Simulating Large-Number Bulk-Product Sampling to Improve Food Safety Sampling Plans

Drawing an accurate conclusion about whether a food ingredient or product is safe based on the result of a test is important to the evaluation and management of food safety risk. It is critical that sampling plans maximize the probability of finding a target hazard in an ingredient or a finished product, particularly with non-uniform and low level contamination. Sample collection in bulk ingredients is typically done manually in the food industry, but manual sampling is time-consuming and laborious, and often results in sampling inconsistency. Therefore, a different is needed for rapid and efficient collection of representative samples of these products. The goal of this project is to build a validated and ready-to-use simulation model of bulk product sampling to improving sampling plans.

Institution: University of Illinois at Urbana-Champaign
Principal Investigator: Matthew Stasiewicz, PhD
Amount Awarded: $109,949
Year Awarded: 2018

Read more: Evaluation of the Impact of Skewness, Clustering, and Probe Sampling Plan on Aflatoxin Detection in Corn

View this project on the Center for Open Science’s Open Science Framework.

Learn more about the IAFNS Food Microbiology Committee.