Uncertainty in photometric analysis: a case study

A procedure for estimation of measurement uncertainty of photometric analysis based on the ISO GUM method is presented. Two variations of the procedure—for the calibration graph and the standard addition method, respectively—are discussed. The variations are based on mathematical models involving 64 and 80 input quantities, respectively. The uncertainty of the result strongly depends on changes in experimental details. These dependencies are explored for a practical example of determination of the iron content of aluminum. The importance of taking uncertainty from sample preparation into account in uncertainty estimation is stressed. The number of effective degrees of freedom is calculated and discussed. The examples are available as GUM Workbench files in the Electronic Supplementary Material.Electronic Supplementary Material  Supplementary material is available for this article at .

Estimation of uncertainty in electrochemical amperometric measurement of dissolved oxygen concentration

A procedure for the estimation of measurement uncertainty of dissolved oxygen (DO) concentration measurement based on the ISO approach is presented. It is based on a mathematical model that involves 14 input parameters. The uncertainty of DO concentration strongly depends on changes in experimental details (temperature difference between calibration and measurement, the time interval between calibration and measurement, etc.). The relative measurement uncertainty is, however, practically independent of the DO concentration itself. The uncertainty is the lowest if the calibration and the measurement are done at the same temperature and on the same day. A calculation tool is provided (in the form of a GUM Workbench file) for practitioners that can be used for uncertainty calculation of DO concentrations at very different experimental conditions.Electronic Supplementary Material The uncertainty calculation example is available as a GUM Workbench calculation file C_O2_meas.smu (GUM Workbench ver. 1.3.3, Metrodata GmbH) together with its data file Input_values.xls (MS Excel 97). For those users who do not have GUM Workbench, the full report of the GUM Workbench calculation is available as a PDF file C_O2_meas.pdf. This material is available via the Internet at .     

From GUM to alternative methods for measurement uncertainty evaluation

Since the advent of the Guide to the expression of Uncertainty in Measurement (GUM) in 1995 laying the principles of uncertainty evaluation numerous projects have been carried out to develop alternative practical methods that are easier to implement namely when it is impossible to model the measurement process for technical or economical aspects. In this paper, the author presents the recent evolution of measurement uncertainty evaluation methods. The evaluation of measurement uncertainty can be presented according to two axes based on intralaboratory and interlaboratory approaches. The intralaboratory approach includes “the modelling approach” (application of the procedure described in section 8 of the GUM, known as GUM uncertainty framework) and “the single laboratory validation approach”. The interlaboratory approaches are based on collaborative studies and they are respectively named “interlaboratory validation approach” and “proficiency testing approach”.     

Measurement uncertainty of selenium determination in the reference material Seronorm Trace Elements Serum by hydride generation atomic fluorescence spectrometry

 An analytical result is not considered to be complete without a statement of the measurement uncertainty associated with it. Evaluation of measurement uncertainty is therefore an essential part of every determination. In the present study the measurement uncertainty of the result of determination of selenium in the reference material Seronorm Trace Elements Serum was studied. The combination of a digestion procedure using sulfuric acid, V₂O₅ in H₂SO₄ and hydrogen peroxide and the continuous flow approach of hydride generation atomic fluorescence spectrometry (HG-AFS) detection was used for selenium determination. The total uncertainty budget was calculated with the help of the GUM Workbench program, in which computations follow the rules of the ’ISO guide to the expression of uncertainty in measurement’. The major sources of uncertainty were found to be due to the recovery of the procedure, measurement of peak heights and the purity of Na₂SeO₃. A selenium mass fraction of 77.1±4.8 ng/g (with a coverage factor of 2.1, 95% probability) was determined, which is comparable with the recommended value of 80 ng/g. Received: 13 September 2002 Accepted: 23 December 2002 Acknowledgements We would like to thank Tinkara Buˇcar, B.Sc. for useful discussions and the IAEA Vienna, for financial support of the project 11553/RO. Presented at CERMM-3, Central European Reference Materials and Measurements Conference: The function of reference materials in the measurement process, May 30–June 1, 2002, Rogaška Slatina, Slovenia Correspondence to V. Stibilj     

Measurement uncertainty of iodine determination in radiochemical neutron activation analysis

The measurement uncertainty of iodine determination in NIST standard reference material (SRM) 1549 using radiochemical neutron activation analysis (RNAA) was studied. This method is based on ignition of the irradiated sample [¹²⁷I(n,)¹²⁸I, t_1/2=25 min, E=422.9 keV] in an oxygen atmosphere, followed by absorption of iodine in a reducing acid solution and its purification by a selective extraction–stripping–reextraction cycle. The purified solution of iodine in CHCl₃ was transferred to a well-type HPGe detector for -ray measurement of the induced radionuclide ¹²⁸I. The detection limit of the method employed under the conditions described was 1 ng/g. The reproducibility of iodine determination in the SRM was 3.6% (12 determinations within 1 month), calculated by the analysis of variance procedure. Using the commercially available software program GUM Workbench and the recommendations of the Eurachem/CITAC Guide, we evaluated the uncertainty budget for this RNAA method and the relative uncertainty obtained was 3.6%. The largest uncertainty contributions were due to the repeatability of the chemical yield determination, the count rate of the induced nuclide in the standard and sample, the mass of the carrier and the mass of the irradiation standard.     

Uncertainty evaluation from Monte-Carlo simulations by using Crystal-Ball software

The first Supplemental Guide to the current edition of the Guide to the expression of uncertainty measurement (GUM) deals with the propagation of distributions, and emphasizes the use of Monte-Carlo Simulation (MCS) for estimating the uncertainty of measurands. In order to carry out MCS we need computer programs for generating pseudo-random numbers and for solving numerically the integral equations arising when simulating the values of the specification variables with a given probability density function (PDF). Crystal-Ball commercial software is very suitable for performing MCS and to estimate the expanded uncertainty for the measurand. The use of Crystal-Ball is illustrated with two working examples dealing with specification models of non-linear features and with correlated variables (such as the slope and intercept of calibration straight lines), respectively.An erratum to this article can be found at     

Reference measurement procedure for Δ9-tetrahydrocannabinol in serum

An isotope dilution gas chromatography/mass spectrometry (ID-GC/MS) reference measurement procedure for Δ9-tetrahydrocannabinol (THC) in serum was developed and validated. The method complies with the concept of a ratio primary reference measurement procedure. The uncertainty was determined for two concentrations of THC in serum (1 ng/mL and 2.4 ng/mL). The calculation procedure is based on the Guide to the Expression of Uncertainty in Measurement (GUM). The relative expanded uncertainty was found to be less than 2% for both concentration levels, corresponding to a 95% confidence interval. For the reference method, it was shown that the measurement of THC within the concentration range covered by the current threshold values is very accurate. The method has the potential to provide traceability for the methods used in practical forensics.     

Implementing the GUM in the certification of reference materials: the revision of ISO Guide 35

The availability of certified reference materials, certified in accordance to the GUM is an important tool for the proper estimation of measurement uncertainty in routine analysis. Many CRMs may suffer from incomplete or wrongly estimated uncertainties, mainly due to lack of guidance on how to implement the GUM in the production of CRMs. In particular the inclusion of the impact of inhomogeneity and instability in the uncertainty budget is often missing. The ongoing revision of ISO Guide 35 aims to fill this gap in providing guidance how (batch) inhomogeneity and instability can be translated into measurement uncertainty. The structure of the current ISO Guide 35 has been maintained as far as possible, but major parts underwent revision to become better aligned with GUM and ISO Guide 34 (2000). Received: 9 April 2001 Accepted: 22 October 2001     

From a single analyzer to multiple instruments—The GUM approach

Assessment and expression of analytical quality have become novel spotlights in medical laboratories since accreditation began in the early 1990s, in Europe. Evaluation of uncertainty of measurement by definition was launched in Finland when the Finnish Accreditation Service (FINAS) accredited the first medical laboratories in the mid 1990s. In spite of all the analytical and statistical knowledge which has been available in medical laboratories for years, evaluation of total uncertainty of measurement has not yet caught on. The concept is still unfamiliar to experts and, indeed, little guidance has been available. National and international activities, with good results, can be shown when the educational aspect is considered. The Guide to the Expression of Uncertainty in Measurement (GUM) remains the main document for uncertainty evaluation. Uncertainty of measurement together with target value of uncertainty can be used as a good measure for analytical quality in large or smaller laboratories over time, because it is a quantitative indication and the evaluation is easy to repeat as running practical tools are available.Presented at the 8th Conference on Quality in the Spotlight, 17–18 March 2003, Antwerp, Belgium     

Uncertainty sources in UV-Vis spectrophotometric measurement

An overview is given of the most important uncertainty sources that affect analytical UV-Vis spectrophotometric measurements. Altogether, eight uncertainty sources are discussed that are expected to have influence in chemical analysis. It is demonstrated that the well-known intrinsic (or “physical”) sources of uncertainty that originate from the instrument itself (repeatability of spectrophotometer reading, spectrophotometer drift, stray light, etc.) often have significantly lower contributions to the combined uncertainty of the result than the “chemical” sources of uncertainty that originate from the object under study (interference from the constituents of the matrix, decomposition of the photometric complex, etc.). Although selectivity of a photometric procedure is often considered more a validation topic than an uncertainty topic, it is very often important to include it also in the uncertainty budget.Usually the most difficult part of uncertainty estimation of a chemical measurement result is to evaluate the magnitude of the actual uncertainty components, especially the chemical ones. For most of the uncertainty sources discussed in this paper, approaches for their evaluation are given. A generic uncertainty budget for absorbance is presented. Electronic Supplementary Material Supplementary material is available for this article at     

Estimation of uncertainty in p<Emphasis Type="Italic">K</Emphasis><Subscript>a</Subscript> values determined by potentiometric titration

A procedure is presented for estimation of uncertainty in measurement of the pK(a) of a weak acid by potentiometric titration. The procedure is based on the ISO GUM. The core of the procedure is a mathematical model that involves 40 input parameters. A novel approach is used for taking into account the purity of the acid, the impurities are not treated as inert compounds only, their possible acidic dissociation is also taken into account. Application to an example of practical pK(a) determination is presented. Altogether 67 different sources of uncertainty are identified and quantified within the example. The relative importance of different uncertainty sources is discussed. The most important source of uncertainty (with the experimental set-up of the example) is the uncertainty of pH measurement followed by the accuracy of the burette and the uncertainty of weighing. The procedure gives uncertainty separately for each point of the titration curve. The uncertainty depends on the amount of titrant added, being lowest in the central part of the titration curve. The possibilities of reducing the uncertainty and interpreting the drift of the pK(a) values obtained from the same curve are discussed.     

Measurement uncertainty and the use of reference materials

The use of (certified) reference materials and quality control materials can form a suitable basis for evaluating measurement uncertainty of routine measurements. In particular when these materials are used for quality control purposes, it is not always evident how the quality control data can be used in the uncertainty budget of a routine measurement. Current guidance documents on the evaluation of measurement uncertainty and the use of reference materials treat this subject only in part, or in very generic terms. ISO/REMCO has established a new working group that will provide practical guidance and examples on how to use quality control data in the evaluation of measurement uncertainty. A short introduction to the subject is given.

Comparison of different approaches to estimate the uncertainty of a liquid chromatographic assay

A measurement result cannot be properly interpreted without knowledge about its uncertainty. Several concepts to estimate the uncertainty of a measurement result have been developed. Here, four different approaches for uncertainty estimation are compared on the example of the RP-high-performance liquid chromatography (HPLC) assay for tylosin for veterinary use: the guide to the expression of uncertainty in measurement (GUM) approach, which derives the uncertainty of a measurement result by combining the uncertainties related to the uncertainty sources of the measurement process; the top-down approach, which uses the reproducibility estimate from an inter-laboratory study as uncertainty estimate; an approach recently presented by Barwick and Ellison, which combines precision, trueness and robustness data to obtain an uncertainty estimate of the measurement result and finally a further approach, which directly estimates the measurement uncertainty from a robustness test. The comparison shows that the different approaches lead to comparable uncertainty estimates.     

Uncertainty estimation in measurement of pKa values in nonaqueous media: A case study on basicity scale in acetonitrile medium

The difficulties in estimating uncertainty of pK_a values determined in nonaqueous media are reviewed and two different uncertainty estimation approaches are presented and applied to the pK_a values of the compounds on a previously established self-consistent spectrophotometric basicity scale in acetonitrile. One approach is based on the ISO GUM methodology (the “ISO GUM” approach) and involves careful analysis of the uncertainty sources and quantifying the respective uncertainty components. The second approach is based on the standard-deviation-like statistical parameter that has been used for characterization of the consistency of the scale (the “statistical” approach). It is demonstrated that the ISO GUM approach somewhat overestimates the uncertainty. The statistical approach is based on long-term within-laboratory statistical data and it is demonstrated that it underestimates the uncertainty. In particular it neglects the laboratory bias effects that are taken into account at least to some extent by the ISO GUM approach. Thus, together these two approaches allow to “bracket” the uncertainties of the pK_a values on the scale. The uncertainties of the pK_a values are defined in two different ways. Definition (a) includes the uncertainty of the pK_a of the reference base (anchor base of the scale) pyridine. Definition (b) excludes it. It is demonstrated that both definitions have their virtues. Definition (a) leads to the uncertainty ranges of 0.12-0.22 and 0.12-0.14 pK_a units at standard uncertainty level for different bases using the ISO GUM and statistical approach, respectively. Definition (b) leads to the uncertainty ranges of 0.04-0.19 and 0.02-0.08 pK_a units, respectively. The uncertainty of the pK_a of a given base is dependent on the quality of the measurements involved and on the distance from the reference base on the scale. The importance of the correlation between the pK_a values of bases belonging to the same scale is stressed.     

Uncertainty evaluation for the quantification of low masses of benzo[a]pyrene: Comparison between the Law of Propagation of Uncertainty and the Monte Carlo method

A proper evaluation of the uncertainty associated to the quantification of micropollutants in the environment, like Polycyclic Aromatic Hydrocarbons (PAHs), is crucial for the reliability of the measurement results. The present work describes a comparison between the uncertainty evaluation carried out according to the GUM uncertainty framework and the Monte Carlo (MC) method. This comparison was carried out starting from real data sets obtained from the quantification of benzo[a]pyrene (BaP), spiked on filters commonly used for airborne particulate matter sampling. BaP was chosen as target analyte as it is listed in the current European legislation as marker of the carcinogenic risk for the whole class of PAHs.     

Estimation of uncertainty in routine pH measurement

A procedure for estimation of measurement uncertainty of routine pH measurement (pH meter with two-point calibration, with or without automatic temperature compensation, combination glass electrode) based on the ISO method is presented. It is based on a mathematical model of pH measurement that involves nine input parameters. Altogether 14 components of uncertainty are identified and quantified. No single uncertainty estimate can be ascribed to a pH measurement procedure: the uncertainty of pH strongly depends on changes in experimental details and on the pH value itself. The uncertainty is the lowest near the isopotential point and in the center of the calibration line and can increase by a factor of 2 (depending on the details of the measurement procedure) when moving from around pH 7 to around pH 2 or 11. Therefore it is necessary to estimate the uncertainty separately for each measurement. For routine pH measurement the uncertainty cannot be significantly reduced by using more accurate standard solutions than ±0.02 pH units – the uncertainty improvement is small. A major problem in estimating the uncertainty of pH is the residual junction potential, which is almost impossible to take rigorously into account in the framework of a routine pH measurement.¹ Received: 11 August 2001 Accepted: 22 February 2002     

Model-based measurement uncertainty evaluation,with applications in testing

Basic concepts and implementations of the model-based approach to uncertainty evaluation are discussed. One implementation is that of the law of propagation of uncertainty with the use of the central limit theorem as recommended in the Guide to the expression of uncertainty in measurement (GUM). Another is the propagation of distributions, the subject of a supplemental guide to the GUM, which is at an advanced stage of development. It falls in the category of other analytical and numerical methods indicated in the GUM. Two testing applications are used to illustrate the principles: tennis-ball rebound and tensile strength.

Evaluation of measurement uncertainty in analytical assays by means of Monte-Carlo simulation

The main limitations of the Guide to the expression of Uncertainty Measurement (GUM) approach for evaluating the measurement uncertainty of analytical assays are presented and explained. The advantages of using Monte-Carlo simulation against the GUM approach are outlined and discussed and the principle of propagation of distributions is explained. The procedure of Monte-Carlo analysis is illustrated by two case studies. A first simple example quoted from the EURACHEM Guide and dealing with the preparation of a calibration standard is used to present the technique with detail in a step-by-step way. In this case the results obtained by both approaches are very similar. A second example deals with the calibration of mass according to a strong non-linear model. In this case, the Monte-Carlo analysis leads to better results.     

Identification of natural dyes used in works of art by pyrolysis–gas chromatography/mass spectrometry combined with in situ trimethylsilylation

Samples of four natural dyes from different organic families—natural madder (anthraquinonoid), curcuma (curcuminoid), saffron (carotenoid) and indigo (indigotic)—were analysed using a new procedure based on pyrolysis–gas chromatography/mass spectrometry (Py–GC/MS), which includes the on-line derivatisation of the natural dyes using hexamethyldisilazane (HMDS). In addition, a previous procedure involving the addition of a 10% H₂SO₄ aqueous solution to the dye and further separation with ethyl acetate has been tested. This procedure enhances the sensitivity of the method by extracting the colouring compounds from the rest of the compounds present in the natural dye. Two possible derivatising reagents—HMDS and tetramethylammonium hydroxide (TMAH)—were compared in order to assess their effectiveness in the proposed method. Characteristic peaks from trimethylsilyl derivatives of alizarin, quinizarin, xanthopurpurin and purpurin were obtained for madder; peaks from safranal, isophorone and trimethylsilyl derivative of crocetin for saffron; peaks from 4-(4-hydroxy-3-methoxy)phenyl-3-buten-2-one and 4-(4-hydroxy-3-methoxy)phenyl-2-butanone, which are primary pyrolysis products of curcuma, and peaks from indole, 2-methylindole and 2,3-dihydroindol-2-one, which are primary pyrolysis products of indigo, among others, were obtained. The reported procedure leads to the unambiguous identification of the four studied dyes from solid samples formed by individual dyes.Electronic Supplementary Material Supplementary material is available for this article at