Tracking coffee roasting at an industrial scale for quality control is challenging. Bean color is a practical gauge for monitoring and regulating the process but only occurs before and after the process. This study highlights the feasibility of monitoring the process throughout using Raman spectroscopy. Strecker degradation and the Maillard reaction contribute to various aromatic compounds that can serve as markers in quality monitoring. Among these are chlorogenic acids (CGAs), recognized as pivotal factors determining the desired aroma. Here, drum and fluidized bed roaster processes were monitored, capitalizing on the chemical alterations induced by high temperatures (140–200 °C), particularly through the Maillard reaction. These chemical changes manifest in the scattered light signal. For real-time monitoring, Raman spectra were taken every 10 ms in selected ranges, with an average calculated every second. Utilizing a calibration matrix from a High-Pressure Liquid Chromatography (HPLC) method, CGA concentration becomes the control variable for assessing roasting progress. This study reveals the potential of Raman spectroscopy for tracking CGA during roasting. It establishes a correlation between inelastic scattered light and CGA validated through laboratory measurements and fixed roasting conditions, resulting in a theoretical CGA concentration that can be used as a process termination criterion.

Objective: A novel high-precision approach [lifetime-decomposition measurement (LTDM)] for the assessment of the glomerular filtration rate (GFR) based on clearance measurements of exogenous filtration marker. Methods: The time-correlated single photon counting (TCSPC) acquisition in combination with a new decomposition method allows the separation of signal and background from transcutaneous measurements of GFR. Results: The performance of LTDM is compared versus the commercially available NIC-kidney patch-based system for transcutaneous GFR measurement. Measurements are performed in awake Sprague Dawley (SD) rats. Using the standard concentration required for the NIC-kidney system [7-mg/100-g body weight (b.w.) FITC-Sinistrin] as reference, the mean difference (bias) of the elimination curves GFR between LTDM and NIC-kidney was 4.8%. On the same animal and same day, the capability of LTDM to measure GFR with a FITC-Sinistrin dose reduced by a factor of 200 (35-μg/100-g b.w.) was tested as well. The mean differences (half lives with low dose using LTDM compared with those using first, the NIC-Kidney system and its standard concentration, and second, LTDM with the same concentration as for the NIC-Kidney system) were 3.4% and 4.5%, respectively. Conclusion: We demonstrate that with the LTDM strategy substantial reductions in marker concentrations are possible at the same level of accuracy. Significance: 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.

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.

To predict chlorogenic acid (CGA) concentration in coffee during roasting, a machine learning algorithm was applied to mid‐infrared (MIR) data. A total of 44 roasting samples between 140 and 220 °C, along with an unroasted control, were dry‐heated in an eddy current roaster and subsequently ground and measured. CGA concentrations were predicted from MIR spectroscopy data using a multilayer perceptron (MLP) regressor and validated against a high‐performance liquid chromatography with diode array detector reference. The algorithm performed spectral preprocessing and selected relevant wavenumber regions. The MLP‐based model achieved a high coefficient of determination, outperforming classic peak evaluation, indicating that automated wavelength selection improves predictive accuracy.