Drawing accurate conclusions about whether an ingredient or finished product is safe based on the results of a test is important to the evaluation and management of food safety risk. With the expected prevalence of contamination in today’s food system at less than 1%, extremely large samples sizes are required to reliably detect contamination, and the potential for false negatives during routine sampling is high. It is therefore critical that samples are representative of the ingredient or product being evaluated, and that sampling plans maximize the probability of finding a target hazard -- particularly as contamination patterns are often heterogeneous rather than uniform. This project will leverage a recently-developed bulk product simulation model to create a publicly available model used to detect low-prevalence, low-level contamination in powdered products and ingredients, such as powdered milk and cocoa powder.
Institution: University of Illinois at Urbana-Champaign
Principal Investigator: Matthew Stasiewicz, PhD
Year Awarded: 2021
- An extended tutorial/user video to utilize the app can be found here
View this project on the Center for Open Science’s Open Science Framework.
- This project was referenced in a Perspectives paper published in the Journal of Dairy Science.
Learn more about the IAFNS Food Microbiology Committee.