ICP-MS determination of Pt in biological fluids of patients treated with antitumor agents: evaluation of analytical uncertainty

The estimation of the uncertainty associated to the analytical methods is necessary in order to establish the comparability of results. Methods of Pt determination in biological fluids lack very often of information about uncertainty of results, with likely implications when results are used to interpret the mechanism of action of platinum compound or when they are considered to optimise the clinical therapies.An inductively coupled plasma-mass spectrometer (ICP-MS) method for the determination of Pt in biological fluids (plasma, ultrafiltrate and urine) of patients treated with antitumor agents has been developed and validated. The limits of quantification (LOQ) in the three matrices were 1.0, 0.1, and 2.0 μg/l, respectively.Intraday and interday precisions and accuracies were in good agreement with the FDA criteria for the validation of analytical methods. The validation study was implemented by assessing the uncertainty evaluation for Pt determination in the different matrices according to EURACHEM/CITAC Guide.     

Estimation of precision and efficiency mass transfer steps for the determination of pesticides in vegetables aiming at the expression of results with reliable uncertainty

A 'bottom-up' approach for the expression of results obtained from analytical methods that include analytical steps with recovery inherently different from 100% [mass transfer steps (MTS): extraction, evaporation, clean-up procedures, digestion, etc.] is presented. The estimation of the combination of all MTS uncertainty involves the comparison of the experimental dispersion of replicated analyses of spiked samples with the estimation of the uncertainty obtained for the combination of all uncertainty sources except MTS ones ('incomplete' estimation). The estimation of MTS uncertainty by difference is performed after evaluating the statistical difference between the 'incomplete' estimation and the experimental dispersion (F-test). When the two estimations are statistically equivalent, the MTS uncertainty is considered to be negligible in relation to the other sources budget. The assumption of constancy of MTS performance within the analytical range is tested through single analyses at several concentration levels and is evaluated by the inclusion of the expected values at the intervals resulting from the combination of the MTS uncertainty estimation performed at one concentration level and the 'incomplete' estimation. The developed methodology can also be useful for method optimisation and validation and for the detection of small trends in results. The determination of pesticides in sweet peppers by GC-NPD was used to explore the above concepts.     

Models to estimate overall analytical measurements uncertainty: assumptions, comparisons and applications

Evaluation of analytical results reliability is of core importance as crucial decisions are taken with them. From the various methodologies to evaluate the fitness of purpose of analytical methods, overall measurement uncertainty estimation is more and more applied. Overall measurement uncertainty allows to combine simultaneously the remaining systematic influences to the random sources of uncertainty and allows assessing the reliability of results generated by analytical methods. However there are various interpretations on how to estimate overall measurement uncertainty, and thus various models for estimating it. Each model together with its assumptions has great impacts on the risks to abusively declare that analytical methods are suitable for their intended purpose. This review paper aims at (i) summarizing the various models used to estimate overall measurement uncertainty, (ii) provide their pros and cons, (iii) review the main areas of application and (iv) as a conclusion provide some recommendations when evaluating overall measurement uncertainty.     

Estimating uncertainty in analytical procedures based on chromatographic techniques

Chromatographic techniques are very frequently used in analytical procedures for the separation, determination and identification of a wide spectrum of analytes present in samples with complex and sometimes variable matrices. However, the estimation of uncertainty of the final results does not include the uncertainties associated with the actual chromatographic process. In effect, such results cannot always be treated as a reliable source of analytical information. In this paper we present the basic terms, sources of uncertainty, and methods of calculating the combined uncertainty that any presentation of final determinations should include.     

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.     

Worst case uncertainty estimates for routine instrumental analysis

A methodology for the worst case measurement uncertainty estimation for analytical methods which include an instrumental quantification step, adequate for routine determinations, is presented. Although the methodology presented should be based on a careful evaluation of the analytical method, the resulting daily calculations are very simple. The methodology is based on the estimation of the maximum value for the different sources of uncertainty and requires the definition of limiting values for certain analytical parameters. The simplification of the instrumental quantification uncertainty estimation involves the use of the standard deviation obtained from control charts relating to the concentrations estimated from the calibration curves for control standards at the highest calibration level. Three levels of simplification are suggested, as alternatives to the detailed approach, which can be selected according to the proximity of the sample results to decision limits. These approaches were applied to the determination of pesticide residues in apples (CEN, EN 12393), for which the most simplified approach showed a relative expanded uncertainty of 37.2% for a confidence level of approximately 95%.     

Estimation of measurement uncertainty of pesticides,polychlorinated biphenyls and polyaromatic hydrocarbons in sediments by using gas chromatography–mass spectrometry

The evaluation of the uncertainty associated to analytical methods is essential in order to demonstrate quality of a result. However, there is often lack of information about uncertainty of methods to estimate persistent organic pollutants concentration in complex matrix. Current work has thoroughly evaluated uncertainty associated to quantification of several organochloride pesticides, PCBs and PAHs in sediments. A discussion of the main contributions to the overall uncertainty is reported, allowing authors to establish the accuracy of results and plan future improvements. Combined uncertainties ranged between 5–9% (pesticides), 4–7% (PCBs) and 5–10% (PAHs), being uncertainty derived of calibration the main contribution. Also, the analytical procedure was validated analysing a standard reference material (IAEA-408).     

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.     

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.     

Uncertainty in analytical results from solid materials with electrothermal atomic absorption spectrometry: a comparison of methods

The combined uncertainty in the analytical results of solid materials for two methods (ET-AAS, analysis after prior sample digestion and direct solid sampling) are derived by applying the Guide to the Expression of Uncertainty in Measurement from the International Standards Organization. For the analysis of solid materials, generally, three uncertainty components must be considered: (i) those in the calibration, (ii) those in the unknown sample measurement and (iii) those in the analytical quality control (AQC) process. The expanded uncertainty limits for the content of cadmium and lead from analytical data of biological samples are calculated with the derived statistical estimates. For both methods the expanded uncertainty intervals are generally of similar width, if all sources of uncertainty are included. The relative uncertainty limits for the determination of cadmium range from 6% to 10%, and for the determination of lead they range from 8% to 16%. However, the different uncertainty components contribute to different degrees. Though with the calibration based on reference solutions (digestion method) the respective contribution may be negligible (precision < 3%), the uncertainty from a calibration based directly on a certified reference material (CRM) (solid sampling) may contribute significantly (precision about 10%). In contrast to that, the required AQC measurement (if the calibration is based on reference solutions) contributes an additional uncertainty component, though for the CRM calibration the AQC is “built-in”. For both methods, the uncertainty in the certified content of the CRM, which is used for AQC, must be considered. The estimation of the uncertainty components is shown to be a suitable tool for the experimental design in order to obtain a small uncertainty in the analytical result.     

To what extent can an uncertainty calculation be general?

 It is argued that results of uncertainty calculations in chemical analysis should be taken into consideration with some caution owing to their limited generality. The issue of the uncertainty in uncertainty estimation is discussed in two aspects. The first is due to the differences between procedure-oriented and result-oriented uncertainty assessments, and the second is due to the differences between the theoretical calculation of uncertainty and its quantication using the validation (experimental) data. It is shown that the uncertainty calculation for instrumental analytical methods using a regression calibration curve is result-oriented and meaningful only until the next calibration. A scheme for evaluation of the uncertainty in uncertainty calculation by statistical analysis of experimental data is given and illustrated with examples from the author's practice. Some recommendations for the design of corresponding experiments are formulated.     

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.     

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     

A new formulation to estimate the variance of model prediction. Application to near infrared spectroscopy calibration

Evaluation of uncertainty affecting predictions is a major trend in analytical chemistry and chemometrics. Several approximate expressions and resampling methods have been proposed for the estimation of prediction uncertainty when using multivariate calibration. This article proposes a new expression for the variance of prediction, adapted to near infrared spectroscopy specificities and particularly to the spectral error structure, induced by the high colinearity of the variables. The proposed analytical expression enables a detailed evaluation of the different contributions and components of uncertainty affecting the model. An application to real data of feedstuff near infrared spectra related to protein content has shown its advantages.     

ICP-AES法测定油漆中总铅量不确定度的讨论

根据EURACHEM／CITAC2000中的规定计算了ICP—AES法测定油漆中总铅量的不确定度,建立了数学模型和根据在测试过程中产生不确定度的变量建立了因果图。通过转化数学模型和因果图,对油漆标准样品中含铅量的测定精密度和准确度进行试验,并计算了此方法的不确定度。结论中提出可根据在日常分析试验中所积累的数据,用B类不确定度的计算方法计算所得的测定过程的不确定度具有更高的真实可信性,并指出要不断积累测试数据,不断更新测定方法的不确定度,这样得到的不确定度更为可信合理。     

A validation concept for purity determination and assay in the pharmaceutical industry using measurement uncertainty and statistical process control

 The analytical chemists in process development in the pharmaceutical industry have to solve the difficult problem of producing high quality methods for purity determination and assay within a short time without a clear definition of the substance to be analyzed. Therefore the quality management is very difficult. The ideal situation would be that every method is validated before use. This is not possible because this would delay the development process. A process-type quality development approach with an estimation type fast validation (measurement uncertainty) is therefore suggested. The quality management process consists of the estimation of measurement uncertainty for early project status. Statistical process control (SPC) is started directly after measurement uncertainty estimation and a classical validation for the end of the project. By this approach a process is defined that allows a fast and cost-efficient way of supporting the development process with the appropriate quality at the end of the process and provides the transparency needed in the development process. The procedure presented tries to solve the problem of the parallelism between the two development processes (chemical and analytical development) by speeding up the analytical development process initially. Received: 25 March 1997 · Accepted: 17 May 1997     

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     

Expression of results with uncertainty for the determination of pesticides in melon--experience in a proficiency test.

The expression of results with an uncertainty through the "bottom-up" approach, involving the estimation and combination of all the sources of uncertainty, represents a challenge when the analytical method includes mass transfer steps (MTS). These steps (e.g. extraction, evaporation, digestion, etc.) with inherently different from 100% recoveries lack models capable of describing their precision and efficiency. Recently, a new methodology was published aimed at the estimation of the performance of these critical steps. Comparison of the experimental dispersion from the replicated analysis of spiked samples with the combination of the uncertainty associated with gravimetric, volumetric and instrumental quantification steps (described by well established models) allows the estimation of the MTS uncertainty. Evaluation of the behaviour of the MTS within the analytical range supports the use of developed estimations over a wide concentration range. This methodology was applied, with success, to the determination of pesticide residues in melon in one particular proficiency test organised by the Food Analysis Performance Assessment Scheme (FAPAS) between November 2000 and February 2001.     

Introduction of measurement uncertainty estimation into analytical instrument software: mission impossible?

In this paper we argue that introduction of ISO GUM based uncertainty estimation into analytical equipment software is a “mission possible” and is wholly realistic at this stage of development of the art. A possible general scheme of implementation of uncertainty estimation into analytical instrument software is presented based on the example of high-performance liquid chromatography (HPLC) but is also applicable to most other analytical instruments. This implementation would be very beneficial for the analysts/practitioners as the uncertainty would be handled within their everyday software environment.