Grants and Contributions:
Title:
Reducing foodborne disease by salmonella control via artificial intelligence networks (FooDSCAN)
Agreement Number:
948226
Agreement Value:
$197,500.00
Agreement Date:
Oct 1, 2020 - Sep 30, 2022
Description:
Salmonella causes the majority of foodborne outbreaks associated with low moisture foods. Approaches to destroy Salmonella in low moisture foods are designed to reduce the concentration of Salmonella in the food by 5 orders of magnitude. However, in certain cases, due to the physical and chemical characteristics of the food, a 5 log reduction may not be possible. In such cases, it is necessary to demonstrate that the concentration and potential growth of Salmonella is low enough that a 5 log reduction is not needed.
The objectives of this research are to investigate the growth kinetics of Salmonella and Enterococcus (a non-pathogenic surrogate used to validate antimicrobial treatments) in low moisture foods including walnuts, sunflower, and wheat, by developing microbial survival data of the Salmonella and Enterococcus in the three foods, to develop predictive mathematical growth models that describe the behavior of the bacteria in low moisture foods
Organization:
National Research Council Canada
Expected Results:
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.
Location:
Guelph, Ontario, CA N1G 2W1
Reference Number:
172-2021-2022-Q4-948226
Agreement Type:
Contribution
Report Type:
Grants and Contributions
Recipient Business Number:
108161829
Recipient Type:
Academia
Additional Information:
This agreement has been amended 1 time(s). The total amended value is 197,500 dollars.
Amendment Date
Mar 24, 2022
Recipient's Legal Name:
University of Guelph
Federal Riding Name:
Guelph
Federal Riding Number:
35032
Program:
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Purpose:
Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.
NAICS Code:
541710
Amendments: