Improve the precision of platelet spectrum quantitative analysis based on ‘‘M+N” theory
Improve the precision of platelet spectrum quantitative analysis based on ‘‘M+N” theory
复制标题
基于α-M N-理论提高血小板谱定量分析精度
DOI：
10.1016/j.saa.2021.120291
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Ling Lin
中科院分区：
文献类型：
--
作者：
Gang Li;Dan Wang;Jing Zhao;Mei Zhou;Kang Wang;Shaohua Wu;Ling Lin
血小板具有促进血液凝固和加速止血的功能，在人体中起着重要的作用。利用光谱分析技术实现血小板临床快速微量检测具有重要的医学意义，是今后临床检测的发展方向。但由于血小板吸收光谱特征不明显的问题，建模分析结果不能满足临床准确性要求。为了提高分析精度，本文基于“M+N”理论，综合考虑被测组分血小板和非被测组分血红蛋白浓度对建模分析的影响，采用基于两组分浓度分布选择训练集的方法。同时，考虑到线性模型的特点，选取两组分浓度两端的样本作为训练集，结合三次项拟合方法对血小板浓度进行建模和预测。设计了以下实验：采用四种不同的方法选取训练集进行建模，预测血小板浓度，并对不同方法的建模结果进行比较。通过对222个样本的建模和预测，结果表明，选择两组分浓度分布的训练集的方法可以有效提高所建立模型的预测精度，得到了性能更好的模型，相关系数Rc达到0.63，比对所有样本进行完全建模的结果提高了24.98%，RMSE降低了10.02%。在建模中考虑非测量分量的影响对提高测量分量的预测精度具有重要意义，选择两个分量浓度两端的样本作为训练集可以进一步提高模型的性能和精度。
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.