| Title |
Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses |
| Authors |
Bolmanis, Emils ; Uhlendorff, Selina ; Pein-Hackelbusch, Miriam ; Galvanauskas, Vytautas ; Grigs, Oskars |
| DOI |
10.3389/fbioe.2025.1609369 |
| Full Text |
|
| Is Part of |
Frontiers in bioengineering and biotechnology.. Lauzanne : Frontiers Media SA. 2025, vol. 13, art. no. 1609369, p. 1-13.. ISSN 2296-4185 |
| Keywords [eng] |
in-situ ; permittivity ; dielectric spectroscopy ; signal preprocessing ; dynamic threshold ; static threshold ; anomaly validation ; Pichia pastoris |
| Abstract [eng] |
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. |
| Published |
Lauzanne : Frontiers Media SA |
| Type |
Journal article |
| Language |
English |
| Publication date |
2025 |
| CC license |
|
