Taking stock of literature for future food flavours

Within foods research at Unilever, we look for healthy natural flavours for our brands. Unilever believes food can be a force for good and looks for ways in which its food brands can be better for people and better for the planet. The trend is towards vegan, to reduce the impact on the planet. Flavours that people appreciate and that can be produced from non-animal food ingredients often make use of an assembly of specific chemical reactions, collectively named as the Maillard reaction. The Maillard reaction brings the authentic colours, aromas and tastes resulting from familiair home cooking processes (boiling, grilling, roasting etc). The Maillard reaction has been widely researched in thousands of experiments in different conditions all over the world, but despite all the research, the Maillard reaction remains unpredictable due to its complexity.

Although being unpredictable and not amenable for modelling, the huge amount of publications & patents with specific data on the Maillard reaction out in the open domain might give new insights and potential if efficiently processed (read, relevant data extracted and analysed). Will this hold clues to better modelling of the reaction? Can we make the future stock cube by taking stock of large volumes of the current literature on Maillard?

Better learning 

New insights from literature can lead to better, more sustainable and natural processes for current flavour preparation, while making use of AI to search the literature could guide to less and more critical experiments to get to these processes, i.e. experimentation in a more sustainable way. Learnings from this could easily be applied to other food industrial areas having already loads of publications and patents.  

Access to a well-prepared subset of research literature

For this challenge our information specialists have compiled a subset of relevant literature. No time will be lost organizing access. For setting up the rules for selecting relevant passages, you will work with chemists and food experts as well as data scientists.

Goal

To test if automated analysis of existing scientific literature, helps us better predict the outcome of Maillard experiments. 

Can we create a model that predicts if an article or paragraph is relevant when looking for a specific outcome of the Maillard reaction? Can we automatically create an overview of all experimental conditions under which the Maillard reaction was tested and link these conditions to outcomes?

Who are we looking for?

We could use as team members: Aspiring text-mining or search specialists, food technologists and chemists who like adding some flavour to their skills! We have experts with mentoring abilities.

About Unilever

Unilever is one of the world’s leading suppliers of Foods & Refreshment, Beauty & Personal Care and Home Care products with sales in over 190 countries and reaching 2.5 billion consumers a day. Unilever has around 400 brands found in homes all over the world, including Knorr, Dove, Dirt Is Good, Rexona, Hellmann’s, Lipton, Wall’s, Lux, Magnum, Axe, Sunsilk and Surf. Since 2010 Unilever has been taking action through the Unilever Sustainable Living Plan to help more than a billion people improve their health and well-being, halve their environmental footprint and enhance the livelihoods of millions of people as they grow their business.