Uncertainty evaluation

The advantage of this approach for mass bias correction is that any temporal changes in the instrumental mass bias during the measurement process are accounted for. However, matrix-induced mass bias cannot be fidly compensated with this type of mass bias correction and the corrected isotope amount ratios can show a dependence on the sample concentration [56]. Hence a near-perfect matrix separation, and dose matching of analyte and calibrant concentrations are required to yield accurate isotope amount ratios. 

Note that the cahbrator element in this method serves as the mass bias correction proxy. Hence, for example, even though the value obtained for the Zr isotope ratio may itself be biased due to the limitations of the mass bias correction models employed (e.g., assumptionysr= ), this bias is largdy negated in the second step of the calibration (Zr Sr). Owing to error cancellation with this method, it is akin to the use of isotope dilution methods wherein reverse and direct isotope dilution protocols are performed in tandem. The error cancellation, however, occurs only when matrix separation and concentration matching are fully attained. 

Related to the above-described inter-elemental correction method, the need for a proxy element can be obviated at the expense of resorting to sequential analysis of the sample and the calibrator, that is, with external gravimetric calibration. Although this technique is not favored by all, it has played a major role, for example, in the recent re-evaluation of the atomic weight of zinc [13]. 

An accurate uncertainty statement of the measurement result is arguably as important as the accuracy of the measurement result itself. However, since the process of isotope amount ratio measurement is far from trivial, so is also the evaluation of the uncertainty. Consider the following example as an illustration of current problems in conceptual understanding of the uncertainty evaluation of the measurement results. 

Exponential mass bias correction of N( Hg)/N( °Hg) is performed using the 

---

Frey, H.C. and E.S. Rubin, Evaluate Uncertainties in Advanced Process Technologies, Chemical Engineeiing Piogiess, May 1992, 6.3-70. (Uncertainty evaluation)... 

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. 

The uncertainty evaluation process can be broken down into four stages specification, identification, quantification and combination. 

When evaluating uncertainty, it is important to understand the distinction between empirical and non-empirical methods, as this influences how the uncertainty is evaluated. In the case of non-empirical methods, any bias in the results which is due to the method of analysis or, for example, a particular sample type, needs to be considered as part of the uncertainty evaluation process. For example, if a method was intended to determine the total amount of cadmium present in a soil sample, but for some reason only 90% of the cadmium present was extracted from the sample, then this 10% bias would need to be accounted for in the uncertainty estimate. One approach would be to correct results to take account of the bias. However, there would be an uncertainty associated with the correction as there will be some uncertainty about the estimate of the bias. For empirical methods, the method bias is, by definition, equal to zero (the method defines the result obtained). However, when evaluating the uncertainty associated with results obtained from an empirical method, we still need to consider the uncertainty associated with any bias introduced by the laboratory during its application of the method. One approach is to analyse a reference material that has been characterized by using the same empirical method. If no suitable reference material is available, then any bias associated with carrying out the individual stages of the method in a particular laboratory will need to be evaluated. 

LGOVAM Development and Harmonisation of Measurement Uncertainty R inciples Rart(d). Protocol ter uncertainty evaluation fromvalidation data (www.vam.org uk) Niemela, S.I. Uncertainty of quantitative determinations derived by cultivaBon of microorganisms. MIKES-Rublication Ji /2003(www mkes.fi)... 

EUROLAB (2007) Measurement uncertainty revisited Alternative approaches to uncertainty evaluation, available from www.eurolab.org... 

LGC/VAM (2000) Development and Harmonisation of Measurement Uncertainty Principles Part(d) Protocol for uncertainty evaluation from validation data, available from www.vam.org.uk... 

In so far as the input parameters in a risk analysis try to describe reality, they are not known with certainty, and in so far they are known with certainty, that is, they can be expressed by a single-point value, they do not refer to reality. This reminds ns that risk considerations on uncertainty evaluation becomes a key variable that cannot be neglected. The nse of fuzzy logic to evaluate solvents is innovative. This section will only develop these concepts briefly, allowing more rigorous texts to explain it in detail (Ronvray, 1997). 

Ingersoll, C.G., Ankley, G.T., Baudo, R., Burton, G.A., Lick, W., Luoma, S.N., MacDonald, D.D., Reynoldson, T.F., Solomon, K.R., Swartz, R.C. and Warren-Hicks, W. (1997) Work group summary report on an uncertainty evaluation of measurement endpoints used in sediment ecological risk assessments, Chapter 18, EPA/600/A-96/097, U.S. Environmental Protection Agency, National Biological Service, Corvallis, OR, USA. 

When the uncertainty is negligible in comparison with S, the estimations by Eq. (7) do not differ from the estimation used in the ordinary least squares technique. However, the uncertainty increase will influence the result of such estimation. Theoretically it may even happen that o > S, and Eq. (7) lead to an absurd result. In such cases p]=°° is accepted and no absurd results will be obtained [6]. This influence is very important not only for determination of the analyte concentration X0 corresponding to the response Y0 by the calibration curve, but also for the correct uncertainty evaluation in the determination result. 

According to the rules of combined uncertainty evaluation [8, 9], ox can be considered as negligible, if it leads to increase of SXo for less then one-third of its initial value (calculated by ordinary least squares technique). An example of the ox influence on the calibration parameters bh b0 and SXo, and corresponding lifetime of the traceability chain are analysed below. 

International comparability and traceability of measurements to stable references are required in measurements for environmental monitoring and protection, international trade, clinical practice, health and safety, and industrial production. In this respect, this paper presents some practical aspects of traceability using certified reference materials (CRMs) and some examples regarding the uncertainty evaluation in spectrochemical measurements. 

Some results of the calibration uncertainty evaluation, due to the linear calibration curve of copper determination cm by molecular absorption spectro (photo)-metry using a Cecil 2020 instrument are illustrated in Table 2. Note that at the end of the linear range (0-10) mg/1 the calibration uncertainty is bigger than in the middle of the linear range for a concentration of 0.987 mg/1 copper the uncertainty component due to the calibration is 3% and for a concentration of at 6.010 mg/1 copper the uncertainty component due to the calibration is 0.56%. 

This paper presents some aspects regarding the uncertainty evaluation and traceability assurance of spectro-chemical results using spectrometric RMs. 

Present-day analytical laboratories are increasingly under pressure to supply objective evidence of their technical competence, of the reliability of their results and performance, and to seek formal certification or accreditation. This pressure may come from the laboratory s customers (e.g., industry and national bodies) but may also be due to scientific considerations. A QM system in place, validation of methods, uncertainty evaluation, the use of primary standards and CRMs, participation in ILCs, and PT, all serve to assure and demonstrate the quality of measurements. Compared to, say, 30 years ago, the stability of the equipment now available is much improved, and a greater range of RMs for method validation and calibration is accessible. Nevertheless, to achieve mutual (international) acceptance of various bodies of evidence for QA activities, a number of protocols have been developed. The most widely recognized protocols used in chemical measurements and testing are the ISO Guide 9000 2000, ISO/IEC 17025 2005, and OECD Guidelines for GLP, as well as its national and sector equivalents. 

Precision %RSD< 15 % at>3 levels n>5 at each level %RSD<20 % at LLOQ %RSD<20% n=4-7 CV<2.2 % Uncertainty evaluation Coefficient of variation (CV) 1 % Uncertainty of volumetric error 0.3 % Uncertainty of reference standard 0.1 % Uncertainty of weighing 0.5 % Uncertainty of other systematic errors Combined standard uncertainty Coverage factor Expanded uncertainty Relative expanded uncertainty (%) Intraassay CV=2-9 % Interassay CV=4-12%... 

The TrainMiC-Uncertainty Module Measurement uncertainty is an important ISO/IEC 17025 requirement. This module explains and demystif>es the approach of the ISO-GUM to estimate and report the uncertainty of a measurement result obtained following a specibc measurement procedure. A clear description of all steps needed for uncertainty evaluation is presented, with the respective examples relevant to the environmental, clinical, and/or food sectors. 

Chajduk, E., Polkowska-Motrenko, H., Dybczynski, R.S. A definitive RNAA method for selenium determination in biological samples. Uncertainty evaluation and assessment of degree of accuracy. Accred. Qual. Assur. 13, 443 51 (2008)... 

Validation of the uncertainty evaluation for the determination of metals in solid samples by atomic spectrometiy... 

Abstract A protocol has been developed illustrating the link between validation experiments, such as precision, trueness and ruggedness testing, and measurement uncertainty evaluation. By planning validation experiments with uncertainty estimation in mind, uncertainty budgets can be obtained from validation data with little additional effort. The main stages in the uncertainty estimation process are described, and the use of true-... 

An uncertainty evaluation must consider the full range of variability likely to be encountered during application of the method. This includes parameters relating to the sample (analyte concentration, sample matrix) as well as experimental parameters associated with the method (e.g. temperature, extraction time, equipment settings, etc.). Sources of uncertainty not adequately covered by the precision and trueness studies require separate evaluation. There are three main sources of information calibration certificates and manufacturers specifications, data published in the literature and spe-... 

The individual sources of uncertainty, evaluated through the precision, trueness, ruggedness and other studies are combined to give an estimate of the standard uncertainty for the method as a whole. Uncertainty contributions identified as being proportional to analyte concentration are combined using Eq. (11) ... 

We have applied this protocol to the evaluation of the measurement uncertainty for a method for the determination of three markers (Cl solvent red 24, Cl solvent yellow 124 and quinizarin (1,4-dihydroxyanthra-quinone)) in road fuel. The method requires the extraction of the markers from the sample matrix by solid phase extraction, followed by quantification by HPLC with diode array detection. The uncertainty evaluation involved four experimental studies which were also required as part of the method validation. The studies were precision, trueness (evaluated via the analysis of spiked samples) and ruggedness tests of the extraction and HPLC stages. The experiments and uncertainty calculations are described in detail in Part 2. A summary of the uncertainty budget for the method is presented in Fig. 3. 

We have developed a protocol which describes how data generated from experimental studies commonly undertaken for method validation purposes can be used in measurement uncertainty evaluation. This paper has illustrated the application of the protocol. In the example described, the uncertainty estimate for three analytes in different oil matrices was evaluated from three experimental studies, namely precision, recovery and ruggedness. These studies were required as part of the method validation, but planning the studies with uncertainty evaluation in mind allowed an uncertainty estimate to be calculated with little extra effort. A number of areas were identified where additional experimental work may be required to refine the estimates. However the necessary data could be generated by carrying out additional analyses alongside routine test samples. Again this would minimise the amount of laboratory effort required. 

In terms of numerical methods, the dominance by FEM with equivalent continuum approach might not be most suitable for sparsely or moderately fractured hard rocks and more advanced methods and codes using discrete approach are needed. The issue of applicability of the equivalent media approach, the associated scale effects, and uncertainty evaluations need to be fully explored. The processes are dominated by coupled stress-flow problems and effects of thermo-chemical effects need more attention. More works for soils, clays, sands and other similar media, which are equally, if not more, important in the fields of geo-engineering and environments, seem also needed. 

---