利用反向传播(BP)神经网络预测方法，通过光纤将红外光谱仪、拉曼光谱仪和旋光测量系统结合在一起，建立了基于多光谱测量血糖含量的分析模型,提出了数据融合的处理方法。选择了30个人体血液样品，分别测量旋光光谱、红外光谱、拉曼光谱。将光谱数据进行了预处理与归一化处理，建立BP神经网络模型，预测血液样品的糖含量值。使用克拉克误差网格分析法分别分析了三种测量方法和数据融合后的血糖值，结果应用BP人工神经网络模型预测血糖值的拟合精度为0.9992，预测误差低于0.2 mmol/L，满足临床医学的精度要求，并且具有较高的稳健性和较强的容错能力。

Based on the back-propagation (BP) neural network prediction method and combined with the infrared spectrometer, Raman spectrometer and polarimetry analysis system through the optical fiber, a multi-spectral blood glucose measurement system is developed and a processing method of data fusion is proposed. 30 human blood samples were measured to obtain the optical rotatory dispersion spectrum, infrared spectrum and Raman spectrum, respectively. Spectral data was preprocessed and normalized. The BP neural network model was established to predict the blood glucose content. We use the Clarke error grid analysis to compare the blood glucose content obtained by the three measurement methods and by data fusion. Results show that the fitting precision of the fusion data is 0.9992, and the prediction error is lower than 0.2 mmol/L, which can meet the accuracy of clinic medicine. This method also has high robustness and strong tolerance.