AI-powered foodome characterization
Forthcoming
Status as published by the data source.
Expected Outcome:
Project results are expected to contribute to all the following expected outcomes:
• an Artificial Intelligence (AI) tool able to accelerate the analysis of complex foodomics data to speed up the identification, structural characterisation, and quantification of chemicals present in food;
• improving capacity and synergies in foodomics research between academia, research infrastructures and industry. Scope:
Through food and beverages, the population is exposed to an immense variety of chemicals (as contaminants and as ingredients). The “foodome” is the complete set of compounds present in a food sample at a given time; it includes both endogenous biomolecules (produced through a species' metabolic processes) and exogenous compounds (natural and synthetic substances from production, processing, handling, and packaging). The current knowledge is largely confined to a narrow set of approximately 150 food components catalogued by traditional databases. These components represent only a very small fraction of the total chemical complexity present in foods, while thousands (>100,000) of compounds and contaminants remain largely unquantified and underexplored. Understanding this “nutrient dark matter” would require identifying, cataloguing and quantifying the full spectrum of substances present in our food and present in accessible, AI-ready databases. The need for such information is today a bottleneck to make the nutrition field predictive and data driven, and has the potential to make food processing more efficient in bringing new foods items on the market. Efforts to create such datasets are underway but owing to the quantity of samples (hundreds of thousands of compounds across thousands of foods), such databases often rely on high-throughput experimental methods which do not allow a precise characterisation of the molecules identified.
Proposals should address the following activities:
• develop and test validated AI algorithms capable of interpreting and annotating high-throughput foodomics data, for instance from untargeted mass-spectrometry or nuclear magnetic resonance;
• the AI algorithms should be capable to predict or infer the presence and concentration of compounds based on (incomplete) foodomics data via innovative approaches, for instance by considering phylogenic relationships between species. Moreover, this should help identify analytical methods and potential food safety hazards based on their similarity to known compounds;
• develop a European research and innovation network between Member States and Associated Countries in food exposome research to support the development, standardization, valorisation and dissemination of the AI results and considering existing initiatives such as the Centre for Excellence in the Periodic Table of Food Initiative from Wageningen University. The multi-actor approach is encouraged. Proposals should include industry in the consortia and academia, startups, SMEs and international initiatives. Collaboration with international partners is encouraged as well as with existing private companies in the EU/Associated countries.
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Activities are expected to start from TRL 5 in order to achieve TRL 7 by the end of the project – see General Annex B.
- Status
- Forthcoming
- Deadline
- (time not stated)
- Opens
- Published
- Total budget
- €7,800,000
- Grant range
- €7,800,000 – €7,800,000
- Country
- European Union (EU-wide)
- Programme
- Horizon Europe (HORIZON)
- Official page
- Open at the source portal
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All calls under this programme
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Where this came from
- Source document
- https://ec.europa.eu/info/funding-tenders/opportunities/data/topicDetails/horizon-cl6-2027-02-farm2fork-08.json
- Document fingerprint
c0d805fde321e7ac(SHA-256, first 16 hex characters)- Retrieved
- First recorded here
What has changed
- url first recorded as https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/horizon-cl6-2027-02-farm2fork-08 on
- currency first recorded as EUR on
- amount_max first recorded as 7800000.00 on
- amount_min first recorded as 7800000.00 on
- budget_total first recorded as 7800000.00 on
- deadline first recorded as 2027-09-23 on
- opens_on first recorded as 2027-04-20 on
- published_on first recorded as 2025-12-12 on
- status_basis first recorded as source_status on
- status first recorded as forthcoming on
- title first recorded as AI-powered foodome characterization on
Data source
© European Union, 2026. Source: EU Funding & Tenders Portal. Reused under Commission Decision 2011/833/EU — CC BY 4.0.
Retrieved from the source on .
The source last updated this document on Mon, 02 Mar 2026 09:38:21 GMT.
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