@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{13223, author = {{Uhlendorff, Selina and Burankova, Tatsiana and Dahlmann, Katharina and Frahm, Björn and Pein-Hackelbusch, Miriam}}, location = {{Chemnitz}}, title = {{{Application Constraints of Linear Multivariate Regression Models for Dielectric Spectroscopy in Inline Bioreactor Viable Cell Analysis}}}, year = {{2025}}, } @misc{13315, abstract = {{Determining cell density and viability with (semi‐)automated methods enables rapid cell cultivation analysis. However, scientific validation is required, typically by comparing results to manual counting. To address this, we present a method to evaluate and compare two cell counting methods exemplified by a manual and a semi‐automated method (Countstar BioTech). Experiments followed validation parameters aligned with the International Council for Harmonization (ICH) Q2(R1) guideline and a dilution series design based on ISO 20391‐2:2019. Both the semi‐automated and manual methods showed comparable specificity and linearity for Chinese hamster ovary (CHO)‐K1 cells. The semi‐automated method exhibited superior repeatability for total cell density, whereas cell viability results showed no significant difference.}}, author = {{Uhlendorff, Selina and Odefey, Ulrich and Frahm, Björn and Pein-Hackelbusch, Miriam}}, booktitle = {{Chemie Ingenieur Technik}}, issn = {{1522-2640}}, keywords = {{Cell density, Cell enumeration, Cell viability, CHO-K1}}, number = {{1-2}}, pages = {{57–68}}, publisher = {{Wiley}}, title = {{{Performance Comparison between Semi‐Automated and Manual Cell Counting for Animal Cell Culture}}}, doi = {{10.1002/cite.70048}}, volume = {{98}}, year = {{2025}}, } @unpublished{12400, abstract = {{Determining cell density and cell viability is fundamental for any cell cultivation process. In addition to the manual counting method using hemocytometers, (semi-)automated methods offer advantages such as lower variability and shortened analysis times. However, these methods should provide at least comparable results to the manual method, which is why a comparison of methods is essential. We conducted a dilution series experimental design according to ISO 20391-2:2019 and compared two cell counting methods based on validation parameters aligned with the ICH Q2(R1) guideline. Regarding specificity and linearity, the manual (hemocytometer) and semi-automated (Countstar BioTech®) method exhibited similar results in the two evaluated characteristics total cell density and cell viability of CHO-K1 cells. Regarding repeatability of determining total cell density, the semi-automated method achieved significant (α = 0.05) better results with average relative standard deviations of < 6 %, than the manual method with average relative standard deviations of > 9 %. Concerning repeatability of the cell viability measurement, no significant difference between the two methods were shown. These results show the suitabililty of the dilution series experimental design. For the applied example, they indicate that the investigated semi-automated method is an appropriate alternative to the manual method.}}, author = {{Ramm, Selina and Odefey, Ulrich and Frahm, Björn and Pein-Hackelbusch, Miriam}}, publisher = {{bioRxiv}}, title = {{{Semi-automated vs. manual: Comparative study of cell culture counting methods using validation parameters}}}, doi = {{10.1101/2024.05.30.596619}}, year = {{2024}}, } @misc{11381, author = {{Hernández Rodriguez, Tanja and Ramm, Selina and Lange-Hegermann, Markus and Frahm, Björn}}, location = {{Berlin, Germany}}, publisher = {{DECHEMA e.V.}}, title = {{{A systematic, model-based workflow for risk-based decision making in upstream development}}}, year = {{2023}}, } @misc{11487, author = {{Ramm, Selina and Hernández Rodriguez, Tanja and Frahm, Björn and Pein-Hackelbusch, Miriam}}, location = {{Berlin, Germany}}, publisher = {{DECHEMA e.V.}}, title = {{{Comparison of Preprocessing Methods of Dielectric Spectroscopy Data and the Effects on Linear Regression }}}, year = {{2023}}, } @misc{13650, author = {{Uhlendorff, Selina and Hernández Rodriguez, Tanja and Frahm, Björn and Pein-Hackelbusch, Miriam}}, location = {{Lemgo, Germany}}, title = {{{Systematic Preprocessing of Dielectric Spectroscopy Data and Estimating Viable Cell Densities}}}, year = {{2023}}, } @misc{10201, author = {{Hernández Rodriguez, Tanja and Ramm, Selina and Lange-Hegermann, Markus and Frahm, Björn}}, location = {{Recklinghausen, Germany}}, title = {{{A systematic, model-based workflow for risk-based decision making in upstream development}}}, year = {{2023}}, } @misc{10216, abstract = {{Wet granulation is a frequent process in the pharmaceutical industry. As a starting point for numerous dosage forms, the quality of the granulation not only affects subsequent production steps but also impacts the quality of the final product. It is thus crucial and economical to monitor this operation thoroughly. Here, we report on identifying different phases of a granulation process using a machine learning approach. The phases reflect the water content which, in turn, influences the processability and quality of the granule mass. We used two kinds of microphones and an acceleration sensor to capture acoustic emissions and vibrations. We trained convolutional neural networks (CNNs) to classify the different phases using transformed sound recordings as the input. We achieved a classification accuracy of up to 90% using vibrational data and an accuracy of up to 97% using the audible microphone data. Our results indicate the suitability of using audible sound and machine learning to monitor pharmaceutical processes. Moreover, since recording acoustic emissions is contactless, it readily complies with legal regulations and presents Good Manufacturing Practices.}}, author = {{Fulek, Ruwen and Ramm, Selina and Kiera, Christian and Pein-Hackelbusch, Miriam and Odefey, Ulrich}}, booktitle = {{Pharmaceutics}}, issn = {{1999-4923 }}, keywords = {{wet granulation, acoustic classification, machine learning, convolutional neural networks}}, number = {{8}}, publisher = {{MDPI}}, title = {{{A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions}}}, doi = {{https://doi.org/10.3390/pharmaceutics15082153}}, volume = {{15}}, 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}}, } @misc{9627, author = {{Ramm, Selina and Fulek, Ruwen and Eberle, Veronika Anna and Kiera, Christian and Odefey, Ulrich and Pein-Hackelbusch, Miriam}}, location = {{Rosenberg}}, title = {{{Compression Density as an Alternative to Identify an Optimal Moisture Content for High Shear Wet Granulation as an Initial Step for Spheronisation}}}, year = {{2023}}, } @misc{7932, author = {{Hernández Rodriguez, Tanja and Ramm, Selina and Lange-Hegermann, Markus and Frahm, Björn}}, location = {{Barcelona, Spain}}, title = {{{A systematic, model-based workflow for risk-based decision making in upstream development}}}, year = {{2022}}, } @misc{9568, abstract = {{Pellet production is a multi-step manufacturing process comprising granulation, extrusion and spheronisation. The first step represents a critical control point, since the quality of the granule mass highly influences subsequent process steps and, consequently, the quality of final pellets. The most important parameter of wet granulation is the liquid requirement, which can often only be quantitatively evaluated after further process steps. To identify an alternative for optimal liquid requirements, experiments were conducted with a formulation based on lactose and microcrystalline cellulose. Granules were analyzed with a Powder Vertical Shear Rig. We identified the compression density (ρpress) as the said alternative, linking information from the powder material and the moisture content (R2 = 0.995). We used ρpress to successfully predict liquid requirements for unknown formulation compositions. By means of this prediction, pellets with high quality, regarding shape and size distribution, were produced by carrying out a multi-step manufacturing process. Furthermore, the applicability of ρpress as an alternative quality parameter to other placebo formulations and to formulations containing active pharmaceutical ingredients (APIs) was demonstrated.}}, author = {{Ramm, Selina and Fulek, Ruwen and Eberle, Veronika Anna and Kiera, Christian and Odefey, Ulrich and Pein-Hackelbusch, Miriam}}, booktitle = {{Pharmaceutics}}, issn = {{1999-4923}}, keywords = {{wet granulation, liquid requirement, granulation endpoint, compression density}}, number = {{11}}, publisher = {{MDPI}}, title = {{{Compression Density as an Alternative to Identify an Optimal Moisture Content for High Shear Wet Granulation as an Initial Step for Spheronisation.}}}, doi = {{https://doi.org/10.3390/pharmaceutics14112303}}, volume = {{14}}, year = {{2022}}, }