Improvement in fresh fruit and vegetable logistics quality: berry logistics field studies

Real-Time Monitoring System for Shelf Life Estimation of Fruit and Vegetables

“…The solutions available make it possible to measure at different points in order to determine the variations that may exist in the thermal conditions of the commodities. In an example, during transport, air and product temperatures do not have the same dynamics, so fluctuations will differently affect both [17][18][19].Radio Frequency Identification (RFID) solutions allow a wide deployment of sensors throughout the load and can be used up to battery-less operation [20]. A systematic review covering the 2004-2018 period was conducted in [21], citing the advantages and disadvantages of this technology and its practical implementation in temperature monitoring.Wireless sensor networks (WSN) deployments are also widely employed since they allow multi-hop node architectures, which solves coverage problems among nodes at times.…”

“…The refrigeration unit of the transportation system will then compensate the heat coming from the exterior by transmission or irradiation, and the respiration heat produced by the load [25].Due to air thermal dynamic is higher than that of the commodities, since they have a 'thermal inertia', air temperature changes much faster than that of the product [26]. For this reason, the use of air temperature measurement systems requires appropriate statistical indicators that will allow the integration of differences in the dynamics between air and product temperatures [18].Modelling methods such as accelerated shelf life testing (ASLT) [27], or the multivariate accelerated shelf life testing (MASLT) [28] based on principal component analysis (PCA) [27], have been proposed for shelf-life modelling. Recently, artificial neuronal networks (ANNs) have been regarded as a flexible modelling tool, because neural networks can model any type of relationship within the data with high accuracy [29].…”

“…Recently, artificial neuronal networks (ANNs) have been regarded as a flexible modelling tool, because neural networks can model any type of relationship within the data with high accuracy [29]. As a recent evolution, machine learning (ML) techniques are used with artificial neural networks in the field of agricultural processes both in irrigation management [30] and in postharvest, specifically for shelf life prediction [18,31]. In [22], a specific ML is designed for the management of the cold chain for perishable food products.…”

“…The solutions available make it possible to measure at different points in order to determine the variations that may exist in the thermal conditions of the commodities. In an example, during transport, air and product temperatures do not have the same dynamics, so fluctuations will differently affect both [17][18][19].…”

“…Due to air thermal dynamic is higher than that of the commodities, since they have a 'thermal inertia', air temperature changes much faster than that of the product [26]. For this reason, the use of air temperature measurement systems requires appropriate statistical indicators that will allow the integration of differences in the dynamics between air and product temperatures [18].…”