光谱学与光谱分析 |
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Study of Chance Correlation in Blood Glucose Sensing |
ZHAO Bo1, CAO Yu-zhen1*, LIU Rong2, XU Ke-xin2 |
1. College of Precision Instrument & Opto-electronics Engineering, Tianjin University, Tianjin 300072, China 2. State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China |
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Abstract In the noninvasive blood glucose sensing by the near-infrared spectroscopy, chemometrics is applied to achieve the quantitative analysis of unknown samples. In modeling and validation process, however, there usually introduces a certain degree of chance correlation, thus affecting the stability of the model. In the present paper, normally distributed random numbers were used to simulate spectral data and reference concentration. In this way, it can investigate the probability level of chance correlation from the number of selected modeling wavelengths and different probable cross validation methods. Chance correlation exists in the process of modeling. In this paper, there has also given the best level of modeling wavelengths and the optimal cross validation method to reduce the chance correlation. In addition, the in vitro experiment of glucose aqueous solution at different temperature is conducted. In this experiment, the relationship between the temperature and the glucose concentration was obtained, according to which the temperature effect in practice was reduced.
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Received: 2011-08-08
Accepted: 2011-11-28
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Corresponding Authors:
CAO Yu-zhen
E-mail: yzcao@tju.edu.cn
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[1] McClure W F. Anal. Chem., 1994, 66(1): 43A. [2] LIANG Yi-zeng(梁逸曾). White-Gray-Black Analysis of Complex Multi-Component System and Its Method of Chemometrics(白灰黑复杂多组份分析体系及其化学计量学算法). Changsha: Hunan Science &Technology Press(长沙:湖南科学技术出版社),1996. 12. [3] Kubinyi H. Quant. Struct. Act., 1994, 13(3): 285. [4] Todeschini R, Consonni V, Maiocchi A. Chemom. Int. Lab. Syst., 1999, 46(1): 13. [5] Todeschini R, Consonni V, Mauri A, et al. Anal. Chim. Acta, 2004, 515(1): 199. [6] Topliss J G, Edwards R P. J. Med. Chem., 1979, 22(10): 1238. [7] Richard D C. Discovery and Design, 1993, 1(2): 269. [8] Liu R, Chen W L, Gu X Y, et al. J. Phys. D: Appl. Phys., 2005, 38(15): 2675. [9] Golbraikh A, Tropsha A. J. Mol. Graph. Mod., 2002, 20(4): 269. [10] Hawkins D. J. Chem. Inf. Comput. Sci., 2004, 44(1): 1. [11] Geisser S. J. Am. Statist. Assoc., 1975, 70(350): 320. [12] Baumann K. QSAR Comb. Sci., 2005, 24(9): 1033. [13] YI Zhong-sheng(易忠胜). Chinese Journal of Analytical Chemistry(分析化学), 2009, 37(3): 374. |
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