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Integrating predictive models and sensors to manage food stability in supply chains
Food products move through complex supply chains, which require effective logistics to ensure food safety and to maximize shelf-life. Predictive models offer an efficient means to monitor and manage the safety and quality of perishable foods, however models require environmental data to estimate changes in microbial growth and sensory attributes. Currently, several companies produce Time-Temperature Indicators that react at rates that closely approximate predictive models; these devices are simple and cost-effective for food companies. However, even greater outcomes could be realized using sensors that transfer data to predictive models in real-time. This report describes developments in predictive models designed for supply chain management, as well as advances in environmental sensors. Important innovation can be realized in both supply chain logistics and food safety management by integrating these technologies.
History
Publication title
Food MicrobiologyVolume
75Pagination
90-94ISSN
0740-0020Department/School
Tasmanian Institute of Agriculture (TIA)Publisher
Academic Press Ltd Elsevier Science LtdPlace of publication
24-28 Oval Rd, London, England, Nw1 7DxRights statement
Copyright 2017 Elsevier Ltd.Repository Status
- Restricted