Using experimental data designs and multivariate modeling to assess the effect of glycated serum protein concentration on glucose prediction from near-infrared spectra of human serum

Investigation of the effect of clinically relevant interferents on glucose monitoring using near-infrared spectroscopy

Near infrared spectroscopy (NIR) is a promising technique for continuous blood glucose monitoring for diabetic patients. Four interferents, at physiological concentrations, were introduced to study how the glucose predictions varied with a standard multivariate calibration model. Lactate and ethanol were found to interfere strongly with the glucose predictions unless they were included in the calibration models. Lactate was mistaken for glucose and gave erroneously high glucose predictions, with a dose response of 0.46 mM/mM. The presence of ethanol resulted in too low glucose predictions, with a dose response of -0.43 mM/mM. Acetaminophen, a known interferent in the glucose monitoring devices used for diabetes management today, was not found to be an interferent in NIR spectroscopy, nor was caffeine. Thus, interferents that may appear in high concentrations, such as ethanol and lactate, must be included in the calibration or model building of future NIR-based glucose measurement devices for diabetes monitoring.

Glucose sensing by absorption spectroscopy using lensed optical fibers

The bulkiness of common transmission spectroscopy probes prevents applicability at remote locations such as within the body. We present the fabrication and characterization of lensed fibers for transmission spectroscopy in the near-infrared. Eigenmode simulations and measurements of the coupling efficiency are presented and applied to design the setup corresponding to the sample absorption. Sensing capabilities are demonstrated on aqueous glucose samples ranged 80 to 500 mM, obtaining a mean absolute percentage error of calibration of 4.3%. With increased flexibility, transmission spectroscopic sensors at remote locations may be achievable, for example, applied to in vivo continuous glucose monitoring.