Challenge tests often represent a significant investment. It’s not uncommon for companies to raise their eyebrows at the price tag of such a study. “Why does this have to take so long? Why so many measurement points? Why are multiple strains needed? And does it really all have to be documented in such detail?” In this article, we discuss the importance of a thorough challenge test and how you can save costs through a smart combination of testing, predictive models, and product grouping.
A poorly conducted challenge test isn’t just a waste of money—it can lead to misleading conclusions, incorrect shelf life estimates, and, in the worst case, foodborne illnesses, recalls, and damage to your brand.
The reason a proper challenge test requires so much time and care is that food safety is rarely black and white in practice. European guidelines, such as those in the EU Technical Guidance Document on Shelf-life Studies for Listeria monocytogenes, rightly emphasize a realistic, well-founded, and reproducible approach. Because food chains are complex and pathogens rarely behave predictably, such a test must approximate the real world as closely as possible.
This means, for example, that the test product must be identical to the commercial end product. Even small differences in pH, water activity, or packaging can have a significant effect on the growth of microorganisms. Conducting a test under “ideal” laboratory conditions may seem convenient, but it says nothing about what happens in a distribution chain with temperature fluctuations or minor deviations in real-world conditions.
The number of measurement points is also very important. If you only check on day 0 and day 10, you can easily miss crucial growth phases. The growth of, for example, Listeria monocytogenes can start slowly but accelerate as soon as competitive flora decreases. Without interim analyses, you won’t see that happening. And without that data, you can’t calculate the maximum growth rate or lag phase—precisely the parameters that are essential for risk assessment or modeling.
Another important aspect is the choice of strains used for inoculation. Using a single laboratory strain is not representative. Different strains—and certainly wild-type strains derived from similar products or production environments—behave differently. Only by using a mix can you obtain a reliable picture of the worst-case scenario. Furthermore, these strains often need to be prepared, adapted, and mixed, which in itself requires time and expertise.
The same applies to the product’s natural flora. In many challenge tests, the product is first pasteurized or filtered to ensure a “clean” start. But it is precisely this flora that often plays a role in either suppressing or stimulating pathogenic growth. By eliminating the flora, you create an artificial situation. That is why, depending on the product type, it is sometimes necessary to conduct a test both with and without flora.
In addition, regulations also require testing under conditions that are not only ideal but also realistic and even unfavorable. Think of storage at 7°C instead of just at 4°C, or simulating temperature fluctuations. These worst-case scenarios aren’t meant to cause panic, but to ensure preparedness for realistic situations in the supply chain. After all, consumers rarely store products exactly as indicated on the label.
Finally, every step in the test must be thoroughly documented: which protocol was followed, which strains were used, how and when samples were taken, and which criteria were predetermined to decide whether growth is “acceptable.” Without that documentation, a test is useless during audits or inspections and cannot be defended in the event of incidents or inquiries from regulatory authorities.
Actually, yes—working cost-consciously and ensuring food safety don’t have to be mutually exclusive, provided you take the right approach. The key lies in a well-thought-out combination of challenge tests and predictive models, as well as in the smart grouping of products. Theoretical models, such as ComBase or FSSP, can provide valuable insight in advance into the growth potential of pathogens in your products. They are useful for assessing which product categories are at increased risk and which are more resilient. Models are not a substitute for testing, but they can support targeted decisions, such as which product to test and under what conditions. Read more here about the hybrid approach that combines models and challenge testing. In addition, you can group products based on shared characteristics: pH, water activity, composition, storage temperature, and packaging. Provided this is well-founded, you can then select a representative “worst-case” product that covers the entire group. Keep the following points in mind:
A well-thought-out product group, combined with a robust worst-case challenge test and supported by models, ensures that you do not have to test every product individually. This way, you avoid duplicate costs without compromising on safety or legal justification.
A thorough challenge test is rightly considered an investment, but it is also a powerful tool for identifying risks, accurately determining shelf life, and avoiding disputes with regulatory agencies or auditors. Those who recognize the complexity of such a test and use it wisely can work more efficiently in the long run. Combine models with practical studies, build well-founded product groups, and document your choices transparently. This way, you not only save money but also enhance the credibility of your food safety policy.
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