ReviewFrontiers in bioengineering and biotechnology2021
Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review.
Review in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Metabolic state-driven monitoring and control of abnormal pigment formation in sodium gluconate fermentation bySynthetic and systems biotechnology · 2027Article
- Observability- and Identifiability-Guided Sensor-Set Design for Digital-Twin-Assisted Consolidated Bioprocessing.Sensors (Basel, Switzerland) · 2026Article
- A Framework for Digital Technologies Application in Soft Sensing Monitoring to Recombinant Nanobody Production inACS omega · 2026Article
- Integrating artificial intelligence and conventional approaches in sugarcane bagasse biorefineries: a review towards a circular bioeconomy.Frontiers in bioengineering and biotechnology · 2026Review
- Innovative Formulation Strategies for Biosimilars: Trends Focused on Buffer-Free Systems, Safety, Regulatory Alignment, and Intellectual Property Challenges.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Batch Process Design Including Initial and Operating Conditions and Online Property Estimation in Acrylic Resin Polymerization.ACS omega · 2025Article
- A Hybrid Soft Sensor Approach Combining Partial Least-Squares Regression and an Unscented Kalman Filter for State Estimation in Bioprocesses.Bioengineering (Basel, Switzerland) · 2025Article
- Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses.Frontiers in bioengineering and biotechnology · 2025Article
- Collaborative Multiple Players to Address Label Sparsity in Quality Prediction of Batch Processes.Sensors (Basel, Switzerland) · 2024Article
- Synergic kinetic and physiological control to improve the efficiency of Komagataella phaffii recombinant protein production bioprocesses.Microbial biotechnology · 2024Article
- Soft bioreactor systems: a necessary step toward engineered MSK soft tissue?Frontiers in robotics and AI · 2024Review
- Continuous Process Verification 4.0 application in upstream: adaptiveness implementation managed by AI in the hypoxic bioprocess of theFrontiers in bioengineering and biotechnology · 2024Article
- Soft Sensing of LPG Processes Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration.Sensors (Basel, Switzerland) · 2023Article
- Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha.Journal of biological engineering · 2023Article
- rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing.Bioengineering (Basel, Switzerland) · 2023Review
- Online 2D Fluorescence Monitoring in Microtiter Plates Allows Prediction of Cultivation Parameters and Considerable Reduction in Sampling Efforts for Parallel Cultivations ofBioengineering (Basel, Switzerland) · 2022Article
- Ensemble-based adaptive soft sensor for fault-tolerant biomass monitoring.Engineering in life sciences · 2022Article
- An Analysis of Monitoring Solutions for CAR T Cell Production.Healthcare technology lettersReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among the greatest challenges in soft sensor development for bioprocesses are variable process lengths, multiple process phases, and erroneous model inputs due to sensor faults. This review article describes these three challenges and critically discusses the corresponding solution approaches from a data scientist's perspective. This main part of the article is preceded by an overview of the status quo in the development and application of soft sensors. The scope of this article is mainly the upstream part of bioprocesses, although the solution approaches are in most cases also applicable to the downstream part. Variable process lengths are accounted for by data synchronization techniques such as indicator variables, curve registration, and dynamic time warping. Multiple process phases are partitioned by trajectory or correlation-based phase detection, enabling phase-adaptive modeling. Sensor faults are detected by symptom signals, pattern recognition, or by changing contributions of the corresponding sensor to a process model. According to the current state of the literature, tolerance to sensor faults remains the greatest challenge in soft sensor development, especially in the presence of variable process lengths and multiple process phases.
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