Measurement Uncertainty in Microbiology: Estimating via Validation Data

Microbiological testing plays a vital role in ensuring the safety and quality of food, water, and many other products. Whether it's counting bacteria, estimating the presence of pathogens, or confirming the absence of contamination, microbiology labs provide critical data. But how confident can we be in those results?

Here concept of Measurement Uncertainty (MU) comes into play. Why it’s important, and how one common and practical method of estimating uncertainty is by using data from standard method validations, such as those published by ISO, AOAC, or other competent bodies/organizations.

What is Measurement Uncertainty?

Simply put, measurement uncertainty tells us how much doubt there is in a test result.

Let’s say your lab analyzes a food sample and reports a result of 150 CFU/g (colony forming units per gram). That number is an estimate, not an exact count. Due to natural variation and many influencing factors during the test, the actual value could be slightly higher or lower. Measurement uncertainty defines the range within which the true value is likely to lie.

For example:  Result: 150 CFU/g ± 50 CFU
This means the actual number of bacteria is likely between 100 and 200 CFU/g.

Why is Measurement Uncertainty Important?

Measurement uncertainty is not about questioning the reliability of a lab. In fact, it’s quite the opposite. It shows transparency, scientific integrity, and compliance with international standards.

Why MU matters:

• Compliance with ISO/IEC 17025:2017: All accredited labs must evaluate uncertainty for quantitative results.

• Supports decision-making: Whether a product passes or fails a specification can depend on MU.

• Builds trust: Regulators, clients, and auditors appreciate honest and traceable uncertainty reporting.

Sources of Uncertainty in Microbiology

Microbiology isn’t like weighing a sample on a balance and autoclaving a media. We’re working with living organisms, and many things can influence their detection and growth.

Common sources of uncertainty include:

• Sample handling: Differences in homogenization or sub-sampling.

• Dilutions: Small volume errors can cause big changes in results.

• Media preparation: pH, temperature, or composition variations.

• Incubation: Time and temperature inconsistencies.

• Counting error: Overlapping colonies or subjective interpretations.

• Operator variability: Different analysts may interpret or perform steps slightly differently.

In microbiology, organisms reproduce exponentially. That’s why results are often expressed as log10 values to make variability easier to manage statistically.

How Do Labs Estimate Measurement Uncertainty?

There isn’t just one method for calculating measurement uncertainty. In fact, the ISO 19036:2019 standard (for food chain microbiology) and guidelines describe multiple models based on available data and testing conditions.

Some commonly used approaches include:

1. Technical Replicates: Measuring the same spiked sample repeatedly to assess variation.

2. Recovery Studies: Comparing how well a method recovers known organisms from different matrices.

3. Intra-laboratory Reproducibility: Running duplicate tests over time, with different analysts and equipment, to assess routine variation.

4. Validation Data from Standard Methods: Using published validation studies to estimate uncertainty.

Here will discussed only about the estimation of MU by using Validation Data from Standard Methods:

Using Method Validation Data to Estimate Uncertainty

One practical approach—especially for labs using internationally recognized methods—is to use validation data from standard methods like those from ISO, AOAC, or EN standards.

These organizations often conduct interlaboratory validation studies. That means multiple accredited labs test the same samples using the same method, and the results are analyzed for repeatability (how well results agree within the same lab) and reproducibility (how well results agree between labs).

So how does this help?

If your lab is using a validated method, and you can show that your performance is consistent with what was seen in the validation study, then you can use their reproducibility data as a reasonable estimate of your own uncertainty.

This is a perfectly valid approach under ISO 21748:2017, which guides the use of such estimates for MU.

Of course, you still need to:

• Confirm your lab has acceptable bias and repeatability (by doing some internal checks).

• Make sure the validation study included all steps of the method, like sample preparation.

• Consider any extra variability in your process (e.g., manual weighing or pipetting) that wasn’t part of the validation.

Once all that is done, you apply a coverage factor (usually k = 2 for 95% confidence) to get your expanded uncertainty (U).

For example, if the published reproducibility for a method is 10%, and your lab shows consistent results, you may report a result like:

Result: 150 CFU/g ± 15 CFU (10%)

If your internal checks show slightly more or less variation, you can adjust this accordingly.

Key Benefits of This Approach

Time-saving: No need to generate fresh datasets from scratch.
Standardized: Uses globally recognized data and methods.
Efficient for routine testing: Especially useful in food, pharma, or water labs running high-throughput tests.
Auditor-approved: Acceptable under ISO/IEC 17025 and ISO 19036 if implemented correctly.

Final Thoughts

Measurement uncertainty in microbiology might sound complex at first, but it's really about being honest about variation and ensuring test results are reliable and reproducible.

Using method validation data—when done correctly—is a smart and efficient way to estimate MU. It bridges the gap between high scientific standards and the practical needs of everyday lab work.

By embracing uncertainty, microbiology labs actually build greater confidence in the results they report.

Learn More

• Check out ISO 19036:2019 – The go-to standard for microbiological uncertainty.

• Refer to ISO 21748 – For using reproducibility data from validation studies.