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Real-time quality control

IT meets food technology

Compliance with high standards in food production ensures comprehensive consumer protection, the economical use of raw materials and the avoidance of higher production costs and complaints. Overall, these are high requirements for the quality assurance process, which is currently "often only carried out on a random and retrospective basis", says Professor Oliver Niggemann, Director of the Institute for Industrial Information Technology (inIT).

Partners of the participating research institutes and companies at the kick-off project meeting at the Centrum Industrial IT (CIIT): Professor Oliver Niggemann (6th from right) and Professor Jan Schneider (2nd from left) coordinate the research activities in Lemgo. (Image: CIIT)

Researchers at inIT and the Institute for Food Technology NRW (ILT.NRW), both institutes at OWL University of Applied Sciences in Lemgo, are therefore working with partners from the (food) industry to develop a methodology that enables continuous, predictive monitoring of the entire food production process. This is where the IP1 research project comes in: A model of the products, a so-called "virtual image", is to be generated from sensor data and raw material information during production.

The virtual images can be used in quality control to monitor standards and specification values.

"The virtual image can be used to access real-time information such as product properties and food quality, largely without time-consuming laboratory tests or additional costly sensor technology," explains project manager Niggemann. To this end, the inIT researchers are harnessing the benefits of cyber-physical systems: Model formalisms are being developed that both allow the prediction of all relevant product properties and support model learning during operation.

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The virtual images learned can be used in quality control, for example, to monitor standards and specification values. The target values determined along the process chain are compared directly and immediately with the actual values derived from the virtual images. "This approach makes it possible to identify faulty batches and safety risks while production is still underway and remove them from circulation in order to minimize safety risks and protect consumers," says Professor Jan Schneider, Deputy Director of the ILT.NRW, explaining one of the aims of the research project. Furthermore, due to the real-time forecasts of process or raw material fluctuations, the aim is to improve "space, time and raw material yield", which can give food producers a competitive advantage. as

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