Raman spectroscopy as a method to replace off-line pH during mammalian cell culture processes
| Autor: | David C. Latshaw, Carl Rafferty, Barbara Burgoyne, Rosemary Rea, Jim O'Mahony, Karin M. Balss |
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| Rok vydání: | 2019 |
| Předmět: |
Chemistry
Cell Culture Techniques Bioengineering Temporal complexity CHO Cells Carbon Dioxide Hydrogen-Ion Concentration Spectrum Analysis Raman Applied Microbiology and Biotechnology symbols.namesake Bioreactors Cricetulus Cell culture Robustness (computer science) Scientific method Cricetinae Partial least squares regression symbols Animals Lactic Acid Error reduction Biological system Raman spectroscopy Off line Biotechnology |
| Zdroj: | Biotechnology and bioengineeringREFERENCES. 117(1) |
| ISSN: | 1097-0290 |
| Popis: | Raman spectroscopy is a robust, well-established tool utilized for measuring important cell culture process variables for example, feed, metabolites, and biomass in real-time. This study further expands the functionality of in-line Raman spectroscopy coupled with partial least squares (PLS) regression modelling to develop a pH measurement tool. Cell line specific models were developed to enhance the robustness for processes with different pH setpoints, deadbands, and cellular metabolism. The modelling strategy further improved robustness by reducing the temporal complexity of pH shifts by splitting data sets into two time zones reflective of major changes in pH. In addition, models were developed to assess if lactate and partial pressure of carbon dioxide (pCO2 ) could be used in a PLS model for pH. Splitting the data sets into early and late for the process resulted in errors of 0.035 pH and 0.034 pH for the two respective Raman cell lines models which was within acceptance criteria. The lactate and pCO2 PLS model with values provided by Raman models had a further 0.001 pH error reduction. This study illustrates the potential to eliminate off-line samples to correct for in-line measurements of pH and further illustrates the capabilities of Raman to measure additional process variables. |
| Databáze: | OpenAIRE |
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