Our research projects

Photo: Manuel Gutjahr


Differenzierung von Autofluoreszenzsignaturen zur Online-Erfassung bakterieller Kontaminanten in der automatisierten Fleischzerlegung.
Coordinating Institute
Leibniz-Institut für Agrartechnik und Bioökonomie e.V. (ATB)
Leibniz-Universität Hannover

Minimal processing of meat cutting not only necessities a gentle handling with the meat himself, but also demands for a careful use of resources. The work of the entire cluster intends to a high-quality meat processing requiring a minimal amount of energetic reserves, manufacturing and cleaning agent materials. The aim of the ATB subproject is the development of a method to predict/detect bacterial contamination on meat surfaces (e.g. pork tenderloin, pork ham) for online monitoring in meat processing. Therefore the method has to be fast and non-invasive in order to be integrated in an automated dissection process. In terms of a minimal processing the contaminated samples should be discharged as soon as possible to prevent cross-contamination. A possible contamination of the devices should also be detected to initiate a following cleaning step. An improved quality together with high throughput accompanied by lower product losses and reduced holding times represents a competitive advantage for small and medium sized companies. In this project basic knowledge of the detection of bacterial fluorescence on meat surfaces will be investigated. Therefore, the matrix effects influencing the autofluorescence signals have to be examined. Furthermore, changes of the fluorescence signals will be correlated to the microbiological growth behavior. Finally, chemometrical data processing combined with different data pre-processing methods will be considered for the prediction of the bacterial contamination directly on the meat surface. The attention will be addressed on the formation, the physiology, and the metabolism of the microorganisms. The fluorescence signals of the bacteria itself and/or of their products of metabolism shall be utilized for the non-invasive optical prediction of bacterial contamination.

Deutsche Forschungsgemeinschaft (DFG)
Grant agreement number
BO 3582/1-1 und HI 476/6-1 und SCHL 851/3-1


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