Project details
Funding: MSCA Postdoctoral Fellowships 2023. HORIZON-MSCA-2023-PF-01
Project coordinator: IMDEA Materials
Project period: 01/10/2024 – 30/09/2026
IMDEA Materials' researchers
Supervisor: Dr. Maciej Haranczyk
Fellow: Sabina Lachowicz-Wiśniewska
Abstract
A diet rich in plant-based foods, abundant in beneficial polyphenolic compounds (PCs), is pivotal in preventing civilization-related diseases. PCs are valued for their robust antioxidant properties and prebiotic potential. However, their inherent instability and sensitivity to environmental factors and processing methods pose challenges. Rapid oxidation and limited bioavailability in the human body underscore the need for effective stability enhancers and delivery systems. In principle, the encapsulation technique provides prolonged and controlled release of food ingredients and increases the stability and bioavailability of bioactive compounds. The complexity of the capsule design, however, has consumed considerable resources in the food science field but has yet to result in satisfactory results in terms of PCs delivery.
This project offers a fresh perspective by treating capsule design as a materials engineering task. We aim to leverage cutting-edge material design, preparation, and characterization techniques to enhance the bioavailability of encapsulated PCs. Our approach will involve layer-by-layer methods to create model systems with precise control over the morphology of polysaccharide and protein encapsulation layers. Co-focal microscopy, SEM imaging, and nanomechanics will provide insights into structure, stability, and PC release in vitro. To optimize layer composition efficiently, we will integrate robot-aided experimentation and machine learning. These efforts will culminate in developing a Material Acceleration Platform, a versatile methodology for designing polyphenolic compound delivery systems for food applications. To achieve these objectives, the Researcher will be provided training-through-research in a broad spectrum of preparation and characterization techniques aimed at layer-by-layer systems and data-driven experimentation approaches such as the Design of Experiments and Bayesian optimization.
Partners
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Funded by the European Union under Grant Agreement 101151044. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.