Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses
IntroductionIn-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...
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Frontiers Media S.A.
2025-07-01
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| author | Emils Bolmanis Emils Bolmanis Emils Bolmanis Selina Uhlendorff Miriam Pein-Hackelbusch Vytautas Galvanauskas Oskars Grigs |
| author_facet | Emils Bolmanis Emils Bolmanis Emils Bolmanis Selina Uhlendorff Miriam Pein-Hackelbusch Vytautas Galvanauskas Oskars Grigs |
| author_sort | Emils Bolmanis |
| collection | DOAJ |
| description | IntroductionIn-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.MethodsThis 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 discussionWe 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. |
| format | Article |
| id | doaj-art-e095d96d1ad84a7f9bed001e0a74eb3f |
| institution | Kabale University |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-e095d96d1ad84a7f9bed001e0a74eb3f2025-08-20T03:56:05ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-07-011310.3389/fbioe.2025.16093691609369Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocessesEmils Bolmanis0Emils Bolmanis1Emils Bolmanis2Selina Uhlendorff3Miriam Pein-Hackelbusch4Vytautas Galvanauskas5Oskars Grigs6Laboratory of Bioengineering, Latvian State Institute of Wood Chemistry, Riga, LatviaK. Tars Lab, Latvian Biomedical Research and Study Centre, Riga, LatviaInstitute of Biomaterials and Bioengineering, Riga Technical University, Riga, LatviaInstitute for Life Science Technologies ILT.NRW, OWL University of Applied Sciences and Arts, Lemgo, GermanyInstitute for Life Science Technologies ILT.NRW, OWL University of Applied Sciences and Arts, Lemgo, GermanyDepartment of Automation, Kaunas University of Technology, Kaunas, LithuaniaLaboratory of Bioengineering, Latvian State Institute of Wood Chemistry, Riga, LatviaIntroductionIn-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.MethodsThis 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 discussionWe 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.https://www.frontiersin.org/articles/10.3389/fbioe.2025.1609369/fullin-situpermittivitydielectric spectroscopysignal preprocessingdynamic thresholdstatic threshold |
| spellingShingle | Emils Bolmanis Emils Bolmanis Emils Bolmanis Selina Uhlendorff Miriam Pein-Hackelbusch Vytautas Galvanauskas Oskars Grigs Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses Frontiers in Bioengineering and Biotechnology in-situ permittivity dielectric spectroscopy signal preprocessing dynamic threshold static threshold |
| title | Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses |
| title_full | Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses |
| title_fullStr | Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses |
| title_full_unstemmed | Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses |
| title_short | Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses |
| title_sort | anomaly detection and removal strategies for in line permittivity sensor signal used in bioprocesses |
| topic | in-situ permittivity dielectric spectroscopy signal preprocessing dynamic threshold static threshold |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1609369/full |
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