The application of the measurement uncertainty concept to in-process control in pharmaceutical development

 Any analytical data is used to provide information about a sample. The "possible error" of the measurement can be of extreme importance in order to have complete information. The measurement uncertainty concept is a way to achieve quantitative information about this "possible error" using an estimation procedure. On the basis of the analytical result, the chemist makes a decision on the next step of the development process. If the uncertainty is unknown, the information is not complete; therefore this decision might be impossible. The major problem for the in-process control (IPC) procedure is that not only the repeatability but also the intermediate precision (which expresses the variations within laboratories related to different days, different analysts, different equipment, etc.) has to be good enough to make a decision. Unfortunately, the statistical information achieved from one single analytical run only gives information about the repeatability. This paper shows that the estimation of the measurement uncertainty for IPC is a way to solve the problem and gives the necessary information about the quality of the procedure. An example demonstrates that an estimate of uncertainty based on the standard deviations of an analytical method gives a value similar to one based on the standard deviations obtained from a control chart. Therefore, the estimation is both a very useful and also a very cost-effective tool. Though measurement uncertainty cannot replace validation in general, it is a viable alternative to validation for all methods that will never be used routinely. Received: 24 May 1996 Accepted: 10 August 1996     

Measurement uncertainty – a reliable concept in food analysis and for the use of recovery data?

 Steps which are taken to implement the concept of measurement uncertainty in analytical chemical laboratories should take full account of existing internationally agreed protocols for analytical quality assurance and reflect the needs of particular analytical sectors. For the food sector this may mean that for official purposes the use of the term measurement uncertainty is replaced by the term measurement reliability and that a quantitative estimation of this is made based on existing collaborative trial data. In many analytical sectors, the differing strategies currently followed for the determination and use of recovery information are an important cause of the non-comparability of analytical results. Guidelines which are being prepared for the estimation and use of recovery information in analytical measurement may provide a more unified approach which includes measurement uncertainty as a key concept in the use of recovery data. Received: 4 November 1997 · Accepted: 3 February 1998     

Uncertainty profiles for the validation of analytical methods

This article aims to expose a new global strategy for the validation of analytical methods and the estimation of measurement uncertainty. Our purpose is to allow to researchers in the field of analytical chemistry get access to a powerful tool for the evaluation of quantitative analytical procedures. Indeed, the proposed strategy facilitates analytical validation by providing a decision tool based on the uncertainty profile and the β-content tolerance interval. Equally important, this approach allows a good estimate of measurement uncertainty by using data validation and without recourse to other additional experiments.In the example below, we confirmed the applicability of this new strategy for the validation of a chromatographic bioanalytical method and the good estimate of the measurement uncertainty without referring to any extra effort and additional experiments. A comparative study with the SFSTP approach [1] showed that both strategies have selected the same calibration functions.The holistic character of the measurement uncertainty compared to the total error was influenced by our choice of profile uncertainty. Nevertheless, we think that the adoption of the uncertainty in the validation stage controls the risk of using the analytical method in routine phase.     

Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling

Measurement uncertainty is a vital issue within analytical science. There are strong arguments that primary sampling should be considered the first and perhaps the most influential step in the measurement process. Increasingly, analytical laboratories are required to report measurement results to clients together with estimates of the uncertainty. Furthermore, these estimates can be used when pursuing regulation enforcement to decide whether a measured analyte concentration is above a threshold value. With its recognised importance in analytical measurement, the question arises of 'what is the most appropriate method to estimate the measurement uncertainty?'. Two broad methods for uncertainty estimation are identified, the modelling method and the empirical method. In modelling, the estimation of uncertainty involves the identification, quantification and summation (as variances) of each potential source of uncertainty. This approach has been applied to purely analytical systems, but becomes increasingly problematic in identifying all of such sources when it is applied to primary sampling. Applications of this methodology to sampling often utilise long-established theoretical models of sampling and adopt the assumption that a 'correct' sampling protocol will ensure a representative sample. The empirical approach to uncertainty estimation involves replicated measurements from either inter-organisational trials and/or internal method validation and quality control. A more simple method involves duplicating sampling and analysis, by one organisation, for a small proportion of the total number of samples. This has proven to be a suitable alternative to these often expensive and time-consuming trials, in routine surveillance and one-off surveys, especially where heterogeneity is the main source of uncertainty. A case study of aflatoxins in pistachio nuts is used to broadly demonstrate the strengths and weakness of the two methods of uncertainty estimation. The estimate of sampling uncertainty made using the modelling approach (136%, at 68% confidence) is six times larger than that found using the empirical approach (22.5%). The difficulty in establishing reliable estimates for the input variable for the modelling approach is thought to be the main cause of the discrepancy. The empirical approach to uncertainty estimation, with the automatic inclusion of sampling within the uncertainty statement, is recognised as generally the most practical procedure, providing the more reliable estimates. The modelling approach is also shown to have a useful role, especially in choosing strategies to change the sampling uncertainty, when required.     

Software support for the Nordtest method of measurement uncertainty evaluation

Software support for the Nordtest method of measurement uncertainty evaluation is described. According to the Nordtest approach, the combined measurement uncertainty is broken down into two main components??the within-laboratory reproducibility (intermediate precision) s _Rw and the uncertainty due to possible laboratory bias u(bias). Both of these can be conveniently estimated from validation and quality control data, thus significantly reducing the need for performing dedicated experiments for estimating detailed uncertainty contributions and thereby making uncertainty estimation easier for routine laboratories. An additional merit of this uncertainty estimation approach is that it reduces the danger of underestimating the uncertainty, which continues to be a problem at routine laboratories. The described software tool??MUkit (measurement uncertainty kit)??fully reflects the versatility of the Nordtest approach: it enables estimating the uncertainty components from different types of data, and the data can be imported using a variety of means such as different laboratory data systems and a dedicated web service as well as manual input. Prior to the development of the MUkit software, a laboratory survey was carried out to identify the needs of laboratories related to uncertainty estimation and other quality assurance procedures, as well as their needs for a practical tool for the calculation of measurement uncertainty.     

Estimation of Uncertainty for Measuring Codeine Phosphate Tablets Formulation Using UV-Vis Spectrophotometry

Analytical results represent a very important part in a quality control program. Uncertainty estimation is an important step in method validation. The objective of this paper is to study the uncertainty of measurement estimation in the quantitative determination of codeine phosphate from pharmaceutical formulations using UV-VIS spectrophotometry. The uncertainty estimation was performed using the Ishikawa diagram. The estimation of uncertainty components proved to be a good way for the experimental model to obtain low contribution of uncertainty to the analytical result.     

Proficiency tests for laboratories: a systematic review

Laboratories analytical results must be reliable. Proficiency tests (PT) main objective is to provide independent demonstrations of laboratory competence. The demand for these activities is increasing on the world scenario, as well as its importance. The main objective of this study is to identify the important publications of the PT theme from 2005 to 2012 based on a systematic review procedure. The method proposed reached a total of 113 papers published in indexed journals and another 34 additional references, including standards, guidelines and recommendations of international or regional accreditation body cooperation, international standards organization and international metrological institutes. All selected references were clustered based on its approach, and then the main practices were presented. The approaches identified were related to performance assessment, calculation method for performance assessment, use of PT for validation and/or estimation of measurement uncertainty and management of PT. Results indicate some opportunities to develop researches, such as: project management related to PT, the importance of analyses of data probability distribution function when consensus value is used, criteria to select the parameter to homogeneity and stability tests and to explore the link between PT, method validation and measurement uncertainty, among others.     

Validation requirements for chemical methods in quantitative analysis – horses for courses?

 Although the validation process necessary to ensure that an analytical method is fit for purpose is universal, the emphasis placed on different aspects of that process will vary according to the end use for which the analytical procedure is designed. It therefore becomes difficult to produce a standard method validation protocol which will be totally applicable to all analytical methods. It is probable that far more than 30% of the methods in routine laboratory use have not been validated to an appropriate level to suit the problem at hand. This situation needs to change and a practical assessment of the degree to which a method requires to be validated is the first step to a reliable and cost effective analytical industry. Received: 22 September 1997 · Accepted: 28 November 1997     

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

 Every analytical result should be expressed with some indication of its quality. The uncertainty as defined by Eurachem ("parameter associated with the result of a measurement that characterises the dispersion of the values that could reasonably be attributed to the, . . ., quantity subjected to measurement") is a good tool to accomplish this goal in quantitative analysis. Eurachem has produced a guide to the estimation of the uncertainty attached to an analytical result. Indeed, the estimation of the total uncertainty by using uncertainty propagation laws is components-dependent. The estimation of some of those components is based on subjective criteria. The identification of the uncertainty sources and of their importance, for the same method, can vary from analyst to analyst. It is important to develop tools which will support each choice and approximation. In this work, the comparison of an estimated uncertainty with an experimentally assessed one, through a variance test, is performed. This approach is applied to the determination by atomic absorption of manganese in digested samples of lettuce leaves. The total uncertainty estimation is calculated assuming 100% digestion efficiency with negligible uncertainty. This assumption was tested. Received: 3 November 1997 · Accepted: 2 January 1998     

Measurement uncertainty and its implications for collaborative study method validation and method performance parameters

ISO principles of measurement uncertainty estimation are compared with protocols for method development and validation by collaborative trial and concomitant "top-down" estimation of uncertainty. It is shown that there is substantial commonality between the two procedures. In particular, both require a careful consideration and study of the main effects on the result. Most of the information required to evaluate measurement uncertainty is therefore gathered during the method development and validation process. However, the information is not generally published in sufficient detail at present; recommendations are accordingly made for future reporting of the data.     

Do we need to consider metrological meanings of different measurement uncertainty estimations?

Along the years, several approaches for measurement uncertainty estimation have been suggested. Emphasis has been put on the general metrological interpretation of measurement uncertainty, but not on its different meanings when it is associated to given conditions of measurement where analytical work is performed and errors are originated. Three different definitions for uncertainty are proposed for reproducibility and intermediate precision conditions of measurement. These definitions inherit features from the VIM 3 definition of measurement uncertainty. It is argued that if a high performance laboratory keeps errors under control with proper validation and quality assurance programs, measurement uncertainty from intermediate precision condition of measurement is justified as a suitable estimation of its capability to attribute values to a measurand. Alternatively, a laboratory that does not keep errors under control should use uncertainty from reproducibility condition of measurement as the cost of its imperfections. Selection of information sources for measurement uncertainty estimation should be in harmony with its metrological meaning.     

Speciation analysis of chromium in drinking water samples by ion-pair reversed-phase HPLC–ICP-MS: validation of the analytical method and evaluation of the uncertainty budget

The approach presented in this article refers to the modification of a method for the detection and quantitative determination of chromium species in water by high-performance liquid chromatography inductively coupled plasma mass spectrometry. The main aim of this work was to establish a detailed validation of the analytical procedure and an estimation of the budget of measurement uncertainty which was helpful in recognizing the critical points of the presented method. As a result of the method validation experiment, the obtained limit of quantification, repeatability and intermediate precision were satisfied for the quantification Cr(III) and Cr(VI) in water matrices. The trueness of the method was verified via an estimation of the recovery of the spiked real samples. The recovery rate of both determined analytes was found to be between 93 and 115 %. Considering that the validation of the method and the evaluation of measurement uncertainty are crucial for quantitative analysis, the above-mentioned assessment of the uncertainty budget was performed in two different ways: a modelling approach and a single-laboratory validation approach. The measurement uncertainties of the results were found to be 4.4 and 7.8 % for Cr(III), 4.2 and 7.9 % for Cr(VI) using the classical concept and method validation data, respectively. This paper is the first publication to presenting all the steps needed to evaluate the measurement uncertainty for the speciation analysis of chromium species. In summary, the obtained results demonstrate that the method can be applied effectively for its intended use.     

Quantifying the measurement uncertainty of results from environmental analytical methods

The Eurachem-CITAC Guide Quantifying Uncertainty in Analytical Measurement was put into practice in a public laboratory devoted to environmental analytical measurements. In doing so due regard was given to the provisions of ISO 17025 and an attempt was made to base the entire estimation of measurement uncertainty on available data from the literature or from previously performed validation studies. Most environmental analytical procedures laid down in national or international standards are the result of cooperative efforts and put into effect as part of a compromise between all parties involved, public and private, that also encompasses environmental standards and statutory limits. Central to many procedures is the focus on the measurement of environmental effects rather than on individual chemical species. In this situation it is particularly important to understand the measurement process well enough to produce a realistic uncertainty statement. Environmental analytical methods will be examined as far as necessary, but reference will also be made to analytical methods in general and to physical measurement methods where appropriate. This paper describes ways and means of quantifying uncertainty for frequently practised methods of environmental analysis. It will be shown that operationally defined measurands are no obstacle to the estimation process as described in the Eurachem/CITAC Guide if it is accepted that the dominating component of uncertainty comes from the actual practice of the method as a reproducibility standard deviation.     

Quantifying the measurement uncertainty of results from environmental analytical methods

The Eurachem–CITAC Guide Quantifying Uncertainty in Analytical Measurement was put into practice in a public laboratory devoted to environmental analytical measurements. In doing so due regard was given to the provisions of ISO 17025 and an attempt was made to base the entire estimation of measurement uncertainty on available data from the literature or from previously performed validation studies. Most environmental analytical procedures laid down in national or international standards are the result of cooperative efforts and put into effect as part of a compromise between all parties involved, public and private, that also encompasses environmental standards and statutory limits. Central to many procedures is the focus on the measurement of environmental effects rather than on individual chemical species. In this situation it is particularly important to understand the measurement process well enough to produce a realistic uncertainty statement. Environmental analytical methods will be examined as far as necessary, but reference will also be made to analytical methods in general and to physical measurement methods where appropriate. This paper describes ways and means of quantifying uncertainty for frequently practised methods of environmental analysis. It will be shown that operationally defined measurands are no obstacle to the estimation process as described in the Eurachem/CITAC Guide if it is accepted that the dominating component of uncertainty comes from the actual practice of the method as a reproducibility standard deviation.     

Using target measurement uncertainty to determine fitness for purpose

The methods an analytical laboratory uses must be validated to be fit for purpose. The fitness for purpose of a quantitative method used to determine the concentration of a substance when assessing compliance to requirements can be described by the maximum measurement uncertainty. This is called the target measurement uncertainty. Acceptance criteria for precision and bias in the method validation are then established in terms of the target measurement uncertainty. The target measurement uncertainty can be decided by following a process which involves determining the required concentration range of the measurand; determining the acceptable level of risks of incorrect decisions of compliance; developing a suitable decision rule, with guard bands if appropriate; using the probability of making an incorrect decision of compliance based on the decision rule; and assessing the impact of bias. A key participant in this process is the end user of the data, the laboratory customer. This paper presents the concepts concerning target measurement uncertainty introduced in recently published international guidelines to the practicing analytical chemist who is not generally familiar with these concepts. Three examples are used to illustrate the process.     

Accuracy profiles from uncertainty measurements

Accuracy profiles of chemical assays are introduced and derived from the uncertainty of the analytical result. The calculation of accuracy profiles is based on the estimation of the measurement uncertainty of the analytical assay from validation data. For the sake of illustration, a case study dealing with the spectrofluorimetric determination of quinine in tonic water is explained in detail.     

Analysis of the relationships between proficiency testing,validation of methods and estimation of measurement uncertainty: a qualitative study with experts

Proficiency testing (PT) is an important part in the process ensuring quality of results. Validation of methods and estimation of measurement uncertainty are also vital elements in a laboratory qualification process. The main objective of this paper is to identify the relationships between PT, validation of methods and calculation of measurement uncertainty through the drawing up of a Mind Map. The method proposed used the Web-based Delphi method to conduct a qualitative research with experts of the area (laboratory assessors and accreditation body members). Experts from five accreditation bodies (Brazil, USA, France, Portugal and China) took part in the research. The study led to the drawing up of a Mind Map showing the connections of cause and effect between the factors under study. It emerged that PT effectively contributes to the reliability of results, since it is directly related to the validation of methods and to measurement uncertainty, so as to increase laboratory’s competitiveness.     

Practical digest for evaluating the uncertainty of analytical assays from validation data according to the LGC/VAM protocol

The estimation of the measurement uncertainty of analytical assays based on the LGC/VAM protocol from validation data is fully revisited and discussed in the light of the study of precision, trueness and robustness.     

Gas chromatography analysis: method validation and measurement uncertainty evaluation for volume fraction measurements of gases in simulated reformate gas stream

It is important to understand each analytical system and its limitations when performing any chromatographic measurements. In the present paper, a methodology for method validation and measurement uncertainty evaluation for the measurement of volume fractions of selected gases (CO₂, CO, CH₄, H₂) in simulated reformate gas streams by using gas chromatography was developed. A detailed procedure for in-house method validation based on a simple experimental design and consistent statistics is presented. The analytical protocol allowed us to quantify gases in volume fractions from 2.00 to 100.0 mL/(100 mL) with satisfactory recoveries. We proved that the method was selective for the measurement of gases in simulated reformate gas stream. In addition, a step-by-step illustration of modelling approach for measurement uncertainty evaluation of each component is also provided. Uncertainty arising from repeatability and trueness is relatively low, while the contribution from reproducibility is of higher level for all the analytes tested. The main reason for this is changes in atmospheric pressure that affect gas chromatographic measurements. Solution of this problem could be more frequent calibration of apparatus, yielding to higher costs and more time-consuming process, or by measuring the atmospheric pressure and using it to correct the response of the gas chromatograph for resulting variations in sample size. The obtained results confirm that it is imperative to fully characterize the analytical system before proceeding with an analysis.