Introduzione ai differenti approcci alla stima dell'incertezza di misura

2. Il contenuto della presentazione può non riflettere necessariamente
la posizione ufficiale di ARPAL

3. • Eurachem/CITAC Guide CG4, Quantifying Uncertainty in
Analytical Measurement, Second edition, 2000
(http://www.eurachem.org);
• EUROLAB Technical Report No 1/2007, Measurement
uncertainty revisited: Alternative approaches to uncertainty
evaluation (http://www.eurolab.org);
• Nortdtest TR537, Handbook for Calculation of Measurement
Uncertainty in Environmental Laboratories, 2004
(http://www.nordicinnova tion.net).
The application of the GUM general principles to measurements
in chemistry is described in these guides:

4. After more than ten years since publication of the 1st edition, the Guide to the Expression
of Uncertainty in Measurement, known as the GUM, is acknowledged as the master
document on measurement uncertainty throughout the testing community and the
However, when it comes to evaluating the uncertainty of the results for a test procedure,
the GUM is often criticised as inapplicable.
This impression is due to the fact that the GUM almost exclusively treats a single approach
for uncertainty evaluation: the “modelling approach” based on a comprehensive
mathematical model of the measurement procedure.
This is therefore often (mis)conceived as being “the GUM approach” for uncertainty
evaluation.
Actually the GUM principles admit a variety of approaches
GUM principles are fully accepted.

5. Where we are…
Introduction

6. TR 1/2007
Very often, a combination of the different approaches
needs to be used to assess the uncertainty

7. • Modelling approach
— Uncertainty of an individual result of a measurement using a
measurement procedure in the laboratory
• Single laboratory validation & quality control approach
— Typical uncertainty of results obtained using a measurement
procedure in the laboratory
• Interlaboratory validation approach
— Uncertainty of results obtained using the same measurement
procedure in different laboratories
Uncertainty by different approaches

8. The “modelling approach” is based on a comprehensive mathematical model of the
measurement procedure, where every uncertainty contribution is associated with a
dedicated input quantity, the uncertainty contributions are evaluated individually and
combined as variances.
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Uncertainty of an individual result of a measurement using a measurement
procedure in the laboratory
Uncertainty by different approaches
Modelling approach

9. If the demand on uncertainty is low, it can be possible to directly use the sR from
interlaboratory comparisons as an approximation of uc.
This may be an overestimate depending on the quality of the laboratory – worst-case
scenario.
It may also be an underestimate due to sample inhomogeneity or matrix variations.
u = sR
Reproducibility between laboratories sR
Data given in standard method
In order to use a figure taken directly from the standard method, the laboratory
must prove that they are able to perform in accordance with the standard method
Data from interlaboratory comparisons
Interlaboratory comparisons are valuable tools in uncertainty evaluation. The
reproducibility between laboratories is normally given directly in reports from the
exercises as sR.
Interlaboratory validation approach
Uncertainty by different approaches

10. Typical uncertainty of results obtained using a measurement
procedure in the laboratory
Intermediate precision
(within-laboratory reproducibility)
Measurement uncertainty = Intermediate precision & uncertainty on bias
Method and lab bias
•Reference material
•Interlab comparison
•Validation
 22
)( biasuRuu w 
Uncertainty by different approaches
Single laboratory validation &
quality control approach

11. the most common ways of estimating the Intermediate precision u(Rw):
control samples and / or duplicate analyses of real samples
Intermediate precision, Rw
When a synthetic control solution is used for quality control, and the matrix type
of the control sample is not similar to the natural samples, we have to take into
consideration uncertainties arising from different matrices. Example: To estimate
the repeatability in different matrices
Always from within laboratory data
 22
)( biasuRuu w 
Laboratory validation & quality control

12. Method and Laboratory bias – u(bias)
For every estimation of the uncertainty from the method and laboratory bias, two
components have to be estimated to obtain u(bias):
I. the root mean square (RMS) of the bias values
II. the uncertainty of the nominal/certified value, u(Cref) or u(Crecovery)
bias = mean deviation of replicate measurement results from the reference value
 22
)( biasuRuu w 
Laboratory validation & quality control

13. Method and Laboratory bias – u(bias)
bias = mean deviation of replicate measurement results from the reference value
 22
)( biasuRuu w 
the most common ways of estimating the bias components are:
 the use of CRM,
 recovery tests,
 participation in interlaboratory comparisons (proficiency tests).
from within laboratory data
Laboratory validation & quality control

14. Method and Laboratory bias – u(bias)
bias = mean deviation of replicate measurement results from the reference value
 22
)( biasuRuu w 
The uncertainty of the bias, u(bias) can be estimated by
   22
CrefuRMSbiasu bias 
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With more bias data available:
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data
bias
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bias
RMS
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2
Laboratory validation & quality control