EIT Health, in partnership with Abbott has launched the first in a series of challenges and is actively seeking a solution to enhance the development of nutritional products by addressing the challenge of ingredient stability during processing. The objective is to predict these stability challenges early in the development process to avoid time-consuming and resource-heavy testing.
We are looking for solutions that can:
- Predict the stability of nutritional ingredients under specified conditions of treatment based on existing published literature
At present, the stability of ingredients is determined through small-scale formulation and analytical testing, which is followed by shelf-life assessments. However, this approach is costly and time-consuming, especially when evaluating multiple ingredients. Abbott is looking for a solution that can streamline this process by predicting ingredient stability based on published literature. Ideally, this solution would be an app or database of food/nutritional ingredients with search features tied to public literature databases that would be able to make data-driven recommendation on ingredient stability under various physico-chemical conditions. Such a system would help prioritise ingredients or identify potential instability early, enabling faster and more efficient product development.
This database/ programme may utilise AI-technology to scan existing published literature databases, identify studies tied to the specified ingredients as it relates to stability in various physico-chemical conditions or in presence of various micro/ macronutrients. Product users should be able to input specific processing parameters (like pH, temperature, and time) and receive predictions on how well an ingredient will perform under those conditions. The system should also generate a “stability score” and allow for multiple users to access and save search results. Ideally, this tool would be user-friendly, web-based, and capable of producing detailed, exportable reports. The ultimate goal is to have a fully functional database by Q4 2025.
For more detailed information on the challenge, please read the document here.