Tamplin, ML, Integrating predictive models and sensors to manage food stability in supply chains, Food Microbiology, 75 pp. 90-94. ISSN 0740-0020 (2018) [Refereed Article]
Copyright 2017 Elsevier Ltd.
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.
|Item Type:||Refereed Article|
|Keywords:||predictive models, sensors, food safety, spoilage, supply chains|
|Research Division:||Biological Sciences|
|Research Field:||Microbiology not elsewhere classified|
|Objective Group:||Public health (excl. specific population health)|
|Objective Field:||Food safety|
|UTAS Author:||Tamplin, ML (Professor Mark Tamplin)|
|Year Published:||2018 (online first 2017)|
|Web of Science® Times Cited:||8|
|Deposited By:||Tasmanian Institute of Agriculture|
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