利用统计处理方法提高动态光谱的检测精度

近红外光谱法成分检测因其检测方法的优越性，已成为生物医学领域的研究热点。目前国内外相关研究虽然广泛，可是还没有进入实际临床应用的报道。关键是现有检测方法中存在着个体差异，测量条件的影响，使光谱检测精度达不到要求，因此提高检测精度是血液成分无创检测得以实现的关键。文章在最近提出的一种新的近红外血液成分检测方法-动态光谱法的基础上，利用传统的统计处理方法，通过剔除含有粗大误差的奇异点和多次测量统计平均来提高动态光谱检测精度。实验结果表明含粗大误差的奇异点剔除和多次测量统计平均能够提高动态光谱的检测精度。

Using the Statistic Preprocessing Method to Raise the Measurement Accuracy of Dynamic Spectrum

The development of the non-invasive measurement of blood compositions by nearinfrared spectroscopy has attracted significant interests in recent years.But it has not yet been applied to the clinical field except the oximeter.The most important and difficult problem is that the instrument accuracy does not meet the requirement of the non-invasive measurement of blood compositions due to the effect of the individual discrepancy and complicated measurement conditions.So how to reach the instrument accuracy required is its key part.Based on the dynamic spectroscopy,a new non-invasive measurement method of blood compositions is presented,where the measurement accuracy is raised through the outlier eliminating and the assembly average of several measurement results.It has been proved by the experiment that the measurement accuracy can be raised by the outlier eliminating and the assembly average of several measurement results.