光谱学与光谱分析 |
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Rapid Detection of Alanine Aminotransferase with Near-Infrared Spectroscopy |
HUANG Fu-rong1,ZHANG Jun1*,LUO Yun-han1,LI Shi-ping1,ZHENG Shi-fu2,CHEN Xing-dan1, 3 |
1. Key Laboratory of Disaster Forecast and Control in Engineering,Ministry of Education(Jinan University), Guangzhou 510632, China 2. Clinical Laboratory Center, the First Affiliated Hospital(Jinan University), Guangzhou 510632, China3. Chanchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
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Abstract Near infrared transmission spectroscopy of Whole blood are investigated with different thickness(0.5,1,2,4 mm)in order to explore the feasibility of detecting alanine aminotransferase rapidly by near-infrared spectra. The results show that the whole blood sample with 0.5 mm thickness is more suitable for spectral analysis. And then Near infrared spectroscopy of 176 samples were collected. Multiplicative scatter correction and second-order differential method have been used to spectral pretreatment. Stepwise multiple linear regression method and partial least squares regression method have been employed to establish quantitative detection model to predict content of alanine aminotransferase in whole blood. The alanine aminotransferase measured presents best result in calibration and prediction by Near-Infrared Spectroscopy with partial least squares regression calibration model, and the calibration correlation coefficient, the standard error of calibration and the standard error of prediction are 0.98,2.42 and 7.22 respectively.
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Received: 2009-10-06
Accepted: 2010-01-08
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Corresponding Authors:
ZHANG Jun
E-mail: ccdbys@163.com
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[1] ZENG Jin-feng, LI Huo, WU Guo-guang(曾劲峰,李 活,吴国光). Chinese Journal Blood Transfusion(中国输血杂志), 2004, 17(4):269. [2] DU Yong, DAI Jin-hua(杜 勇,戴金华) . Jiangxi J. Med. Lab(江西医学检验),2005,123(4):374. [3] LU Wan-zhen(陆婉珍). Modern Near Infrared Spectroscopy Analytical Technology(Second Edition)(现代近红外光分析技术·第2版). Beijing: China Petrochemical Press(北京: 中国石化出版社), 2007. [4] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄, 赵龙莲, 韩东海, 等). Principle and Application of Near Infrared Spectroscopy(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京: 中国轻工业出版社), 2005. [5] ZHANG Jun, CHEN Xin-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. [6] LIU Hong-xin, ZHANG Jun, WANG Bo-guang, et al(刘宏欣,张 军,王伯光,等). Optics and Precision Engineering(光学精密工程),2009, 17(3):525. [7] LUO Yun-ping, ZHANG Jun, ZHENG Shi-fu, et al(罗运平,张 军,郑仕富,等). Chinese Journal of Spectroscopy Laboratory(光谱实验室),2007,24(5):773. [8] DING Dong, ZHANG Hong-yan, WANG Li-qiu(丁 东,张洪艳,王丽秋). Chinese J. Anal. Chem.(分析化学),2003,31(4):468. [9] HE Zhong-hai, LUO Yun-han, GU Xiao-yu, et al(贺忠海,罗云瀚,谷筱玉,等). Acta Optica Sinica(光学学报),2006,26(4):591. [10] XIAO Jun, WANG Long, LUO Qing-ming, et al(肖 君, 王 龙, 骆清铭, 等). Journal of Optoelectronics·Laser(光电子·激光),2007,18(9):1135. [11] Thygesen L G J. J . Near Infrared Spectrosc., 2000, 8:183. [12] LI Chang-hou(李昌厚). UV-Vis Spectrophotometer(紫外-可见分光光度计). Beijing: Chemical Industry Press(北京:化学工业出版社), 2005.
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