In this task the mathematical growth models for meat pathogens and the continuous temperature monitoring with TTI will be used as the elements in the SMAS algorithm. This program core will allow the evaluation of the safety risk of individual product units (e.g small pallets or even single packs) at strategic control point of the chill chain. Based on the distribution of the product safety potential it is possible to make decisions for optimal handling, destination and stock rotation, aiming to obtain a narrow distribution of safety at the point of consumption. Based on the quality of each product unit relative to this distribution, decisions about its further handling are made. For the most abused products the decision will be to shorten the time before consumption i.e. to advance them for consumption as quick as possible or for products that SMAS estimates an unacceptable predicted risk at the time of consumption to withhold them and discard or channel to a different process that will eliminate risk.

The developed SMAS algorithm in Sub-workpackage 4.2 will be the basis of a practical tool of chill chain management. This will be achieved by developing a user friendly software that will integrate the meat safety and quality prediction models (WP1), the TTI response kinetics and correlation routine (software developed in WP3) and risk assessment data from WP2. The software will be developed in appropriate computer programming language. Visual Basic will be a practical and widely applicable environment. More advanced application packages will be considered if deemed necessary from the evaluation of software performance in the filed testing (WP5). At the chill chain decision point the TTI response and product characteristics will be input to the software and the software will generate as output instructions with regards the management of the product.

To assess the application of the developed safety monitoring and assurance management system based on SMAS the Monte Carlo numerical simulation technique will be applied. It will be based on the generation of hypothetical, realistic ‘scenarios’ of handling, storage and transport throughout the meat distribution chain, using temperature data provided by the obtained database(see WP2, Task 2.1.1.)

The final quality calculation procedure is repeated many times, including cases of good as well as problematic marketing practice. Eventually, the analysis leads to a frequency, instead of a single point value, for the output of interest i.e. the level of risk and quality (expressed as remaining shelf life) that has taken into account the probability distribution of temperature conditions.