Measurement of hemoglobin in whole blood using a partial least squares regression model with selected second derivative near infrared transmission spectral signals

In this work, we propose a signal selection procedure for determination of hemoglobin (Hb) concentration in whole blood using near infrared (NIR) transmission spectral signals. A dataset of 190 whole blood NIR transmission spectra with reference Hb concentrations was used to evaluate the method. Spectral signals were selected based on the squared correlation coefficient (R2) between the signal and the Hb concentration. An improved uninformative variable elimination (UVE) procedure was performed to remove redundant signals from the primary selected signal set. A partial least squares (PLS) regression model was built with the final selected signals and the corresponding Hb concentrations. The results indicate that the proposed method is effective at increasing the predictive power of the NIR–PLS spectral model for determining Hb concentration in whole blood samples.

► We smooth the NIR spectra of whole blood.
► Calculating the second-derivative spectra.
► Selecting wavelengths for hemoglobin determination.
► Building a NIR spectral model for whole blood hemoglobin concentration determination.
► Using the selected NIR signals can augment the hemoglobin prediction power of the model.