| 光谱学与光谱分析 |
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| Study of Cholesterol Concentration Based on Serum UV-Visible Absorption Spectrum |
| ZHU Wei-hua1, 2,ZHAO Zhi-min1*,GUO Xin1,CHEN Hui1 |
1. College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2. College of Science, Hohai University, Nanjing 210098, China |
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Abstract In the present paper, UV-visible absorption spectrum and neural network theory were used for the analysis of cholesterol concentration. Experimental investigation shows that the absorption spectrum has the following characteristics in the wave band of 350-600 nm: (1) There is a stronger absorption peak at 416 nm for the test sample with different cholesterol concentration; (2) There is a shoulder peak between 450 and 500 nm, whose central wavelength is 460 nm; (3) There is a weaker peak at 578 nm; (4) Absorption spectrum’s shape of different cholesterol concentration is different obviously. The absorption spectrum of serum is the synthesis result of cholesterol and other components (such as sugar), and the information is contained at each wavelength. There is no significant correlation between absorbance and cholesterol content at 416 nm, showing a random relation, so whether cholesterol content is abnormal is not determined by the absorbance peak at 416 nm. Based on the evident correlation between serum absorption spectrum and cholesterol concentration in the wave band of 455-475 nm, a neural network model was built to predict the cholesterol concentration. The correlation coefficient between predicted cholesterol content output A and objectives T reaches 0.968, which can be regarded as better prediction, and it provides a spectra test method of cholesterol concentration.
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Received: 2007-10-20
Accepted: 2008-01-26
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Corresponding Authors:
ZHAO Zhi-min
E-mail: zhaozhimin@nuaa.edu.cn
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[1] SUN Zhou(孙州编译). Nanjing Daily(南京日报)，2004-02-20.
[2] WANG Le-xin，ZHAO Zhi-min，YAO Hong-bing，et al(王乐新，赵志敏，姚红兵, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2002，22(6)：980.
[3] ZHAO Zhi-min，CHEN Yu-ming, YU Xiao-lei, et al(赵志敏，陈玉明, 俞晓磊, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2003，23(5)：922.
[4] ZHAO Zhi-min，GUO Lin-feng, YU Xiao-lei, et al(赵志敏，郭林峰，俞晓磊，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2005，25(12)：2056.
[5] ZHU Wei-hua, ZHAO Zhi-min, ZHENG Min, et al(朱卫华, 赵志敏, 郑 敏, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007，27(12)：2531.
[6] LAN Xiu-feng, LIU Jian-gang, LIU Ying, et al(兰秀风, 刘建刚, 刘 莹, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(3): 467.
[7] GU Huai-min, XING Da(谷怀民，邢 达). Acta Photonica Sinica(光子学报)，2003, 32(5): 640.
[8] YU Chang-qing，LIU Fan-guang，GU Ying, et al(于常青, 刘凡光，顾 瑛，等). Chin. J. Laser Med. Surg.(中国激光医学杂志)，2004, 13: 41.
[9] LI Jian-dong，DING Jian-hua，WU Min, et al(李建东，丁建华，吴 敏，等). Applied Laser(应用激光)，2004，24(2)：112.
[10] MENG Ji-wu, ZHENG Rong-er, ZHANG Yun-dong(孟继武, 郑荣儿，掌蕴东). Chin. J. Laser Med. Surg.(中国激光医学杂志), 2003, 12(1): 43.
[11] CHEN Rong, LI Bu-hong，CHEN Jin-kai, et al(陈 荣, 李步洪, 陈金铠, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2003，23(4)：748.
[12] GE Xiang-hong, ZHAO Yuan-li, ZHANG Feng-jiu, et al(葛向红，赵元黎，张凤秋, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2004，24(7)：841.
[13] SHI Xiao-feng, MA Jun, MAO Wei-zheng, et al(史晓凤, 马 君，毛伟征, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006，26(2)：295.
[14] GAO Shu-mei, LIU Ying, LAN Xiu-feng, et al(高淑梅, 刘 莹, 兰秀风, 等). Chinese Journal of Lasers(中国激光), 2004，31(7)：893.
[15] QIN Shao-ping, DING Jian-hua, LI Jian-dong(秦少平，丁建华，李建东). Journal of Optoelectronics·Laser(光电子·激光), 2006，17(5)：634.
[16] WU Gen-hua, HE Chi-yang, CHEN Rong(吴根华，何池洋，陈 荣). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2002，22(5)：813.
[17] XU Fang-wei, CAI Kun-bao(徐方维, 蔡坤宝). Journal of Chongqing University(重庆大学学报), 2004，27(8)：35.
[18] MENG JOO E R. IEEE Transaction on Neural Network, 2002, 13(3): 15. |
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