A prompt reaction to deviations in product quality is only feasible if the required information is provided in real-time. Measurements of supply and return air temperature, as provided by standard telematics units are insufficient for predicting product temperatures and resulting quality deviations. The ‘intelligent container’ can capture spatial temperature deviations via use of a network of 10 to 20 wireless sensor nodes. The nodes measure product temperature directly inside the packaging, and biological models calculate the effect of various deviations in environmental parameters on the product quality. Gas sensors are able to monitor additional factors influencing quality. Ethylene gas in particular is related to the ripening process of several fruits.
The results from the project were verified using case studies with bananas and different meat products. The case studies included temperature mapping for truck and container transport, development and parameterisation of shelf life models, and test transport with our prototype of the ‘intelligent container’. Possible decision points for implementing the FEFO principle were defined after a detailed analysis of the supply chain of three project partners.
- Lang, W.; Jedermann, R.; Mrugala, D.; Jabbari, A.; Krieg-Brückner, B.; Schill, K.: The Intelligent Container - A cognitive sensor network for transport management. In: IEEE Sensors Journal Special Issue on Cognitive Sensor Networks, 2011, Vol. 11(3), pp. 688-698 [pdf] DOI (Digital Object Identifier): 10.1109/JSEN.2010.2060480
- Jedermann, R.; Nicometo, M.; Uysal, I.; Lang, W.: Reducing food losses by intelligent food logistics In: Philosophical Transactions of the Royal Society A, May/June 2014, Vol. 372(2017), 20130302. DOI: 10.1098/rsta.2013.0302
Steffen Janssen Email
Reiner Jedermann Email
Walter Lang Email
Institute for Microsensors-, actuators and -systems, University of Bremen, Germany