| 光谱学与光谱分析 |
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| Research on the Effective Signal Extraction in the Noninvasive Blood Glucose Sensing by Near Infrared Spectroscopy |
| DING Hai-quan1, 2, LU Qi-peng1*, WANG Dong-min3, CHEN Xing-dan1 |
1.State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2.Graduate University of Chinese Academy of Sciences，Beijing 100049, China
3.College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China |
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Abstract Diabetes seriously endanger human health, and noninvasive glucose sensing is the expectation of both doctors and patients.Physiological background is complicated, volatile and mixed with a variety of tissue information, resulting in direct measurement of the body’s near infrared spectra difficult to truly reflect the concentration change in glucose.As a matter of fact, blood volume is always changing, but human tissue background and the concentration of blood components are constant in a short period.Taking advantage of this, subtracted blood volume spectrometry is propounded, which could eliminate the interference of human tissue background and obtain effective spectrum information of blood.To verify the effectiveness of the method, a experimental system was developed.The system noise is better than 20 μAU, and the signal to noise ratio of the effective spectrum signal at 1 250 nm is 20 000∶1.Finally, the feasibility and advantages of subtracted blood volume spectrometry are clarified in clinical application of near infrared non-invasive glucose sensing.
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Received: 2009-01-05
Accepted: 2009-04-06
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Corresponding Authors:
LU Qi-peng
E-mail: luqipeng@126.com
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[1] Williams P, Norris K.Near-Infrared Technology in the Agricultural and Food Industries(Second Edition).American Association of Cereal Chemists, Inc., 2001.
[2] DING Hai-quan，LU Qi-peng，PIAO Ren-guan，et al(丁海泉, 卢启鹏, 朴仁官, 等).Optics and Precision Engineering(光学精密工程), 2007, 15(12): 1946.
[3] ZHANG Jun, CHEN Xing-dan, PIAO Ren-guan, et al(张 军, 陈星旦, 朴仁官, 等).Optics and Precision Engineering(光学精密工程), 2008, 16(6): 986.
[4] Chen J, Arnold M A, Small G W.Analytical Chemistry, 2004, 76(18): 5405.
[5] Maruo K, Tsurugi M, Tamura M, et al.Applied Spectroscopy, 2003, 57(10): 1236.
[6] Saptari V, Youcef-Toumi K.Applied Optics, 2004, 43(13): 2680.
[7] Olesberg J T, Arnold M A, Mermelstein C, et al.Applied Spectroscopy, 2005, 59(12): 1480.
[8] Arnold M A, Small G W.Analytical Chemistry, 2005, 77(17): 5429.
[9] CHEN Xing-dan(陈星旦).Optics and Precision Engineering(光学精密工程), 2008, 16(5): 759.
[10] LIU Rong, GU Xiao-yu, XU Ke-xin(刘 蓉，谷筱玉，徐可欣).Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(8): 1772.
[11] Hertzman A B.Proceeding of Soc.Exp.Biol.Med., 1937, 37: 529.
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