Examples of Measurement Uncertainty Budgets in Analytical Chemistry

For help with concepts you are welcome to visit the Online Course of Measurement Uncertainty Estimation in Analytical Chemistry

If you do not have online access to the cited publications, please contact ivo.leitout.ee

Measurement

Complexity of measurement

Elaboration level

Extent of comments

Description

Available files

Determination of acrylamide in snacks by LC-MS

High

High

High
(incl. video explanations)

Measurement uncertainty estimation of acrylamide determination in snacks by liquid chromatography mass spectrometry (LC-MS) using the Nordtest approach. The within-lab reproducibility component is evaluated as a standard deviation of measurement results in a CRM (crisp bread) made over a time period of more than a year. The bias component was evaluated from the results obtained with two CRMs (crisp bread and potato snacks).

XL calculation files (initial and solved) as well as a slideshow and a video explaining the calculations: Determination of acrylamide in snacks by LC-MS

Ammonium by Photometry

High

Medium (somewhat simplified, suitable for most labs)

High
(incl. video explanations)

A mainstream measurement of NH4+ by photometry. The measurement uncertainty (MU) estimation uses the simplification that slope and intercept of the calibration graph are considered independent parameters.

 

This is a tricky example. After several years of discussion and careful study we now believe that the MU in this example is not underestimated if the determination is carried out carefully and if there is no strong chemical interference. In any case, use with care! For deeper coverage of MU sources in photometric analysis see the paper L.Sooväli, E.-I. Rõõm, A. Kütt, I. Kaljurand, I. Leito. Accred. Qual. Assur. 2006, 11, 246-255 and the PhD thesis of Lilli Sooväli (defended on June 20, 2006).

The example is explained in detail together with the ISO GUM modeling approach as section 9 of the MU web course. Calculation of the combined standard uncertainty using the Kragten approach is explained in section 9.7, where also the XL calculation files (both initial and solved) are found.

Ammonium by Photometry

High

High (uncertainty estimated at full rigor, suitable for experts)

High

The same example as previous, solved in GUM Workbench with full rigor. Contains XLS import. The corresponding SMU and XLS file must be placed in the same folder. In case of problems, please use the PDF printout.

In English:

GUM Workbench

PDF printout

Auxiliary XLS

Measurement uncertainty in Kjeldahl nitrogen determination

Medium

High

High

Determination of protein content in food products by the photometric version of the Kjeldahl method (using the Digesdahl sample mineralization equipment). The uncertainty is estimated using the Nordtest approach. Within-lab reproducibility is estimated from a long-term standard deviation of determination results with a standard sample. The bias component is estimated using two CRMs.

This example is presented as three self-test exercises (with solutions and explanations: finding the reproducibility component, finding the bias component, finding the combined standard uncertainty

Measurement uncertainty due to the matrix effect in LC-ESI-MS (LC-ESI-MSMS)

High

High

High

This work (A. Kruve, K. Herodes, I. Leito. J. AOAC International 2010, 93, 306-314) presents an empirical approach—the matrix effect graph approach—for estimating the uncertainty due to the matrix effect in LC-MS (with the electrospray (ESI) ion source) analysis of pesticide residues in fruits and vegetables. At certain time intervals (1 month), a calibration graph using extracts of different fruits/vegetables as calibration solutions is prepared, and a regression line is fitted through these data. These fruits/vegetables may be either from the commodity group of the samples or from different commodity groups. The relative residuals of the calibration point peak areas are calculated and plotted against the measurement time – the matrix effect graph is then obtained. The root mean square of the relative residuals is calculated and used as the estimate of relative uncertainty of the sample peak areas caused by the matrix effect. The matrix effect graph obtained over fruits/vegetables from different commodity groups can also be used to identify fruits/vegetables with extreme matrix effects.

Full text of the article (please contact us if you do not have online access to this article)

Measurement uncertainty of measurement with amperometric sensors

Medium

High

High

This tutorial review (I. Helm, L. Jalukse, I. Leito Sensors 2010, 10, 4430-4455) focuses on measurement uncertainty estimation in amperometric sensors (both for liquid and gas-phase measurements). The main uncertainty sources are reviewed and their contributions are discussed with relation to the principles of operation of the sensors, measurement conditions and properties of the measured samples. The discussion is illustrated by case studies based on the two major approaches for uncertainty evaluation–the ISO GUM modeling approach and the Nordtest approach. This tutorial is expected to be of interest to workers in different fields of science who use measurements with amperometric sensors and need to evaluate the uncertainty of the obtained results but are new to the concept of measurement uncertainty. The tutorial is also expected to be educative in order to make measurement results more accurate.

Full text of the article (it is an open-access article, so the full text is freely available)

Electron probe microanalysis (SEM-EDS)

High

High

High

Determination of iron in ink writing on paper manuscripts using electron probe microanalysis (SEM-EDS). Full information, with detailed explanations on uncertainty sources and their quantification is available in publication K.Virro, E.Mellikov, O.Volobujeva, V.Sammelselg, J.Asari, L.Paama, J.Jürgens, I.Leito Microchimica Acta, published online 06.08.2007.

XLS

Analysis of gold alloys by flame-AAS

High

High

High

Detailed example, covering not only uncertainty estimation but also validation and establishing traceability

Chapter 2 in Practical Examples on Traceability, Measurement Uncertainty and Validation in Chemistry

Volume 1

Determination of calcium in serum by spectrophotometry

High

High

High

Detailed example, covering not only uncertainty estimation but also validation and establishing traceability

Chapter 3 in Practical Examples on Traceability, Measurement Uncertainty and Validation in Chemistry

Volume 1

Determination of radium in water by a-spectrometry

High

High

High

Detailed example, covering not only uncertainty estimation but also validation and establishing traceability

Chapter 4 in Practical Examples on Traceability, Measurement Uncertainty and Validation in Chemistry

Volume 1

Determination of polar pesticides by liquid chromatography mass spectrometry (LC-MS)

High

High

High

Detailed example, covering not only uncertainty estimation but also validation and establishing traceability

Chapter 5 in Practical Examples on Traceability, Measurement Uncertainty and Validation in Chemistry

Volume 1

Determination of ammonium in water by flow analysis (CFA) and spectrometric detection

High

High

High

Detailed example, covering not only uncertainty estimation but also validation and establishing traceability

Chapter 6 in Practical Examples on Traceability, Measurement Uncertainty and Validation in Chemistry

Volume 1

Simple weighing

Simple

Medium

Medium

Uncertainty of simple weighing

GUM Workbench

PDF printout

XLS

Volume of 50 ml volumetric flask

Simple

Medium

Medium

Uncertainty of volume of solution contained in 50 ml volumetric flask.

GUM Workbench

PDF printout

XLS

Volume of 10 ml pipette

Simple

Medium

Medium

Uncertainty of volume of solution delivered by 10 ml bulb pipette.

GUM Workbench

PDF printout

XLS

Nonvolatile matter by gravimetry

Medium

Medium

Medium

Routine determination of nonvolatile matter by gravimetry. The sample was weighed before and after drying in oven at a specified temperature (please see the presentation ISO GUM Uncertainty in Chemistry).

In English:

GUM Workbench

PDF printout

In Estonian:

GUM Workbench

PDF printout

pH measurement

Medium

High

Low

The uncertainty calculation for pH is available as a web application (server-based, written in PHP). This means that calculation can be carried out immediately in the browser and there is no need to install any software. The result can be displayed either as a simple or as a detailed result. In the latter case the measurement equation and detailed uncertainty budget are also displayed. Additional information is available in the help file of the web application and in the articles I.Leito, L.Strauss, E.Koort, V.Pihl Accred. Qual. Assur. 2002, 7, 242-249 and E.Koort, K.Herodes, V.Pihl, I.Leito. Anal. Bioanal. Chem. 2004, 379, 720-729. For more information see also the PhD thesis of Eve Koort (defended on June 20, 2006).

In English:

Web application

Dissolved oxygen concentration measurement

Medium

High

High

This uncertainty estimation procedure is intended for the mainstream dissolved oxygen concentration measurement with the galvanic type of equipment. Details can be found in the article: L.Jalukse, I.Leito Measurement Science and technology 2007, 18, 1877-1886 and also in the PhD thesis of Lauri Jalukse.

In English:

XLS calculation file

Complexonometric titration

Medium

Medium

Medium

Complexonometric determination (EDTA) of total hardness of water

GUM Workbench

PDF printout

Nitrite by Photometry

High

High

High

Photometric determination of nitrite using the NEDA-sulfanilamide method. For deeper coverage of uncertainty sources in photometric analysis see the paper L.Sooväli, E.-I. Rõõm, A. Kütt, I. Kaljurand, I. Leito. Accred. Qual. Assur. 2006, 11, 246-255 (published online on 25.04.06) and the PhD thesis of Lilli Sooväli (defended on June 20, 2006).

GUM Workbench

PDF printout

 

Butanol in acetone by GC

High

High

Medium

Measurement of butanol content in acetone by GC. Very small solution volumes are used in this method and all solutions are prepared by weighing. The largest uncertainty contributions are due to the imperfections of integrating peaks on the chromatogram and drift of the balance, which is mainly due to the volatility of acetone.

In English:

GUM Workbench

PDF printout

 

Sorbic acid by HPLC

High

Low

Medium

Mainstream liquid chromatography (HPLC) method for determination of preservatives (Sorbic acid in this example). Main parameters of the method: Isocratic elution (Acetate buffer : MeOH, 70:30), RP C18 column, UV-Vis photometric detection at 235 nm.

In English:

GUM Workbench

PDF printout

In Estonian:

GUM Workbench

PDF printout

 

Quality control of a drug product by HPLC

High

High

High

Liquid chromatography (HPLC) determination of Simvastatin in tablets. The method is a mainstream HPLC method with UV-Vis photometric detection at 238 nm. Two varieties are provided: 5-point calibration and single point calibration. This uncertainty estimation has been published in the following paper: S. Leito, K. Mölder, A. Künnapas, K. Herodes, I. Leito J. Chrom. A 2006, 1121, 55-63.

In English:

5-point calibration:

GUM Workbench

PDF printout

Single-point calibration:

GUM Workbench

PDF printout

 

Phosphorus Content in Feed by Photometry

High

High

High

Measurement uncertainty estimation example on photometric determination of phosphorus in feed using the molybdatovanadate reagent. The largest uncertainty contribution is due to the sample preparation. For deeper coverage of measurement uncertainty sources in photometric analysis see the following paper: L.Sooväli, E.-I. Rõõm, A. Kütt, I. Kaljurand, I. Leito. Accred. Qual.Assur. 2006, 11, 246-255.

GUM Workbench
PDF printout

Lead in Soil by AAS

High

High

High

Measurement of Lead content of soil by graphite furnace atomic absorption spectrometry.

GUM Workbench

PDF printout

XLS

 

Comments

Files

The uncertainty budgets are available in files of following types:

  • GUM Workbench files (extension SMU) have been created by GUM Workbench TrainMiC 1.3 (Metrodata GmbH)
  • PDF printouts of the SMU files (not all people have the GUM Workbench software. The printout contains all the essential information about the uncertainty example)
  • Excel files (extension XLS) have been created by MS Excel 97 or 2003 (Microsoft Inc.). Some of them are standalone uncertainty budgets, some are just auxiliary files (containing input data) for SMU files.

Most of the files are in English. In some cases files in other languages are also available.

 

Complexity, elaboration level and extent of comments

The “complexity of measurement” refers to the intrinsic complexity of the measurement itself (the more there are operations and measurements, the higher the complexity)

The “elaboration level” refers to the extent to which various uncertainty sources have been identified and taken into account. Low elaboration level does not necessarily mean that there are important uncertainty sources that have not been taken into account: instead it usually means that here and there several different uncertainty sources have been grouped. For example, instead of identifying all the repeatability contributions, they may have been grouped to give the general repeatability of the procedure that can be estimated from overall repeatability studies.

The “extent of comments”: indicates how much comment is added to the file to increase the readability by users.

 

Estimates of uncertainty components

Generally the uncertainty components have been estimated according to the particular equipment and working practices used in our lab (or in the labs of our collaborators). In some cases reasonable estimates (based on experience or literature data) are used. The obtained uncertainty values have proved to be adequate for those conditions. However, these uncertainty values are not directly applicable to results obtained in other laboratories using different instrumentation and working practices (even if exactly the same measurement procedures are used) because they are dependent on the conditions. These values should thus be used for guidance only. The users of the examples are strongly recommended to do their own estimation of uncertainty components based on their own equipment and working practices and then insert the uncertainty data into the files from this page. Also, it is strongly recommended to see the presentations ISO GUM Uncertainty in Chemistry (presented in 2004 in Stockholm on MiC teaching workshop) and Different Approaches to Estimation of Measurement Uncertainty in Analytical Chemistry (presented in 2010 in Tel Aviv at IsrAnalytica XIII).
The material on this site is provided on “as is” basis, the compilers of this site accept no responsibility or liability whatsoever with regard to the material on this site.

 

Feedback

Any feedback (comments, error reports, criticism) is most welcome! The feedback should be sent to Ivo Leito (ivo.leito[at]ut.ee, +372 5 184 176, University of Tartu, Testing Centre, Ravila 14a, 50411 Tartu, Estonia). You can also submit your own examples for posting on this site. The examples will be examined and then decision will be made, whether they are suitable. Preference will be given to detailed and fully commented examples.

Other topics

See also other research topics

 

 

This page has been created at University of Tartu with support from the EC JRC Institute for Reference Materials and Measurements in the framework of the TrainMiC programme.
© University of Tartu, EC-JRC IRMM

 

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Last edited:  Monday, 04-Aug-2014 17:02:38 EEST