Frequent glucose monitoring is a fundamental part of diabetes management, and good glucose control is important for long-term health outcomes. New types of electrochemical sensors that allow for continuous glucose monitoring (CGM) have become an important tool for diabetes management, although they still have drawbacks such as short lifetime and a need for frequent calibration. Other technologies are still being researched for CGM, in an attempt to replace the electrochemical sensors. Optical methods have several advantages for CGM, including potentially long sensor lifetimes and short measurement times, and many developments have been made over the last decades. This paper will review optical measurement methods for CGM, their challenges, and the current research status. The different methods will be compared, and the future prospects for optical methods will be discussed.

No abstract

We describe a system designed to re-bunch positron pulses delivered by an accumulator supplied by a positron source and a Surko-trap. Positron pulses from the accumulator are magnetically guided in a 0.085 T field and are injected into a region free of magnetic fields through a μ-metal field terminator. Here positrons are temporally compressed, electrostatically guided and accelerated towards a porous silicon target for the production and emission of positronium into vacuum. Positrons are focused in a spot of less than 4 mm FWTM in bunches of ∼8 ns FWHM. Emission of positronium into the vacuum is shown by single shot positron annihilation lifetime spectroscopy

The development of rapid and accurate biomedical laser spectroscopy systems in the mid-infrared has been enabled by the commercial availability of external-cavity quantum cascade lasers (EC-QCLs). EC-QCLs are a preferable alternative to benchtop instruments such as Fourier transform infrared spectrometers for sensor development as they are small and have high spectral power density. They also allow for the investigation of multiple analytes due to their broad tuneability and through the use of multivariate analysis. This article presents an in vitro investigation with two fiber-coupled measurement setups based on attenuated total reflection spectroscopy and direct transmission spectroscopy for sensing. A pulsed EC-QCL (1200–900 cm−1) was used for measurements of glucose and albumin in aqueous solutions, with lactate and urea as interferents. This analyte composition was chosen as an example of a complex aqueous solution with relevance for biomedical sensors. Glucose concentrations were determined in both setup types with root-mean-square error of cross-validation (RMSECV) of less than 20 mg/dL using partial least-squares (PLS) regression. These results demonstrate accurate analyte measurements, and are promising for further development of fiber-coupled, miniaturised in vivo sensors based on mid-infrared spectroscopy.

Convolutional neural networks (CNNs) are widely used for image recognition and text analysis, and have been suggested for application on one-dimensional data as a way to reduce the need for pre-processing steps. Pre-processing is an integral part of multivariate analysis, but determination of the optimal pre-processing methods can be time-consuming due to the large number of available methods. In this work, the performance of a CNN was investigated for classification and regression analysis of spectral data. The CNN was compared with various other chemometric methods, including support vector machines (SVMs) for classification and partial least squares regression (PLSR) for regression analysis. The comparisons were made both on raw data, and on data that had gone through pre-processing and/or feature selection methods. The models were used on spectral data acquired with methods based on near-infrared, mid-infrared, and Raman spectroscopy. For the classification datasets the models were evaluated based on the percentage of correctly classified observations, while for regression analysis the models were assessed based on the coefficient of determination (R 2 ). Our results show that CNNs can outperform standard chemometric methods, especially for classification tasks where no pre-processing is used. However, both CNN and the standard chemometric methods see improved performance when proper pre-processing and feature selection methods are used. These results demonstrate some of the capabilities and limitations of CNNs used on one-dimensional data.