The improvements in sensors and wireless technology offer an effective way to enhance food safety and certification along all the perishable goods supply-chain, in order to reduce food waste and losses, while guaranteeing a high degree of quality and preventing diseases directly related to the use of expired or harmful products. In this paper, a complete system for continuous environmental parameters (i.e. temperature, light exposition and relative humidity) acquisition and real-time shelf-life prediction of monitored product is proposed. An algorithm based on a 1st order kinetic model of the product quality decay with a variation rate evaluated accordingly to the Arrhenius law is proposed. A case study is also shown, i.e.: data during the storage phase of agricultural product (tomatoes) have been acquired through a wireless sensor networks and uploaded to a cloud service. The collected data, a sample per 15 minutes, are processed by the computation algorithm implemented on laptop: the overall delay due to data download and processing is just about 0,3 s. As consequence, the remaining shelf-life of the food can be estimated with a 5% uncertainty with a 2K temperature sensor, highlighting critical situation in the manufacturing environment and allowing timely intervention.

On-line shelf-life prediction in perishable goods chain through the integration of WSN technology with a 1st order kinetic model / Annese, Valerio F.; DE VENUTO, Daniela. - (2015), pp. 605-610. (Intervento presentato al convegno 15th IEEE International Conference on Environment and Electrical Engineering, EEEIC 2015 tenutosi a Roma, Italy nel June 10-13, 2015) [10.1109/EEEIC.2015.7165232].

On-line shelf-life prediction in perishable goods chain through the integration of WSN technology with a 1st order kinetic model

DE VENUTO, Daniela
2015

Abstract

The improvements in sensors and wireless technology offer an effective way to enhance food safety and certification along all the perishable goods supply-chain, in order to reduce food waste and losses, while guaranteeing a high degree of quality and preventing diseases directly related to the use of expired or harmful products. In this paper, a complete system for continuous environmental parameters (i.e. temperature, light exposition and relative humidity) acquisition and real-time shelf-life prediction of monitored product is proposed. An algorithm based on a 1st order kinetic model of the product quality decay with a variation rate evaluated accordingly to the Arrhenius law is proposed. A case study is also shown, i.e.: data during the storage phase of agricultural product (tomatoes) have been acquired through a wireless sensor networks and uploaded to a cloud service. The collected data, a sample per 15 minutes, are processed by the computation algorithm implemented on laptop: the overall delay due to data download and processing is just about 0,3 s. As consequence, the remaining shelf-life of the food can be estimated with a 5% uncertainty with a 2K temperature sensor, highlighting critical situation in the manufacturing environment and allowing timely intervention.
2015
15th IEEE International Conference on Environment and Electrical Engineering, EEEIC 2015
978-1-4799-7992-9
On-line shelf-life prediction in perishable goods chain through the integration of WSN technology with a 1st order kinetic model / Annese, Valerio F.; DE VENUTO, Daniela. - (2015), pp. 605-610. (Intervento presentato al convegno 15th IEEE International Conference on Environment and Electrical Engineering, EEEIC 2015 tenutosi a Roma, Italy nel June 10-13, 2015) [10.1109/EEEIC.2015.7165232].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/81865
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