Daniel Onwude

Assistant Professor 

Tier II Canada Research Chair in Computational Food Systems & Precision Nutrition (ComputFood Lab)

Biography
Dr. Daniel Onwude is an Assistant Professor and Tier II Canada Research Chair in Computational Food Systems & Precision Nutrition in the Department of Food Science, University of Guelph. Prior to joining the University of Guelph, he spent over seven years in Switzerland, serving as a lecturer at ETH Zürich, a Senior Scientist and Team Lead at Empa – Swiss Federal Laboratories for Materials Science and Technology, and a Senior Digital Sustainability Expert at Digital for Planet. His research focuses on developing new ways to process and preserve foods through a systems approach that reduces food loss and waste while improving nutrition, sustainability, and food system resilience. He studies how foods change as they move from production through processing, storage, and transportation to our plates, and how these changes influence the nutrients our bodies ultimately absorb. He does this by creating physics-based digital models that act like virtual copies of real foods and food processing systems, integrated with data and artificial intelligence. By testing ideas in these virtual environments before applying them in the real world, he helps design better ways to make and preserve food, so it remains healthier, safer, and more sustainable. 

Academic History

Post-Doctorate in Food Processing, ETH Zürich - Federal Institute of Technology, Zürich

Ph.D. in Agricultural Process Engineering (Specialization in Food Process Engineering), University of Putra, Malaysia

M.Sc. in Agricultural Process Engineering (Specialization in Food Process Engineering), University of Putra, Malaysia

Research area 
The interdisciplinary Lab applies computational and experimental approaches to understand and improve how foods are processed, preserved, and distributed across the food system. The lab develops digital twins (so virtual models), data-driven tools, and advanced processing technologies to study food quality, nutrient retention, and waste across supply chains. By integrating food science, engineering, and data analytics, the lab aims to design innovative processing and preservation strategies that support sustainable food manufacturing and resilient food systems. ComputFood Lab’s current research themes include:

• Digital twins for food systems: The lab develops digital twin models and predictive tools to simulate how foods change across the value chain, from production and processing to storage, transportation, and consumption. These models help identify when and why food quality loss, waste, and nutrient degradation occur.
• Food processing for precision nutrition and health: The group studies how processing and preservation conditions influence nutrient stability, digestibility, and bioavailability to support personalized nutrition and improved health outcomes.
• Sustainable food production, processing, and side-stream upcycling: The lab designs sustainable processing strategies that reduce environmental impacts, apply dynamic life cycle assessment approaches (LCA, S-LCA, LCC), while transforming food by-products and side streams into valuable ingredients and functional foods.
• Innovative food preservation technologies: The lab develops advanced preservation techniques (e.g., drying, heating, cooling, packaging) that improve shelf life, maintain food quality, and reduce food loss across supply chains.

Featured publications [Full list in Google Scholar]

Onwude, D., Hashim, N., Janius, R. B., Nawi, N. M., and Abdan, K. (2016). Modeling the Thin-Layer Drying of Fruits and Vegetables - A Review. Comprehensive Reviews in Food Science and Food Safety, 15: 599-618. DOI

Onwude, D., Iranshahi, K., Martynenko, A., Defraeye, T., (2021). Electrohydrodynamic drying: Can we scale-up the technology to make dried fruits and vegetables more nutritious and appealing? Comprehensive Reviews in Food Science and Food Safety. 202120:;5283–5313. DOI

Onwude, D., Cronje, P., North, J., Defraeye, T. (2024). Digital replica to unveil the impact of growing conditions on orange postharvest quality. Scientific Report 14, 14437. DOI

Onwude, D., Bahrami, K., Shrivastava, C., Schudel, S., Crenna, E., Shoji, K., Cronje, P., Berry, T., North, J., Kristen, N., & Defraeye, T. (2022). Physics-driven digital twins to quantify the impact of pre- and postharvest variability on the quality evolution of orange fruit. Resources, Conservation and Recycling, 186, 106585. DOI

Onwude, D., Iranshahi, K., Rubinetti, D., Martynenko, A., Defraeye, T., (2021). Scaling-up electrohydrodynamic drying for energy-efficient food drying via physics-based simulations. Journal of Cleaner Production 329, 129690. DOI  

Wittkamp, T., Defraeye, T., Yegon, R., & Onwude, D. (2025). Enhancing postharvest storage in low-and middle-income countries: Evaluation of the passive evaporative cooling blanket for fruits and vegetables. Energy for Sustainable Development, 88, 101787. DOI

Defraeye, T., and Onwude, D. (2020). The future of digital twins for drying. Drying technology, 1-2. DOI