Improving the analysis accuracy of components in blood by SSP-MCSD and multi-mode spectral data fusion.

In recent years, spectral quantitative analysis for blood components has been a research hotspot in biomedical engineering. But researches have been limited to the application of high-sensitivity spectroscopy instruments and the complexity of blood components-the overlapping of absorption curves for many components is severe. This has led to the difficulty in achieving satisfactory results when using spectroscopy to quantify components in blood. In order to enhance the model robustness and improve the model performance, this paper proposed a sample set partitioning strategy based on multi-component spatial distance (SSP-MCSD). Different from the other sample set partitioning strategies, which only consider the uniformity of the concentration distribution of the target component, this strategy also concerns to the concentration distribution of non-target components. The concentration of the target component and non-target components are used to construct a multi-dimensional space, and the Euclidean Distance of sample points in this space is used as the criterion to partition the sample set. At the same time, the spectra collected in multi-modes are fused for increasing the amount of information. So as to enhance the model robustness and to improve the analysis accuracy of the target components. In order to verify the effectiveness of this strategy, the serum of 101 volunteers was analyzed. Taking total protein in serum as the non-target component, the regression model for bilirubin concentration was established by transmission spectra, fluorescence spectra, and the joint spectra after fusion of the above two spectra, respectively. The experimental results showed that the prediction accuracy of the model established by SSP-MCSD combined with multi-mode spectral fusion is obviously higher than that of other methods. It can effectively improve the analysis accuracy of blood components.