(i.e. regulatory requirement). If a hazard has been known to occur, infrequently detecting the target with testing may occur from sampling design insufficiencies, limitations in the testing methodology or a combination of both. The second scenario is generally much harder to diagnose and interpret since it can be influenced by numerous factors that require investigation. Irrespective of the technical reason why few positives are detected, a related issue when analyzing testing programs is that negatives on samples are rarely questioned since they support the preferred outcome and expectation. In general, negative samples lead one to interpret that the process is under control and that all food safety efforts “pass the test.” Positive feedback in this scenario is the receipt of a negative test result. Quite simply, in food safety, a negative dataset is very comfortable, but ultimately not very valuable in reducing risk to consumers since it is only when detections are found that we modify behavior (e.g. destroy product, conduct additional sanitation, etc.). The change in behavior, and not the test result, is what would reduce risk in the system. Evolving to a preventative, risk-based food safety paradigm is an exciting concept and one that food safety professionals have anxiously been waiting for. Receiving a call
from regulators or public health officials that your product has been linked to an outbreak is one that no person or company wants to receive; it is the worst fear and ultimate signal of failure. One illness has always been one too many. While the improvement of testing technology and computational power creates the technical infrastructure for the shift toward risk- based management and preventative food safety, it is critical that the industry not overlook the need to establish a food safety culture where receiving failing results has been normalized and encouraged. Shifting goals to manage risks and not absolutes ultimately requires mentally accepting that there is some level of residual risk that we may have, especially in systems without processing interventions, and that testing programs with zero to few detections should not be the goal. Testing and analysis only contribute to reducing risk when information is learned from the result. Detections teach us and allow us to focus behaviors to drive residual risks continually lower. A perfect score from pathogen testing is generally not the signal of a healthy system, but a testing program that may require attention and optimization to detect the targets. Regardless of how large the testing program is, negative test results provide
no motivation for behavior change over time, and thereby do nothing to reduce risk to consumers. This is counterintuitive since many testing efforts require enormous financial and labor resources to execute; they surely are not easy. Alongside advancements in testing and analysis tools, data-sharing platforms are also being developed to help support broader and faster learning by providing an opportunity to see not just how one product, plant or industry performs, but to create enhanced visibility about how they perform relative to other operations. The increased visibility via data-sharing may further exacerbate inherent sensitivities toward observing detections, and the food industry and regulatory agencies must focus on building a social and regulatory framework that incentivizes finding failing test results. The advent of preventative, risk-based food safety management is an exciting and revolutionary time for the food industry. As the world connects through data analysis, new insights into the interrelatedness of our food and production ecosystems will offer new understanding and strategies for producing the safest, most sustainable food system possible.
JANUARY | FEBRUARY 2024
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Western Grower & Shipper | www.wga.com
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