Details
| Original language | English |
|---|---|
| Pages (from-to) | 636-645 |
| Number of pages | 10 |
| Journal | Journal of biotechnology |
| Volume | 168 |
| Issue number | 4 |
| Publication status | Published - 12 Aug 2013 |
Abstract
Process analytical technology (PAT) is a guide to improve process development in biotech industry. Optical sensors such as near and mid infrared spectrometers fulfill an essential part for PAT. NIRS and MIRS were investigated as non-invasive on line monitoring tools for animal cell cultivations in order to predict critical process parameters, like cell parameters as well as substrate and metabolite concentrations. Eight cultivations were performed with frequent sampling. Variances between cultivations were induced by spiking experiments with intent to break correlations between analytes; to keep causality of the models; and to increase model robustness. Calibration models were built for each analyte using partial least-squares regression method. Cultivations chosen for validation were not part of the calibration set. Glucose concentration, cell density and viability were predicted by NIRS with a root mean square error of prediction (RMSEP) of 0.36g/L, 3.9 106cells/mL and 3.62% respectively. Based on MIR spectra glucose and lactate concentrations were predicted with a RMSEP of 0.16 and 0.14g/L respectively. Results show that MIRS has higher accuracy regarding the prediction of single analytes. For prediction of a main course of a cultivation, NIRS is much better suited than MIRS.
Keywords
- CHO cell cultivation, Infrared spectroscopy, Multivariate data analysis, Online monitoring
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biotechnology
- Chemical Engineering(all)
- Bioengineering
- Immunology and Microbiology(all)
- Applied Microbiology and Biotechnology
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In: Journal of biotechnology, Vol. 168, No. 4, 12.08.2013, p. 636-645.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Comparative study of non-invasive monitoring via infrared spectroscopy for mammalian cell cultivations
AU - Sandor, M.
AU - Rüdinger, F.
AU - Bienert, R.
AU - Grimm, C.
AU - Solle, D.
AU - Scheper, T.
N1 - Funding information: The authors would like to thank Sartorius Stedim Biotech GmbH for financial and technical support.
PY - 2013/8/12
Y1 - 2013/8/12
N2 - Process analytical technology (PAT) is a guide to improve process development in biotech industry. Optical sensors such as near and mid infrared spectrometers fulfill an essential part for PAT. NIRS and MIRS were investigated as non-invasive on line monitoring tools for animal cell cultivations in order to predict critical process parameters, like cell parameters as well as substrate and metabolite concentrations. Eight cultivations were performed with frequent sampling. Variances between cultivations were induced by spiking experiments with intent to break correlations between analytes; to keep causality of the models; and to increase model robustness. Calibration models were built for each analyte using partial least-squares regression method. Cultivations chosen for validation were not part of the calibration set. Glucose concentration, cell density and viability were predicted by NIRS with a root mean square error of prediction (RMSEP) of 0.36g/L, 3.9 106cells/mL and 3.62% respectively. Based on MIR spectra glucose and lactate concentrations were predicted with a RMSEP of 0.16 and 0.14g/L respectively. Results show that MIRS has higher accuracy regarding the prediction of single analytes. For prediction of a main course of a cultivation, NIRS is much better suited than MIRS.
AB - Process analytical technology (PAT) is a guide to improve process development in biotech industry. Optical sensors such as near and mid infrared spectrometers fulfill an essential part for PAT. NIRS and MIRS were investigated as non-invasive on line monitoring tools for animal cell cultivations in order to predict critical process parameters, like cell parameters as well as substrate and metabolite concentrations. Eight cultivations were performed with frequent sampling. Variances between cultivations were induced by spiking experiments with intent to break correlations between analytes; to keep causality of the models; and to increase model robustness. Calibration models were built for each analyte using partial least-squares regression method. Cultivations chosen for validation were not part of the calibration set. Glucose concentration, cell density and viability were predicted by NIRS with a root mean square error of prediction (RMSEP) of 0.36g/L, 3.9 106cells/mL and 3.62% respectively. Based on MIR spectra glucose and lactate concentrations were predicted with a RMSEP of 0.16 and 0.14g/L respectively. Results show that MIRS has higher accuracy regarding the prediction of single analytes. For prediction of a main course of a cultivation, NIRS is much better suited than MIRS.
KW - CHO cell cultivation
KW - Infrared spectroscopy
KW - Multivariate data analysis
KW - Online monitoring
UR - http://www.scopus.com/inward/record.url?scp=84888824939&partnerID=8YFLogxK
U2 - 10.1016/j.jbiotec.2013.08.002
DO - 10.1016/j.jbiotec.2013.08.002
M3 - Article
C2 - 23948256
AN - SCOPUS:84888824939
VL - 168
SP - 636
EP - 645
JO - Journal of biotechnology
JF - Journal of biotechnology
SN - 0168-1656
IS - 4
ER -