基于平行因子分析法提高近红外无创血糖校正模型稳健性的研究

Enhanced Robustness of Calibration Models Using Parallel Factor (PARAFAC) Analysis with NIR Spectral Data for Non-invasive Blood Glucose Monitoring

采用近红外光谱进行无创血糖检测时,样品背景变动造成的预测集样本与校正集样本量测体系不一致的问题是导致预测精度低的原因之一.提出一种将母体背景作为变量引入回归建模中,结合各个母体背景下的样本光谱信息构建三维光谱矩阵以提高校正模型稳健性的分析方法.将平行因子分析(PARAFAC)与多元线性回归(MLR)相结合,对人体三层皮肤模型的蒙特卡罗模拟实验和葡萄糖水溶液及其混合物的离体实验进行了验证.实验结果表明,与传统的单一母体背景所建立的偏最小二乘模型相比,将母体背景作为建模元素采用PARAFAC-MLR法所建立的校正模型具有更好的预测能力和稳健性.

The main cause of the low prediction accuracy in non-invasive blood glucose monitoring with near-infrared （NIR） spectroscopy is that the variations induced by the changes of the measuring system in the prediction data set are inconsistent with those in the calibration data set. In this paper, a method to improve the robustness of the calibration models is proposed, in which the information of the matrix background is introduced as a variable into the calibration data and the three-way tensor is used to build the regression model. The idea of constructing regression models based on the hybrid algorithms consists of two steps. The first is to build a parallel factor （PARAFAC） model with its second-order advantage and calculate the scores and loadings. Then a multivariate linear regression （MLR） calibration model is built from the PARAFAC sample scores combined with the reference concentration values for quantification purposes. For the validation and prediction, the PARAFAC loadings are used to calculate the predicted scores with the validation and prediction data sets, and then the predicted concentration values can be deduced from the MLR model. The proposed method has been successfully applied to two NIR spectroscopy experiments. One is a Monte-Carlo simulation experiment of skin. The changes of the absorption coefficients and scattering coefficients of dermis are considered as the variations of the matrix background. The other is an in vitro experiment including glucose, haemoglobin and albumin solutions and the mixed composition solutions. The determination coefficients and root mean square error of prediction （RMSEP） values obtained from the PARAFAC-MLR models are compared with those obtained from traditional chemometrics tools such as partial least squares （PLS）. The results show that the PLS model cannot handle uncalibrated variations whereas the way of introducing the matrix background to generate tensor data and the regression method based on the combination of PARAFAC and MLR perform better in model robustness and prediction precision.