为消除数据采集过程中各种因素对基于动态光谱法无创血液成分测量精度的影响, 需要对动态光谱数据建立一个质量评价标准, 以提高建模的稳定性和预测的准确度。 在对110名志愿者的测量数据进行分析后, 提出了动态光谱数据的一个质量评价指标——稳定波长数, 并依此选取出60例优秀样本。 利用BP神经网络对此样本的总胆固醇、 血糖、 血红蛋白进行了建模和预测。 预测结果较之对照组均有所改善, 平均相对误差分别从13.8%, 15.8%, 5.4%降低到6.5%, 6.5%, 2.1%, 证明将稳定波长数作为动态光谱数据质量评价标准的有效性。 引入稳定波长数指标, 可对测量数据进行预先的质量评估, 提高实际仪器预测的可靠性, 为动态光谱法血液无创检测走向临床应用铺平了道路。

To eliminate the influence of all factors on non-invasive measurement precision of blood components by dynamic spectrum method during data acquisition process, a quality evaluation criterion of dynamic spectrum data needs to be established to improve the stability of the model and precision of the prediction. The number of stable wavelength as a quality evaluation index for the dynamic spectrum data was presented in this article after further analysis of 110 samples, which were all obtained by in-vivo measurements, and 60 samples were picked up as the satisfactory samples. BP artificial neural network was used to establish the calibration model of subjects’ total cholesterol, glucose and hemoglobin values against dynamic spectrum data. The prediction result of the experiment group was improved compared with the control group. The average relative error was decreased from 13.8%, 15.8% and 5.4% to 6.5%, 6.5% and 2.1% respectively, by which the effectiveness of the number of stable wavelength as a quality evaluation index could be proved. By evaluating the quality of the dynamic spectrum data measured, more reliable prediction result can be obtained, which can make the non-invasive measurement of the blood components come to the clinical application sooner.