Enhancing real-time cell culture process monitoring through the integration of advanced machine learning techniques: A comparative analysis of Raman and capacitance spectroscopies.

Abstract

Machine learning (ML) techniques have emerged as an important tool improving the capabilities of online process monitoring and control in cell culture process for biopharmaceutical manufacturing. A variety of advanced ML algorithms have been evaluated in this study for cell growth monitoring using spectroscopic tools, including Raman and capacitance spectroscopies. While viable cell density can be monitored real-time in the cell culture process, online monitoring of cell viability has not been well established. A thorough comparison between the advanced ML techniques and traditional linear regression method (e.g., Partial Least Square regression) reveals a significant improvement in accuracy with the leading ML algorithms (e.g., 31.7% with Random Forest regressor), addressing the unmet need of continuous monitoring viability in a real time fashion. Both Raman and capacitance spectroscopies have demonstrated success in viability monitoring, with Raman exhibiting superior accuracy compared to capacitance. In addition, the developed methods have shown better accuracy in a relatively higher viability range (>90%), suggesting a great potential for early fault detection during cell culture manufacturing. Further study using ML techniques for VCD monitoring also showed an increased accuracy (27.3% with Raman spectroscopy) compared to traditional linear modeling. The successful integration of ML techniques not only amplifies the potential of process monitoring but also makes possible the development of advanced process control strategies for optimized operations and maximized efficiency.