A Machine Vision Approach for Bioreactor Foam Sensing. (August 2021)

• Record Type: Journal Article
• Authors: Austerjost, Jonas; Söldner, Robert; Edlund, Christoffer; Trygg, Johan; Pollard, David; Sjögren, Rickard
• Is Part Of: SLAS technology. Volume 26:Number 4(2021)
• Page Start: 408
• Page End: 414
• Publication Date: 2021-08
• Subjects: foam sensor -- machine vision -- deep learning -- process analytical technology -- bioprocessing; Medical laboratory technology -- Periodicals; Laboratories -- Equipment and supplies -- Periodicals; Diagnosis, Laboratory -- Periodicals; 616.075
• DOI: 10.1177/24726303211008861
• Languages: English
• ISSNs: 2472-6303

Abstract

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.