Trueness of the results

In fact, there exists a great difference between the two types of needs in terms of traceability. On the one hand, the regulation compliance need and more often the scientific need require us to be as confident as possible about the trueness of the results (or at least the closeness between the results and true values) and to store the data for further exploitation. On the other hand, process control and hazards prevention are based on the real-time exploitation of the results. 

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 other major use of CRMs is to ascertain the trueness of the results found. A common (but unregulated) practice is to assume that the results for the samples are correct if the result of the CRM, analyzed along with the samples, is within the 95 percent CL It should be kept in mind that the analyst knows the certified levels of the analytes in the CRM prior to the analysis, which unavoidably makes him/her biased. Therefore, good CRM results may indicate that the results of the samples the CRM was analyzed with are just as good, but are not useful as a guarantee for the trueness of results. 

Procedure blanks are performed at each occasion of measurement and their results are discussed before correction can be applied. Finally, all results are corrected for the water content in the sample. Particular attention is given to the reliability of the signal of the detector and the calibration of this signal. For detectors with a poor linear dynamic range the calibration must be performed in an adequate concentration so that extrapolation is possible to the signal of the unknown sample. Finally, all chromatograms — samples with and without internal standard(s), blanks, calibration solutions — are examined by all participants collectively. Any remaining unresolved question or any doubt on one or the other step in a procedure that may affect the trueness of the result leads to rejection of the data. The methods and data that successfully passed this technical evaluation are used to calculate the certified value and its uncertainty. 

It is widely accepted that the precision or reproducibility of isotope ratio measurements can be improved dramatically by using an MC instrument. However, further correction or careful procedural protocols are required to also improve the trueness of the resulting isotopic data. There are two major considerations for better quality isotope ratio measurements using LA-ICP-MS analysis the isotope fractionation during the LA and/or ionization process in the ICP, and the contribution of the matrix effect (non-spectral interference) to the mass bias effect. The latter is discussed in Section 4.6. First, the level of isotope fractionation during LA or ionization processes is discussed in this section. 

In our example, spiking is no longer required in spite of matrix effects being evident. The additional effort would be nothing but a waste of resources. By comparing calibrations and their respective uncertainty shown in the two plots of Fig. 10, we even improve the quality of our analytical result in spite of reduced measurement efforts. The trueness of the result remains imchanged, but we are able to report an improved precision. 

Obviously there are also some disadvantages associated with the direct analysis of sohd samples, such as difficulties in finding proper standards for calibration, and a greater imprecision due to sample inhomogeneity. The latter problem, however, is often counterbalanced by a better trueness of the results because of the reduced risk of analyte loss or contamination. 

The trueness of the analytical results or their inaccuracy, respectively, characterized by the bias 6 = x — xtrue see Sect. 7.1.3. 

The situation becomes more complex when aspects of the trueness of analytical results are included in the assessment. Trueness of information cannot be considered neither by the classical Shannon model nor by Kullback s divergence measure if information. Instead, a model that takes account of three distributions, viz the uniform expectation range, po(x), the distribution of the measured values, p(x), and that of the true value, r(x), as shown in Fig. 9.5, must be applied. 

Measurements are subject to systematic errors as well as the random errors covered in Section 4.3.2. Bias is the difference between the mean value of a large number of test results and an accepted reference value for the test material. The bias is a measure of trueness of the method. It can be expressed in a number of ways, i.e. simply as a difference or as a ratio of the observed value to the accepted value. This latter representation, when expressed as a percentage, is often termed recovery. This represents how much of the analyte of interest has been extracted from the matrix and measured. This is dealt with in Section 4.6.3. 

T raceability is the property of result of measurement or the value of a standard whereby it can be related to stated references, usually national or international standards, through an unbroken chain of comparisons all having stated uncertainty [16]. Traceability is the proof of trueness of a result. Traceability is also a measure to build up trust in measurement results because it includes a complete documentation of calibration certificates (Figs. 7 and 8). 

This is a procedure that is regularly given more importance than deserved in the efforts to guarantee the trueness of experimental results when determining trace elements in foodstuffs. The information value of recovery tests in element analysis by AAS in food is very limited. Recovery information is important in chemical analysis where there is an extraction step in order to elucidate the efficiency of... 

There are many known ways of detecting results with gross errors. Each is applied in certain specific conditions [2]. After eliminating results with gross errors, the trueness of the obtained final determination (most often the mean value of the measurement series) is influenced by biases and/or random errors. 

The evaluation of the distribution of means is performed through a Dixon test (Nalimov). If outlying mean values remain after the technical discussion, it demonstrates that biased results remain and that this technical examination was unreliable. The parameter cannot be certified as a doubt remains on the trueness of the data. 

Accuracy Formerly the closeness of a measurement result to the true value now the quality of the result in terms of trueness and precision in relation to the requirements of its use. (Section 1.8 figure 1.6)... 

These experiments are designated as phases II and III of a method validation or an analyst familiarization for those using the approach recommended in the USDA/FSIS Chemistry Laboratories Guidebook (see QA section of the posted methods)7 The results provide an assessment of the recovery (trueness) and the analyst precision attained with the method under routine conditions of use. In addition, the data generated may be used to calculate statistical estimates of the reliability of the results, including estimates of MU. i ... 

A measurement result is an estimate and may be considered to be without error to measure the correct values, and then the analytical procedures may be considered to be correct or accurate. The accuracy of an analytical procedure and result combines precision and trueness. ISO 5725 [5] defines accuracy as the closeness of agreement between test results and the accepted reference value , and is described by the terms trueness and precision . Trueness refers to the closeness of agreement between the average value of a large number of test results and the true or accepted reference value. Trueness of the analytical procedure is normally measured as a bias. Precision refers to the closeness of agreement between test results. Two measures of precision, termed repeatability and reproducibility, have been established for describing the variability of a measurement method. The first describes the minimum and the second the maximum variability of results, and between these intermediate conditions exist [17]. 

Appraising trueness and accuracy has to proceed with an evaluation of precision in order to finally ensure and prove the traceability of analytical results to known standards. Thus, in the examples given herein, the focus is on calibration data and characteristics in order to derive an accurate estimate of the precision and trueness of the method. 

If analytical methods are validated in inter-laboratory validation studies, documentation should follow the requirements of the harmonized protocol of lUPAC. " However, multi-matrix/multi-residue methods are applicable to hundreds of pesticides in dozens of commodities and have to be validated at several concentration levels. Any complete documentation of validation results is impossible in that case. Some performance characteristics, e.g., the specificity of analyte detection, an appropriate calibration range and sufficient detection sensitivity, are prerequisites for the determination of acceptable trueness and precision and their publication is less important. The LOD and LOQ depend on special instmmentation, analysts involved, time, batches of chemicals, etc., and cannot easily be reproduced. Therefore, these characteristics are less important. A practical, frequently applied alternative is the publication only of trueness (most often in terms of recovery) and precision for each analyte at each level. No consensus seems to exist as to whether these analyte-parameter sets should be documented, e.g., separately for each commodity or accumulated for all experiments done with the same analyte. In the latter case, the applicability of methods with regard to commodities can be documented in separate tables without performance characteristics. 

The example given in Table 7.2 is taken from a study to verify the trueness of clinical analyses (Streck [2004]). Recovery rates have been used as the criterion to accept a good agreement between the measured results and the reference values as it is frequently done by analysts. 

It goes without saying that you should make all measurements to the best of your ability. However, a value to the highest level of precision and trueness is not always required. The aim is that the result produced should be accurate enough to be of use to the customer, for the intended purpose (see Chapter 4). Customers may want the technical details of the method used but more often this will not be... 

Bias is a measure of trueness . It tells us how close the mean of a set of measurement results is to an assumed true value. Precision, on the other hand, is a measure of the spread or dispersion of a set of results. Precision applies to a set of replicate measurements and tells us how the individual members of that set are distributed about the calculated mean value, regardless of where this mean value lies with respect to the true value. 

Accuracy (Trueness and Precision) of Measurement Methods and Results - Part 3 Intermediate Measures of the Precision of a Standard Measurement Method , ISO 5725-3 1994, International Organization for Standardization (ISO), Geneva, Switzerland, 1994. 

Trueness Closeness of agreement between the average value obtained from a large series of test results and an accepted reference value. Trueness is normally expressed in terms of bias. 

Some authors use the word trueness instead of accuracy to describe the closeness of the mean of many replicate analyses to the true value. This allows the word accuracy to carry a more general meaning which relates to the accuracy or difference of a single result from the true value, as a conse-... 

International Organization for Standardization (ISO), Statistical methods for quality control, Vol. 2, 4th Edition, Accuracy (trueness and precision) of measurement methods and results - Part 3 Intermediate measures of the precision of a standard measurement method, ISO 1994(E), 5725-3. 

Accuracy expresses the closeness of a result to the true value. Accuracy = trueness + precision. Under specific conditions it is quantified by the measurement uncertainty. Measurement uncertainty may vary under changing conditions and method validation determines the degree. 

Method validation seeks to quantify the likely accuracy of results by assessing both systematic and random effects on results. The properly related to systematic errors is the trueness, i.e. the closeness of agreement between the average value obtained from a large set of test results and an accepted reference value. The properly related to random errors is precision, i.e. the closeness of agreement between independent test results obtained under stipulated conditions. Accnracy is therefore, normally studied as tmeness and precision. 

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