Macroscopic functional prediction of protein ingredients

Nutritious and functional plant-based proteins are necessary to provide sustainable food security. Plant-based proteins must possess many properties, such as good nutritional value, digestibility and the ability to replace animal-based products, by creating foams, emulsions and gelled structures. For a sustainable future, we need to find suitable ingredients from various crops. But protein-based ingredients must be elaborately tested for many factors, such as safety, health, taste and functionality, before their food application. One can imagine that these explorative processes are laborious and slow down the search for food ingredients from new sources. Therefore, this project aims to find predictors for the ingredient’s performance to facilitate predictability, especially with a focus on functionality. These tests require a heavy workload, as a multi-length scale analysis is necessary, from molecular properties to mesoscale, from mesoscale to macroscopic properties. The project aims to find molecular properties on the protein’s, such as hydrophobicity or nativity, which can directly predict or indicate the macroscopic properties (e.g. foaming, emulsifying and gelling), thereby omitting the mesoscale analysis. In addition, the project aims to find the minimal information necessary to predict macroscopic functionality, which requires the determination of quantitative relationships between all length scales. These so-called molecular predictors of macroscopic functionality have been previously defined for dairy proteins, and should also be determined for plant-based (and alternative) sources to facilitate the protein transition and food formulation in general.