Improve the precision of platelet spectrum quantitative analysis based on "M+N" theory

Improving the accuracy of quantitative spectroscopic analysis of leukocyte by suppressing the influence of the "M" factors based on "M + N" theory

Leukocytes play a crucial role in clinical diagnosis. The noninvasive and immediate detection of this low blood component has both important application and academic significance. The "M + N" theory makes it obvious that suppressing the influence of N factors and reducing the influence of M factors are both necessary in order to accurately detect the low content of blood components like leukocytes. Therefore, based on the strategy of "fixing the influencing factors" in the "M + N" theory, this paper proposes the method of "partition modeling based on the content of large concentrations of non-target components." Firstly, a dynamic spectral acquisition system was built to achieve the noninvasive acquisition of spectra. Then this paper applies the method proposed above to the modeling process of the samples. In order to lessen the impact of the M factors, the approach first divides the samples into groups based on the concentrations of the major blood components (platelets and hemoglobin). This reduces the range of fluctuation of the non-target components in each interval. The modeling of the leukocyte content was then carried out independently for each sample contained in each compartment. Compared with the result of modeling the sample directly, the related coefficient of the calibration set (Rc) improved by 11.70%, and the root mean square error (RMSEC) decreased by 76.97%; the related coefficient of the prediction set (Rp) improved by 32.68%, and the root mean square error (RMSEP) decreased by 52.80%. When the model was applied to predict all samples, the related coefficient (R-all) increased by 16.67%, and the root mean square error (RMSE-all) dropped by 63.00%. It was shown that, as compared to direct modeling of leukocyte concentration, the method of "partition modeling based on the content of large concentrations of non-target components" considerably increased the accuracy of quantitative analysis of leukocytes. The method can be used to analyze other blood components as well, offering a fresh approach and technique to increase the precision of spectral analysis of the blood's small content components.

Noninvasive and simultaneous quantitative analysis of multiple human blood components based on the grey analysis system

Noninvasive detection of human blood components is the dream of human beings and the goal of clinical detection. From the perspective of mathematical analysis, based on the grey analysis system, the principle of spectral chemical quantitative analysis and the solution method of multivariate linear equation, this paper pioneers the spectrum elimination method, and obtains a complete, high-precision, synchronous and noninvasive detection system for a variety of human blood components. The spectral elimination method applies the principle of elimination method in mathematics to the noninvasive quantitative analysis of human blood components by spectral method, reduces the influence of non-target components on the detection of target components, and improves the accuracy of noninvasive quantitative analysis of human blood components. To demonstrate the effectiveness of the method, taking the analysis of the contents of seven blood components (hemoglobin, red blood cell count, neutrophils, lymphocytes, monocytes, eosinophils and basophils) in blood as an example, fourteen models were established by two different methods. From the comparison of modeling results, it can be concluded that when the seven models established by spectral elimination method predict the corresponding seven components of all samples, the predicted correlation coefficients are more than 0.9500. The experimental results show that the spectral elimination method and non-invasive detection system proposed can predict the content of human blood components with high accuracy. This paper studies a high-precision, simultaneous and noninvasive quantitative analysis system of multiple human blood components for the first time, which not only makes great progress in the non-invasive chemical quantitative analysis of human blood components by spectroscopy, but also has great application value for clinical medical treatment and disease diagnosis.

Non-invasive quantitative analysis of human blood components in renal three items using spectral compensation method based on spectral data and component content correlation

BACKGROUND AND OBJECTIVE

The non-invasive measurement of the blood component contents in humans using spectroscopic methods has important clinical application value. In the absorption spectrum of human blood, for a target component with a small content, the absorption spectrum line is low, the absorption spectrum line of the target component changes weakly at different wavelengths, and the absorption spectrum lines of the target and non-target components overlap. The above reasons cause the spectral line difference between the target and non-target components to be small. However, non-target components also have physical properties such as scattering, which will reduce the contribution of the target component in the absorption spectrum of blood, that is, the spectral line difference between the target and non-target components becomes smaller, thus making the accuracy of the target component model lower.

METHODS

To increase the accuracy of modeling analysis, according to the correlation between spectral data and the content of the target component, this paper proposes a spectral compensation method. The method compensates for the influence caused by the non-target components on the target components through the mathematical relations between the data of the spectrum and the contents of the target components. Thus, a high-precision, non-invasive quantitative analysis system of human blood components is obtained. Taking the analysis of the three components of urea nitrogen, creatinine, and uric acid as examples, the experiment used spectral data before and after spectral compensation and partial least squares (PLS) methods to build two models and used them to predict the content of the target component.

RESULTS

When the models of different components established by the spectral compensation method respectively predict the all samples' contents of urea, creatinine, and uric acid, the three correlation coefficients can reach more than 0.9700.

CONCLUSION

It can be obtained from the experimental results that the spectral compensation method can greatly increase the performance of each component model. Spectral compensation is a new and comprehensive method for improving the quality of spectral data. Therefore, this paper obtains a non-invasive quantitative analysis system of human blood components with higher precision, and can quantitatively analyze blood urea nitrogen, creatinine, and uric acid in human blood with high precision, which has great practical application value in clinical practice.

Quantitative analysis of urea in serum by synchronous modulation and demodulation fluorescence spectroscopy

High-precision spectral data is a necessary prerequisite for quantitative analysis of complex solution components. In order to improve the accuracy of spectral data, this paper proposes a method of synchronous modulation and demodulation. This article also combines the "M + N" theory, cleverly uses the excitation fluorescence of the components in the serum and its self-absorption phenomenon, collects the fluorescence spectrum of the serum sample, and then uses the partial least squares (PLS) method and the cubic optimization model method to establish a model to analyze the urea concentration of serum. At the same time, in order to verify the effectiveness of synchronous modulation and demodulation method, the unmodulated fluorescence spectrum is used to establish the regression model of urea concentration. Compared with the unmodulated fluorescence spectrum modeling results, the fluorescence spectrum modeling results after modulation and demodulation have been significantly improved. In the modeling results of fluorescence spectrum after synchronous modulation and demodulation, the Rc is 0.916753, the RMSEC is 2.05848 mmol/L, the Rp is 0.79663, and the RMSEP is 3.16812 mmol/L, the Rp-all is 0.88879, and the RMSEP-all is 2.32114 mmol/L. The results show that the method of synchronous modulation and demodulation proposed in this paper not only reduces the influence of dark current, ambient light and background noise on the signal-to-noise ratio of the spectral data, but also effectively avoids the error caused by the non-synchronization of the chopper and the spectrometer. Therefore, the method used in this paper not only improves the signal-to-noise ratio and accuracy of spectral data, but also improves the accuracy of spectral quantitative analysis of complex solutions.