Highlights
- Two FDA detection methods were modeled for C. cayetanensis.
- Assay performance was modeled for detecting contamination during cilantro production.
- Frequent water testing most powerfully detects water contamination.
- End of season produce testing most powerfully detects in-field contamination.
Abstract
As of August 2023, the two U.S. Food and Drug Administration (FDA) official detection methods for C. cayetanensis are outlined in the FDA Bacteriological Analytical Manual (BAM) Chapters 19b (produce testing) and 19c (agricultural water testing). These newly developed detection methods have been shown to not always detect contamination when present at low levels. Yet, industry and regulators may choose to use these methods as part of their monitoring and verification activities while detection methods continue to be improved. This study uses simulation to better understand the performance of these methods for various produce and water sampling plans. To do so, we used published FDA test validation data to fit a logistic regression model that predicts the methods’ detection rate given the number of oocysts present in a 10 L agricultural water or 25 g produce sample. By doing so, we were able to determine contamination thresholds at which different numbers of samples (n = 1,2,4,8,16, and 32) would be adequate for detecting contamination. Furthermore, to evaluate sampling plans in use cases, a simulation was developed to represent C. cayetanensis contamination in agricultural water and on cilantro throughout a 45-day growth cycle. The model included uncertainty around the contamination sources, including scenarios of unintentionally contaminated irrigation water or in-field contamination. The results demonstrate that in cases where irrigation water was the contamination source, frequent water testing proved to be more powerful than produce testing. In scenarios where contamination occurred in-field, conducting frequent produce testing or testing produce towards the end of the season more reliably detected contamination. This study models the power of C. cayetanensis detection methods to understand the sampling plan performance and how these methods can be better used to monitor this emerging food safety hazard.
DOI