optimization SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1698-1703. |
[6] |
LI Quan-lun1, CHEN Zheng-guang1*, SUN Xian-da2. Rapid Detection of Total Organic Carbon in Oil Shale Based on Near
Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1691-1697. |
[7] |
YU Zhi-rong, HONG Ming-jian*. Near-Infrared Spectral Quantitative Analysis Network Based on Grouped Fully Connection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1735-1740. |
[8] |
WANG Ming-xuan, WANG Qiao-yun*, PIAN Fei-fei, SHAN Peng, LI Zhi-gang, MA Zhen-he. Quantitative Analysis of Diabetic Blood Raman Spectroscopy Based on XGBoost[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1721-1727. |
[9] |
MENG Fan-jia1, LUO Shi1, WU Yue-feng1, SUN Hong1, LIU Fei2, LI Min-zan1*, HUANG Wei3, LI Mu3. Characteristic Extraction Method and Discriminant Model of Ear Rot of Maize Seed Base on NIR Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1716-1720. |
[10] |
JI Jiang-tao1, 2, LI Peng-ge1, JIN Xin1, 2*, MA Hao1, 2, LI Ming-yong1. Study on Quantitative Detection of Tomato Seedling Robustness
in Spring Seedling Transplanting Period Based on VIS-NIR
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1741-1748. |
[11] |
PENG Yan-fang1, WANG Jun1, WU Zhi-sheng2*, LIU Xiao-na3, QIAO Yan-jiang2*. NIR Band Assignment of Tanshinone ⅡA and Cryptotanshinone by
2D-COS Technology and Model Application Tanshinone Extract[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1781-1785. |
[12] |
WANG Li-qi1, YAO Jing1, WANG Rui-ying1, CHEN Ying-shu1, LUO Shu-nian2, WANG Wei-ning2, ZHANG Yan-rong1*. Research on Detection of Soybean Meal Quality by NIR Based on
PLS-GRNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1433-1438. |
[13] |
FU Yan-hua1, LIU Jing2*, MAO Ya-chun2, CAO Wang2, HUANG Jia-qi2, ZHAO Zhan-guo3. Experimental Study on Quantitative Inversion Model of Heavy Metals in Soda Saline-Alkali Soil Based on RBF Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1595-1600. |
[14] |
LI Jia-yi1, YU Mei1, LI Mai-quan1, ZHENG Yu2*, LI Pao1, 3*. Nondestructive Identification of Different Chrysanthemum Varieties Based on Near-Infrared Spectroscopy and Pattern Recognition Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1129-1133. |
[15] |
CHEN Chu-han1, ZHONG Yang-sheng2, WANG Xian-yan3, ZHAO Yi-kun1, DAI Fen1*. Feature Selection Algorithm for Identification of Male and Female
Cocoons Based on SVM Bootstrapping Re-Weighted Sampling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1173-1178. |
|
|
|
|