Steps toward a digital twin for functional food production with increased health benefits

Lactobacillus rhamnosus (L. rhamnosus) is a commensal bacterium with health-promoting properties and with a wide range of applications within the food industry. To improve and optimize the control of L. rhamnosus biomass production in batch and fed-batch bioprocesses, this study proposes the application of artificial neural network (ANN) modelling to improve process control and monitoring, with potential future implementation as a basis for a digital twin.Three ANNs were developed using historical data from ten bioprocesses. These ANNs were designed to predict the biomass in batch bioprocesses with different media compositions, predict biomass in fed-batch bioprocesses, and predict the growth rate in fed-batch bioprocesses.The immunomodulatory effect of the L. rhamnosus samples was examined and found to elicit an anti-inflammatory response as evidenced by the inhibition of IL-6 and TNF-alpha secretion.Overall, the findings of this study reinforce the potential of ANN modelling for bioprocess optimization aimed at improved control for maximising the volumetric productivity of L. rhamnosus as an immunomodulatory agent with applications in the functional food industry.

Automated summary

This study proposes the application of artificial neural network (ANN) modelling for improved control and monitoring of Lactobacillus rhamnosus biomass production. Three ANN models were developed to predict biomass in batch and fed-batch bioprocesses with different media compositions, as well as the growth rate. The immunomodulatory effect of L. rhamnosus samples was examined, showing anti-inflammatory response. The findings highlight the potential of ANN modelling for optimizing L. rhamnosus bioprocesses as an immunomodulatory agent in the functional food industry.