Hoang Minh Phan

I am a PhD researcher at KU Leuven, working in the MeBioS Postharvest group led by Prof. Bart Nicolai. My research is at the intersection of postharvest technology, food engineering, advanced predictive modelling, and applied data science to improve the sustainability of the food cold chain. I focus on building a digital twin of a commercial cold storage system for pear fruit — a virtual replica of the physical system that enables smarter control, more accurate prediction, and systematic optimisation of both fruit quality and storage operations.

This work is part of the ENOUGH Project (H2020 EU), a European initiative driving sustainable innovation across the food sector.

My Background

Driven by a passion for advancing sustainable food systems, I pursued a joint Master’s in Food Technology (Magna cum laude) at KU Leuven and Ghent University, Belgium. My master’s thesis bridged AI and postharvest science — applying deep learning and X-ray CT imaging to develop a non-destructive sorting technology for pears affected by internal browning disorder, translating advanced AI techniques into practical postharvest sorting solutions. Earlier, my foundation in food science was built at the University of Technology (HCMUT-VNU), Vietnam, where I earned a B.Eng in Food Technology (First Class Honours). There, I developed core engineering competencies in process design, quality management, and food systems.

Beyond academic research, I worked as a Chemical Engineer in the food industry, leading technology transfer & CapEx projects across multiple production lines. This industry chapter gave me a perspective that runs from bench scale all the way to the factory floor, and a deep appreciation for what it takes to make innovation work in practice.

Outside of the professional world, I enjoy reading, watching anime, learning new things, and exploring culinary cultures around the world. My favourite Vietnamese dish is Bún Bò Huế — a bold, spicy noodle soup from Central Vietnam that I highly recommend. More broadly, noodles in all their forms are close to my heart. I also like staying active on the badminton court.

Research & Expertise

Digital twins & predictive modelling — food systems monitoring, quality prediction, and operational optimisation.
Postharvest technology & cold chain — cold storage, fruit quality management, shelf life extension, and energy efficiency.
Food processing & engineering — process design, optimisation, and technology transfer.
Applied data science & AI — machine learning, deep learning, statistical modelling, and uncertainty quantification.

What Drives Me

I believe the most meaningful progress in (agri)-food systems comes from bridging rigorous science with practical engineering. Whether through research, consultancy, or hands-on engineering, my goal is to help build systems that are smarter, more efficient, and more sustainable — from the storage room to the supply chain.

If you are working on challenges at the intersection of food, technology, and sustainability, I would love to connect.

An integrated dynamic model of ethylene biosynthesis and respiration of Conference pear (Pyrus communis) during storage and shelf life

Aug 25, 2026

Respiration and ethylene biosynthesis rates change dynamically in maturing and ripening climacteric fruit. However, typically in modelling work, under low-temperature controlled atmosphere storage, the temporal changes of these metabolic processes have often been neglected. This paper proposes a simplified modelling approach to describe and integrate the temporal changes of respiration and ethylene biosynthesis during storage and subsequent shelf-life. The model starts from transcriptome levels of the involved enzymes and incorporates enzyme synthesis and degradation. The interaction between oxygen and ethylene metabolism at the pathway and signalling level is explicitly incorporated in the kinetic equations. These dynamic models of respiration and ethylene biosynthesis were calibrated using mainly shelf-life data (18 °C in regular air) obtained after storage under various conditions. The models were then validated using data from a storage experiment under standard CA (-1 °C, 3 kPa O2, 0.7 kPa CO2) and DCA (-1 °C) conditions in both laboratory and industry settings. The goodness of fit, expressed by the adjusted coefficient of determination R2adj, for the coupled model of ethylene and respiration was 0.68. Overall, the model was able to capture the dynamic pattern of respiration and ethylene biosynthesis, aligning acceptably with the measured data in shelf-life. Specifically, the exchange rate of respiratory gases generally increases and stabilises after one week in shelf-life, while the peak of the ethylene exchange rate usually shifts earlier with later shelf-life stages from different storage conditions.

Improving energy efficiency in pear storage through dynamic controlled atmosphere (DCA)

Feb 1, 2026

The energy efficiency of ‘Conference’ pear storage was assessed for different storage strategies, including dynamic controlled atmosphere (DCA) at different temperatures and controlled atmosphere (CA) at varying temperatures and O2 levels. Storage at -1 °C in 3 kPa O2 and 0.7 kPa CO2 was used as a benchmark. Direct respiration measurements during the storage period showed that DCA reduced respiratory heat by 30–40 % compared with the benchmark, even at slightly elevated temperatures. A simulation-based energy assessment revealed that DCA could reduce the total heat load in a storage room by 8–16 %. Fan operation was found to account for the largest share of the total heat load (up to 50 %), while the respiratory heat contributed around 10–30 %. Among all experimental strategies, DCA at -1 °C reduced the total heat load by ∼8 %, and maintained good firmness and skin colour without inducing internal browning after long-term storage. This makes it the most optimal approach to balance fruit quality and energy savings.

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