Published Guides

IUPAC/CITAC Guide: Evaluation of risks of false decisions in conformity assessment of a substance or material with a mass balance constraint (IUPAC Technical Report) (2023)

Francesca R. Pennecchi, Ilya Kuselman* and D. Brynn Hibbert

Pure Appl. Chem. 2023; 95(12): 1217–1254  

A Bayesian multivariate approach to the evaluation of risks of false decisions on conformity of chemical composition of a substance or material due to measurement uncertainty is adapted to cases for which the composition is subject to a mass balance constraint. The constraint means that sum of the actual (“true”) values of the composition component contents under conformity assessment is equal to 1 (or 100 %) or another positive value less than 1 (less than 100 %). As a consequence, the actual values of the component contents are intrinsically correlated. Corresponding measured values of the component contents are correlated also. Any correlation can influence evaluation of risks of false decisions in conformity assessment of the substance or material chemical composition. A technique for appropriate evaluation of the relevant risks, including evaluation of the conformance probability of a subject or material composition, is discussed for different scenarios of the data modeling, taking into account all observed correlations. A Monte Carlo method is applied in the R programming language for the necessary calculations. Examples of evaluation of the risks are provided for conformity assessment of chemical composition of a platinum-rhodium alloy, pure potassium trioxidoiodate, a sausage, and synthetic air.

EURACHEM /CITAC Guide: Assessment of performance and uncertainty in qualitative chemical analysis, 1st edition, Eurachem  (2021)

Editors: R Bettencourt da Silva and S L R Ellison 

Many decisions with socio-economic or individual impact depend on qualitative analysis, including decisions related to food safety, clinical diagnosis, and forensic evidence, are based primarily on qualitative, rather than quantitative, chemical analysis. Qualitative analysis is analysis that returns a classification rather than a numerical value.

Qualitative analysis, like quantitative analysis, needs to be demonstrably reliable. Part of the purpose of the guide is accordingly to show how the performance of a qualitative analysis procedure can be quantified to ensure its fitness for purpose. Practical difficulties and limitations in reliable quantification of low false result rates are discussed, and recommendations are made for checking the validity of these analyses. Brief recommendations are also made for ensuring that any measurements undertaken in the course of a qualitative analysis are reliable.


EURACHEM /CITAC Guide: Use of Uncertainty Information in Compliance Assessment, 2nd edition (2021)    

Editors: Alex Williams (UK), Bertil Magnusson (SE) 

This guide provides guidance on how uncertainty may be taken into account in deciding compliance with a limit.

The guide is applicable to decisions on compliance with regulatory or manufacturing limits where a decision is made on the basis of a decision rule, together with a measurement value and the associated measurement uncertainty.

The guide includes a discussion and general recommendations, including the use of “guard bands” to improve the probability of correct acceptance or correct rejection. This is followed by more detailed guidance on establishing rules for interpretation and by several examples.

This second edition has been amended to take into account the developments in other international guides and Standards, including ILAC G8, “Guidelines on Decision Rules and Statements of Conformity” and JCGM 106, “Evaluation of measurement data – The role of measurement uncertainty in conformity assessment”.

IUPAC/CITAC Guide: Evaluation of risks of false decisions in conformity assessment of a multicomponent material or object due to measurement uncertainty  (IUPAC Technical Report). Pure Appl. Chem. 2021; 93(1): 113–154   

Ilya Kuselman*, Francesca R. Pennecchi, Ricardo J. N. B. da Silva and David Brynn Hibbert

Risks of a false decision on conformity of the chemical composition of a multicomponent material or object due to measurement uncertainty are defined using the Bayesian approach. Even if the conformity assessment for each particular component of a material is successful, the total probability of a false decision (total consumer’s risk or producer’s risk) concerning the material as a whole might still be significant. This is related to the specific batch, lot, sample, environmental compartment, or other item of material or object (specific consumer’s and producer’s risks), or to a population of these items (global consumer’s and producer’s risks). A model of the total probability of such false decisions for cases of independent actual (‘true’) concentrations or contents of the components and the corresponding measurement results is formulated based on the law of total probability. It is shown that the total risk can be evaluated as a combination of the particular risks in the conformity assessment of components of the item. For a more complicated task, i.e. for a larger number of components under control, the total risk is greater. When the actual values of the components’ concentrations or contents, as well as the measurement results, are correlated, they are modelled by multivariate distributions. Then, a total global risk of a false decision on the material conformity is evaluated by the calculation of integrals of corresponding joint probability density function. A total specific risk can be evaluated as the joint posterior cumulative function of actual property values of a specific item lying outside the multivariate specification (tolerance) domain when the vector of measured values obtained for the item is inside this domain. The effect of correlation on the risk is not easily predictable. Examples of the evaluation of risks are provided for conformity assessment of denatured alcohols, total suspended particulate matter in ambient air, a cold/flu medication, and a PtRh alloy.

EURACHEM/EUROLAB/CITAC/Nordtest/UK RSC Analytical Methods Committee Guide: Measurement uncertainty arising from sampling. 2nd edition (2019)

Editors: Michael H.Ramsey (University of Sussex, UK), Stephen L R Ellison (LGC, UK), Peter Rostron (University of Sussex, UK)

This Guide aims to describe various methods that can be used to estimate the uncertainties arising from the processes of sampling and the physical preparation of samples. It is intended primarily for specialists such as sampling planners and for analytical chemists who need to estimate the uncertainty associated with their measurement results.

The Guide deals with the case where the measurand is defined in term of the value of the analyte concentration in a sampling target, rather than in just the sample delivered to the laboratory. In this case, the sampling process affects the result and its uncertainty, and sampling is necessarily considered as part of the measurement process. This Guide takes a holistic view of the measurement process to include sampling and sample preparation as well as the analytical process.

This Second Edition includes a number of additions and changes. These include:

  • the expression of uncertainty of measurement as an ‘uncertainty factor’ (FU) when the frequency distribution describing the sampling uncertainty is log-normal rather than normal, and guidance on the use of an uncertainty factor in an uncertainty budget.
  • the use of an unbalanced design to estimate uncertainty more cost-effectively than can be achieved using the balanced ‘duplicate method’ design;
  • updates to definitions and references to reflect current international documents and literature, including applications of these methods to on-site and in situ measurements, made at both the macro and the micro scale.

EURACHEM/CITAC leaflet: Setting Target Measurement Uncertainty, (July 16, 2018)

Ricardo Bettencourt da Silva, Alex Williams

Measurement results are only fit for purpose if the reported uncertainty is correct and has a magnitude small enough for the intended use. The target measurement uncertainty (target MU) is the maximum admissible uncertainty defined for a specific measurement goal. This information leaflet provides a short and accessible introduction to the idea of target measurement uncertainty.

EURACHEM/CITAC Guide: Guide to Quality in Analytical Chemistry – An Aid to Accreditation, 3rd Edition (2016)                   

Vicki Barwick    

The aim of this guide is to provide laboratories with guidance on best practice for the analytical operations they carry out. The guidance covers both qualitative and quantitative analysis carried out on a routine or non-routine basis. A separate guide covers research and development work (Quality Assurance for Research and Development and Non-routine Analysis,1998 ).

This third edition is a revision of the CITAC/Eurachem Guide published in 2002. The 2002 edition was developed from CITAC Guide 1 (which in turn was based on the Eurachem/WELAC Guide). The third edition reflects changes that were introduced with the publication of the 2005 version of ISO/IEC 17025. The terminology has also been updated to take account of ISO/IEC 17000:2004, ISO 9000:2015 and the 3rd edition of the International Vocabulary of Metrology – Basic and general concepts and associated terms (JCGM 200:2012 – VIM).

The Guide focuses on the requirements of ISO/IEC 17025, however the content should also be of use to organisations seeking accreditation or certification against the requirements of standards such as ISO 15189 or ISO 9001, or compliance with the Principles of Good Laboratory Practice

IUPAC/CITAC Guide: Classification, modeling and quantification of human errors in a chemical analytical laboratory (IUPAC Technical Report). Pure Appl. Chem. Vol. 88, No. 5, 477-515, 2016

Ilya Kuselman, Francesca Pennecchi

The classification, modeling, and quantification of human errors in routine chemical analysis are described. Classifications include commission errors (mistakes and violations) and omission errors (lapses and slips) in different scenarios at different steps of the chemical analysis. A Swiss cheese model is used to characterize error interaction with a laboratory quality system. The quantification of human errors in chemical analysis, based on expert judgments, i.e. on the expert(s) knowledge and experience, is applied. A Monte Carlo simulation of the expert judgments was used to determine the distributions of the error quantification scores (scores of likelihood and severity, and scores of effectiveness of a laboratory quality system against the errors). Residual risk of human error after the error reduction by the laboratory quality system and consequences of this risk for quality and measurement uncertainty of chemical analytical results are discussed. Examples are provided using expert judgments on human errors in pH measurement of groundwater, multiresidue analysis of pesticides in fruits and vegetables, and elemental analysis of geological samples by inductively coupled plasma mass spectrometry.

EURACHEM/CITAC Guide: Setting and Using Target Uncertainty in Chemical Measurement, 1st Edition (2015)

Ricardo Bettencourt da Silva, Alex Williams

The Eurachem/CITAC Measurement uncertainty and traceability working group has prepared this Guide to as part of a sequence of guidelines that aims at promoting the production of measurement results that are traceable to an adequate reference and are reported with reliable and sufficiently low uncertainty for the intended use of the measurement. These features are essential for the adequate interpretation of the measurement result which is discussed in the Eurachem/CITAC guide, ‘Use of uncertainty information in compliance assessment’.

This document discusses how to set a maximum admissible uncertainty, defined in the third edition of the International Vocabulary of Metrology as the “target uncertainty”, to check whether measurement quality quantified by the measurement uncertainty is fit for the intended purpose.

This guideline is applicable to analytical fields where the target uncertainty is not set by the regulator or the client, or where a minimum difference of the studied parameter in the same or different items must be detected in R&D work. This guide discusses how to set the target uncertainty for process development and for applied or fundamental research using information about the smallest difference or system trend that must be distinguished in a reliable way.

This guideline can also be useful for authorities and stakeholders that feel the need to define or upgrade criteria for measurements quality. The setting of target values for the so called conventional performance characteristics (precision, trueness, etc.) can miss the control of important uncertainty components included in sound uncertainty evaluations

IUPAC/CITAC Guide: Investigating out-of specification test results of chemichal composition based on metrological concepts (IUPAC Technical Report). Pure Appl. Chem. Vol. 84, No. 9, 1939-1971, 2012,, 9 July 2012

Ilya Kuselman, Francesca Pennecchi, Cathy Burns, Aleš Fajgelj, Paolo de Zorzi 

A metrological background for investigating out-of-specification (OOS) test resultsof chemical composition is discussed. When an OOS test result is identified, it is importantto determine its root causes and to avoid reoccurrence of such results. An investigation of thecauses based on metrological concepts is proposed. It includes assessment of validation dataof the measurement process and its metrological traceability chains, evaluation of measure-ment uncertainty, and related producer’s and consumer’s risks. This approach allows distin-guishing between OOS test results that indicate an actual change in chemical composition ofan analyzed object, and OOS test results that are metrologically related with a certain confi-dence probability, i.e., caused by measurement problems, while the analyzed object stillmeets the specification requirements at the time of testing. Practical examples illustrating applications of the described approach in environ mentaland food analysis, as well in drug analysis and stability study of drug products, are described.Acceptance limits, warning and action lines for the test results, and corresponding producer’sand consumer’s risks are discussed.

EURACHEM/CITAC Guide CG4: Quantifying Uncertainty in Analytical Measurement, 3rd Edition (2012)

WG Chairman – Alex Williams

This guide has been produced by a joint EURACHEM/CITAC Measurement Uncertainty Working Group.

The first edition of the EURACHEM Guide for “Quantifying Uncertainty in Analytical Measurement” was published in 1995 based on the ISO “Guide to the Expression of Uncertainty in Measurement”. The second edition was prepared in collaboration with CITAC in 2000 in the light of practical experience of uncertainty estimation in chemistry laboratories and the even greater awareness of the need to introduce formal quality assurance procedures by laboratories. The second edition stressed that the procedures introduced by a laboratory to estimate its measurement uncertainty should be integrated with existing quality assurance measures, since these measures frequently provide much of the information required to evaluate the measurement uncertainty.

This third edition retains the features of the second edition and adds information based on developments in uncertainty estimation and use since 2000. The additional material provides:

  • Improved guidance on the expression of uncertainty near zero; 
  • New guidance on the use of Monte Carlo methods for uncertainty evaluation;
  • Improved guidance on the use of proficiency testing data:
  • Improved guidance on the assessment of compliance of results with measurement uncertainty.

The guide therefore provides explicitly for the use of validation and related data in the construction of uncertainty estimates in full compliance with the formal ISO Guide principles set out in the ISO Guide to the Expression of Uncertainty in measurement. The approach is also consistent with the requirements of ISO/IEC 17025:2005.

IUPAC-CITAC-Guide: Selection and use of proficiency testing schemes for a limited number of participants – chemical analytical laboratories (IUPAC Technical Report). Pure Appl. Chem., Vol. 82, No 5, 2010, 1099-1135

Ilya Kuselman, Ales Fajgelj     

A metrological background for implementation of proficiency testing (PT) schemes for a limited number of participating laboratories (fewer than 30) is discussed. Such schemes should be based on the use of certified reference materials (CRMs) with traceable property values to serve as PT items whose composition is unknown to the participants. It is shown that achieving quality of PT results in the framework of the concept “tested once, accepted everywhere” requires both metrological comparability and compatibility of these results. The possibility of assessing collective/group performance of PT participants by comparison of the PT consensus value (mean or median of the PT results) with the certified value of the test items is analyzed. Tabulated criteria for this assessment are proposed. Practical examples are described for illustration of the issues discussed.

EURACHEM/CITAC Guide:  Use of uncertainty information in compliance assessment. 

WG Chairman –  Alex Williams 

This guide is applicable to decisions on compliance with regulatory or manufacturing limits where a decision is made on the basis of a measurement result accompanied by information on the uncertainty associated with the result. It covers cases where the uncertainty does not depend on the value of the measurand, and cases where the uncertainty is proportional to the value of the measurand.

Traceability in Chemical Measurement. A guide to achieving comparable results in chemical measurement (2003) 

This guide has been produced primarily by a joint EURACHEM/CITAC Working Group in collaboration with representatives from AOAC International and EA. Production of the guide was in part supported under the contract with the UK Department of Trade and Industry as part of the National Measurement System Valid Analytical Measurement (VAM) Programme 

Quality Assurance for Research and Development and Non-routine Analysis (1998)

This guide, produced by a joint Eurachem/CITAC working party representing industrial, academic, and governmental interests, promotes and describes the concepts of quality assurance in the non-routine environment. The guide promotes a nested approach to quality assurance, dealing with it at a general organisational level, a technical level and a project specific level. It is intended to promote the use of QA as an effective tool for establishing and maintaining quality in R&D and non-routine operations. It does not seek to set criteria for accreditation of R&D although there is a section describing various methods for third party assessment of quality systems.The guidance may form the basis on which accreditation criteria can be set in the future. The guidance is intended to complement the existing CITAC guide (CG1) which describes QA in the routine environment.It is primarily directed towards analytical chemistry establishments but is, in principle, applicable to other sectors. An extensive bibliography is included. English language versions (edition 1.0 1998) are available from the Office of Reference Materials, LGC. Alternatively, you may download the guide from this website. Use Acrobat Reader.

Translated Guides

Guides translated to Portuguese

Letter from the Brazilian National Agency for Health

“Guide to Quality in Analytical Chemistry” – “Guia para Qualidade em Química Analítica”

“Selection, use and interpretation of proficiency testing (PT)” – “Seleção, Uso e Interpretação de Programas de Ensaios de Proficiência (EP)”

“Accreditation for Microbiological Laboratories” – “Habilitação para Laboratórios de Microbiologia”

Guides translated to other languages

Quantifying Uncertainty in Analytical Measurement in Farsi (Persian Language) from Mohammad Rahmani

Quantifying Uncertainty in Analytical Measurement in Spanish from Pedro Morillas

Information Leaflets

Metrological Traceability of Analytical Results:
in English
in Spanish
Use of uncertainty information in compliance assessment:
in English
in Turkish
in Ukrainian

Publication Archive

Ilya Kuselman, Francesca Pennecchi, Cathy Burns, Ales Fajgelj, Paolo de Zorzi 

Investigating out-of-specification test results of chemical composition based on metrological concepts
Accred. Qual. Assur. (2010), 15, 283-288Abstract: A metrological background for investigating out-of-specification (OOS) test results of chemical composition is discussed. When an OOS test result is identified, it is important to determine its root causes and to avoid reoccurrence of such results. An investigation of the root causes based on metrological concepts would be beneficial. It includes (1) assessment of validation data of the measurement process, (2) evaluation of the measurement uncertainty contributions, and (3) assessment of metrological traceability chains critical for measurement parameters and environmental conditions influencing the test results. The questions, how can the validation data be applied for this investigation, and how can measurement uncertainty contributions and/or metrological traceability chains change a probability of OOS test results, are analyzed.
IUPAC/CITAC project 2008-030-1-500

Superseded Guides

Quantifying Uncertainty in Analytical Measurement, The Second Edition (2000)

This guide has been produced primarily by a joint EURACHEM/CITAC Working Group in collaboration with representatives from AOAC International and EA. Production of the guide was in part supported under the contract with the UK Department of Trade and Industry as part of the National Measurement System Valid Analytical Measurement (VAM) Programme. The first version of this guide, which was published in 1995, has been very well received. It has been very widely used and two successful workshops on its utilisation have been held since its publication. Following from these workshops and the many helpful comments the working group has received on the contents of the first edition, many significant changes and improvements have been made in this second edition. The most important change deals with the use of method performance data and in particular the use of method validation data, from both collaborative validation studies and from in-house studies. The new sections dealing with the use of method performance data show that in many cases such data gives all, or nearly all information required to evaluate the uncertainty. The format of the guide is very similar to that of the first edition with chapters 1 and 2 dealing with the scope and the concept of uncertainty as before. Chapter 3, Analytical Measurement and Uncertainty, is completely new and covers the process of method validation and conduct of experimental studies to determine method performance and their relationship to uncertainty estimation. There is also a new section on traceability. The chapter on uncertainty estimation in the previous guide has been considerably expanded and split into four separate chapters, dealing with the four steps involved in estimating uncertainty. Step 1 deals with the specification of the measurand, Step 2 with identifying the uncertainty sources, Step 3, which has been considerably expanded to cover the use of existing method validation data, deals with quantifying the uncertainty and Step 4 covers the calculation of the combined uncertainty. The examples have been completely revised and new ones added. They are now all in a standard format, which follow the four steps described above. They all utilise the cause and effect diagram as an aid to identifying the sources of uncertainty and to ensuring that all the significant ones are included in the evaluation of the uncertainty. In addition a web site has been set up at URL which contains an indexed HTML version of the Guide. This site hosts a discussion forum on the application of the guide and has a section for the publication of additional examples. Printed hard copy will be available soon; please send enquiries either to the Eurachem Secretariat or the Office of Reference MaterialsLGC for UK copies.

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