Stability of Mozzarella cheese was studied under 8 modified atmospheres (air, vacuum and mixtures carbon dioxide/nitrogen) during 8 wk. Samples, packaged in barrier bags and stored at 10ЊC, were periodically evaluated to investigate microbiological quality and composition of headspace gases. Both consumption of oxygen and production of carbon dioxide occurred in many packages. Modified atmospheres containing carbon dioxide efficiently stabilized lactic and mesophilic flora, while inhibiting staphylococci, molds and yeast. Psychrotrophs grew in all samples but were less numerous in high CO 2 atmospheres. Levels of 75% CO 2 were optimal to repress undesirable organisms and reduce gas formation.

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Current temperature tracking systems lack the convenience and accuracy demanded by the real conditions of a fast-paced produce supply chain. In recent years RFID technology has been suggested to be an enhanced method for temperature tracking because of its many benefits, such as using little instrumentation, offering the quick readings necessary for real-time decision making, and allowing the capture of long-duration temperature profiles. However its limitation lies in that probeless systems fail to provide accurate temperature readings in some of the critical points of the pallet and the load. The objective of this work was to study the use of RFID in temperature monitoring by comparing the performance of RFID temperature tags versus conventional temperature tracking methods, as well as RFID temperature tags with probe versus RFID temperature tags without probes and their utilization along the supply chain. Therefore, the temperature mapping of a shipping trial comprising pallets of crownless pineapples instrumented using different RFID temperature dataloggers and traditional temperature dataloggers and packed in two kinds of packages (corrugated boxes and reusable plastic containers) inside a container was performed. The results showed that RFID temperature tags are analogous with regards to accuracy to the conventional methods, but have a superior performance because they allow quick instrumentation and data recovery, and the possibility of accessing the sensor program and data at any point of the supply chain without line of sight. In addition, the use of RFID tags with probe was justified by its role in determining the efficiency of the pre-cooling operations and low temperature abuse tracking during transportation and refrigerated storage; while the RFID tags without probe proved useful for high temperature abuse monitoring during transportation and refrigerated storage. The creation of a RFID sensor with a probe, able to record both ambient and pulp temperatures simultaneously is suggested.

No abstract

Shelf life of fresh fruits and vegetables is greatly influenced by environmental conditions. Increasing temperature usually results in accelerated loss of quality and shelf-life reduction, which is not physically visible until too late in the supply chain to adjust logistics to match shelf life. A blackberry study showed that temperatures inside pallets varied significantly and 57% of the berries arriving at the packinghouse did not have enough remaining shelf life for the longest supply routes. Yet, the advanced shelf-life loss was not physically visible. Some of those pallets would be sent on longer supply routes than necessary, creating avoidable waste. Other studies showed that variable pre-cooling at the centre of pallets resulted in physically invisible uneven shelf life. We have shown that using simple temperature measurements much waste can be avoided using ‘first expiring first out’. Results from our studies showed that shelf-life prediction should not be based on a single quality factor as, depending on the temperature history, the quality attribute that limits shelf life may vary. Finally, methods to use air temperature to predict product temperature for highest shelf-life prediction accuracy in the absence of individual sensors for each monitored product have been developed. Our results show a significant reduction of up to 98% in the root-mean-square-error difference between the product temperature and air temperature when advanced estimation methods are used.