Browsing by Person "Schaum, Alexander"

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Application of a PAT/QbD concept onto a Pharmaceutical Bioprocess
(2025) Graf, Alexander; Schaum, Alexander
In 2022 close to a hundred billion units of pharmaceuticals were sold in Germany alone (Radtke, 2023). Patient safety and high efficacy are the most critical factors in producing these drugs. This results in a need for manufacturers to constantly output high-quality products. Consequently, there has been an ongoing adoption of Quality by Design (QbD) and Process Analytical Technologies (PAT) in the industry over the last decades. This thesis focused on advancing different spectroscopic methods as PAT tools in the context of QbD. First, a novel 2D-fluorescence (2DF) sensor was investigated for its usability in-process monitoring of mammalian cell cultures – qualitative and quantitative. Second, Raman spectroscopy was examined for its use in bioprocess development and as a control device in production bioreactors. In the first part of this work, the 2DF technology demonstrated its versatility as, on the one hand, an effective method for golden batch monitoring, i.e., for promptly detecting deviations within the process. On the other hand, the fluorescence signals can be correlated to cell count and viability, making it a suitable in-line alternative to traditional off-line cell counting. In the context of QbD, fluorescence spectroscopy can furthermore give the user more insight into the cellular metabolism, as, for example, co-enzymes like NADH can be detected. The second part focuses on Raman spectroscopy as a valuable tool during process development and for in-line process control of critical process parameters. First, a Raman spectrometer was integrated into two automated mini-bioreactor systems – one with 15 mL and the other with 250 mL single-use vessels. These systems are commonly used in cell line development and upstream process development campaigns, especially for economic execution of Design of Experiment studies. Integrating a Raman spectrometer in these highly automated systems made it possible to efficiently generate a large Raman dataset for robust modeling of several essential process parameters, such as glucose, lactate, glutamine, glutamate, and target protein titer. In the case of the glucose model, scale-up to a 50 L bioreactor was successfully made for in-line monitoring of said parameter. Finally, Raman spectroscopy was integrated into a perfusion process. In combination with a biocapacitance probe for in-line cell count control, the Raman system was successfully utilized for on-line control of the glucose concentration. This paper proves that PAT sensors can be utilized as enablers for process intensification and, consequently, as a step toward continuous processing.
Improved prediction of wheat baking quality by three novel approaches involving spectroscopic, rheological and analytical measurements and an optimized baking test
(2025) Ziegler, Denise; Buck, Lukas; Scherf, Katharina Anne; Popper, Lutz; Schaum, Alexander; Hitzmann, Bernd
Baking quality, defined as loaf volume, is one of the most important quality attributes of wheat. An accurate and rapid determination is of great interest for the wheat supply chain. However, this remains difficult to date, because reported predictions based on other wheat characteristics (e.g. protein content) or flour spectroscopy are poor. This study investigates three novel approaches to improve the prediction of specific loaf volume determined by an optimized mini-baking test. The predictions are based on a large variety of rheological and analytical data as well as fluorescence, near-infrared (NIR) and Raman spectroscopy of flour and flour fractions. Furthermore, the influence of data fusion on the predictions is investigated. All three approaches presented promising results and showed great potential for practical application with R2CV > 0.90 for various regression models. For example, the combination of farinograph data with solvent retention capacity data or NIR flour spectra yielded R2CV of 0.91 in both cases. Combining Raman spectra of the < 32 μm and 75–100 μm fractions as well as NIR spectra of gluten, flour and starch both also yielded R2CV of 0.91. The results underline that loaf volume is a complex quality characteristic that can be better predicted when different data types are combined. Different rheological and analytical tests and different spectroscopic methods capture specific wheat quality characteristics that have different relations to baking volume and can therefore provide complementary information for improved predictions. Furthermore, the importance of rheological tests (especially farinograph, extensograph, alveograph) and the baking procedure for the prediction of baking quality are emphasized.
Improved prediction of wheat quality and functionality using near-infrared spectroscopy and novel approaches involving flour fractionation and data fusion
(2025) Ziegler, Denise; Buck, Lukas; Scherf, Katharina Anne; Popper, Lutz; Schaum, Alexander; Hitzmann, Bernd; Ziegler, Denise; Department of Process Analytics, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany; Buck, Lukas; Department of Bioactive and Functional Food Chemistry, Institute of Applied Biosciences, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Scherf, Katharina Anne; Department of Bioactive and Functional Food Chemistry, Institute of Applied Biosciences, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Popper, Lutz; Mühlenchemie GmbH & Co. KG, Ahrensburg, Germany; Schaum, Alexander; Department of Process Analytics, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany; Hitzmann, Bernd; Department of Process Analytics, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany
The accurate and rapid determination of wheat quality is of great importance for the wheat supply chain. Near-infrared (NIR) spectroscopy has become an established method for this purpose. So far, however, predictions for most wheat quality characteristics are not accurate enough to replace reference measurements, with the exception of protein content. This study investigates the potential to improve the prediction of 41 wheat quality parameters (protein- and starch-related parameters, solvent retention capacity, farinograph, extensograph, alveograph) based on a flour fractionation approach (sieve fractionation, dough preparation, gluten washing) and data fusion using the established techniques of NIR spectroscopy and chemometrics. Results show that preprocessing of flour significantly altered the composition of the samples, which reflected in spectral differences of their NIR spectra. This also led to a change in the prediction accuracy for many wheat quality parameters. Compared to the prediction using flour spectra, flour fractionation with or without data fusion improved the RMSECV between 5.6 and 28.6% for 35 out of the 41 quality parameters tested, leading to R2CV between 0.80 and 0.96 for many of them. Gluten, dough, and the 50–75 µm and the 75–100 µm fractions were particularly important for the improved predictions. The best predictions were often based on data fusion of spectra from different sample types, demonstrating the importance of using complementary information from different data sources to improve predictions. The results underline the potential of this novel approach to be established in the industry as an extension of conventional NIR spectroscopy to improve wheat quality prediction.
Monitoring a coffee roasting process based on near‐infrared and raman spectroscopy coupled with chemometrics
(2025) Munyendo, Leah; Schuster, Katharina; Armbruster, Wolfgang; Babor, Majharulislam; Njoroge, Daniel; Zhang, Yanyan; von Wrochem, Almut; Schaum, Alexander; Hitzmann, Bernd
Roasting is a fundamental step in coffee processing, where complex reactions form chemical compounds related to the coffee flavor and its health‐beneficial effects. These reactions occur on various time scales depending on the roasting conditions. To monitor the process and ensure reproducibility, the study proposes simple and fast techniques based on spectroscopy. This work uses analytical tools based on near‐infrared (NIR) and Raman spectroscopy to monitor the coffee roasting process by predicting chemical changes in coffee beans during roasting. Green coffee beans of Robusta and Arabica species were roasted at 240°C for different roasting times. The spectra of the samples were taken using the spectrometers and modeled by the k‐nearest neighbor regression (KNR), partial least squares regression (PLSR), and multiple linear regression (MLR) to predict concentrations from the spectral data sets. For NIR spectra, all the models provided satisfactory results for the prediction of chlorogenic acid, trigonelline, and DPPH radical scavenging activity with low relative root mean square error of prediction (pRMSEP < 9.649%) and high coefficient of determination ( R 2 > 0.915). The predictions for ABTS radical scavenging activity were reasonably good. On the contrary, the models poorly predicted the caffeine and total phenolic content (TPC). Similarly, all the models based on the Raman spectra provided good prediction accuracies for monitoring the dynamics of chlorogenic acid, trigonelline, and DPPH radical scavenging activity (pRMSEP < 7.849% and R 2 > 0.944). The results for ABTS radical scavenging activity, caffeine, and TPC were similar to those of NIR spectra. These findings demonstrate the potential of Raman and NIR spectroscopy methods in tracking chemical changes in coffee during roasting. By doing so, it may be possible to control the quality of coffee in terms of its aroma, flavor, and roast level.
On the structural analysis and optimal input design for joint state and parameter estimation
(2025) Lepsien, Arthur; Kügler, Philipp; Schaum, Alexander
This paper addresses the problem of joint state and parameter estimation for nonlinear affine-input systems with positive parameters including the design of a closed-loop optimal input adaptation to increase an identifiability measure for the system. The identifiability itself is considered in the context of structural observability of the system dynamics based on structural analysis of the system including the unknown parameters as additional states. In particular, the network graph-based interpretation of structural observability is employed at this point. This analysis motivates to include time derivatives of the measurements as additional system outputs to enhance the structural observability properties. For this purpose robust exact differentiation is considered, relying on the super-twisting algorithm to obtain finite time convergent estimates of these signals. Using the extended measurement signal, a continuous-discrete Extended Kalman filter is proposed that ensures strictly positive estimates for the parameters. Based on the estimates of states and parameters the input signal is determined using a moving horizon optimal predictive control that evaluates the condition number of the Fisher information matrix, thus maximizing the information content of the measurements with respect to the parameters. The proposed scheme extends and combines different previously discussed approaches from the literature and is evaluated by means of a thermal process example in simulation and experiment, showing high potential for similar system identification problems.