LTDM aims to resolve the issue of the currently necessary large doses of fluorescence tracer required for transcutaneous GFR measurement. Due to substantially less influences from autofluorescence and artifacts, the proposed method outperforms other existing techniques for accurate percutaneous organ function measurement.

In the presented investigation, the chemical composition of malt during roasting is estimated using diffuse reflectance mid-infrared fourier transform (DRIFT-MIR) spectroscopy and multiple linear regressions. Accordingly, the corresponding test setup is presented and evaluated. A total number of sixty-five stop roasting, having temperature range from 140 to 220ºC, and one unroasted sample of 1500 g Avalon malt are performed in an eddy current roaster. Roasted and unroasted malt samples are milled and then analysed. Additionally, analytical standard reference methods are performed for colour, spectral tristimulus L*a*b* -values, colour difference (E), iron-content, quantitative radical generation and the formation of specific intermediates, such as 5-(hydroxymethyl) furfural (HMF) as well as 3-deoxy-hexosulose (3-DH) and end products of Maillard reaction on all sixty-six samples. Multiple linear regression models were used to predict analysed references based on mid-infrared data, modified with spectral pre-processing for better prediction performance. The obtained results indicate that DRIFT-MIR spectrometry, combined with pre-processing and selection of evaluated wave number areas, is a useful analytical tool for the measurement of quality attributes of malt and therefore, shows potential for application in quality and process control.

To meet the demands of the chemical and pharmaceutical process industry for a combination of high measurement accuracy, product selectivity, and low cost of ownership, the existing measurement and evaluation methods have to be further developed. This paper demonstrates the attempt to combine future Raman photometers with promising evaluation methods. As part of the investigations presented here, a new and easy-to-use evaluation method based on a self-learning algorithm is presented. This method can be applied to various measurement methods and is carried out here using an example of a Raman spectrometer system and an alcohol-water mixture as demonstration fluid. The spectra’s chosen bands can be later transformed to low priced and even more robust Raman photometers. The evaluation method gives more precise results than the evaluation through classical methods like one primarily used in the software package Unscrambler. This technique increases the accuracy of detection and proves the concept of Raman process monitoring for determining concentrations. In the example of alcohol/water, the computation time is less, and it can be applied to continuous column monitoring.