Lecture
Digital Food Twins – when Bits meets Bites
- at -
- ICM Saal 4b
- Type: Lecture
Lecture description
Digital Food Twins offer a transformative framework for understanding, analyzing, and predicting the dynamic behavior of food products across their lifecycle. Unlike traditional digital twins in engineering, food twins must account for natural variability, physicochemical changes, and complex interactions within biological matrices [1]. A central element of Digital Food Twins is the integration of multimodal data—ranging from imaging systems and hyperspectral sensors to electronic noses, classical process measurements, and chemical or microbiological analytics [2]. These heterogeneous data streams enrich the representation of food systems and form the foundation for advanced AI-based predictions.
We further discuss machine learning methods that enable Digital Food Twins to model, classify, and forecast food quality attributes [3]. Beyond conventional data-driven approaches, the incorporation of Physics-Informed Machine Learning ensures that domain knowledge—such as diffusion processes, thermodynamics, or biochemical reactions—is embedded directly into the learning process. In addition, recent advances in Generative AI opens new opportunities for integrating process knowledge, scientific literature, and contextual data into Food Twins, enabling more intelligent, transparent, and flexible decision support [4].
Finally, the talk highlights key practical challenges for implementing Digital Food Twins in real-world settings, including data availability, multimodal data fusion, model explainability, transferability across products, and the lack of standardization in food data representation. By addressing these challenges, Digital Food Twins have the potential to fundamentally reshape food analysis and quality assessment.
Literature:
[1] Jox, D.; Hummel, D.; Hinrichs, J.; Krupitzer, C.: A Conceptual Framework for Predictive Digital Dairy Twins: Integrating Explainable AI and Hybrid Modeling. Proc. FoodOps, 2024.
[2] Anker, M.; Borsum, C.; Zhang, Y.; Zhang, Y.; Krupitzer, C.: Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices. Processes, 2024, 12, 266.
[3] Senge, J.M.; Kaltenecker, F.; Krupitzer, C.: Integrating Sensor Data, Laboratory Analysis, and Computer Vision in Machine Learning-Driven E-Nose Systems for Predicting Tomato Shelf Life. Chemosensors, 2025, 13, 255.
[4] Krupitzer, C.: Generative artificial intelligence in the agri-food value chain - overview, potential, and research challenges. Frontiers in Food Science and Technology, 2024, 4, 1473357.
We further discuss machine learning methods that enable Digital Food Twins to model, classify, and forecast food quality attributes [3]. Beyond conventional data-driven approaches, the incorporation of Physics-Informed Machine Learning ensures that domain knowledge—such as diffusion processes, thermodynamics, or biochemical reactions—is embedded directly into the learning process. In addition, recent advances in Generative AI opens new opportunities for integrating process knowledge, scientific literature, and contextual data into Food Twins, enabling more intelligent, transparent, and flexible decision support [4].
Finally, the talk highlights key practical challenges for implementing Digital Food Twins in real-world settings, including data availability, multimodal data fusion, model explainability, transferability across products, and the lack of standardization in food data representation. By addressing these challenges, Digital Food Twins have the potential to fundamentally reshape food analysis and quality assessment.
Literature:
[1] Jox, D.; Hummel, D.; Hinrichs, J.; Krupitzer, C.: A Conceptual Framework for Predictive Digital Dairy Twins: Integrating Explainable AI and Hybrid Modeling. Proc. FoodOps, 2024.
[2] Anker, M.; Borsum, C.; Zhang, Y.; Zhang, Y.; Krupitzer, C.: Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices. Processes, 2024, 12, 266.
[3] Senge, J.M.; Kaltenecker, F.; Krupitzer, C.: Integrating Sensor Data, Laboratory Analysis, and Computer Vision in Machine Learning-Driven E-Nose Systems for Predicting Tomato Shelf Life. Chemosensors, 2025, 13, 255.
[4] Krupitzer, C.: Generative artificial intelligence in the agri-food value chain - overview, potential, and research challenges. Frontiers in Food Science and Technology, 2024, 4, 1473357.
