The Korea Food Research Institute (KFRI) said Wednesday its researchers have developed a technology to manage food-poisoning bacteria in food materials safely in variable temperature environments of the food supply chain, such as storage and distribution.

Researchers at the Korea Food Research Institute (KFRI) have developed a real-time safety management technology to detect food poisoning bacteria in the food supply network's variable temperature environments.
Researchers at the Korea Food Research Institute (KFRI) have developed a real-time safety management technology to detect food poisoning bacteria in the food supply network's variable temperature environments.

KFRI’s “dynamic prediction model” can predict the proliferation of food poisoning and contamination of food supplies in real-time using the Internet of Things by linking the temperature provided by the food supply network.

Most of the food ingredients in group meals are safe. Still, some food products contaminated with food poisoning bacteria may increase due to temperature changes during the distribution-storage process. This explains why real-time safety management technology is needed in the process, KFRI said in a news release.

The research team developed the dynamic prediction model by assuming egg yolk was contaminated by six salmonella types and three types of staphylococcus. They could then predict the proliferation of salmonella and staphylococcus with the accuracy of root mean square error (RMSE) of 0.095-0.31 due to confirming them in variable temperature cycles in the range of 10-25 and 15-30 degrees. 

The model could predict salmonella’s increase with RMSE 0.04-0.48 accuracy in egg rolls, confirming its applicability in managing food materials in school cafeterias and egg-processing factories.

The model can also predict yellow staphylococcus' proliferation with various toxic gene profiles at high accuracy of RMSE 0.05-0.23. It applies to food separators with diverse traits aside from standard strains used for development. 

KFRI plans to install the developed predictive model in its own dynamic safety management system to enhance food management and develop more predictive models for food processing and distributing companies in the future.

The research results were published in the latest issue of Food Control, an international journal for food safety control. 

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