Treatment of uncorrected measurement bias in uncertainty estimation for chemical measurements

Consistent treatment of measurement bias, including the question of whether or not to correct for bias, is essential for the comparability of measurement results. The case for correcting for bias is discussed, and it is shown that instances in which bias is known or suspected, but in which a specific correction cannot be justified, are comparatively common. The ISO Guide to the Expression of Uncertainty in Measurement does not provide well for this situation. It is concluded that there is a need for guidance on handling cases of uncorrected bias. Several different published approaches to the treatment of uncorrected bias and its uncertainty are critically reviewed with regard to coverage probability and simplicity of execution. On the basis of current studies, and taking into account testing laboratory needs for a simple and consistent approach with a symmetric uncertainty interval, we conclude that for most cases with large degrees of freedom, linear addition of a bias term adjusted for exact coverage ("U(e)") as described by Synek is to be preferred. This approach does, however, become more complex if degrees of freedom are low. For modest bias and low degrees of freedom, summation of bias, bias uncertainty and observed value uncertainty in quadrature ("RSSu") provides a similar interval and is simpler to adapt to reduced degrees of freedom, at the cost of a more restricted range of application if accurate coverage is desired.     

Effect of insignificant bias and its uncertainty on the coverage probability of uncertainty intervals Part 1. Evaluation for a given value of the true bias

This paper investigates the coverage probability of the uncertainty intervals determined in compliance with the GUM and EURACHEM Guide, which are defined by expanded uncertainty U about the results uncorrected with the insignificant biases and corrected with the significant biases. This coverage probability can significantly fall below the chosen level of confidence in some cases as Maroto et al. discovered by using the Monte Carlo method. Their numerical results obtained provided that only the β errors have occurred in the test significance and findings that the coverage reduction depends on the mutual proportions of the magnitudes of the systematic error, overall uncertainty and bias uncertainty are confirmed in this paper by using probability calculus and numerical integration. This problem is also studied when all possible experimental biases, both significant and insignificant, are considered. From this point of view, the reduction of the coverage probability turns out to be less severe than from the previous one. The coverage probability is also investigated for some uncertainty intervals computed in different ways than the above mentioned documents recommend. The intervals defined by U about the results corrected with both significant and insignificant bias give always the same coverage probability equalling the chosen level of confidence. The intervals with some uncertainties modified or enlarged with the insignificant biases remove or moderate the coverage reduction.     

Effect of insignificant bias and its uncertainty on the coverage probability of uncertainty intervals: Part 2. Evaluation for a found insignificant experimental bias

This paper continues in studying the coverage probability of uncertainty intervals. It particularly investigates the uncertainty intervals determined in compliance with the GUM and EURACHEM Guide in case of the results uncorrected for the systematic error, since the experimental bias has been found insignificant. The problem is solved for known values of the experimental bias, its standard uncertainty and the overall standard uncertainty. The obtained findings given in graphs and tables show that coverage probability of the uncertainty intervals defined by expanded uncertainty about the uncorrected results can considerably fall below the chosen level of confidence; this depression depends only on the ratio of the bias and the overall uncertainty. The bias uncertainty does not directly influence this depression, it only determines whether the bias is significant or not and thereby determines whether the results will be corrected or not. The paper proposes three methods how to remove this coverage probability reduction: to apply a higher level of confidence in the significance test, to correct the results with the insignificant but too high biases and to compute the uncertainty intervals defined by some type of the uncertainties enlarged with the insignificant bias.     

Should non-significant bias be included in the uncertainty budget?

The bias of an analytical procedure is calculated in the assessment of trueness. If this experimental bias is not significant, we assume that the procedure is unbiased and, consequently, the results obtained with this procedure are not corrected for this bias. However, when assessing trueness there is always a probability of incorrectly concluding that the experimental bias is not significant. Therefore, non-significant experimental bias should be included as a component of uncertainty. In this paper, we have studied if it is always necessary to include this term and which is the best approach to include this bias in the uncertainty budget. To answer these questions, we have used the Monte-Carlo method to simulate the assessment of trueness of biased procedures and the future results these procedures provide. The results show that non-significant experimental bias should be included as a component of uncertainty when the uncertainty of this bias represents at least a 30% of the overall uncertainty. Received: 29 May 2001 Accepted: 10 December 2001     

Comparison of different methods to estimate the uncertainty in composition measurement by chromatography

Natural gas is a mixture that contains hydrocarbons and other compounds, such as CO₂ and N₂. Natural gas composition is commonly measured by gas chromatography, and this measurement is important for the calculation of some thermodynamic properties that determine its commercial value. The estimation of uncertainty in chromatographic measurement is essential for an adequate presentation of the results and a necessary tool for supporting decision making. Various approaches have been proposed for the uncertainty estimation in chromatographic measurement. The present work is an evaluation of three approaches of uncertainty estimation, where two of them (guide to the expression of uncertainty in measurement method and prediction method) were compared with the Monte Carlo method, which has a wider scope of application. The aforementioned methods for uncertainty estimation were applied to gas chromatography assays of three different samples of natural gas. The results indicated that the prediction method and the guide to the expression of uncertainty in measurement method (in the simple version used) are not adequate to calculate the uncertainty in chromatography measurement, because uncertainty estimations obtained by those approaches are in general lower than those given by the Monte Carlo method.     

A simplified approach to the estimation of analytical measurement uncertainty

The present study summarizes the measurement uncertainty estimations carried out in Nestlé Research Center since 2002. These estimations cover a wide range of analyses of commercial and regulatory interests. In a first part, this study shows that method validation data (repeatability, trueness and intermediate reproducibility) can be used to provide a good estimation of measurement uncertainty.In a second part, measurement uncertainty is compared to collaborative trials data. These data can be used for measurement uncertainty estimation as far as the in-house validation performances are comparable to the method validation performances obtained in the collaborative trial.Based on these two main observations, the aim of this study is to easily estimate the measurement uncertainty using validation data.     

A new terminology for the approaches to the quantification of the measurement uncertainty

A new terminology for the approaches to the quantification of the measurement uncertainty is presented, with a view to a better understanding of the available methodologies for the estimation of the measurement quality and differences among them. The knowledge of the merits, disadvantages and differences in the estimation process, of the available approaches, is essential for the production of metrologically correct and fit-to-purpose uncertainty estimations. The presented terminology is based on the level of the analytical information used to estimate the measurement uncertainty (e.g., supralaboratory or intralaboratory information), instead of the direction of information flow (“bottom-up” or “top-down”) towards the level of information where the test is performed, avoiding the use of the same designation for significantly different approaches. The proposed terminology is applied to the approaches considered on 19 examples of the quantification of the measurement uncertainty presented at the Eurachem/CITAC CG4 Guide, Eurolab Technical Report 1/2002 and Nordtest Technical Report 537. Additionally, differences of magnitude in the measurement uncertainty estimated by various approaches are discussed.     

Customers' needs in relation to uncertainty and uncertainty budgets

 The general requirement of Quality Management standards to include in test reports a statement of the uncertainty of the results reflects the fact that a test result is rather useless without a knowledge of its accuracy. After an outline of the basic concepts of uncertainty, the need for uncertainty statements is illustrated for different ranges of applications. Received: 23 December 1997 · Accepted: 3 March 1998     

Model-based measurement uncertainty evaluation,with applications in testing

Basic concepts and implementations of the model-based approach to uncertainty evaluation are discussed. One implementation is that of the law of propagation of uncertainty with the use of the central limit theorem as recommended in the Guide to the expression of uncertainty in measurement (GUM). Another is the propagation of distributions, the subject of a supplemental guide to the GUM, which is at an advanced stage of development. It falls in the category of other analytical and numerical methods indicated in the GUM. Two testing applications are used to illustrate the principles: tennis-ball rebound and tensile strength.

How to encompass an uncorrected bias into the expanded uncertainty with a fixed coverage probability: calculation procedures

Accreditation and Quality Assurance - The practice in analytical and medical laboratories often necessitates evaluating the uncertainty of measurement in such a way that incorporates the bias in...     

分析测试不确定度的评定与表示(Ⅰ)

就“不确定度”概念的来历和意义;基本术语;误差与不确定度;化学分析测量不确定度的来源;不确定度的评定;标准物质的不确定度;不确定度的评定实例等7个方面概述分析测试不确定度的评定与表示,介绍不确定度的基本知识与应用。     

Estimates of uncertainty of measurement from proficiency testing data: a case study

The results obtained by a laboratory over a number of proficiency testing/external quality assessment schemes (PT/EQAS) rounds can give information on the uncertainty of its measurements for a given test, provided that conditions such as full coverage of the routine analytical range, traceability, and small uncertainty of the assigned values (compared to the spread of the results) are met and provided that systematic deviations and any other sources of uncertainty are considered. As organisers of the Italian EQAS (ITEQAS) in occupational and environmental laboratory medicine, we tested this hypothesis using as model data from well-performing laboratories taking part in ITEQAS for lead in blood over the last 2 years. We also investigated how different PT/EQAS features (frequency of trials and number of samples) would affect a laboratory estimate of its uncertainty. Such information can be helpful in improving PT/EQAS organisation and define, for a given test: (a) the state of the art of the uncertainty of current measurement procedures, (b) identify needs for improvement of analytical methodologies and (c) set targets for acceptable uncertainty values.Presented at the Eurachem PT Workshop September 2005, Portorož, Slovenia.Papers published in this section do not necessarily reflect the opinion of the Editors, the Editorial Board and the Publisher.     

Towards a new edition of the “Guide to the expression of uncertainty in measurement”

The “Guide to the expression of uncertainty in measurement” (GUM) is an extremely important document. It unifies methods for calculating measurement uncertainty and enables the consistent interpretation and comparison of measurement results, regardless of who obtained these measurements and where they were obtained. Since the document was published in 1995, it has been realised that its recommendations do not properly address an important class of measurements, namely, non-linear indirect measurements. This drawback prompted the initiation of the revision of the GUM in the Working Group 1 of the Joint Committee for Guides in Metrology, which commenced in October 2006. The upcoming revision of the GUM provides the metrological community with an opportunity to improve this important document, in particular, to reflect developments in metrology that have occurred since the first GUM publication in 1995. Thus, a discussion of the directions for this revision is important and timely. By identifying several shortcomings of the GUM and proposing directions for its improvement, we hope this article will contribute to this discussion. Papers published in this section do not necessarily reflect the opinion of the Editors, the Editorial Board and the Publisher.     

Effect of non-significant proportional bias in the final measurement uncertainty

The trueness of an analytical method can be assessed by calculating the proportional bias of the method in terms of apparent recovery. If the apparent recovery does not differ significantly from one, the analytical method has not a significant bias. If this is the case, the bias is neglected and the uncertainty associated with this bias is included in the uncertainty budget of results. However, when assessing trueness there is always a probability of incorrectly concluding that the proportional bias is not significant. Therefore, the uncertainty of results may be underestimated. In this paper, we study how non-significant bias affects the uncertainty of analytical results. Moreover, we study how to avoid the underestimation of uncertainty by including the non-significant bias calculated in the uncertainty budget. To answer these questions, we have used the Monte-Carlo method to simulate the process of estimating the apparent recovery of a biased analytical method and, subsequently, the future results this method provides. The results of the simulation show that non-significant bias may underestimate the uncertainty of analytical results when bias contributes in more than 20% to the overall uncertainty. Uncertainty is specially underestimated when bias contributes in more than 50% to the overall uncertainty.     

From GUM to alternative methods for measurement uncertainty evaluation

Since the advent of the Guide to the expression of Uncertainty in Measurement (GUM) in 1995 laying the principles of uncertainty evaluation numerous projects have been carried out to develop alternative practical methods that are easier to implement namely when it is impossible to model the measurement process for technical or economical aspects. In this paper, the author presents the recent evolution of measurement uncertainty evaluation methods. The evaluation of measurement uncertainty can be presented according to two axes based on intralaboratory and interlaboratory approaches. The intralaboratory approach includes “the modelling approach” (application of the procedure described in section 8 of the GUM, known as GUM uncertainty framework) and “the single laboratory validation approach”. The interlaboratory approaches are based on collaborative studies and they are respectively named “interlaboratory validation approach” and “proficiency testing approach”.     

火焰原子吸收法测定土壤中铜的测量不确定度评定

根据GB／T17138-1997规定的测量步骤对土壤中的铜进行了测定,对影响测定结果的各不确定度来源进行了分析,并对一个样品的测定结果进行了不确定度评定。     

Estimation of measurement uncertainty in organic analysis: two practical approaches

Estimation of measurement uncertainty has become a more regularly performed part of the whole analytical process. However, there is still much on-going discussion in the scientific community about ways of building up the uncertainty budget. This study describes two approaches for estimation of measurement uncertainty in organic analysis: one which can be used for single sets of measurements and the other based on validation studies. In both cases the main contributions to the uncertainty are presented and discussed for the analysis of PCBs in mussel tissue, but the approaches can be extended to other organic pollutants in environmental/food samples. The main contributions to the uncertainty budget arise from calibration, sample preparation, and GC–MS measurements. A comparison of the relevant sources and their contributions to the expanded uncertainty is presented.     

HPLC法测定辣椒油中苏丹红染料的测量不确定度评定

建立了HPLC法分析辣椒油中苏丹红染料的不确定度模型,分析了方法中的不确定度分量及其来源,计算了各不确定度分量和合成标准不确定度。     

Introducing the concept of uncertainty of measurement in testing in association with the application of the standard ISO/IEC 17025

ISO/IEC 17025 requests laboratories to estimate the overall uncertainty associated with a test result. In many cases this means a new requirement to laboratories. It is essential to understand the importance of the knowledge of the uncertainty of measurements as well as the principle that not always the smallest possible uncertainty is needed, but fitness for purpose has to be achieved. The paper suggests a policy on the implementation of the concept of uncertainty and guidance on how to proceed, taking into account the present state of the art of understanding the problem.     

Role of measurement uncertainty in conformity assessment in legal metrology and trade

There is increasing interest in the role of measurement uncertainty in conformity assessment, particularly where measurements are in support of fair trade, health & safety and the environment. A lack of clarity, however, in treating measurement uncertainties and in setting specification limits for compliance may sometimes lead to drastic consequences. Examples concerning conformity assessment, particularly in the measurement of trace quantities (Cd in plastics and antibiotic trace residues in shrimps), are amongst cases identified in the ongoing EU project MetroTrade, which can be significant impediments to international trade. This paper considers the present status of the role of measurement uncertainty in conformity testing, covering both recent work in international standardisation and physical metrology (e.g. OIML recommendations on weighing). One conclusion is that, while much has been achieved, there still exists some work to be done in order to facilitate acceptance of conformity from both customer and supplier and avoid perceived barriers to trade and serious international trade disputes.Presented at the MetroTrade Workshop on Traceability and Measurement Uncertainty in Testing, 30–31 January 2003, Berlin, GermanyAn erratum to this article can be found at