Abstract
We previously proposed in [1] a novel standalone fluorescence measurement device that can identify fluorophore substances in a light conducting environment. In this paper, we adapt this work to the particular problem of predicting blood glucose levels based on near-infrared (NIR) absorption spectroscopy. We have focused this investigation on the combination (5000–4000 cm−1) and first-overtone (6500–5500 cm−1) spectral bands known to be dominated by glucose absorption information. An array of electrically pumped vertical-cavity surface-emitting lasers (VCSELs) is used to cover the two selected spectral bands; with each laser diode emitting light at a specific wavelength. A Quantum well infrared (QWI) photodetector operating in the 1.2–2.5 µm range is used for optical power detection. Then, an artificial neural network (ANN) based model is used to determine the glucose concentrations from the obtained blood absorption spectra.
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