Seafood CRC: time-temperature management to maximise returns through the prawn supply chain
University of Tasmania (UTAS)
There are many opportunities for product to deteriorate in the value chain. However appropriate correction actions imply a transparent view of handling conditions. For seafood in general, temperature has the greatest effect on product quality. However, it is not just temperature but exposure time. As such, knowing the specific time-temperature profile of a product is essential to interpret effects of steps in the supply chain that maximise quality, and those that do not. Knowing where correct handling occurs will allow an industry to focus its resources on where mistakes are made. Currently, knowledge about the performance of prawn chains is mostly anecdotal and doesn’t ensure that remedial actions are appropriately targeted. A remedy to this problem is Time-Temperature Indicators (TTIs) that provide clear evidence about chain performance and permit fisheries and the ACPF to plan corrective actions. However the effects of time-temperature on quality parameters cannot be extrapolated across all product forms. For example, microbiological changes that affect quality occur at different rates for raw versus cooked product, and for frozen versus chilled product. Such differences are influenced by physical process that can reduce microbial load, inactivate/activate chemical reactions and increase water activity. In addition, microbial load and types of spoilage organisms can differ by fishery. For example, tropical conditions select for species of bacteria that do not survive well under refrigeration, whereas fisheries in cooler environments do. For these reasons, this project will develop predictive tools that consider the effect of fishery and product type on changes in prawn quality, thus providing industry with robust tools for improving handling practices. However, successfully using these tools assumes that industry collects time-temp data. Therefore, this project will test and identify TTIs that are suitable (accurate, robust, cost-effective) for prawn supply chains.
1. Produce predictive models for King prawns that consider fisheries and product type
2. Map supply time-temperature profiles to identify points that reduce product quality
3. Validate predictive models in commercial supply chains
4. Identify appropriate TTIs for industry to evaluate the performance of supply chains