@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}},
}