Speaker
Description
Lactobacillus rhamnosus (L. rhamnosus) represent a valuable potential for applications in the continuously growing multi-billion euro functional-food industry. This commensal microorganism is known for its multiple health benefits including immunomodulatory and gut-stimulating properties. Optimizing the production of L. rhamnosus biomass can enhance the efficacy of its use. In this study, three artificial neural networks (ANNs) were developed to predict biomass and growth rate in batch and fed-batch bioprocesses using a dairy-based substrate. Additionally, the immunomodulatory effect of L. rhamnosus was examined, revealing anti-inflammatory properties.
This research aims to maximise the production of L. rhamnosus biomass through optimized and robust control of bioprocesses. L. rhamnosus was cultivated in bench-scale studies on industrially suitable dairy-based feedstocks, namely skim milk powder, 90% demineralised whey and whey permeate.
Based on on-line and at-line measurements of bioprocess data, three ANNs were developed to predict biomass and growth rate in batch and fed-batch bioprocesses. This can be used to estimate biomass and growth rate digitally in real-time, creating a digital twin of the process. This estimator can be used to regulate the addition of feed to the bioprocess, leading to more precise control and increased productivity. These findings demonstrate the potential of ANN modelling and digital twin development for bioprocess optimisation of L. rhamnosus for application as a functional food ingredient. The immunomodulatory effect of L. rhamnosus was furthermore investigated and in-activated cell samples were found to have anti-inflammatory properties.
Overall, this research provides a comprehensive approach to the optimization of L. rhamnosus production through ANNs, digital twin development, and process control. The findings are significant for the functional food industry, demonstrating the potential for using L. rhamnosus as a functional food ingredient.