Zukunftslabor2030: Pioneering AI and novel technologies for advanced food freshness monitoring and spoilage predictio

ألمانية. Introduction: Safety and quality are essential for enjoying food without hesitation, but they depend on dynamic physiochemical and microbial changes of the products. Real-time assessment of food freshness requires continuous monitoring throughout the entire process chain from production, storage and distribution to sale. Goals: The objective of the research project "Zukunftslabor2030" (Future lab 2030), which is funded by the German Federal Ministry for Food and Agriculture (project number 28DK126F20), is to employ artificial intelligence and novel technologies to develop an efficient and sustainable system for monitoring food products in order to achieve improvements regarding food quality and safety, consumer protection and food waste reduction. Platform architecture and standards: A fundamental component of the Zukunftslabor2030 project are digital twins which serve as digital representations of the properties of food products. Using measurement data and modelling algorithms, the digital twins enable a dynamic assessment and prediction of food spoilage depending on environmental parameters such as temperature. Communication between the individual modules of the Zukunftslabor2030 application, such as the data platforms, the predictive models and the digital twins, takes place via an EPCIS 2.0 service (EPCIS = Electronic Product Code Information Services), an exchange format for event-based product tracking and process documentation in the food trade. Assessment and prediction of food spoilage are generated by predictive models developed within the project and provided in the harmonised exchange format FSKX (FSKX = FAIR Scientific Knowledge eXchange). Conclusion: The Zukunftslabor2030 project aims to enhance food safety and quality by integrating artificial intelligence and new technologies for real-time food freshness monitoring and spoilage prediction, leveraging digital twins, predictive modelling, and the EPCIS standard for data exchange.