Researchers from the Critical Analytics for Manufacturing Personalized-Medicine (CAMP) interdisciplinary research group of the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, in collaboration with MIT, A*STAR Skin Research Labs, and the National University of Singapore, have unveiled a novel method to rapidly detect microbial contamination in biomanufacturing processes, offering a promising advancement for real-time quality assurance in therapeutic production.
Described in their recent publication, “Rapid, untargeted microbial contamination detection using elastic light scatter phenotyping and one-class machine learning,” the approach combines elastic light scatter technology with one-class machine learning to detect contaminants with high sensitivity — even at early stages. This technique significantly reduces the detection time compared to conventional culture-based assays, allowing for earlier intervention and reduced risk of batch failure.
The work is part of SMART’s broader mission to advance smart manufacturing tools that support a risk-based framework for biologics development. Future research will focus on integrating this method into current process monitoring systems to streamline contamination control.
Learn more: MIT News Article
