Measurement Uncertainty in Peptide Testing

Measurement uncertainty expresses the range of values reasonably associated with a reported result.

Sources of uncertainty

Potential components include:

• balance performance
• volumetric preparation
• reference-standard assignment
• calibration model
• repeatability
• instrument response
• sample recovery
• environmental conditions
• analyst variation

Near specification limits

Uncertainty becomes especially important when a result lies close to an acceptance limit.

Estimation approaches

Laboratories may use bottom-up calculations, top-down performance data, proficiency testing, control samples, or combinations.

Decision rules

A decision rule defines how uncertainty is considered when stating conformance.

Frequently asked questions

Is uncertainty the same as error?

No. Error is the difference from a true value; uncertainty describes the range around the result.

Does every result need uncertainty printed?

Not always, but the laboratory should understand it where relevant.

Why is reference-standard uncertainty important?

It propagates into quantitative results.

Can uncertainty improve over time?

Yes. Better controls and more performance data can reduce or refine the estimate.

Key takeaways

Measurement uncertainty provides context for numerical results and supports defensible decisions near specifications.

References

1. FDA. Data Integrity and Compliance With Drug CGMP: Questions and Answers.
2. OECD Principles of Good Laboratory Practice.
3. ISO/IEC 17025 overview.

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TSMS Labs educational disclaimer: For laboratory research and educational purposes only. Not for human consumption. This content is not medical, clinical, or regulatory advice.