Universität Stuttgart

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Institute: search.filters.UBSInstitut.Institut für SystemdynamikAuthor: search.filters.author.Giessen, Harald

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    Adaptive method for quantitative estimation of glucose and fructose concentrations in aqueous solutions based on infrared nanoantenna optics
    (2019) Schuler, Benjamin; Kühner, Lucca; Hentschel, Mario; Giessen, Harald; Tarín, Cristina
    In life science and health research one observes a continuous need for new concepts and methods to detect and quantify the presence and concentration of certain biomolecules-preferably even in vivo or aqueous solutions. One prominent example, among many others, is the blood glucose level, which is highly important in the treatment of, e.g., diabetes mellitus. Detecting and, in particular, quantifying the amount of such molecular species in a complex sensing environment, such as human body fluids, constitutes a significant challenge. Surface-enhanced infrared absorption (SEIRA) spectroscopy has proven to be uniquely able to differentiate even very similar molecular species in very small concentrations. We are thus employing SEIRA to gather the vibrational response of aqueous glucose and fructose solutions in the mid-infrared spectral range with varying concentration levels down to 10 g/l. In contrast to previous work, we further demonstrate that it is possible to not only extract the presence of the analyte molecules but to determine the quantitative concentrations in a reliable and automated way. For this, a baseline correction method is applied to pre-process the measurement data in order to extract the characteristic vibrational information. Afterwards, a set of basis functions is fitted to capture the characteristic features of the two examined monosaccharides and a potential contribution of the solvent itself. The reconstruction of the actual concentration levels is then performed by superposition of the different basis functions to approximate the measured data. This software-based enhancement of the employed optical sensors leads to an accurate quantitative estimate of glucose and fructose concentrations in aqueous solutions.
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    Regression methods for ophthalmic glucose sensing using metamaterials
    (2011) Rapp, Philipp; Mesch, Martin; Giessen, Harald; Tarín, Cristina
    We present a novel concept for in vivo sensing of glucose using metamaterials in combination with automatic learning systems. In detail, we use the plasmonic analogue of electromagnetically induced transparency (EIT) as sensor and evaluate the acquired data with support vector machines. The metamaterial can be integrated into a contact lens. This sensor changes its optical properties such as reflectivity upon the ambient glucose concentration, which allows for in situ measurements in the eye. We demonstrate that estimation errors below 2% at physiological concentrations are possible using simulations of the optical properties of the metamaterial in combination with an appropriate electrical circuitry and signal processing scheme. In the future, functionalization of our sensor with hydrogel will allow for a glucose-specific detection which is insensitive to other tear liquid substances providing both excellent selectivity and sensitivity.
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    Machine learning methods of regression for plasmonic nanoantenna glucose sensing
    (2021) Corcione, Emilio; Pfezer, Diana; Hentschel, Mario; Giessen, Harald; Tarín, Cristina
    The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in the literature, with surface-enhanced infrared absorption (SEIRA) spectroscopy combining the sensitivity of plasmonic systems and the specificity of standard infrared spectroscopy. The challenge addressed in this paper is to determine the best method to estimate the glucose concentration in aqueous solutions in the presence of fructose from the measured reflectance spectra. This is referred to as the inverse problem of sensing and usually solved via linear regression. Here, instead, several advanced machine learning regression algorithms are proposed and compared, while the sensor data are subject to a pre-processing routine aiming to isolate key patterns from which to extract the relevant information. The most accurate and reliable predictions were finally made by a Gaussian process regression model which improves by more than 60% on previous approaches. Our findings give insight into the applicability of machine learning methods of regression for sensor calibration and explore the limitations of SEIRA glucose sensing.