Halfway to Automated Feeding of Chinese Hamster Ovary Cells

Abstract

This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction. It also describes the development of a simulation environment whose core is a model of CHO cell kinetics. The latter allows the development of advanced control algorithms for nutrient dosing and the observation of the effects of different parameters on the growth and productivity of CHO cells. All developed models were validated and demonstrated to have a high robustness and predictive accuracy, which were reflected in a 40% reduction in the root mean square error compared to established methods. The results of this study provide valuable insights into the practical application of these methods in the field of monitoring and automated cell feeding and make an important contribution to the further development of process analytical technology in the bioprocess industry.

Intelligent Soft Sensors

“…In bioprocessing, where the goal is to improve accuracy and efficiency, soft sensors are becoming increasingly useful, especially when combined with Raman spectroscopy and machine learning. A study on the automated feeding of Chinese hamster ovary cells (CHO) [ 7 ] is presented, highlighting the importance of integrating Raman spectroscopy and chemometrics to develop models for monitoring and controlling CHO bioprocesses. The profound implications of the careful preprocessing and wise selection of multivariate analysis methods to improve accuracy in the monitoring of important process variables are demonstrated.…”