eCite Digital Repository

Integrating predictive models and sensors to manage food stability in supply chains


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 Statement

Copyright 2017 Elsevier Ltd.

DOI: doi:10.1016/


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 Details

Item Type:Refereed Article
Keywords:predictive models, sensors, food safety, spoilage, supply chains
Research Division:Biological Sciences
Research Group:Microbiology
Research Field:Microbiology not elsewhere classified
Objective Division:Health
Objective Group:Public health (excl. specific population health)
Objective Field:Food safety
UTAS Author:Tamplin, ML (Professor Mark Tamplin)
ID Code:123484
Year Published:2018 (online first 2017)
Web of Science® Times Cited:15
Deposited By:Tasmanian Institute of Agriculture
Deposited On:2018-01-10
Last Modified:2018-12-13

Repository Staff Only: item control page