Summary of Awards to Date

Exploring the relationship between product testing and risk

Date

Jan. 1, 2019 - Dec. 31, 2019

Funding Agency

Center for Produce Safety

Amount Awarded

$64,095.00

Investigator

Emma Hartnett, Ph.D.
Risk Sciences International, Canada

Co-Investigator(s)

Donald Schaffner, Ph.D.

Summary

Risk to consumers is directly related to prevalence and concentration of pathogens in products. Sampling to determine if levels of pathogens are at acceptable levels if one approach adopted to manage the consumer risk. However, the relationship between different sampling options, and the reduction in risk provided by implementing those options has not been well described.  We will develop a sampling-risk model that quantifies the relationship between product testing and the risk to consumers. This model will consider factors including sample size (mass), number of samples, lot size, and many others. The result will be a series of tables and charts that describe the relationship between the factors and risk.  Such analyses can be used to explore the efficacy of alternate risk management strategies, helping answer questions such as “if I increase the sample mass what is the impact on risk?” , or “If I sample at point X instead of point Y what will the benefit be?”. 

Technical Abstract

Microbiological sampling is one tool available to help ensure product safety, but where and how to sample in terms of the most effective risk-reduction, and efficient use of resources (money, labour, time) is not always clear. To effectively employ sampling as a risk-management strategy, it is essential to have a clear understanding of the (sometimes complex) relationship between sampling activities in the supply chain and the residual consumer risk. Such an understanding can be supported by the quantitative definition of the relationship (i.e. a model), that is employed to explore how the components of the relationship interact to influence the risk. To meet this need, we will develop a sampling-risk model that quantifies the relationship between product testing, lot rejection rates, and risk and perform detailed analyses of the relationship between product sampling variables driving the risk. The results from this work can support risk reduction initiatives by providing analyses that explicitly enable the exploration of risk management options, facilitating selection of actionable sampling strategies that have the biggest impact on risk reduction. This information (combined with feasibility and cost considerations) can be used by industry, facilitating scientifically supported, risk-based decision-making.