Currently, objective assessment of intermediate and final product quality in industrial bakery production is limited due to a lack of suitable technical sensors. Consequently, quality assessment relies mainly on subjective judgement by specialists who are increasingly unavailable. Process corrections due to perceived quality defects can only be made after they have occurred, resulting in production waste.

The aim of the project is therefore to investigate and demonstrate the potential of sensor-based process control for increasing resource efficiency, using the example of the production of baked goods from wheat dough. Cause-effect relationships between raw materials, processes and final product quality will be identified using image processing methods, descriptive sensory assessment and a baking line equipped with measurement technology. In particular, the analysis of 3D models of the baked goods enables precise assessment of product quality. Artificial intelligence (AI) helps to identify new correlations and determine quality characteristics. The project is making a significant contribution to improving the flow of information along the value chain and to a better understanding of baking processes. It thus lays the foundation for the auto-adaptive production of baked goods from wheat dough.

Project leader: Prof. Dr. Ulrich Müller | FB Life Science Technologies, Labor Verfahrenstechnik

Partners: Dr. August Oetker Nahrungsmittel KG, WP Kemper GmbH, ISI-Automation GmbH, Brabender GmbH & Co. KG