WSN-based near Real-time Environmental Monitoring for Shelf Life Prediction through Data Processing to Improve Food Safety and Certification

Abstract

This position paper aims to support a control technique in the perishables goods supply-chain through a combination of near real-time wireless sensor network (WSN) for environmental monitoring and further data processing to predict the shelf life of the product. This approach returns a low cost, versatile and efficient tool that can significantly improve the safety and food certification through the organoleptic qualities control using three different sensors, i.e. temperature, light and humidity. In this article, therefore, the advantages of the proposed technique are explained and a case study is presented to support this approach, as well as an example of processing algorithm for shelf life evaluation.

Spatio-Temporal Optimization of Perishable Goods’ Shelf Life by a Pro-Active WSN-Based Architecture

“…A number of literature studies [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ] have showed that, among the extracted environmental variables, the temperature is the parameter that significantly emphasizes worsening food quality.…”

“…Since the Arrhenius law parameters are typically determined with non-linear regression of empirical data, such as the one in [ 6 , 21 , 22 ], in the following, we will insert the kinetic parameters, provided by a tomatoes-centered study, in a general formula for k rate derivation, suitable for any temperature: with k ref : k value at the dedicated reference temperature T ref .…”

“…There are several predictive SL models [ 5 , 6 ], which identify the amount of product degradation during the ageing process, basing their computing on temperature, humidity and illumination [ 5 ].…”

“…By combining the environmental real-time monitoring capability of the Wireless Sensor Networks (WSN) with the integration, on portable devices and platforms, of predictive SL models, it becomes possible to detect interruptions in the cold chain, and act in advance to compensate abnormal SL failure [ 6 , 7 ].…”

Wearable platform for automatic recognition of Parkinson Disease by muscular implication monitoring

“…The solution aims to be a diagnostic tool for the early detection ofthe disease, as well as a useful clinical support tool for monitoring the therapy impact. Future perspectives include the design and fabrication of an Application Specific Integrated Circuit (ASIC) [23][24][25][26][27][28][29][30], the use of biocompatible and flexible electronics in order to increase the wearability degree of the system [31,32] and the optimization of the wireless network [33][34][35][36].…”