Application of partial robust M-regression in noninvasive measurement of human blood glucose concentration with near-infrared spectroscopy

In the study of non-invasive measurement of human blood glucose concentration with near-infrared spectroscopy, the partial robust M-regression (PRM) is proposed in the present paper to solve the robustness of calibration model affected by outliers existing in the spectra data set. While keeping the good properties of M-estimators if an appropriate weighting scheme is chosen, PRM inherits the speed of computation and easy realization of the iterative reweighted partial least squares (IRPLS) algorithm, but is robust to all types of outliers. With the pretreatment of spectra based on PRM, the root mean square error of prediction (RMSEP) of calibration model was presented and compared with partial least squares (PLS). Experimental results show that the robust calibration model PRM produces better prediction of glucose than the model of PLS when the components of the samples increase which is significant for non-invasive prediction of blood glucose levels.