Setting Target Measurement Uncertainty

Importance of the topic

Reliable measurement results require an explicitly defined target measurement uncertainty (MU) that is appropriate for the intended decision. The target MU is the maximum admissible uncertainty that still allows a measurement result to be fit for purpose — whether for regulatory compliance, protecting public health, ensuring industrial product quality, or enabling fair commercial transactions. Setting an appropriate target MU balances the risk of incorrect decisions (false acceptance or false rejection) against the cost and complexity of reducing uncertainty.

Objectives and overview of the study

This guidance focuses on principles and practical approaches for setting and using target uncertainty in chemical measurement. It explains why target MU must be defined, how it affects compliance decisions, and offers a hierarchy of information sources and methods that laboratories and decision makers can use to derive pragmatic target uncertainties aligned with the intended use of results. A fictional case study (a farmer selling oranges) is used to illustrate how differing uncertainties influence commercial decisions despite metrological compatibility of results.

Methodology and approach to defining target MU

The document describes a decision-oriented, risk-based approach to define a target MU. Key elements include:

• Start from the decision context: determine the critical decision limits (e.g., regulatory action limits, acceptance thresholds, profitability breakpoints).
• Derive uncertainty requirements from the ability to discriminate relevant outcomes at specified confidence (for example, enabling reliable detection of exceedance of a limit).
• Use available sources in descending order of preference: formally defined regulatory or customer performance criteria; health- or environment-based guidance; method validation and inter-laboratory study data; proficiency testing and historical laboratory performance; and if necessary, judgement-based risk assessment.
• Express target MU as a standard uncertainty (utg) and, when reporting, as an expanded uncertainty (Utg) using an appropriate coverage factor (commonly k = 2 for ~95 % confidence).
• Iterate and document: the target MU should be set early, reviewed with stakeholders, and updated when better information or changing decision needs arise.

The guide also presents a hierarchy of adequacy for information sources (ranked from most to least suitable) which laboratories should consult when defining a target MU.

Main results and discussion

The guidance emphasizes practical consequences of mismatched MU by presenting a fictional example:

• Scenario: Oranges are evaluated for thiabendazole (pesticide) and Brix (sweetness). The buyer accepts produce with thiabendazole < 1 mg kg-1 and pays premium for Brix > 65 °Bx; baseline premium threshold is 55 °Bx.
• Laboratory C reports: thiabendazole (0.592 ± 0.019) mg kg-1 (k = 2) and Brix (70 ± 25) °Bx (k = 2). The pesticide uncertainty is extremely small (expensive to achieve) while the Brix uncertainty is far too large to support a reliable premium decision.
• Buyer’s laboratory reports: thiabendazole (0.51 ± 0.20) mg kg-1 and Brix (61.2 ± 1.1) °Bx (k = 2). These results are metrologically compatible with Laboratory C but lead to a different commercial decision because of the contrasting MU magnitudes.

This example highlights two common problems:

• Excessively small MU: resources may be wasted reducing uncertainty for a measurand where such precision provides no additional decision value (e.g., pesticide level well below the limit with no finer discrimination required).
• Excessively large MU: measurement cannot support the operational decisions that depend on a result (e.g., determining premium payment for Brix), leading to economic loss or disputes.

The guide therefore recommends that laboratories define target MU even when the customer does not, and use objective criteria or documented stakeholder requirements to set practical, cost-sensitive uncertainty goals.

Benefits and practical applications of the method

Setting and using target MU delivers several practical benefits:

• Decision relevance: ensures measurement quality is aligned with the consequences of decisions based on results (regulatory enforcement, product acceptance, health protection).
• Cost-effectiveness: avoids unnecessary expenditure to reduce uncertainty beyond what is needed to support the decision.
• Transparency and traceability: documented target MU supports clear communication with customers and regulators and explains why a measurement is or is not fit for use.
• Improved comparability: harmonised target MUs across stakeholders reduce conflicts when different laboratories perform tests on the same samples.

Practical implementation steps for laboratories include establishing a documented uncertainty budget, using validation and inter-laboratory data to set realistic targets, and expressing results with their associated expanded uncertainty and coverage factor to inform the recipient about decision reliability.

Future trends and potential applications

Anticipated developments that will influence target MU practices:

• Greater harmonization of performance specifications across sectors (regulatory, commercial, clinical) to reduce ambiguity in target setting.
• Improved use of inter-laboratory data and big data analytics to derive empirically supported target uncertainties and to benchmark laboratory capability.
• Automation and software tools to calculate uncertainty budgets quickly and to perform sensitivity analyses linking MU to decision risk.
• Adoption of real-time uncertainty assessment in process analytics and quality-by-design frameworks, enabling dynamic decision rules tied to instantaneous MU estimates.
• Increased emphasis on user-friendly reporting formats that combine numerical uncertainty with plain-language guidance on decision implications.

These trends will expand the practical use of target MU beyond traditional laboratory reporting to integrated decision-support systems in manufacturing, environmental monitoring, food safety, and clinical diagnostics.

Conclusion

A clearly defined target measurement uncertainty is essential to ensure measurement results are fit for their intended use. The Eurachem/CITAC guidance provides a structured, risk-based pathway to derive target uncertainty from the decision context, regulatory or customer requirements, and empirical performance data. Laboratories should proactively define and document target MU to support transparent, cost-effective, and comparable measurement services. The fictional orange case underscores the real-world consequences of mismatched uncertainty: metrological compatibility does not guarantee consistent decision outcomes when MU magnitudes differ.

Reference

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