Characterization of glycated hemoglobin based on Raman spectroscopy and artificial neural networks

The World Health Organization has declared the glycated hemoglobin (HbA1c) as a gold standard biomarker for diabetes diagnosis; this has led to relevant research on the spectral behavior and characterization of HbA1c. This paper presents an analysis of Raman peaks of commercial lyophilized HbA1c, diluted in distilledwater, using concentrations of 4.76% and 9.09%, as well as pure powder (100% concentration). Vibrational Raman peak positions of HbA1c powder were found at 1578, 1571, 1536, 1436, 1311, 1308, 1230, 1222, 1114, 1106, 969, 799 and 665 cm-1; these values are consistent with results reported in other works. Besides, a nonlinear regression model based on a Feed-Forward Neural Network (FFNN) was built to quantify percentages of HbA1c for unknown concentrations. Using the Raman spectra as independent variables, the regression provided a Root Mean Square Error in Cross-Validation (RMSECV) of 0.08% +/- 0.04. We also include a detailed molecular assignment of the average spectra of lyophilized powder of HbA1c. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study analyzed the vibrational Raman peak positions of lyophilized HbA1c powder and built a regression model to quantify percentages of HbA1c for unknown concentrations. The regression based on Raman spectra achieved a Root Mean Square Error in Cross-Validation of 0.08% +/- 0.04, and included detailed molecular assignment of the average spectra.