血糖检测中建模方法和温度引入的偶然相关研究

摘要

近红外光谱分析技术应用在血糖检测中,需要借助化学计量学方法建立模型来实现对未知样品的定量分析.在模型建立和验证的过程中通常会引入一定程度的偶然相关,从而影响模型的稳健性.采用随机数模拟光谱数据及参考浓度的研究方式,从建模波长数的选择和交互验证方法两方面考察了不同建模方法在建模的过程中存在偶然相关的概率水平,并给出了最佳的建模波长数以及最优的交互验证方法,以降低引入的偶然相关.此外通过离体实验,研究温度对葡萄糖浓度检测的影响并指导如何在实际血糖检测中降低温度引入的偶然相关.%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.