Abstract: Raman spectroscopy has found extensive use in monitoring and controlling cell culture processes. In this context, the prediction accuracy of Raman-based models is of paramount importance. However, models established with data from manually fed-batch cultures often exhibit poor performance in Raman-controlled cultures. Thus, there is a need for effective methods to rectify these models. The objective of this paper is to investigate the efficacy of Kalman filter (KF) algorithm in correcting Raman-based models during cell culture. Initially, partial least squares (PLS) models for different components were constructed using data from manually fed-batch cultures, and the predictive performance of these models was compared. Subsequently, various correction methods including the PLS-KF-KF method proposed in this study were employed to refine the PLS models. Finally, a case study involving the auto-control of glucose concentration demonstrated the application of optimal model correction method. The results indicated that the original PLS models exhibited differential performance between manually fed-batch cultures and Raman-controlled cultures. For glucose, the root mean square error of prediction (RMSEP) of manually fed-batch culture and Raman-controlled culture was 0.23 and 0.40 g·L^-1. With the implementation of model correction methods, there was a significant improvement in model performance within Raman-controlled cultures. The RMSEP for glucose from updating-PLS, KF-PLS, and PLS-KF-KF was 0.38, 0.36 and 0.17 g·L^-1, respectively. Notably, the proposed PLS-KF-KF model correction method was found to be more effective and stable, playing a vital role in the automated nutrient feeding of cell cultures.

Key words: Raman spectroscopy, Model correction, Algorithm, Model-predictive control, Bioprocess

摘要： Raman spectroscopy has found extensive use in monitoring and controlling cell culture processes. In this context, the prediction accuracy of Raman-based models is of paramount importance. However, models established with data from manually fed-batch cultures often exhibit poor performance in Raman-controlled cultures. Thus, there is a need for effective methods to rectify these models. The objective of this paper is to investigate the efficacy of Kalman filter (KF) algorithm in correcting Raman-based models during cell culture. Initially, partial least squares (PLS) models for different components were constructed using data from manually fed-batch cultures, and the predictive performance of these models was compared. Subsequently, various correction methods including the PLS-KF-KF method proposed in this study were employed to refine the PLS models. Finally, a case study involving the auto-control of glucose concentration demonstrated the application of optimal model correction method. The results indicated that the original PLS models exhibited differential performance between manually fed-batch cultures and Raman-controlled cultures. For glucose, the root mean square error of prediction (RMSEP) of manually fed-batch culture and Raman-controlled culture was 0.23 and 0.40 g·L^-1. With the implementation of model correction methods, there was a significant improvement in model performance within Raman-controlled cultures. The RMSEP for glucose from updating-PLS, KF-PLS, and PLS-KF-KF was 0.38, 0.36 and 0.17 g·L^-1, respectively. Notably, the proposed PLS-KF-KF model correction method was found to be more effective and stable, playing a vital role in the automated nutrient feeding of cell cultures.

关键词: Raman spectroscopy, Model correction, Algorithm, Model-predictive control, Bioprocess