Sørensen, Helena Mylise (2024) Development and optimisation of a microbial fermentation from media selection to pilot-scale production. PhD thesis, Dublin City University.
Abstract
Lactic acid bacteria (LAB) are microorganisms with significant importance within the continuously growing multi-billion-euro functional food industry, particularly in fermented dairy manufacturing. Consumption of functional foods containing LAB or their metabolites has demonstrated pronounced multiple health benefits including immunomodulatory effects, thereby raising consumer appeal. To meet the demands of this growing market, the continuous improvement and refinement of biotechnological tools for bioprocess optimisation are imperative.
In this work, the production, purification, and health benefits of functional exopolysaccharides (EPS) excreted from Lactobacillus species are reviewed. Strain screening identified Lactobacillus rhamnosus LRH30 (L. rhamnosus LRH30) as a suitable candidate, supported by extensively documented health benefits in literature as well as high experimental biomass yields. Dairy-based growth media for L. rhamnosus LRH30 was optimised through response surface modelling in a shake flask, identifying skim milk powder as the optimal media base for high biomass yields. Furthermore, the impact of temperature and airflow rate on both biomass and EPS production from L. rhamnosus LRH30 was optimised by utilising design of experiments methodology in a small-scale bioprocess setup which was subsequently validated in a larger-scale process.
The immunomodulatory potential of both L. rhamnosus cells LRH30 and EPS was evaluated in murine macrophages through cytokine excretion using enzyme-Linked Immunosorbent Assay. The structural properties of EPS were additionally analysed through Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and high-performance liquid chromatography (HPLC).
Data collected online throughout both batch and fed-batch bioprocesses was utilised to develop a neural network-based digital twin with the capability to predict biomass and growth rate. Leveraging this predictive model, an estimator was devised for the addition of feed to the bioprocess, enabling more precise control of the bioprocess, ultimately leading to a more efficient process with increased productivity.
Metadata
Item Type: | Thesis (PhD) |
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Date of Award: | August 2024 |
Refereed: | No |
Supervisor(s): | Freeland, Brian, Loscher, Christine and Brabazon, Dermot |
Subjects: | Biological Sciences > Biotechnology Humanities > Biological Sciences > Biotechnology Biological Sciences > Food technology Humanities > Biological Sciences > Food technology |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Science and Health DCU Faculties and Schools > Faculty of Science and Health > School of Biotechnology |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 License. View License |
Funders: | Science Foundation Ireland |
ID Code: | 30223 |
Deposited On: | 25 Nov 2024 14:16 by Brian Freeland . Last Modified 25 Nov 2024 14:16 |
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