期刊
ENGINEERING IN LIFE SCIENCES
卷 20, 期 1-2, 页码 26-35出版社
WILEY
DOI: 10.1002/elsc.201900076
关键词
bioprocess engineering; chemometric modeling; multivariate adaptive regression spline; process monitoring; Quality by Design
资金
- Osterreichische Forschungsforderungsge-sellschaft [859219]
In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on-line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real-time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D-fluorescence data and process data, was developed for a comprehensive study with a 20-L experimental design, for Escherichia coli fed-batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D-fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D-fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression-model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on-line, enabling Quality by Design.
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