E-MUSE

A. Set-up and execution of plant-based fermentations: Upon selection and preparation of plant based ingredients, large numbers of lactic acid bacteria (with known genome sequences) will be used to perform fermentations using in vitro high throughput methodologies. Relevant phenotypic data and fermentation characteristics will be analysed, namely: Acidification, Proteolysis, Relevant enzymatic activities, Flavour compounds (GC-MS). B. Identification of favourable fermentation outcomes by data analysis of the large datasets (e.g. ingredients, strains, fermentation outcomes): By mapping phenotypic traits and fermentation outcomes of the different strains (e.g. flavour development, acidification rates impacting safety) on different substrates versus the genome content (gene-trait matching), clustering of well-performing strains for different applications will be performed. C. Assess the impact of the fermentation process and storage/ripening with respect to potential spoilage and safety: For well-performing plant-protein / strain combinations, control of sporeformers spoilers and pathogen during fermentation and shelf life will be assessed. D. Integrate data for predictive modelling approaches: In collaboration with other early stage researchers in the EU project, deep learning methods will be developed to predict strain- and condition-dependent properties to produce different plant-based cheeses.