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On-line multiple component analysis for efficient quantitative bioprocess development

pmid: 22484127
On-line monitoring devices for the precise determination of a multitude of components are a prerequisite for fast bioprocess quantification. On-line measured values have to be checked for quality and consistency, in order to extract quantitative information from these data. In the present study we characterized a novel on-line sampling and analysis device comprising an automatic photometric robot. We connected this on-line device to a bioreactor and concomitantly measured six components (i.e. glucose, glycerol, ethanol, acetate, phosphate and ammonium) during different batch cultivations of Pichia pastoris. The on-line measured data did not show significant deviations from off-line taken samples and were consequently used for incremental rate and yield calculations. In this respect we highlighted the importance of data quality and discussed the phenomenon of error propagation. On-line calculated rates and yields depicted the physiological responses of the P. pastoris cells in unlimited and limited cultures. A more detailed analysis of the physiological state was possible by considering the off-line determined biomass dry weight and the calculation of specific rates. Here we present a novel device for on-line monitoring of bioprocesses, which ensures high data quality in real-time and therefore refers to a valuable tool for Process Analytical Technology (PAT).
- Polish Academy of Sciences Poland
- Institute of Chemical Engineering Poland
- TU Wien Austria
- TU Wien Austria
Glycerol, Ethanol, Robotics, Online Systems, Pichia, Culture Media, Photometry, Bioreactors, Glucose, Fermentation, Biomass, Biotechnology
Glycerol, Ethanol, Robotics, Online Systems, Pichia, Culture Media, Photometry, Bioreactors, Glucose, Fermentation, Biomass, Biotechnology
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).29 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
