Food waste is an increasing concern both because of costs to industry productivity and because of the need to assure food security.
Australia is a relatively large producer of red meat (beef and lamb) and exports those products to many nations. The products are usually exported in vacuum-packs (VP), and shipped at low temperatures (~ -1°C). Under these conditions, there is no perceptible loss of quality for up to 160 days for beef products, and ~90 days for lamb products. If, however, temperature control is lost, the quality of the product can deteriorate more rapidly. The ability to quantify the loss of quality or shelf life due to such lapses of temperature control would enable better decisions to be made about the disposition of such products, i.e., rather than simply discarding products for which temperature control has been temporarily lost.
Quality standards for VP meats are often expressed in terms of ‘total viable counts’ but in VP products TVC is dominated by lactic acid bacteria that do not necessarily cause spoilage.
We studied changes in TVC and organoleptic quality in vacuum packed beef and lamb primals stored at a range of temperatures (-1, 2, 4, 8°C).
From the microbiological and organoleptic assessments we were able to develop a predictive mathematical model for the end of quality shelf life of Australian VP beef and lamb products.
The reliability of the predictive model was evaluated by a series of trials in export supply chains including Japan, and the Middle East and a domestic supply chain of a national retailer.
The results supported the reliability of the model for vacuum-packed beef and lamb products, but also showed that the model was less reliable for product in modified atmosphere packaging, or reprocessed and repackaged in an aerobic overwrap.
The model has been incorporated into a software tool available to Australian meat processors to enable them to better manage their value chains and reduce product waste as well as maintaining the quality reputation of the Australian meat industry.
This poster describes the development of the model, the relationship between microbiological and sensory quality metrics, and the evaluation of the model against independent ‘real world’ data.