Influencing Factors of Mid-Infrared Spectrum Blood Glucose Detection

Mid-infrared attenuated total reflection spectroscopy has the natural advantage of fast and green blood glucose detection in humans. However, the presence of other components in human blood can affect the accuracy of glucose detection. Therefore, we study the interference degree of the presence of cholesterol, albumin and urea in human blood on the detection of blood glucose by infrared spectroscopy. Taking 117 parts of glucose mimicry solution containing different interferences and different mass concentrations as the research object, the original spectrum is smoothed by Savitzky-Golay to construct a partial least squares regression model, and the Clarke Error Grid and comparison plot of predicted value and true value are constructed for further analysis. The results show that the prediction set correlation coefficient (R-p) and root mean square error (RMSEP) of the prediction set of the total interferer model are 0.9785 and 40.0187, respectively, and 85.7% of the prediction results fall in the Clarke Error Grid A region. The R-p and RMSEP of the missing cholesterol model are 0.9042 and 175.7292, respectively, and 40% of the predictions fall in region A. The R-p and RMSEP of the missing albumin model are 0.9616 and 103.6627, respectively, and 42.9% of the predictions fall in region A. The R-p and RMSEP of the urea deletion model are 0.9742 and 38.6716, respectively, and all predictions fall in region A. It can be seen that cholesterol has the greatest degree of interference, followed by albumin, and urea produces the least interference. This study has certain help and reference value for improving the accuracy of glucose detection by infrared spectroscopy.