Mechanical damage of agricultural products is one of the main problems in the harvest and postharvest chain due to the large economic loss that the shelf life reduction could generate. Measurements of change in the chemical components of highly perishable products and microbial growth under different static and dynamic loads is the first step in the development of intelligent logistic units that could help in predicting the residual shelf life in order to minimize losses along the supply chain. In our research, the effects of vibration along the supply chain of a case study were analyzed on strawberry (Fragaria x ananassa) and woodland strawberry (Fragaria vesca) in terms of microbiological and quality assessment. Fruits were subjected to vibration in a temperature-controlled environment, simulating the transport conditions. Microbiological and quality analyses were conducted in three different positions along the column of the crates. The results were compared with the nonvibrated strawberries stored in the same environmental conditions along the whole cold chain, showing that vibrations cause a significant decrease in the qualitative characteristics of both fruits. Practical Applications: The practical application obtainable from our research is the development of ad hoc economically affordable sensors based on the volatile organic compounds (VOCs) emitted by the microorganisms showing the most rapid increase. The measurement of the VOCs of the dominant microorganisms could be implemented in a smart logistic unit as it provides information on the microbial evolution in real time. The research proposed configures as a first step to achieve such objective, and toward the development of a supply chain monitoring and control infrastructure relaying on the correlation of vibration phenomena with the VOCs originated by the microbiological activity. The methodology consists of measuring the effects of vibration along a reference supply chain in terms of microbiological and quality assessment, with the aim of extrapolating a mathematical correlation that can further be generalized into a replicable model.
|Numero di pagine||17|
|Rivista||Journal of Food Process Engineering|
|Stato di pubblicazione||Published - 2016|
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