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

Platelets have the functions of promoting blood coagulation and accelerating hemostasis, playing an important role in human body. It is of great medical significance to realize clinical rapid micro-detection of platelets by spectral analysis, which is the development direction of clinical detection in the future. However, due to the problem of unobvious characteristic of platelet absorption spectrum, the results of modeling and analysis cannot meet the clinical accuracy requirements. In order to improve the analysis accuracy, based on the “M+N” theory, this paper comprehensively considers the influence of the concentrations of measured component platelet and non-measured component hemoglobin on modeling analysis, and uses the method of selecting training set based on the concentration distribution of two components. At the same time, considering the characteristic of the linear model, the samples at both ends of the concentration of two components are selected as the training set, and the cubic term fitting method is combined to model and predict the concentration of platelet. The following experiments were designed: the training sets were selected by four different methods and used for modeling to predict the platelet concentration, and compared the modeling results of different methods. Through the modeling and prediction of 222 samples, the result showed that the method of selecting the training set with the concentration distribution of two components could effectively improve the prediction accuracy of the established model, and got a better model with better performance, the correlation coefficient Rc reached 0.63, which was 24.98% higher than the result of full modeling for all samples, and RMSE decreased by 10.02%. Considering the influence of non-measured components in modeling is of great significance to improve the prediction accuracy of measured components, and selecting samples from both ends of the concentration values of two components as the training set can further improve the performance and accuracy of the model.