Detecting trace pathogen levels does not always mean health risk, study finds

Researchers warn ultra-sensitive pathogen tests may trigger unnecessary recalls and food waste despite trace detections posing limited health risk.

Detecting trace pathogen levels does not always mean health risk, study findsDetecting trace pathogen levels does not always mean health risk, study finds


Detecting trace levels of foodborne pathogens does not necessarily mean a product poses a health risk to consumers, according to new research questioning strict zero-detection expectations in food safety.

The study, published in Frontiers in Science, warns that increasingly sensitive testing methods can detect extremely small quantities of microbes unlikely to cause illness. However, these detections can still lead to product disposal, recalls or additional controls.

While food safety remains a critical global concern – with foodborne pathogens responsible for around 600 million illnesses and 420,000 deaths each year – the researchers argue that protecting public health does not always require zero-detection standards.

Instead, they say regulators and industry should move towards evidence-based targets for what they describe as “sufficiently safe” food. Such an approach would allow food safety decisions to be balanced alongside factors such as food security, sustainability and nutrition.

Lead author Professor Martin Wiedmann from Cornell University said:

Although the public expects food to be completely safe, there will always be some risk of foodborne illness. Zero risk doesn’t exist, and we shouldn’t be aiming for that either. Just as we don’t limit highway speeds to 10 miles per hour to minimize road deaths, we need to take a balanced approach that considers possible negative consequences of extreme food safety measures.”

Trace detection and food safety decisions

According to the researchers, many food safety standards and purchasing requirements rely heavily on pathogen detection. In some cases, any positive result is treated as unacceptable without considering factors such as pathogen levels, consumer exposure or whether the food can support microbial growth.

For example, foods may be classified as contaminated if testing detects Listeria monocytogenes, regardless of the amount present. Yet modern ultra-sensitive testing technologies can identify extremely small quantities of microbes that may not pose a realistic risk to consumers.

In other situations, tests may detect bacteria that are not harmful themselves but act as indicators of possible contamination. As a result, strict zero-tolerance responses to such findings can lead companies to discard edible food, reducing supply and wasting production resources.

Co-author Professor Sophia Johler from Ludwig Maximilian University of Munich explained:

A tremendous amount of food is wasted that would have been sufficiently safe to eat. Too often, trade-offs such as environmental or economic costs are only considered after a traditional microbial risk assessment. We cannot afford to carry on like this at a time when we desperately need to reduce our impact on the planet and assure not only food safety but food security.”

Moving from hazard detection to risk assessment

More broadly, the authors argue that current food safety frameworks often prioritise hazard-based assessments. Under this approach, the presence of a pathogen may trigger corrective action regardless of the level of risk posed to consumers.

In practice, this may divert attention and resources away from interventions that more effectively reduce contamination risks.

Co-author Dr Sriya Sunil from Cornell University added:

There’s well-established evidence that focusing on end-product testing is generally ineffective to ensure safety. Overemphasis on end-product testing may distract from other food safety measures (e.g., applying validated and verified process controls), which can provide greater public health benefits.”

Looking ahead, the researchers say improved computational tools could help regulators and industry prioritise risks more effectively. Models combining geographic data, artificial intelligence and genomic information could support more accurate assessments of acceptable risk levels across food systems.

Ultimately, they conclude that adopting more flexible, risk-based food safety approaches could maintain strong public health protections while reducing unnecessary food waste and supply disruptions.

Source: newfoodmagazine.com

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