Image-guided Quality Control of Biomanufacturing Process

Technical advances in 3D metrology bring the increasing availability of imaging data, which are critical to quality inspection and process improvement. Dealing with 3D imaging data has become a general problem facing both traditional and next-generation innovation practices in biotechnology. Traditional methodologies in statistical quality control focus on key characteristics of the product, and are limited in the ability to model spatiotemporal patterns in imaging streams. This paper presents a dynamic network methodology for monitoring and control of high-dimensional imaging streams. The developed methodology is implemented and evaluated for process monitoring of living cells during the synthesis of bio-products.