Work

A Validated Preharvest Sampling Simulation Shows that Sampling Plans with a Larger Number of Randomly Located Samples Perform Better than Typical Sampling Plans in Detecting Representative Point-Source and Widespread Hazards in Leafy Green Fields

Year: 2022

Type: article

Abstract: This study addresses a need for improved preharvest sampling plans for pathogen detection in leafy green fields by developing and validating a preharvest sampling simulation model, avoiding the expensive task of physical sampling in many fields. Validated preharvest sampling simulations were used to develop guidance for preharvest sampling protocols. Sampling simulations predicted that sampling plans with randomization are less variable in their power to detect low-prevalence point source contamination in a 1-acre field. Collecting larger total sample masses improved the power of sampling plans in detecting widespread contamination in 1-acre fields. Hence, the power of typical sampling plans that collect 150 to 375 g per composite sample can be improved by taking more, randomized smaller samples for larger total sample mass. The improved sampling plans are subject to feasibility constraints or to meet a particular food safety objective.

Source: Applied and Environmental Microbiology

DOI: 10.1128/aem.01015-22

Authors: Jorge Quintanilla Portillo, Xianbin Cheng, Alexandra M. Belias, Daniel L. Weller, Martin Wiedmann, Matthew J. Stasiewicz

Institutions: University of Illinois Urbana-Champaign, Cornell University, University of Rochester

Language: English

Topic: Listeria monocytogenes in Food Safety

Subfield: Biotechnology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Zero hunger

Open Access status: hybrid

Funders: Center for Produce Safety, International Life Sciences Institute