Measurement uncertainties analytical methods

AMC (1995) The Analytical Methods Committee Uncertainty of measurement - implications for its use in analytical science. Analyst 120 2303... 

To assess homogeneity, the distribution of chemical constituents in a matrix is at the core of the investigation. This distribution can range from a random temporal and spatial occurrence at atomic or molecular levels over well defined patterns in crystalline structures to clusters of a chemical of microscopic to macroscopic scale. Although many physical and optical methods as well as analytical chemistry methods are used to visualize and quantify such spatial distributions, the determination of chemical homogeneity in a CRM must be treated as part of the uncertainty budget affecting analytical chemistry measurements. 

The results of activation analysis are subject to well known and common analytical sources of uncertainty, as well as method specific uncertainties, e.g. summarized by Greenberg (1997), and also in Section 2.2. In order for INAA experiments to measure differences in induced activity, i.e. differences due to heterogeneity in the amount of analyte in a given test portion, the experimental procedure is designed to allow only the following uncertainties to be part of the result ... 

LGC - VAM Publications (i) The Fitness for Purpose of Analytical Methods, A Laboratory Guide to Method Validation and Related Topics, (2) Practical Statistics for the Analytical Scientist A Bench Guide By TJ Farrant, (3) Trace Analysis A structured Approach to Obtaining Reliable Results By E Pritchard, (4) Quantifying Uncertainty in Analytical Measurement, and (5) Quality in the Analytical Chemistry Laboratory. LGC/RSC Publications, London, England. 

As probabilistic exposure and risk assessment methods are developed and become more frequently used for environmental fate and effects assessment, OPP increasingly needs distributions of environmental fate values rather than single point estimates, and quantitation of error and uncertainty in measurements. Probabilistic models currently being developed by the OPP require distributions of environmental fate and effects parameters either by measurement, extrapolation or a combination of the two. The models predictions will allow regulators to base decisions on the likelihood and magnitude of exposure and effects for a range of conditions which vary both spatially and temporally, rather than in a specific environment under static conditions. This increased need for basic data on environmental fate may increase data collection and drive development of less costly and more precise analytical methods. 

As shown in Sect. 7.1, signal-to-noise ratio S/N can be used to characterize the precision of analytical methods. Noise is a measure of the uncertainty of dynamic blank measurements (of the background ). 

EURACHEM (1995) Quantifying uncertainty in analytical measurement. Teddington EURACHEM (1998) The fitness for purpose of analytical methods. Teddington... 

This chapter deals with handling the data generated by analytical methods. The first section describes the key statistical parameters used to summarize and describe data sets. These parameters are important, as they are essential for many of the quality assurance activities described in this book. It is impossible to carry out effective method validation, evaluate measurement uncertainty, construct and interpret control charts or evaluate the data from proficiency testing schemes without some knowledge of basic statistics. This chapter also describes the use of control charts in monitoring the performance of measurements over a period of time. Finally, the concept of measurement uncertainty is introduced. The importance of evaluating uncertainty is explained and a systematic approach to evaluating uncertainty is described. 

Note that some organizations may not use the terminology used in this book and may not distinguish between SOPs and WIs. Standard Operating Procedures provide details of how a series of operations are carried out. An example of a SOP would be the detailed instruction for carrying out a particular analytical method. Work Instructions give details of how a specific operation is carried out. What might be classed as a WI is how to operate a particular instrument, how to estimate measurement uncertainty or how to calibrate a piece of equipment. 

After five years as an analyst, Vicki moved within LGC to work on the DTI-funded Valid Analytical Measurement (VAM) programme. In this role, she was responsible for providing advice and developing guidance on method validation, measurement uncertainty and statistics. One of her key projects involved the development of approaches for evaluating the uncertainty in results obtained from chemical test methods. During this time, Vicki also became involved with the development and delivery of training courses on topics such as method validation, measurement uncertainty, quality systems and statistics for analytical chemists. 

Although ocean water is well mixed with respect to Li, and many laboratories measure Li isotopes in seawater, the range in values reported in the literature (8 Li = +29.3 to +33) cast some uncertainty on this reservoir. As precision of analytical methods improves, a check of the viability of the seawater standard should be carried out. 

An important consideration for quality control in industry and commerce relates to the trend of developing faster analytical methods than those described in official sfandards the question in such cases is whether a proposed method is acceptable as replacement for the standard. This problem relates to the concepts fitness for purpose and measurement uncertainty, the latter serving for the estimation of the LOD and LOQ parameters of analytical quality. An example of this dilemma relating to the peroxide value is discussed in Section IV.B.5. 

It was proposed to replace the final titration of Is in the standard method with a redox potentiometric method, which is less laborious, fast and prone to automation. The LOD is 0.16 meqkg, allowing determination of POV in fresh oil. A method based on the potentiometric determination of the equilibrium in equation 54, in aqueous solution containing a large excess of I, with a Pt electrode vs. SCSE, was proposed to replace the standard iodometric titrations of Section IV.B.2 for determination of the POV of oils. The proposed method is fit for purpose, based on the measurement uncertainties, as compared to those of the standards based on iodine titration with thiosulfate solution. The analytical quality of the potentiometric method is similar to that of the standards based on titrations for oils with POV >0.5 meqkg however, for fresh oils, with much lower POV, the potentiometric method is bettef . 

MCM-41, olefin epoxidation, 418, 421-2 MCM-48, olefin epoxidation, 418 MDA see Malondialdehyde Measurement uncertainties analytical methods, 624 potentiometry, 663 Meat... 

In the ordinary weighted least squares method, the most probable values of source contributions are achieved by minimizing the weighted sum of squares of the difference between the measured values of the ambient concentration and those calculated from Equation 1 weighted by the analytical uncertainty of those ambient measurements. This solution provides the added benefit of being able to propagate the measured uncertainty of the ambient concentrations through the calculations to come up with a confidence interval around the calculated source contributions. 

Measurement uncertainty is the most important criterium in both method validation and IQC. It is defined as a parameter, associated with the result of a measurement, that characterizes the dispersion of the values that could reasonably be attributed to the measurand [14]. The measurand refers to the particular quantity or the concentration of the analyte being measured. The parameter can be a standard deviation or the width of a confidence interval [14, 37]. This confidence interval represents the interval on the measurement scale within which the true value lies with a specified probability, given that all sources of error are taken into account [37]. Within this interval, the result is regarded as being accurate, that is, precise and true [11]. 

The terms validation and QA are widely used. However, a lot of analysts and laboratories do not know the exact meaning—neither the difference nor the relationship between the two terms. Validating a method is investigating whether the analytical purpose of the method is achieved, which is obtaining analytical results with an acceptable uncertainty level [4]. Analytical method validation forms the first level of QA in the laboratory (Figure 6). AQA is the complete set of measures a laboratory must undertake to ensure that it is able to achieve high-quality data continuously. Besides the use of validation and/or standardized methods, these measures are effective IQC procedures (use of reference materials, control charts, etc.), participation in proficiency-testing schemes, and accreditation to an international standard, normally ISO/IEC 17025 [2,4, 6]. 

The ISO definition of validation is confirmation by examination and provision of objective evidence that the particular requirements of a specified intended use are fulfilled [15]. Method validation is needed to confirm the fitness for purpose of a particular analytical method, that is, to demonstrate that a defined method protocol, applicable to a specified type of test material and to a defined concentration rate of the analyte —the whole is called the analytical system — is fit for a particular analytical purpose [4]. This analytical purpose reflects the achievement of analytical results with an acceptable standard of accuracy. An analytical result must always be accompanied by an uncertainty statement, which determines the interpretation of the result (Figure 6). In other words, the interpretation and use of any measurement fully depend on the uncertainty (at a stated level of confidence) associated with it [8]. Validation is thus the tool used to demonstrate that a specific analytical method actually measures what it is intended to measure and thus is suitable for its intended purpose [11,55,56]. 

The purpose of an analytical method is the deliverance of a qualitative and/or quantitative result with an acceptable uncertainty level. Therefore, theoretically, validation boils down to measuring uncertainty . In practice, method validation is done by evaluating a series of method performance characteristics, such as precision, trueness, selectivity/specificity, linearity, operating range, recovery, LOD, limit of quantification (LOQ), sensitivity, ruggedness/robustness, and applicability. Calibration and traceability have been mentioned also as performance characteristics of a method [2, 4]. To these performance parameters, MU can be added, although MU is a key indicator for both fitness for purpose of a method and constant reliability of analytical results achieved in a laboratory (IQC). MU is a comprehensive parameter covering all sources of error and thus more than method validation alone. 

Trueness or exactness of an analytical method can be documented in a control chart. Either the difference between the mean and true value of an analyzed (C)RM together with confidence limits or the percentage recovery of the known, added amount can be plotted [56,62]. Here, again, special caution should be taken concerning the used reference. Control charts may be useful to achieve trueness only if a CRM, which is in principle traceable to SI units, is used. All other types of references only allow traceability to a consensus value, which however is assumed not to be necessarely equal to the true value [89]. The expected trueness or recovery percent values depend on the analyte concentration. Therefore, trueness should be estimated for at least three different concentrations. If recovery is measured, values should be compared to acceptable recovery rates as outlined by the AOAC Peer Verified Methods Program (Table 7) [56, 62]. Besides bias and percent recovery, another measure for the trueness is the z score (Table 5). It is important to note that a considerable component of the overall MU will be attributed to MU on the bias of a system, including uncertainties on reference materials (Figures 5 and 8) [2]. 

Recovery is often treated as a separate validation parameter (Table 5). Analytical methods intend to estimate the true value of the analyte concentration with an uncertainty that is fit for the intended purpose. However, in such analytical methods, the analyte is transferred from the complex matrix to a simpler solution whereby there is a loss of analyte. As a consequence, the measured value will be lower than the true concentration present in the original matrix. Therefore, assessing the effi-... 

CX/MAS 01/8 (2001), Codex Alimentarius Commission, Codex Committee on Methods of Analysis and Sampling (FAO/WHO), Measurement uncertainty. Relationship between the analytical result, the measurement uncertainty and the specification in Codex standards, agenda item 4a of the 23rd session, Budapest, Hungary, Feb. 26-Mar. 2, 2001. 

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