Mid-infrared spectroscopy with a fiber-coupled tuneable quantum cascade laser for glucose sensing

Abstract

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.

Infrared measurements of glucose in peritoneal fluid with a tuneable quantum cascade laser

“…Silver halide fibres (ART photonics GmbH, Germany) were used to guide light in the transmission setup, and a gap between the fibres was used for sensing. The gap between the fibres was 200 µm, in accordance with the optimal pathlength found previously for our setup [27]. 150 µL fluid was used for each measurement.…”

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

“…A plethora of preprocessing methods exist, for spectral smoothing, removing spectral noise (for example from scattering and path length changes), or choosing spectral ranges with the most information. The choice of preprocessing was found using in-house code publicly available [33], iterating through derivatives, smoothing and scattering correction options. The success of the preprocessing was evaluated based on root mean square error of cross-validation (RMSECV) of a PLSR model.…”

“…We kindly thank Bjorg Narum from Nofima (Norway) for help and facilitation in acquiring the dataset, and Harald Martens (Idletechs and Department of Cyberntics, NTNU) for his help in data analysis. We also thank Ine Jernelv for her assistance in mixing the validation set, and for access to the initial analysis software [33], and Marte Kierulf Åm and Patrick Bösch for providing the porcine intraperitoneal samples.…”

On the Selectivity of Planar Microwave Glucose Sensors with Multicomponent Solutions

“…A variety of proposals for measuring the individual's blood glucose level with different strategies have also been studied, such as the measurement from the breath [16,17], saliva [18,19] or tears [20,21]. With a more general glucose measurement approach, optical methods have been proposed, relying on the principles of mid- [22][23][24] and near-infrared [25,26] spectroscopy. However, the technology attracting the greatest attention for this application during the last years seems to be microwave planar devices [27][28][29][30].…”