@inbook{13712,
  abstract     = {{Biopharmaceutical quality in upstream mammalian cell culture has historically relied on Quality-by-Test (QbT) paradigm, where fixed processes are verified by end-point assays despite limited process observability and substantial biological variability. This chapter discusses how Process Analytical Technology (PAT), aligned with the Quality-by-Design (QbD) framework, enables measurement-driven process understanding and timely control by linking the trajectories of critical process parameters (CPPs) to critical quality attributes (CQAs). We review fit-for-purpose at-line, on-line, and in-line analytical technologies used in mammalian cell culture and summarize their roles across the process development and manufacturing lifecycle. Applications include multivariate process characterization, state-based monitoring and control, and trajectory-based process supervision. Finally, we outline future directions toward IIoT-enabled connectivity, soft sensors, and integrated multi-sensor PAT platforms, which are expected to support adaptive control strategies, digital twins, and ultimately autonomous biomanufacturing.}},
  author       = {{Park, Cheol-Hwan and Jeon, Yunjoo and Uhlendorff, Selina and Pein-Hackelbusch, Miriam and Lee, Dong-Yup}},
  booktitle    = {{Reference Module in Life Sciences}},
  editor       = {{Roitberg, Bernard D. }},
  publisher    = {{Elsevier}},
  title        = {{{Process Analytical Technology in Upstream Mammalian Cell Cultures}}},
  doi          = {{10.1016/b978-0-443-24738-5.00086-0}},
  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}},
}