[01] Yun Han, Department of Data Science, Guangdong Ocean University, Zhanjiang, China. [02] Huihui Zhou, Department of Data Science, Guangdong Ocean University, Zhanjiang, China.

In this study, the relative indicator glycated hemoglobin (HbA1c) was determined by near-infrared (NIR) spectroscopy in human hemolysate samples. Because HbA1c is a percentage indicator, it was indirectly determined via Hb and Hb•HbA1c (absolute HbA1c content). Equidistant combination multiple linear regression (EC-MLR) and moving window partial least squares (MW-PLS) methods were employed for screening key wavelengths. Using the EC-MLR, 6 and 14 wavelengths were selected for Hb and Hb•HbA1c, respectively. Using the MW-PLS, wavebands 940–1750 nm and 1492–1858 nm were selected for Hb and Hb•HbA1c, respectively. The EC-MLR method adopted fewer wavelengths. The HbA1c predicted values were further calculated by predicted values of Hb and Hb•HbA1c. The obtained root mean square error and correlation coefficients of prediction (V_SEP, V_R_P) for HbA1c in validation set were 0.49% and 0.909 with EC-MLR method and 0.41% and 0.919 with MW-PLS method. Both methods achieved good prediction results. The results show that the strategy of measuring relative indicator HbA1c by NIR is feasible, which provides a wider application space for NIR spectroscopy. In addition, the technique is fast and simple compared to traditional methods, so it is a promising tool for screening diabetes in large populations.

Glycated Hemoglobin, Near-Infrared Spectroscopy, Wavelength Selection

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