Comparison of partial least square, artificial neural network, and support vector regressions for real‐time monitoring of CHO cell culture processes using in situ near‐infrared spectroscopy - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Biotechnology and Bioengineering Année : 2022

Comparison of partial least square, artificial neural network, and support vector regressions for real‐time monitoring of CHO cell culture processes using in situ near‐infrared spectroscopy

Résumé

The biopharmaceutical industry must guarantee the efficiency and biosafety of biological medicines, which are quite sensitive to cell culture process variability. Real time monitoring procedures based on vibrational spectroscopy such as NIR spectroscopy, are then emerging to support innovative strategies for retro-control of key parameters as substrates and byproduct concentration. Whereas monitoring models are mainly constructed using Partial Least Squares Regression (PLSR), spectroscopic models based on Artificial Neural Networks (ANNR) and Support Vector Regression (SVR) are emerging with promising results. Unfortunately, analysis of their performance in cell culture monitoring has been limited. This work was then focused to assess their performance and suitability for the cell culture process challenges. PLSR had inferior values of the determination coefficient (R 2) for all the monitored parameters (i.e., 0.85, 0.93, 0.98 respectively for the PLSR, SVR, and ANNR models for glucose). In general, PLSR had a limited performance while models based on ANNR and SVR have been shown superior due to better management of inter-batch heterogeneity and enhanced specificity. Overall, the use of SVR and ANNR for the generation of calibration models enhanced the potential of NIR spectroscopy as a monitoring tool.
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Dates et versions

hal-03844328 , version 1 (08-11-2022)

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Daniel Zavala-Ortiz, Aurélia Denner, Maria Aguilar-Uscanga, Annie Marc, Bruno Ebel, et al.. Comparison of partial least square, artificial neural network, and support vector regressions for real‐time monitoring of CHO cell culture processes using in situ near‐infrared spectroscopy. Biotechnology and Bioengineering, 2022, 119 (2), pp.535-549. ⟨10.1002/bit.27997⟩. ⟨hal-03844328⟩
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