What is a Digital Twin in the Food Industry?

During the past few months, I’ve received several questions about the Digital Twins we’re building at Elytra. I’ve put together this overview to help clarify what a digital twin is — and what it’s not — for our community.

What is a Digital Twin in the Food Industry?

A digital twin is a live, dynamic virtual replica of a real-world physical process — such as fermentation, extrusion, or cell culture — that continuously receives real-time data, learns over time, and helps predict, optimize, and automate decisions.

Instead of running endless physical trials in the lab or on the production floor, you can simulate and adjust virtually until the optimal outcome is reached. Digital twins combine real-world data, mathematical models, and artificial intelligence (AI) algorithms to reflect how a process behaves under real-world conditions.

How is it different from a traditional simulation?

Aspect	Traditional Simulation	Digital Twin
Data input	Static, with fixed user-defined parameters	Continuously updated with real-time or historical data
Interactivity	Runs once and ends	Constantly updated with live data
Learning	Does not evolve or improve	Uses AI to learn and improve predictions over time
Goal	Explore a specific scenario	Continuously and adaptively optimize the process
Example	Simulate pH change during fermentation	Predict the ideal harvest point using real sensor data

Put simply: a simulation is a snapshot. A digital twin is a live feed — with AI as the director.

What role does Artificial Intelligence play?

1. Learn hidden patterns in the data (e.g., how ingredients, process conditions, and outcomes relate).

2. Predict future behavior (ideal fermentation time, expected texture, shelf life).

3. Suggest real-time adjustments to meet specific targets (pH, Brix, yield, elasticity, viscosity).

4. Reduce uncertainty when decisions must be made with limited or noisy data.

5. Automate recommendations, alerts, or even quality validations.

Without AI, the twin is static or rule-based. With AI, it becomes a continuously learning, optimizing assistant.

Real and Relevant Use Cases

1. Precision fermentation (e.g., casein, heme, albumin)

   1. Predicts the optimal endpoint to maximize purity and yield.

   2. Adjusts feeding strategies based on bioreactor conditions and microbial growth phase.

2. Plant-based product formulation

   1. Simulates the effect of proteins, oils, and fibers on texture, mouthfeel, and flavor.

   2. Suggests new formulations based on nutritional and functional targets (e.g., >8% fiber, meat-like bite, high elasticity).

3. Biomass fermentation (e.g., mycelium, microalgae)

   1. Detects anomalies in cell growth before they impact batch quality.

   2. Recommends ideal harvest timing to maximize concentration of active compounds.

4. Cell culture (e.g., cultivated meat)

   1. Models real-time cell density, nutrient usage, and metabolite production.

   2. Helps optimize media costs and scale up processes reliably.

Opportunities for the Food Industry

Opportunity Impact:

• Reduce physical iterations Fewer pilot runs thanks to pre-validated simulations

• Accelerate product development Go from months to weeks for formulation validation

• Optimize every batch Real-time adjustments based on historical and live data

• Prevent losses Early detection of quality, yield, or safety deviations

• Demonstrate compliance End-to-end digital traceability for regulatory audits

Common Challenges

1. Reliable data is critical: Without sensors, structured records, or digitized workflows, the twin can’t learn or operate effectively.

2. Rapidly evolving processes: In emerging technologies like cultivated meat, models need constant retraining and validation.

3. Cultural resistance to change: Teams need guidance and clear value communication to trust AI-supported decisions.

4. Initial infrastructure investment: While upfront effort is needed (data integration, connectivity, modeling), ROI often comes within months.

What a Digital Twin is NOT

• Not a fancy Excel file

• Not just a dashboard or SCADA app

• Not a quality control tool

• Not a business intelligence or KPI reporting system

• Not a replacement for your experts — it works with them to enhance performance

A simple way to explain it:

A digital twin is a smart, predictive version of your food process — helping you make faster, safer, and more confident decisions while avoiding costly trial-and-error and unlocking real innovation.

I hope this summary helps clarify the topic. If you’d like to dive deeper, feel free to leave a comment or send me a message — happy to chat.

Best regards.

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