Authors: Ismail Uysal, Jean-Pierre Emond, Gisele Bennett
Radio frequency identification (RFID) enabled temperature tracking technologies are used to monitor perishables such as fresh produce and pharmaceuticals during storage and transportation to validate the temperature integrity of the supply chain. With the help of RFID readers, the data stored in the memory of an RFID tag can be up-linked to a computer for further information processing. In this study, we develop a computationally-efficient, quality index based shelf life estimation model which operates on the stored temperature data in an RFID tag?s sensor memory to predict the remaining shelf life using a parametric matrix. The advantages of the proposed model over conventional approaches like Arrhenius equation include multi-component quality analysis, scalability to higher dimensions with additional environmental parameters such as humidity, greater control over the trade-off between accuracy and complexity and finally adaptability to application requirements and sensory device capabilities such as memory capacity and sampling speed.