AI Approaches and Optimization of Food Bioprocesses Within a Circular Economy Framework

Today, the agri-food sector must not only 'feed' the world but must also address new challenges related to food-health. The development of innovative processes for producing and transforming nutraceuticals and functional foods (NAFs) requires evaluating their societal (consumer health) and environmental (eco-design) impacts. This research project aligns with the Alliance program of the NSERC, obtained in April 2021, aiming at the Integrated Valorization of Co-products through Eco-efficient Food Technologies within a Circular Economy (VITALE Consortium).

In this context, the goal of this proposed research project, submitted to CRIBIQ for the 115th call for projects, is to use artificial intelligence approaches, specifically machine learning, to optimize food production lines while minimizing their environmental impact and increasing the added value of the final products. The research objectives are:

1. To develop biostatistical and machine learning tools to facilitate the processing and analysis of peptidomics data.
2. To synthesize peptides identified by these tools, demonstrate their bioactivities, and study their mechanisms of action.
3. To develop new electrodialysis membranes.
4. To study membrane/phenolic interactions and develop explanatory models.
5. To optimize the production of lactobionic acid via electrodialysis with bipolar membranes under pulsed electric fields (PEF).