TRACEABILITY AND MEASUREMENT UNCERTAINTY

King, B. (2001), Meeting the measurement uncertainty and traceability requirements of ISO/IEC standard 17025 in chemical analysis, Fresen. J. And. Chem., 371, 714. 

By now, it should be clear what role RMs play in measurement science. This puts great responsibility on the producers of RMs, as they must see how to satisfy the requirements set impHcitly or explicitly by the users regarding matrix, parameters, uncertainty, and traceability. Laboratories use RMs often as a quality control measure, but it this obviously only vahd if the RM is produced under proper conditions. 

In addition to the requirements regarding traceability of measurement results, the measurement methods employed should represent "state-of-the-art in the particular field. Failing to do so would lead to a reference material with an uncertainty that has become too large to serve as a quality control. The better the methods perform in terms of uncertainty and traceability, the better the reference material will serve the interests of the (potential) users. 

Associated with method validation, but not part of it, are two properties of results that have been previously mentioned. These parameters are measurement uncertainty and metrological traceability. Measurement uncertainty is covered in Chapter 6 and metrological traceability in Chapter 5. If considered at the planning stage of method validation, the information obtained during validation is a valuable input into measurement uncertainty evaluation. Traceability depends on the method s operating procedures and the materials being used. 

One or more of these bias components are encountered when analyzing RMs. In general, RMs are divided into certified RMs (CRMs, either pure substances/solu-tions or matrix CRMs) and (noncertified) laboratory RMs (LRMs), also called QC samples [89]. CRMs can address all aspects of bias (method, laboratory, and run bias) they are defined with a statement of uncertainty and traceable to international standards. Therefore, CRMs are considered useful tools to achieve traceability in analytical measurements, to calibrat equipment and methods (in certain cases), to monitor laboratory performance, to validate methods, and to allow comparison of methods [4, 15, 30]. However, the use of CRMs does not necessarely guarantee trueness of the results. The best way to assess bias practically is by replicate analysis of samples with known concentrations such as reference materials (see also Section 8.2.2). The ideal reference material is a matrix CRM, as this is very similar to the samples of interest (the latter is called matrix matching). A correct result obtained with a matrix CRM, however, does not guarantee that the results of unknown samples with other matrix compositions will be correct [4, 89]. 

The certified value is usually taken as the grand mean of the valid results. The organizer uses standard deviation as the basis for calculating the measurement uncertainty. Results from the laboratories will include their own estimates of measurement uncertainty and statements of the metrological traceability of the results. There is still discussion about the best way to incorporate different measurement uncertainties because there is not an obvious statistical model for the results. One approach is to combine the estimates of measurement uncertainty as a direct geometric average and then use this to calculate an uncertainty of the grand mean. Type A estimates will be divided by /n n is the number of laboratories), but other contributions to the uncertainty are unlikely to be so treated. 

Perhaps this sounds unnecessarily complicated, but an understanding of basic concepts and terms in metrology help us appreciate the importance of metrological traceability, measurement uncertainty, and the like. 

For a measurement result to be metrologically traceable, the measurement uncertainty at each level of the calibration hierarchy must be known. Therefore, a calibration standard must have a known uncertainty concerning the quantity value. For a CRM this is included in the certificate. The uncertainty is usually in the form of a confidence interval (expanded uncertainty see chapter 6), which is a range about the certified value that contains the value of the measurand witha particular degree of confidence (usually 95%). There should be sufficient information to convert this confidence interval to a standard uncertainty. Usually the coverage factor ( see chapter 6) is 2, corresponding to infinite degrees of freedom in the calculation of measurement uncertainty, and so the confidence interval can be divided by 2 to obtain uc, the combined standard uncertainty. Suppose this CRM is used to calibrate... 

The standard recognizes the factors that determine the correctness and reliability of test results human factors, accommodation and environment, methods, equipment, sampling, and the handling of test items. In this list, measurement traceability is mentioned, but in fact metrological traceability, with measurement uncertainty and method validation, are really subsumed in methods. (subsection 5.4). The effect of each of these factors on measurement uncertainty will differ considerably among kinds of tests. 

R, the NARL reference analytical value (accompanied by measurement uncertainty estimate) traceable to SI through the validated test method used and the pure substance reference standards used to calibrate the measuring instrument... 

In this approach, calibration uncertainty is an important component of the traceability chain and uncertainty of results depends on the uncertainty of the certified values of RMs used for the calibration. Thus, the results are traceable to the standards used for the instrument calibration. The traceability of certified values of RMs is as important as that of spectrometric measurements. Therefore, it is necessary to use the spectrometric RMs that are characterized in a metrological manner. In this framework, the uncertainty and traceability, as two fundamental metrological concepts, are intimately linked. 

All measurements are made relative to some scale or standard and are therefore traceable to this scale or standard. The uncertainty on the result will be the uncertainty on the realisation of the scale or standard and the uncertainty on making measurements relative to that scale. The concepts of uncertainty and traceability have developed in different ways in physics and chemistry, leading until recently to quite different approaches to measurements in these two disciplines but now a convergence of approach is emerging. 

Note 3 In this definition, uncertainty covers both measurement uncertainty and uncertainty associated with the value of a nominal property, such as for identity and sequence. Traceability covers both metrological traceability of a quantity value and traceability of a nominal property value. ... 

Uncertainty and traceability appear to be different concepts, but, in fact, they are identical twins. If a measurement is traceable, an uncertainty budget can be made that includes the calibration of the reference standard. The uncertainty of this reference standard can only be properly stated if it is traceable to a primary standard. On the other hand, if no uncertainty calculation can be made, a measurement cannot be traceable, and if a measurement is not traceable, no uncertainty can be calculated as the uncertainty of the used references is not known. 

Fluid metering concerns the measurement of fluids, liquids in particular, in the form of flow rate and/or fluid volume. Apart from accuracy and precision, the uncertainty and traceability of... 

ISO/IEC 17025 This standard, entitled General requirements for the competence of testing and calibration laboratories, has two parts, one relating to management structures that covers the ISO 9000 series of quality practices, and the second to practical aspects of testing and calibration. The standard specifies that the laboratory shall use validated methods, determine measurement uncertainty, and ensure the traceability of its results. The laboratory shall also report measurement uncertainty when the client requests it and when it is applicable for example, when a result must be compared to a statutory limit. This standard also allows the analyst to offer interpretations and advice where appropriate. 

CRMs are required in many chemical analyses, in order to perform a calibration and/or validate the method being employed to ensure the accuracy, uncertainty, and traceability of the results obtained. Many modern analytical methods require the use of RMs the matrix of which is sufficiently close to the sample being analyzed to enable calibration and appropriate verification of the measurement method used. 

Fluid metering concerns the measurement of fluids, liquids in particular, in the form of flow rate and/or fluid volume. Apart from accuracy and precision, the uncertainty and traceability of measurements are important factors to be considered. For micro- and nanofluidic applications, the fluid may flow in channels with dimensions of a few millimeters or below, with flow rates ranging typically from microliters to nanoliters per minute, and the fluid may be dispensed in volumes on the order of microliters to picol-iters. 

For a standard of flow rate metering, traceability is the top priority the measurement uncertainty should he sufficiently small, and a system that features single operation, low measurement uncertainty, and high repeatability is desirable. Therefore, approaches based on weighing are, above all others, favorable candidates for such a purpose. As shown in Fig. 6, a gravimetric primary metering standard has been set up to achieve these requirements [9]. Liquid water is driven by a pneumatic pressure control mechanism to obtain flow rates down to less than a microliter per minute. The measurement capabUity depends on the weighing of the collection beaker, the stability of the pressure difference, the time interval, and the variation of the ambient temperature. Buoyancy variation and liquid evaporation are critical concerns for measurements associated with such a small quantity of liquid. 

In this Chapter we highlight the practical considerations that must be understood by all users of RMs and CRMs we look at some of the issues of traceability and make the CRM user aware of the uncertainty budgets that need to be considered with the use of CRMs. No attempt will be made to advise CRM users on the proper use of statistics in the analytical measurement process and no statistical approaches on the establishment of measurement uncertainty will be given. There are a number of good texts on the subject which should be consulted. These are listed in the Further Reading section of the references at the end of this Chapter and include Miller and Miller (1993) and Taylor s work for NIST (Taylor 1985). 

In their broadest application, CRMs are used as controls to verify in a direct comparison the accuracy of the results of a particular measurement parallel with this verification, traceability may be demonstrated. Under conditions demonstrated to be equal for sample and CRM, agreement of results, e.g. as defined above, is proof. Since such possibilities for a direct comparison between samples and a CRM are rare, the user s claims for accuracy and traceability have to be made by inference. Naturally, the use of several CRMs of similar matrix but different analyte content will strengthen the user s inference. Even so, the user stiU has to assess and account for all uncertainties in this comparison of results. These imcertainty calculations must include beyond the common analytical uncertainty budget (i) a component that reflects material matrix effects, (2) a component that reflects differences in the amount of substance determined, (3) the uncertainty of the certified or reference value(s) used, and 4) the uncertainty of the comparison itself AU this information certainly supports the assertion of accuracy in relation to the CRM. However, the requirement of the imbroken chain of comparisons wiU not be formally fulfilled. 

Focusing on the assignment of the reference value by measurement, there are several ways to achieve this. It can be done either by using one measurement method (usually this should be a primaiy method in order to be able to demonstrate measurement traceability and measurement uncertainty), or use of one method by several laboratories or several methods by one or several laboratories. 

Origin, melrologcal traceability and measurement uncertainty of any assigned values... 

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