A modular approach to situation identification of the dynamics of bacterial populations synthesizing poly-β-hydroxybutyrate

Biotechnological processes involving bacteria are strongly nonlinear. Therefore, both their productivity and the final product quality may be considerably improved by applying appropriate control strategies to modulate behavior of the bacteria during transitional states. This requires advance identification of indicative signals by off-line investigation (i.e. experimental analysis) and on-line monitoring, (i.e. real time evaluation). A modular scheme is presented for doing this, which incorporates an Extended Kalman Filter and a prediction filter. If this is based on a suitable process-feature vector, which must be chosen in advance, the system can provide sufficient information to trigger appropriate feedback signals. Thus, it can provide a key element in modular situation control, allowing continuously periodic process management. In this publication the individual modules involved, and their assembly into an integrated system are described. Potential problems concerning selection of the feature vector, and experimental results are also discussed.     

Beitrag zur Kenntnis der Pilzflora Dalmatiens

Ohne Zusammenfassung     

Gefährdete Vegetation und ihre Erhaltung

Ohne Zusammenfassung (Bericht vom 16. Internationalen Symposium über Fragen des Naturschutzes im weiteren Sinne, vom 27.–30. M?rz 1972 in Rinteln/Weser)     

Über eine Mutation in einer reinen Linie von Hordeum distichum L

Molecular Genetics and Genomics -     

Hermaphroditismus im Tierreich vom genetischen Standpunkt

Ohne Zusammenfassung     

Die Bedeutung des Y-Chromosoms im Tierreich

Ohne Zusammenfassung     

Bioprocess in-line monitoring using Raman spectroscopy and Indirect Hard Modeling (IHM): A simple calibration yields a robust model

To increase the process productivity and product quality of bioprocesses, the in-line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in-situ and non-invasively. However, evaluating bioprocess Raman spectra with a robust state-of-the-art statistical model requires effortful model calibration. In the present study, we in-line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics-based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra.