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Many methods are in place, or have been tested, to provide a reliable indicator of the quality of the produce the retailer puts on the shelf for the consumer. Each of these have unique and “tried” methods of prediction, but with so many factors that influence quality and shelf life in the supply chain it would seem to be impossible to provide an all-encompassing method.

With this objective in mind DeltaTrak has been working with the University of South Florida to find a unique method of Quality prediction taking into consideration: The time the produce was picked, the temperature data and pre-cool tunnel duration, the transport times and temperatures to the distribution center, and the final “mile” transportation to the retailer’s shelf.

Working with bagged greens, fresh cut greens and berries, the team has developed an algorithm and Look Up Tables (LUT”s) that when fed into the analysis produce a set of finite Quality Codes that can predict, with a high degree of certainty, the actual quality and shelf life of the product on the retailer’s shelf.

This model and the LUT’s have been validated against other tables like those from the USDA (used for shelf life predicators) and have come up with +99% accuracy level. Next step is to include this Temperature Emulation algorithm into DeltaTrak’s temperature and condition data recorders that track produce conditions from “Farm to Retailer” to ensure that the consumer knows the true quality of the food they eat.

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Peter Norton is a senior logistics and supply chain manager and consultant to the life science industry, with extensive experience in both the domestic US and global markets.

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