Setting Target Measurement Uncertainty

Importance of the topic

Measurement uncertainty (MU) directly determines whether analytical results are fit for purpose. For compliance testing, process control or commercial transactions, the MU must be constrained so that decisions based on results are reliable and cost‑effective. Defining a target MU prevents both false reassurance from overly large uncertainty and unnecessary expense from excessive precision. The Eurachem/CITAC guidance provides a structured approach to set such targets aligned with the intended use of measurements.

Objectives and overview of the guidance

The main aim is to describe how to set a maximum acceptable measurement uncertainty (the target MU) for chemical measurements so results support the decisions they are meant to inform. The guidance presents a hierarchy of information sources and indicators that laboratories and stakeholders can use to derive target standard (utg) and expanded (Utg) uncertainties, ranging from legally defined limits to more subjective quality objectives.

Illustrative scenario and its lessons

A practical example shows the consequences of inappropriate target MUs. A grower sells oranges to a juice producer, which enforces limits on a pesticide (thiabendazole < 1 mg kg−1) and places a premium based on Brix (sweetness). Two laboratories analysed the same fruit but reported very different uncertainties:

• Laboratory C: thiabendazole 0.592 ± 0.019 mg kg−1 (k=2); Brix 70 ± 25 °Bx (k=2).
• Producer's laboratory: thiabendazole 0.51 ± 0.20 mg kg−1 (k=2); Brix 61.2 ± 1.1 °Bx (k=2).

Although the numeric results were metrologically compatible, contrasting uncertainty magnitudes led to different commercial outcomes: one laboratory had an excessively small uncertainty for the pesticide (increasing cost without changing the decision), while the other had an unacceptably large uncertainty for Brix (making price classification ambiguous). The example highlights that both overly tight and overly loose target MUs are undesirable.

Methodology for defining target uncertainty

The guidance recommends a pragmatic, risk‑ and use‑based procedure to set utg and Utg:

• Start from the decision context: what classification, limit or grading is the result intended to support?
• Identify the critical quantities (regulatory limits, contractual thresholds, process tolerances) and the consequences of incorrect decisions (health, economic loss, product rejection).
• Select the most appropriate indicator(s) of required measurement quality from a hierarchy of evidence — e.g. legal or regulatory limits, clinical or toxicological thresholds, performance of downstream processes, historical interlaboratory variability, or stakeholder risk tolerance.
• Translate the chosen indicator into a numerical target uncertainty using decision rules. For example, derive the allowable expanded uncertainty so probability of misclassification is below a chosen risk level, or set utg as a fraction of the tolerance interval around a specification.
• Document assumptions and, if needed, iterate the target based on feasibility, cost and method performance data.

The guide formalises notation by distinguishing target standard uncertainty (utg) and target expanded uncertainty (Utg).

Main results and discussion

Key points from the guidance and the example are:

• Fit‑for‑purpose assessment requires explicitly stated target MU even when regulators or customers do not provide one; the laboratory must assume responsibility.
• Targets should be driven by the decision impact: health and safety limits require tighter control than many routine quality checks, while commercial grading thresholds may accept larger uncertainty depending on economic trade‑offs.
• There is a structured hierarchy of evidence to derive targets; more objective sources (laws, standards) are preferred, but other pragmatic indicators can be used when formal limits are absent.
• The example demonstrates two distinct failure modes: excessive precision (wasting resources) and excessive uncertainty (undermining decision making). Both should be avoided by aligning measurement strategy to the target MU.
• When two laboratories report metrologically compatible results but different uncertainties, stakeholders can still reach disparate decisions; therefore consistency in target MU and reporting is important for fair commercial outcomes.

Practical benefits and applications

Setting and using target MU delivers multiple practical advantages:

• Enables objective go/no‑go decisions for compliance, product release and commercial grading.
• Guides selection and validation of analytical methods appropriate to the measurement purpose, balancing cost and performance.
• Supports laboratory accreditation and technical justification under ISO/IEC 17025 and similar frameworks by linking performance to intended use.
• Improves transparency in contracts and supply chains by making uncertainty expectations explicit, reducing disputes and economic inefficiencies.
• Informs the design of quality control, proficiency testing acceptance criteria and measurement uncertainty budgets.

Future trends and opportunities

Several developments can improve target MU practice:

• Harmonisation of decision rules and wider adoption of the Eurachem/CITAC framework across regulatory and commercial sectors to reduce inconsistent outcomes between laboratories.
• Integration of probabilistic and Bayesian decision support tools to derive uncertainties matched to explicit risk tolerances and economic loss functions.
• Software tools and templates to translate regulatory limits and process tolerances into quantitative target uncertainties, easing routine implementation by laboratories.
• Use of machine learning and advanced statistical methods to characterise complex uncertainty components (matrix effects, non‑normal distributions, lot variability) and update targets dynamically with accumulating data.
• Stronger linkage between accreditation criteria, proficiency testing design and target MU specifications so that external quality assurance aligns with decision needs.

Conclusion

Setting a clear, justified target measurement uncertainty is essential for meaningful analytical results. The target MU should be derived from the decision context, balancing protection of interests (health, safety, product quality) against measurement cost and feasibility. The Eurachem/CITAC guidance offers a structured route to define utg and Utg, and the illustrative example underscores the real commercial and regulatory consequences of poor targeting. Laboratories should adopt and document target MUs to ensure results are both reliable and economically sensible.

Reference

R. Bettencourt da Silva, A. Williams (Eds.), Eurachem/CITAC Guide: Setting and Using Target Uncertainty in Chemical Measurement, 2015. Produced by the Eurachem/CITAC Measurement Uncertainty and Traceability Working Group. First English edition 2018. ISBN 978-989-98723-7-0.