program (EMP) is often documented during food safety audits and U.S. Food and Drug Administration (FDA) inspections as indicating a pattern of inadequate cleaning and sanitation. FDA guidance suggests that findings should occasionally be expected since Listeria is a common organism in the environment, but that such findings should also be remediated without ongoing and repeated occurrences. This is where the realities of production begin to challenge the good intentions of this regulatory guidance. Listeria testing in the industry is routinely conducted as presence/absence testing for all Listeria species, rather than strain typing to determine which isolate(s) are present. If routinely adopted, typing and sequencing techniques would provide the scientific information needed to determine whether repeated positives over time involve the same strain—which more directly addresses whether the organism may have established a harborage point in the plant and avoided control through cleaning and sanitation. However, this type of data is less commonly generated in industry. Why? It comes with additional costs and risks. This testing is not required by regulation or guidance, more information does not change the positive result found or the corrective actions that must be completed, and if additional learnings are generated, the data may also become a more threatening dataset that could be used against an organization in the event of an issue. The incentive to more fully characterize the positive result simply is not there. The legal and business risk overwhelms the operational and scientific benefits. Due to perceived legal risk surrounding the collection of robust Listeria data, there is an understandable fear of creating data that could be misinterpreted. The unintended consequence is that the system creates pressure favoring data gaps—datasets without positives and/or datasets collected vaguely enough to make inappropriate conclusions harder to draw. Compliance needs to evolve. It should not favor missing signals of risk that are known to occur, but instead favor signals of prevention based on a broader dataset—one that captures the effort and actions an operator takes to characterize and control risk. For example,
Joelle Mosso
does the operator know whether it is the same strain over time? Are they evaluating typing information, vectoring, and patterns of control rather than simply overall positivity rates? Are they proactively identifying short- and long-term solutions through increased sampling, sanitation improvements, new chemicals or materials, improved hygienic design, or implementing real-time monitoring of buildup (ATP/bioburden) during production? The failure of compliance under this framework is the absence of data and information around the controls. It is the absence of ever finding positives, the lack of strain-tracking investigations and data points, weak monitoring efforts and the collection of messy or nonexistent datasets that obscure the complete picture of prevention and control. The framework must evolve to capture the effort, intention and reality of what it means to manage a non- zero risk—rewarding operators whose overall efforts, data and programs align with the good, bad and ugly realities in which we operate, rather than simply rewarding the idealized compliance narrative of what we want systems to look like. As long as the bias toward rewarding the appearance of compliance over evidence of well-managed food safety risk remains, we will continue to mistake a fully documented system for a safe one.
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11 Western Grower & Shipper | www.wga.com July – September 2026
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