Partial Least Square with Savitzky Golay Derivative in Predicting Blood Hemoglobin Using Near Infrared Spectrum

Abstract

Near infrared spectroscopy (NIRS) is a reliable technique that widely used in medical fields. Partial least square was developed to predict blood hemoglobin concentration using NIRS. The aims of this paper are (i) to develop predictive model for near infrared spectroscopic analysis in blood hemoglobin prediction, (ii) to establish relationship between blood hemoglobin and near infrared spectrum using a predictive model, (iii) to evaluate the predictive accuracy of a predictive model based on root mean squared error (RMSE) and coefficient of determination rp2. Partial least square with first order Savitzky Golay (SG) derivative preprocessing (PLS-SGd1) showed the higher performance of predictions with RMSE = 0.7965 and rp2= 0.9206 in K-fold cross validation. Optimum number of latent variable (LV) and frame length (f) were 32 and 27 nm, respectively. These findings suggest that the relationship between blood hemoglobin and near infrared spectrum is strong, and the partial least square with first order SG derivative is able to predict the blood hemoglobin using near infrared spectral data.