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

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.