AGROALNEXT ML-NUTRISENS Project: Supervised Machine Learning Techniques for the Design of Extruded Meat Analogues with Improved Nutritional and Sensory Quality
- Type Project
- Scope Autonómico
- Autonomous community Murcia, Región de
- Main source of financing NextGenerationEU
- Project website Web del Proyecto
Description
The project aims to optimize the production of meat analogues with improved texture and nutritional profile using wet extrusion and machine learning techniques. Predictive models and a data repository will be developed, and optimal formulations will be validated in the laboratory.
Contextual description
The growing concern for sustainable food has driven the food industry to develop protein-rich products that can replace meat, including high-moisture extruded foods. The range of possible combinations of different available ingredients and applicable technological processes is vast, and individual cases often require several laboratory tests to achieve a satisfactory result. Machine learning is emerging as a predictive tool capable of contributing to the selection of optimal production conditions.
Results
Creation of a database on nutritional composition and texture characteristics, based on scientific articles. Standardization of the target characteristics for the model.
Contact information
- Coordinator/entity name: Macarena Egea Clemenz
- Mailing address: Not available
- Email coordinator/entity: macarena.egea@um.es
- Phone: Not available
Coordinators
- UMU
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