Blood glucose continuous monitoring (GCM) plays a crucial role in prevention and diagnosis of diabetes. A noninvasive CGM method based on fingertip capillary Dynamic Near Infrared Spectrum (DNIRS) combined with multivariate linear modification algorithm was proposed in this study. In order to control the environmental variables and physicochemical parameters during spectral data acquisition, a Fingertip Fixed Probe Biosensor (FFPB) was designed. In the preliminary experiment, three group of volunteers (healthy young people, middle-aged people and patients with diabetes) were test twice. The model established by the former test could be used for the latter prediction for each individual, and the duration of each test was 120 min. Meanwhile the reference value of blood glucose was measured by the standard blood glucose analyzer. When establishing the prediction model, a multivariate linear modification algorithm was proposed, which has better prediction accuracy and precision than the traditional multiple linear regression model. The root mean square error of validation and root mean square error of prediction are $\text{R}\text{M}\text{S}\text{E}\text{V}\le 15.61\text{m}\text{g}/\text{d}\text{L}$ and $\text{R}\text{M}\text{S}\text{E}\text{P}\le 20.67\text{m}\text{g}/\text{d}\text{L}$ respectively. The correlation coefficient between the prediction and the reference value of blood glucose also reaches 0.87, and the prediction keeps good track of postprandial glucose excursions with the comparison of the reference value. Through the Clark Error Grid Analysis (CEGA), more than 96 % of the test set samples lies within Zone A. The result indicates that the prediction model has good prediction accuracy and robustness. This measurement method can continuously and noninvasively monitor the variance of blood glucose in human body, which has a promising prospect in the future practical application.

血糖连续监测（GCM）在糖尿病的预防和诊断中起着至关重要的作用。提出了一种基于指尖毛细管动态近红外光谱（DNIRS）结合多元线性修正算法的无创CGM方法。为了控制光谱数据采集过程中的环境变量和理化参数，设计了一种指尖固定探针生物传感器（FFPB）。在初步实验中，对三组志愿者（健康的年轻人，中年人和糖尿病患者）进行了两次测试。前一个测试建立的模型可以用于每个人的后一个预测，每个测试的持续时间为120分钟。同时，通过标准血糖分析仪测量血糖的参考值。在建立预测模型时，提出了一种多元线性修正算法，该算法具有比传统的多元线性回归模型更好的预测精度和精度。验证的均方根误差和预测的均方根误差为$\text{[R}\text{中号}\text{小号}\text{Ë}\text{V}\le 15.61\text{米}\text{G}/\text{d}\text{大号}$ 和 $\text{[R}\text{中号}\text{小号}\text{Ë}\text{P}\le 20.67\text{米}\text{G}/\text{d}\text{大号}$分别。血糖预测值与参考值之间的相关系数也达到0.87，通过与参考值的比较，预测值可以很好地跟踪餐后血糖波动。通过克拉克误差网格分析（CEGA），超过96％的测试集样本位于A区中。结果表明，该预测模型具有良好的预测准确性和鲁棒性。该测量方法能够连续，无创地监测人体血糖变化，在未来的实际应用中具有广阔的前景。