Jan. 1, 2019 - Dec. 31, 2020Amount Awarded
Matthew Stasiewicz, Ph.D.
University of Illinois at Urbana-Champaign
Martin Wiedmann, Ph.D.Resources
Effective preharvest, field-level produce sampling is challenging because current practices typically yield few positive samples with fields rarely re-testing positive. Statistical theory suggests one reason is that detecting rare contamination events would require 100s to 1,000s of random samples, or targeting sampling towards higher risk locations in the fields. This project will develop and validate tools for the produce industry to evaluate exiting and improved produce field sampling plans. Specifically, we will develop a simulation model of contaminated in-field produce and the process of picking samples. We will simulate fields in four regions of the US, contaminated by fecal deposits, irrigation water, or low-level soil bacteria. We will simulate convenience, generic, and risk-based sampling plans. Results will be used to communicate to growers the number and location of samples needed to achieve a known power to detect contamination. We will validate these simulations against academic literature, industry partner data, and field-trials of controlled contamination of spinach. Our goal is to provide tools for growers to (i) develop improved sampling plans, (ii) customize those plans to their individual fields, and (iii) quantify the performance and costs of the plan – all to better identify and manage preharvest food safety risks.
Effective preharvest, field-level produce sampling is challenging because current practices (such as compositing 60 samples collected in a Z-pattern) typically yield few positive samples and only rarely do fields re-test positive. Statistical theory suggests one reason is that powerful sampling to detect rare contamination would require 100s to 1,000s of random samples and that not all sampling plans are equally effective at detecting point-source, systematic, or sporadic contamination. An alternative to current industry practice is risk-based sampling, which could incorporate recently developed and validated geospatial models that identify field areas with predicted high or low pathogen prevalence. Thus, the produce industry has a research need for tools to improve preharvest sampling plans, including risk-based sampling. This project will meet this need by developing and validating a produce field simulation model to evaluate sampling plans. These fields will be representative of (i) the Central Valley, CA; (ii) Yuma, AZ; (iii) the Delmarva Peninsula; and (iv) Upstate NY. For each field, we will simulate one of three types of contamination: (i) point sources, e.g. fecal deposits; (ii) systematic sources, e.g. contaminated irrigation water; and (iii) sporadic contamination, e.g. low-level contamination by endemic soil bacteria. We will simulate, in silico, sampling produce from these fields according to (i) convenience sampling plans, e.g. n60 composites of Z-pattern samples; (ii) improved generic plans, e.g. random, systematic, or stratified random sampling; and (iii) risk-based plans, targeting field-specific, high-risk locations. Results will be used to communicate to growers the number and location of samples needed to detect the hazard for a target proportion of contamination events (e.g., 95%). We will validate these simulations against academic literature and industry partner data, including data from n60 composite sampling of fields in the Central Valley, CA and Yuma, AZ. Finally, we will validate simulations against fieldtrials of spinach subject to controlled contamination events, by taking 450 individual samples covering entire fields and evaluating multiple samplings of those fields in silico. The goal of this project is to help growers (i) develop improved sampling plans for in-field produce, (ii) customize those plans to their individual operations, and (iii) quantify the power and costs of the new plans. These results will impact the industry by enabling proactive risk management through efficient detection of pathogens in fields.