Grouping Modeling Strategy for Hematocrit Analysis with Blood Vis-NIR Spectroscopy

Abstract

Vis-NIR spectroscopy combined with equidistant combination-PLS (EC-PLS) method was applied for the rapid and reagent-free analysis of blood Hematocrit (HCT). The multi-parameter optimization platform based on Norris derivative filter (NDF) was constructed to select appropriate spectral preprocessing. Multi-partition modeling and independent validation in calibration-prediction-validation design were adopted to ensure the stability of parameter selection and the objectivity of modeling effect. For male and female groups, the optimal EC-PLS models of the grouping modeling were selected and achieved significantly better validation effects than hybrid modeling. In independent validation, the root mean square error of prediction (SEP) of male, female and mixed sample groups were decreased by 12.7%, 32.4% and 20.4%, respectively. The results showed that the predicted and clinical actual values of the all validation samples have high correlation coefficient of prediction (R_P = 0.93) and low prediction error (SEP = 1.21%), and thus have potential for clinical application.