| 光谱学与光谱分析 |
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| Experimental Study of Differential Spectrum Method for Elimination of Tissue Background in Noninvasive Biochemical Detection |
| DING Hai-quan, LU Qi-peng*, GAO Hong-zhi |
| State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
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Abstract In noninvasive biochemical detection, the differential spectrum method based on the change in blood volume can eliminate the interference of human tissue background in theory, and obtain effective spectrum information of blood. In order to demonstrate the effectiveness of the differential spectrum method, simulated experiment was designed. Biological molecules solutions were used for simulating serum sample, filters with different absorption characteristic were used for simulating interference of tissue background, and an adjustable path-length cell was used for simulating blood volume change. Model accuracies of pre- and post-treatment with differential spectrum method were compared. Thus treated, the root mean square error of cross validation (RMSECV) reduced from 437 to 301 mg·dL-1. The experimental results indicate that using the differential spectrum method can effectively restrain the interference of tissue background, and greatly improve the prediction precision of calibration model.
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Received: 2012-03-26
Accepted: 2012-06-20
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Corresponding Authors:
LU Qi-peng
E-mail: luqipeng@126.com
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[1] Oliver N S, Toumazou C, Cass A E G, et al. Diabetic Medicine, 2009, 26(3): 197.
[2] Barman I, Kong C R, Singh G P, et al. Analytical Chemistry, 2010, 82(14): 6104.
[3] XU Quan-sheng，FENG Shu，YE Da-tian(许全盛，冯 曙，叶大田). Optics and Precision Engineering(光学·精密工程)，2010，18(12): 2688.
[4] Saptari V A，Youcef-Toumi K. Proc. SPIE，2000，4163：45.
[5] Olesberg J T，Arnold M A，Mermelstein C，et al.Applied Spectroscopy，2005，59(12): 1480.
[6] Malin S F，Ruchti T L，Blank T B，et al. Clinical Chemistry，1999，45：1651.
[7] Saptari V，Youcef-Toumi K. Applied Optics，2004，43(13)：2680.
[8] Chen J，Arnold M A，Small G W. Analytical Chemistry，2004，76(18)：5405.
[9] Maruo K，Oota T，Tsurugi M，et al. Applied Spectroscopy，2006，60(4)：441.
[10] Maruo K，Oota T，Tsurugi M，et al. Applied Spectroscopy，2006，60(12)：1423.
[11] HUANG Fu-rong，LUO Yun-han，ZHENG Shi-fu，et al(黄富荣，罗云瀚，郑仕富，等). Acta Optica Sinica(光学学报)，2011，31(10)：1030001.
[12] CHEN Xing-dan，WANG Dong-min，LU Qi-peng，et al(陈星旦，王动民，卢启鹏，等). Acta Optica Sinica(光学学报)，2011，31(9)：0900105.
[13] Arnold M A，Small G W. Analytical Chemistry，2005，77：5429.
[14] CHEN Xing-dan(陈星旦). Optics and Precision Engineering(光学·精密工程)，2008，16(5): 759. |
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