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623.454.8

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The World Health Organization estimates that 347 million people worldwide are diabetic. Although diabetes is presently not curable, continuous glucose monitoring and strict insulin therapy to control the blood glucose levels in diabetic patients can dramatically delay the onset of serious complications. Near infrared (NIR) spectroscopy offers a promising technological platform for continuous glucose monitoring in the human body. The investigations carried out in this research work focus on utilizing NIR spectroscopic data for building reliable glucose prediction models. Principal Component or Partial Least Squares based regression (PLSR) methods are by far the most often used chemometric approaches for the calibration of spectroscopic glucose monitoring sensors. To ensure good prediction performance of the PLSR model, the regression coefficients have to be estimated using a ‘representative’ calibration set, i.e., a data set containing all relevant variation in the measured NIR spectra which can be expected in future test samples. One may never be able to generate such a representative calibration set for in vivo glucose monitoring primarily because of two reasons: First, the chemical composition of the biological fluid may vary over time. Second, a thin tissue layer may grow in the optical path of an implantable in vivo glucose sensor. This will have an adverse effect on the signal to noise ratio of the net collected signal, and could dramatically worsen the prediction ability of multivariate calibration models. This underscores the importance of robust multivariate calibration, and characterization of optical properties of biological tissues.Accordingly, in the first part of this work, in vitro multivariate calibration models have been built for aqueous glucose and human serum solutions. As protein molecules are the most important interferents in human serum, the effect of overall serum protein concentration and glycated serum protein concentration on glucose predictions was studied in detail. In the next part of the work, the possibilities to robustify the multivariate calibration model by inclusion of expert information on chemical interferents and scatterers were investigated. To quantify the effect of presence of a thin tissue layer in the optical path, the bulk optical properties of tissue samples grown on sensor dummies which had been implanted for several months in goats were characterized using Double Integrating Spheres and unscattered transmittance measurements. Overall, it was found that the multivariate calibration models can successfully predict the glucose concentration in aqueous and biological media. Robustification of the multivariate models by utilizing expert knowledge was successful especially when Spectral Interference Subtraction was used to preprocess the NIR data, or when Augmented Classical Least Squares method was used to build multivariate calibration model. Based on the optical characterization of tissues, the diffuse transmittance measurement in the combination band of NIR region was recommended as the optimal configuration for an implantable glucose sensor.

Academic collection --- 547.455.623 --- Glucose --- Theses --- 547.455.623 Glucose

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Silicates. --- 549.623 --- Acid orthosilicates and related minerals --- 549.623 Acid orthosilicates and related minerals

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623.454.8 --- 614.8 --- (4-67EU) --- Polemology --- European Union

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Ordered algebraic structures --- 512.623.3 --- #WWIS:ALTO --- General Galois theory --- Field extensions (Mathematics) --- Galois theory. --- Field extensions (Mathematics). --- 512.623.3 General Galois theory