The significance of this proposed research is to address a long-standing unmet need in precision nutrition and additive manufacturing of foods. Specifically, this research will develop AI-enabled predictive models for 3D printed foods and a gut digestion process.
The specific goals of this proposal are to develop AI-enabled models for additive manufacturing of food for precision nutrition and AI-enabled digital twin of a gut digestion process to predict and optimize the bioaccessibility of micronutrients during digestion based on diverse composition and structure of foods.
Team A: AI-enabled models for food manufacturing and nutrient delivery during digestion, led by N. Nitin, X. Liu, T. Zohdi, Q. Zhao
Team B: Digital Twin and Machine-Learning for Optimized Robotic Production of Complex Multiphase Foods, led by T. Zohdi(unfunded coordinator), K. Mosalam (co-lead), F. Borrelli (co-lead), S. Makiharju, M. Mueller, N. Nitin, I. Tagkopoulos