07 Meal Engine: De\coded Cooking

Future of Cooking
Visual Programming

Case Details

Jenny Hu
Sandeep Hoonjan
Ferdinand Kohle
Yifu Liu
IDE 2021
Dr Stephen Green
Simplifying Complexity

Meal Engine is a novel kitchen tool for recipe creation and adaptation. It leverages big data systems to give home cooks the agency needed to make healthier and environmentally more responsible decisions in the world’s increasingly complex food ecosystems. Meal Engine operates like a node-based visual programming language that transcendences languages and unmasks the recipe’s underlying cooking process.

Meal Engine being used on a tablet device within a home kitchen.
Any textual recipe can be represented in Meal Engine’s adaptable node-based language that is faster and easier to understand and customise


Food is at the centre of some of the biggest global challenges associated with climate change, public health, and ecological damage. However, during shopping, cooking, and eating, the underlying complex and interconnected food systems are hidden to the consumer. Meal Engine makes this complexity transparent and decisions actionable.

To do this, Meal Engine rethinks the recipe. Meal Engine presents recipes as a graphical flow of interdependent nodes representing ingredients and cooking actions.

Meal Engine can be used for remote and multi chef cooking that allows remote, collaborative recipe editing.
Health related such as nutriscores can adaptively suggest changes to a recipe while retaining the original taste and feel.

Due to its node logic, Meal Engine can adapt any recipe to its user’s individual needs. For example, Meal Engine can automatically morph a favourite recipe to incorporate more sustainable and healthier alternatives, while retaining the taste and feel of the original. Existing data sources such as taste and pairing networks, carbon footprints, ingredient seasonality, nutritional information, and others, are feeding into the node logic and can easily be accessed by the home cook.

The nodal flows are automatically rearranged into a linear set of multimedia instructions to instruct the user during the cooking phase

We envision a technology that promotes transparency in the kitchen for us to reconnect with the food we buy, prepare, and eat. Current food systems are responsible for 25% of annual greenhouse gas emissions, while it is projected that by 2025, 44 of the 54 countries in the WHO European region will have increased rates of obesity. Today, it is apparent and ever more pressing that smart meal choices become an everyday point of action.

The nodal flows are automatically rearranged into a linear set of multimedia instructions to instruct the user during the cooking phase