A fiber-coupled transmission spectroscopy setup using a pulsed external-cavity quantum cascade laser (EC-QCL, 1200-900 cm−1 ) has been developed and demonstrated for measurements of aqueous solutions. The system has been characterised with regard to the laser noise and optimal optical pathlength. Solutions with glucose were used to further test the setup, and glucose concentrations down to physiologically relevant levels (0-600 mg/dl) were investigated. Albumin, lactate, urea, and fructose in various concentrations were added as interfering substances as their absorption bands overlap with those of glucose, and because they may be of interest in a clinical setting. Analyte concentrations were predicted using partial least-squares (PLS) regression, and the root-mean-square error of cross-validation for glucose was 10.7 mg/dl. The advantages of using a convolutional neural network (CNN) for regression analysis in comparison to the PLS regression were also shown. The application of a CNN gave an improved prediction error (8.3 mg/dl), and was used to identify important spectral regions. These results are comparable to state-of-the-art enzymatic glucose sensors, and are encouraging for further research on optics-based glucose sensors.
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