@misc{13475, abstract = {{In the context of data-driven bioprocess modeling, selecting appropriate regression models remains a critical challenge. This is especially the case when dealing with time-dependent process dynamics and complex measurement data. The practical relevance of this study lies in its critical assessment of the application constraints associated with multivariate linear regression models in bioprocess monitoring of cell culture processes. The applicability of Partial Least Squares and Ridge regression was evaluated for different cultivation phases. The results emphasize that no single linear modeling approach is universally suitable for capturing the complex behavior of mammalian cell cultures. This is why we present an enhanced segmented modeling approach by learning the optimal transition point from data and introducing a gradual model switch, allowing for smoother and more robust adaptation to process dynamics. This segmented model led to improved predictive performance compared to single-model regression across the entire process duration. Nevertheless, the heterogeneity of the 11 mammalian cell culture datasets used in this study posed significant challenges, with the best-performing models achieving prediction error of around 0.31 of the average offline viable cell density. These results underline the potential of phase-adaptive modeling, while also emphasizing the need for further optimization to robustly handle diverse bioprocess conditions.}}, author = {{Uhlendorff, Selina and Burankova, Tatsiana and Dahlmann, Katharina and Frahm, Björn and Pein-Hackelbusch, Miriam}}, booktitle = {{2025 International Workshop on Impedance Spectroscopy (IWIS)}}, isbn = {{979-8-3315-9323-0}}, keywords = {{cell culture, impedance spectroscopy, partial least squares, ridge regression}}, location = {{Chemnitz}}, pages = {{34--39}}, publisher = {{IEEE}}, title = {{{Application Constraints of Linear Multivariate Regression Models for Dielectric Spectroscopy in Inline Bioreactor Viable Cell Analysis}}}, doi = {{10.1109/iwis69004.2025.11339388}}, year = {{2026}}, } @misc{13101, abstract = {{Introduction: In-line sensors, which are crucial for real-time (bio-) process monitoring, can suffer from anomalies. These signal spikes and shifts compromise process control. Due to the dynamic and non-stationary nature of bioprocess signals, addressing these issues requires specialized preprocessing. However, existing anomaly detection methods often fail for real-time applications. Methods: This study addresses a common yet critical issue: developing a robust and easy-to-implement algorithm for real-time anomaly detection and removal for in-line permittivity sensor measurement. Recombinant Pichia pastoris cultivations served as a case study. Trivial approaches, such as moving average filtering, do not adequately capture the complexity of the problem. However, our method provides a structured solution through three consecutive steps: 1) Signal preprocessing to reduce noise and eliminate context dependency; 2) Anomaly detection using threshold-based identification; 3) Validation and removal of identified anomalies. Results and discussion: We demonstrate that our approach effectively detects and removes anomalies by compensating signal shift value, while remaining computationally efficient and practical for real-time use. It achieves an F1-score of 0.79 with a static threshold of 1.06 pF/cm and a double rolling aggregate transformer using window sizes w1 = 1 and w2 = 15. This flexible and scalable algorithm has the potential to bridge a crucial gap in process real-time analytics and control.}}, author = {{Bolmanis, Emils and Uhlendorff, Selina and Pein-Hackelbusch, Miriam and Galvanauskas, Vytautas and Grigs, Oskars}}, booktitle = {{Frontiers in Bioengineering and Biotechnology}}, issn = {{2296-4185}}, keywords = {{in-situ, permittivity, dielectric spectroscopy, signal preprocessing, dynamic threshold, static threshold, anomaly validation, Pichia pastoris}}, publisher = {{Frontiers Media SA}}, title = {{{Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses}}}, doi = {{10.3389/fbioe.2025.1609369}}, volume = {{13}}, year = {{2025}}, } @misc{12021, author = {{Segermann, Jan and Luttmann, Mario and Blome, André and Feldt, Sebastian and Sivanesan, Sujee and Holst, Christoph-Alexander and Lohweg, Volker and Frahm, Björn and Müller, Ulrich}}, keywords = {{sourdough, fermentation, near-infrared spectroscopy, support vector machine}}, location = {{Lemgo}}, title = {{{Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie}}}, year = {{2024}}, } @misc{10787, abstract = {{Cyber-physical production systems have emerged with the rise of Industry 4.0 in different industrial fields. Especially the food sector, where inhomogeneous input products like beer/yeast suspensions with different qualities and properties have yet slowed down automation, has potential for this evolution. This contribution presents optimization methods for a dynamical cross-flow filtration plant which is driven by an advanced control concept in combination with data driven product monitoring via inline near infrared spectroscopy (NIR) in order to improve energy savings and filtration performance. Using a hierarchical control and optimization structure, the non stationary batch process is steered towards a high production rate with low energy consumption for a variety of different input products.}}, author = {{Tebbe, Jörn and Pawlik, Thomas and Trilling-Haasler, Marc and Löbner, Jannis and Lange-Hegermann, Markus and Schneider, Jan}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}}, editor = {{Jasperneite, Jürgen and Wisniewski, Lukasz and Fung Man, Kim}}, isbn = {{978-1-6654-9314-7 }}, issn = {{1935-4576}}, keywords = {{Spectroscopy, Production systems, Filtration, Velocity control, Optimization methods, Cyber-physical systems, Nonhomogeneous media}}, location = {{Lemgo}}, pages = {{1--7}}, publisher = {{IEEE}}, title = {{{Holistic optimization of a dynamic cross-flow filtration process towards a cyber-physical system}}}, doi = {{10.1109/INDIN51400.2023.10217913}}, year = {{2023}}, } @misc{10788, abstract = {{For process monitoring, an adequate data preprocessing is crucial to link accessible inline process data with offline measured target variables. Literature, however, does not provide systematic preprocessing strategies. The effects of five different preprocessing strategies on data from a Dielectric Spectroscopy system applied to the Viable Cell Density (VCD) of a mammalian cell cultivation were thus evaluated. Single-frequency measurements are typically used to model the VCD over the growth phase using linear regression or the Cole-Cole model and served as a reference. As multi-frequency measurement is promising to model the VCD beyond the growth phase using Partial Least Squares Regression (PLSR), we further aimed to determine, whether replacing linear regression by PLSR shows comparable modeling performance. All five preprocessing strategies led to comparable results. Exemplary, when using capacitance values at a frequency of 3347 kHz, linear regression resulted in a R2 of 0.90 and a standard deviation of 0.4 % on average. Both normalization techniques had the same positive effect on the results of PLSR. The order of smoothing and normalization was irrelevant for both regression methods. Comparing the results of linear regression and PLSR, the latter obtained on average 9 % better results. Therefore, we concluded that PLSR is preferable over linear regression and is potentially suitable to model the VCD beyond the growth phase, which is suggested to be investigated based on more data sets.}}, author = {{Ramm, Selina and Hernández Rodriguez, Tanja and Frahm, Björn and Pein-Hackelbusch, Miriam}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}}, editor = {{Jasperneite, Jürgen and Wisniewski, Lukasz and Fung Man, Kim}}, isbn = {{978-1-6654-9314-7}}, issn = {{1935-4576}}, keywords = {{Spectroscopy, Smoothing methods, Systematics, Phase measurement, Linear regression, Data models, Dielectric measurement}}, location = {{Lemgo}}, pages = {{1--6}}, publisher = {{IEEE}}, title = {{{Systematic Preprocessing of Dielectric Spectroscopy Data and Estimating Viable Cell Densities}}}, doi = {{10.1109/INDIN51400.2023.10218012}}, year = {{2023}}, } @phdthesis{13335, abstract = {{The process of thermal preservation of liquid foods is a safety-relevant process step in the processing of products such as fruit juices and is associated with a high-energy expenditure and safety margin. There are already various approaches to improve this conventionally managed process step in terms of product and resource preservation. Compared to these novel technologies, the use of real-time process analytics offers great potential to improve already existing process plants by implementing inline capable process analytical tools. This allows direct control of the reactions taking place and changes during the running process. Instead of post process, random product control, quality control during the process can be made rendered. The chemical and pharmaceutical industry serves as a reference industry for the use of process analytical tools, although the reactions and product matrices are less complex. In the food industry, on the other hand, there is a greater variation in raw materials and intermediate products. In addition, a large number of reactions can take place in parallel within a process, and the physical states and properties of the individual components can vary. A uniform set of rules for the use of process analytical tools does not exist here. Each product, each process provides its own research potential, so that a large research gap opens up in the area of the food industry. In order to contribute to closing this gap, this thesis presents a novel approach to improve the process of pasteurization of liquid food. For fruit juices as an application, near infrared spectroscopy in combination with chemometric methods was applied to make the process more product specific. Based on known weaknesses of the process, the relevant aspects for a product-specific treatment were identified. In the further course, the suitability of near infrared spectroscopy as a process analytical tool in the process of pasteurization was verified. Moreover, it was investigated whether a sufficiently accurate identification of the product type as well as the microbiologically relevant properties can be achieved by the application of chemometric methods. In the course of this, the suitability of the measurement methodology was confirmed and solutions were established for any process influences. The product classification and description of the microbiologically relevant parameters extract content and pH value were also implemented with sufficient accuracy. Knowing the destruction kinetics of relevant microorganisms, the product-specific determination of target values for the necessary lethal heat input could be realized. In addition, an analysis of the actual values was carried out on the basis of a chemometric regression method by inferring the microbiological pasteurization effect through the chemical reaction of acid hydrolytic sucrose degradation by means of the indirect approach. This required knowledge of the chemical reaction kinetics and mahematical modeling of the degradation behavior. The novel approach could be confirmed by calculations using results from off-line analysis, whereas the use of near infrared spectroscopy as an inline method still revealed potential for optimization with respect to measurement accuracies. In summary, the results of this work provide a promising opportunity to make conventional processes for the preservation of liquid foods more product-specific by using near-infrared spectroscopy as an inline-capable and multimodal sensor technique, leading to an increase in process efficiency and product quality.}}, author = {{Weishaupt, Imke}}, keywords = {{fruit juice pasteurization, near infrared spectroscopy, process optimization, multivariate statistics, inline process analytics, Fruchtsaftpasteurisation, Prozessoptimierung, Nahinfrarotspektroskopie, multivariate Statistik, Inline-Prozessanalytik}}, pages = {{144}}, publisher = {{Technische Universität Berlin}}, title = {{{Near infrared spectroscopy as inline analytical tool to optimize the pasteurization process of liquid foods}}}, doi = {{https://doi.org/10.14279/depositonce-17804}}, year = {{2023}}, } @misc{9213, abstract = {{The function and reliability of electrical connectors in automotive applications is crucial for vehicle safety, especially with regard to E-mobility and autonomous driving. For this reason, electrical connectors are being developed for long-term use applications. However, a small amount of function failures are still being observed in long-term use field vehicles. In this study all electrical connectors of five long-term driven vehicles from various car manufacturers are disassembled and analyzed. The same analysis procedure is followed for every vehicle and the electrical resistance of the connectors is measured to determine electrical failures. The contacts of failed connectors are further analyzed using optical microscopy, XRF spectroscopy, EDS and detailed contact resistance mapping. By comparing the connectors with electrical failures to the same types of connectors with a proper electrical resistance, failure mechanisms can be detected and analyzed. The frequency of various failure mechanisms is statistically evaluated. The results of the analysis provide valuable indications with respect to improvement of the reliability of connectors.}}, author = {{Hilmert, Dirk and Yuan, Haomiao and Song, Jian}}, booktitle = {{Electrical contacts - 2022 : proceedings of the Sixty-Seventh IEEE Holm Conference on Electrical Contacts}}, isbn = {{978-1-6654-5966-2}}, issn = {{2158-9992}}, keywords = {{Connectors, Resistance, Spectroscopy, Optical microscopy, Microscopy, Vehicle safety, Failure analysis}}, location = {{Tampa, FL, USA}}, pages = {{9 -- 16}}, publisher = {{IEEE}}, title = {{{The Analysis of Failure Mechanisms of Electrical Connectors in Long-term Use Field Vehicles}}}, doi = {{10.1109/HLM54538.2022.9969820}}, year = {{2022}}, } @article{7090, abstract = {{The conventional method for the determination of the lethal heat load during pasteurisation (expressed in so-called pasteurisation units (PU)) by measuring temperature and flow rate provides known inaccuracies and requires safety margins in terms of a planned over-pasteurisation to the detriment of the product quality. Based on the hypothesis that chemical conversions correlate with applied heat input, despite the differences in reaction kinetics between chemical conversion and microbiological inactivation, inline near infrared spectroscopy (NIRS) was investigated to identify and quantify applied PU. Acid hydrolytic sucrose degradation was confirmed a favourable marker reaction. In a first step by still using offline analytics (HPLC) and a calculation the feasibility and plausibility in principle could be proved. Compared with conventional PU deviation of only 0.3% were found when using the chemical marker reaction. However, the inline application using NIRS showed too high variations. The too low accuracy of the NIRS model for the sucrose measurement was identified of being the cause for failing the overall goal. Improvements in the inline determination seem to be promising.}}, author = {{Weishaupt, Imke and Neubauer, Peter and Schneider, Jan}}, issn = {{1613-2041}}, journal = {{Brewing science}}, keywords = {{near infrared spectroscopy, apple juice, pasteurisation, acid hydrolytic sucrose degradation, inline measure-ment of heat input, pasteurisation units}}, number = {{1/2}}, pages = {{1--8}}, publisher = {{Carl}}, title = {{{Approach to an inline monitoring of the heat impact in a high temperature short time treatment (HTST) of juice with the help of a chemical marker}}}, doi = {{10.23763/BrSc21-20weishaupt}}, volume = {{75}}, year = {{2022}}, } @article{5425, abstract = {{The feasibility of inline classification and characterization of seven fruit juice varieties was investigated by the application of near-infrared spectroscopy (NIRS) combined with chemometrics. The findings are intended to be used to optimize the flash pasteurization of liquid foods. More precise information of the kind of product in real time had to be achieved to enable a more product-specific process. Using the method of partial least squares discriminant analysis, the fruit juice varieties were classified, showing a classification rate of 100% regarding an internal and 69% regarding an external test sets. A characterization by the extract content, pH value, turbidity, and viscosity was made by fitting a partial least squares regression model. The percentage prediction error of the pH value was <3% for internal and external test sets, and for the Brix value prediction errors were about 4% (internal) and 20% (external). The parameters viscosity and turbidity were found to be unsuitable. Despite this, the strategy applied to gain more product-specific information in real time showed to be feasible. By linking the results to a database containing potentially harmful microorganisms for various types of fruit juices, a more product-specific calculation of the necessary heat input can be performed. To demonstrate the practical relevance, a comparison between conventional and product-adapted process control was performed using two fruit varieties as examples in case of Alicyclobacillus acidoterrestris. Thus, with more accurate product information, achieved through the use of NIRS with chemometrics, a more precise calculation of the heat input can be achieved.}}, author = {{Weishaupt, Imke and Neubauer, Peter and Schneider, Jan}}, issn = {{2048-7177}}, journal = {{Food Science & Nutrition}}, keywords = {{flash pasteurization, fruit juice characterization and classification, inline near-infrared spectroscopy, multivariate data analysis}}, number = {{3}}, pages = {{800--812}}, publisher = {{Wiley}}, title = {{{Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization}}}, doi = {{ https://doi.org/10.1002/fsn3.2709}}, volume = {{10}}, year = {{2022}}, } @article{5424, abstract = {{Near infrared spectroscopy in combination with a transflection probe was investigated as inline measurement in a continuous flash pasteurizer system with a sugar-water model solution. Robustness and reproducibility of fluctuations of recorded spectra as well as trueness of the chemometric analysis were compared under different process parameter settings. Variable parameters were the flow rate (from laminar flow at 30 L/h to turbulent flow at 90 L/h), temperature (20 to 100 degrees C) and the path length of the transflection probe (2 and 4 mm) while the pressure was kept constant at 2.5 bar. Temperature and path length were identified as the most affecting parameters, in case of homogenous test medium. In case of particle containing systems, the flow rate could have an impact as well. However, the application of a PLS model, which includes a broad temperature range, and the correction of prediction results by applying a polynomial regression function for prediction errors, was able to compensate these effects. Also, a path length of 2 mm leads to a higher accuracy. The applied strategy shows that by the identification of relevant process parameters and settings as well as the establishment of a compensation strategy, near infrared spectroscopy is a powerful process analytical tool for continuous flash pasteurization systems.}}, author = {{Weishaupt, Imke and Zimmer, Manuel and Neubauer, Peter and Schneider, Jan}}, isbn = {{0022-1147}}, issn = {{1750-3841}}, journal = {{Journal of Food Science}}, keywords = {{flash pasteurization, inline near infrared spectroscopy, multivariate data analysis, process condition influences, sugar-water-solution model beverage}}, number = {{7}}, pages = {{2020 -- 2031}}, title = {{{Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer}}}, doi = {{10.1111/1750-3841.15307}}, volume = {{85}}, year = {{2020}}, } @article{734, abstract = {{We report on investigations of platinum clusters with about five to 400 atoms that are deposited from a cluster beam on to crystalline graphite surfaces. Scanning tunneling spectroscopy in an UHV environment at liquid helium temperatures reveals the existence of distinct peaks in the conductivity of the cluster-on-surface system. For negative voltages, the peak separations scale with the inverse particle height, which hints at a quantum size effect in these metallic “quantum dots”.}}, author = {{Bettac, A. and Köller, Lars and Rank, V. and Meiwes-Broer, Karl-Heinz}}, issn = {{0039-6028}}, journal = {{Surface Science : a journal devoted to the physics and chemistry of interfaces}}, keywords = {{Clusters, Conductivity, Platinum, Quantum size effect, Scanning tunneling, spectroscopy}}, pages = {{475--479}}, publisher = {{Elsevier}}, title = {{{Scanning Tunneling Spectroscopy on Deposited Platnium Clusters}}}, doi = {{10.1016/S0039-6028(98)00028-4}}, volume = {{402-404}}, year = {{1998}}, }