近红外无创生化检测中不同光程的光谱校正模型研究

摘要

The spectral subtraction approach with different flow blood volume in near infrared noninvasive biochemical sensing can eliminate human tissue background interference, but at the same time the pathlength of subtracted spectrum is unknown. Thus, the model will be insufficiently effective if established directly with pathlength-unknown spectra, which is a big problem for subtracted blood volume spectrometry. In the present paper, a simulated experiment was designed to simulate this issue. The orthogonal signal correction method was proposed to eliminate the influence brought by pathlength. Compared with the PLS model before and after orthogonal signal correction, the root mean square error of cross validation (RMSECV) was from 90.17 mg·dL-1 down to 31. 62 mg·dL-1, and the correlation coefficient was improved from 0. 978 7 to 0. 996 8. The experimental results show that using the orthogonal signal correction method can effectively restrain the interference information of path-length, and improve the prediction precision of calibration model. The conclusion in this paper sets the stage for the practical application of the spectral subtraction approach with different flow blood volume.%近红外无创生化检测中采用不同血流容积光谱相减的方法理论上能够消除人体组织产生的强背景干扰,但人体光谱相减后得到的血液光谱的光程是未知的,这给校正模型的建立带来了困难.通过设计模拟实验并采用正交信号校正的方法对光程信息进行校正,提高不同光程的光谱建立校正模型的预测精度.分别建立了光程校正前后的模型,模型的交叉验证标准偏差从90.17mg·dL-1下降到31.62mg· dL-1,相关系数从0.978 7提高到0.996 8.实验结果表明,采用正交信号校正的方法能够有效抑制光程信息的干扰,提高模型的预测精度,这为不同血流容积光谱相减法的实际应用提供了基础.