Digital Twin Technology Brings Predictive Power to Food Safety and Quality

Editor's Note: This article originally appeared in the November/December 2025 print edition of QA magazine under the headline “Seeing Double.”

Few technologies can simultaneously promise enhanced food quality and safety, improved supply chain efficiency and stronger sustainability — but digital twins, a relatively new innovation in the food industry, have the potential to do just that.

A digital twin is a detailed digital model of a real object or process that’s constantly updated with live data from sensors and control systems. Unlike traditional simulations, which only test a single scenario, a digital twin evolves in step with the real system. It mirrors what’s happening moment by moment, allowing operators to monitor performance, predict issues and safely test “what if” situations before making real-world changes.

As Deniz Turan Kunter, Ph.D., an assistant professor of food quality and design at Wageningen University & Research in the Netherlands, explained, “A digital twin is a virtual representation of something real — like a plant, a package or even an entire food supply chain — that stays continuously aligned with what’s happening on the ground. It shows current conditions, anticipates what’s likely to happen next and helps guide better decisions along the way.”

In the food industry, this technology is beginning to reshape how companies design, monitor and manage everything from product quality to cold-chain logistics. Imagine you’re managing a food-packaging line, and you create a digital twin of it: a real-time digital version that shows exactly what’s going on — from how fast conveyors are running, to if a sensor fired, or whether temperatures are stable.

Because this twin receives continuous data from the actual line, you can use it to simulate, “What if we increase speed by 10%?” or “What if a piece of equipment fails here?” and find out what might go wrong before it occurs.

That’s the power of a digital twin in the food industry: It allows producers to detect risks, optimize processes and make data-driven decisions long before a problem reaches the production floor.

THE ROOTS OF DIGITAL TWINS.

The concept of the digital twin didn’t start in the food industry — it was born in aerospace. During NASA’s Apollo missions in the 1960s, engineers built digital twins in Houston that mirrored the physical spacecraft in real time as data streamed back to Earth.

The approach became critical during the Apollo 13 mission in 1970, when an oxygen tank explosion left the spacecraft severely damaged. Engineers used live data from the real vehicle — the “physical twin”— to update the simulator on the ground, recreating the damaged conditions. That digital model allowed them to test scenarios, run predictions and ultimately guide the decisions that brought the astronauts home safely.

The concept was later adopted across aerospace and automotive industries. The United States Air Force and vehicle manufacturers began using digital twins to improve the design, testing and maintenance of aircrafts and engines. By combining sensor data with computer models, they could predict failures, simulate wear and make design improvements before building physical prototypes.

Advances in the Internet of Things, cloud computing and artificial intelligence eventually transformed digital twins from static 3-D renderings into living, data-driven systems that mirror the physical world in real time. Today, that same technology is finding new purpose in food production and logistics, helping manufacturers and distributors monitor processes, predict spoilage and maintain consistent product quality.

A MORE EFFICIENT FOOD SYSTEM.

Digital twins can improve efficiency by linking environmental data, crop growth and production operations. In agriculture, they combine weather information with plant growth models to predict how temperature, humidity and sunlight affect crop yield and quality.

A recent citrus study published in Scientia Horticulturae last May demonstrated how this approach can simulate fruit development across different regions and guide decisions on harvest timing, packaging and cooling — helping reduce waste and maintain freshness throughout the supply chain.

In manufacturing, digital twins are already being developed to model entire production lines. Sensors track conditions such as temperature, vibration and motor speed, while inspection data adds details about wear or misalignment. This information feeds into a live digital model that mirrors the manufacturing line in real time. Unlike a generic simulation, this twin is unique — it reflects the exact machines, layout and operating history of a specific facility. As the equipment runs, ages and is repaired, the digital twin evolves with it.

With this technology, producers can test new production schedules, predict failures and fine-tune processes before problems occur. The result is a food system that’s smarter, faster and better equipped to meet the challenges of modern production.

IMPROVED FOOD SAFETY.

Digital twins can strengthen food safety by providing a real-time view of how products move and change throughout production, transport and storage. By connecting sensor data such as temperature, humidity and pressure to predictive microbiology models, these systems can identify when conditions might allow harmful microbes to grow.

Recent research has shown how digital twins can pinpoint temperature “risk windows” during refrigerated transport — periods when cooling fluctuations may allow microbial growth or spoilage to accelerate. A 2019 study in Resources, Conservation and Recycling reported that by simulating product-level temperatures instead of relying on ambient readings, these systems can help producers detect hot spots and adjust cooling or logistics before safety is compromised.

Beyond prevention, digital twins improve traceability. Each virtual model records the flow of materials and environmental conditions across every step of the supply chain, creating a digital record that can speed up investigations and recalls when issues arise. They can also simulate “what if” scenarios, such as refrigeration failures or shipping delays, to test how systems would respond and to prevent future risks.

By transforming food safety from a reactive process into a predictive one, digital twins give producers the ability to anticipate problems and protect consumers before products ever leave the facility.

ENHANCED FOOD QUALITY.

Digital twins can help producers improve food quality by linking processing conditions to the final product’s texture, flavor and freshness. By continuously analyzing data from sensors and control systems, these models make it possible to spot quality issues as they develop and even correct them before they affect the final product.

In dairy processing, for example, digital twins could be used to model fermentation in cream cheese production. They would track key variables such as temperature, pH and bacterial growth rate — factors that influence how smooth, thick and flavorful the final product becomes. If the system detected that the pH was dropping too quickly or that texture was changing, it could adjust conditions in real time to maintain consistent quality, as reported in a 2023 study published in Digital Chemical Engineering.

Similarly, in meat freezing, digital twins can prevent texture damage that occurs when large ice crystals form inside the muscle, the study found. By modeling how quickly ice forms under different freezing rates and airflow conditions, producers can fine-tune parameters to preserve juiciness and tenderness after thawing.

Researchers are also using digital twins to improve the quality of fresh produce. Turan Kunter leads work that applies digital twins to fruits like oranges and strawberries. Her models connect real-world data like temperature, humidity and even gas or vision measurements to physical traits such as firmness, moisture and shelf life. By combining physics-based equations with AI models, her research predicts quality losses like chilling injury or dehydration before they happen, giving producers tools to make better storage and transport decisions.

Turan Kunter pointed out that digital twins also can guide smarter packaging design.

“By simulating how different materials or venting patterns affect produce quality during transport, companies can choose packaging that best balances freshness, airflow and shelf stability, not just in lab tests, but across the entire supply chain,” she said.

Whether used for dairy, meat or fresh produce, the goal remains the same: using real data to protect texture, flavor and freshness from harvest to plate. Digital twins give producers the insight they need to keep food at its best every step of the way.

LOOKING AHEAD.

While digital twins are already transforming how the food industry designs, monitors and manages its systems, large-scale deployment remains challenging. Developing robust and scalable models requires advanced mathematical and computational tools, while many companies remain hesitant to adopt new technologies due to high investment costs and data security concerns, according to a study published in April in Frontiers in Sustainable Food Systems.

According to Turan Kunter, one of the biggest hurdles is data governance — specifically, creating standardized formats and sharing frameworks across the entire food chain.

“We need standardized schemas and collaboration between growers, packers, logistics providers and retailers,” she said. “That’s how we’ll break data silos and enable whole-chain twins.”

Integrating digital twins with life-cycle assessments and techno-economic models could also help optimize quality, cost and environmental footprint simultaneously. Open datasets and cross-industry collaboration will be critical to accelerate adoption and innovation.

As the technology matures, digital twins could help the food industry shift from reactive problem-solving to predictive, data-driven decision-making — one virtual model at a time. In doing so, they won’t just improve operations; they’ll redefine how food systems think, adapt and evolve for the challenges ahead.

Abbey Thiel is a food scientist, YouTuber and teacher who loves sharing her passion for food science. With a background in research and education, Thiel has dedicated her career to making complex scientific concepts accessible to a wide audience.