Human being as a part of measuring system influencing measurement results

Abstract

The role of human being as a part of a measuring system in a chemical analytical laboratory is discussed. It is argued that a measuring system in chemical analysis includes not only measuring instruments and other devices, reagents and supplies, but also a sampling inspector and/or analyst performing a number of important operations. Without this human contribution, a measurement cannot be carried out. Human errors, therefore, influence the measurement result, i.e., the measurand estimate and the associated uncertainty. Consequently, chemical analytical and metrological communities should devote more attention to the topic of human errors, in particular at the design and development of a chemical analytical/test method and measurement procedure. Also, mapping human errors ought to be included in the program of validation of the measurement procedure (method). Teaching specialists in analytical chemistry and students how to reduce human errors in a chemical analytical laboratory and how to take into account the error residual risk, is important. Human errors and their metrological implications are suggested for consideration in future editions of the relevant documents, such as the International Vocabulary of Metrology (VIM) and the Guide to the Expression of Uncertainty in Measurement (GUM).

Full text

1 23
Accreditation and Quality Assurance
Journal for Quality, Comparability and
Reliability in Chemical Measurement
ISSN 0949-1775
Volume 21
Number 6
Accred Qual Assur (2016) 21:421-424
DOI 10.1007/s00769-016-1239-3
Human being as a part of measuring
system influencing measurement results
Ilya Kuselman, Francesca Pennecchi,
Walter Bich & D.Brynn Hibbert

1 23
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DISCUSSION FORUM In memory of Paul De Bie
`vre
Human being as a part of measuring system influencing
measurement results
Ilya Kuselman
1
•Francesca Pennecchi
2
•Walter Bich
2
•D. Brynn Hibbert
3
Received: 16 August 2016 / Accepted: 21 September 2016 / Published online: 15 October 2016
Springer-Verlag Berlin Heidelberg 2016
Abstract The role of human being as a part of a measuring
system in a chemical analytical laboratory is discussed. It is
argued that a measuring system in chemical analysis
includes not only measuring instruments and other devices,
reagents and supplies, but also a sampling inspector and/or
analyst performing a number of important operations.
Without this human contribution, a measurement cannot be
carried out. Human errors, therefore, influence the mea-
surement result, i.e., the measurand estimate and the
associated uncertainty. Consequently, chemical analytical
and metrological communities should devote more atten-
tion to the topic of human errors, in particular at the design
and development of a chemical analytical/test method and
measurement procedure. Also, mapping human errors
ought to be included in the program of validation of the
measurement procedure (method). Teaching specialists in
analytical chemistry and students how to reduce human
errors in a chemical analytical laboratory and how to take
into account the error residual risk, is important. Human
errors and their metrological implications are suggested for
consideration in future editions of the relevant documents,
such as the International Vocabulary of Metrology (VIM)
and the Guide to the Expression of Uncertainty in Mea-
surement (GUM).
Keywords Human error Measuring system 
Measurement uncertainty Method validation 
Chemical analysis
Introduction
The International Union of Pure and Applied Chemistry
(IUPAC) and the Cooperation on International Traceability
in Analytical Chemistry (CITAC) have published recently
the joint IUPAC/CITAC Guide: Classification, modeling
and quantification of human errors in a chemical analytical
laboratory (IUPAC Technical Report) [1]. The classifica-
tion includes commission errors (mistakes and violations)
and omission errors (lapses and slips) under different sce-
narios at different steps of the chemical analysis. A ‘‘Swiss
cheese’’ model is used for characterizing the interaction of
such errors with a laboratory quality system including
different components, whose weak points are represented
by holes in slices of the Swiss cheese. Quantification of
human errors in chemical analysis, based on expert judg-
ments, i.e., on the expert’s knowledge and experience, is
applied. Scores related to the error quantification are
defined. They concern the likelihood and severity of the
human errors, and the effectiveness of a laboratory quality
system against these errors. Monte Carlo simulation is used
to propagate variability of the expert judgments,
The author Walter Bich is Convener of the Joint Committee for
Guides in Metrology (JCGM) Working Group 1 (Guide to the
Expression of Uncertainty in Measurement—GUM). The opinion
expressed in this paper does not necessarily represent the view of this
Working Group.
Papers published in this section do not necessarily reflect the opinion
of the Editors, the Editorial Board and the Publisher.
A critical and constructive debate in the Discussion Forum or a Letter
to the Editor is strongly encouraged!
&Ilya Kuselman
ilya.kuselman@bezeqint.net
1
Independent Consultant on Metrology, 4/6 Yarehim St.,
7176419 Modiin, Israel
2
Istituto Nazionale di Ricerca Metrologica (INRIM), 91 Strada
delle Cacce, 10135 Turin, Italy
3
School of Chemistry, UNSW, Sydney, NSW 2052, Australia
123
Accred Qual Assur (2016) 21:421–424
DOI 10.1007/s00769-016-1239-3
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represented by appropriate probability mass functions. The
residual risk of human errors, remaining after the error
reduction by the laboratory quality system, and conse-
quences of this risk for the quality of the laboratory
measurement results are discussed in this Guide. It is
shown also that the measurement uncertainty budget is not
complete without taking into account such residual risk of
human errors [1,2].
For a few fully automated systems, such as a spacecraft
robotic laboratory [3,4] which samples and analyzes
without human participation, only latent human errors (in
development and construction of the system) are possible
[5]. In general, they can be revealed and eliminated during
the system validation for the intended use. There is a rise of
autonomous robots having an ability to perform different
steps of testing, such as sample preparation in analytical
laboratories serving uranium industry [6,7], or some kinds
of blood and urine analysis in clinical laboratories [8].
Nevertheless, using these robots by the laboratory staff
may also provoke a number of scenarios of human errors.
Moreover, in routine laboratories having lower level of
automation, human errors may happen quite easily and
should be taken into proper account.
Therefore, the role of human being in chemical analysis,
still essential in most measurement methods and proce-
dures, is discussed in the present article. It is suggested to
include human being in the updated definition of measuring
system in the International Vocabulary of Metrology
(VIM) [9]. Such update would probably impact also on
other metrological definitions, as well as on the measure-
ment uncertainty evaluation in the Guide to the Expression
of Uncertainty in Measurement (GUM) [10].
Measurement method, procedure and measuring
system
According to the VIM, measurement method [9–2.5] is a
‘‘generic description of a logical organization of operations
used in a measurement,’’ while measurement procedure
[9–2.6] is a ‘‘detailed description of a measurement
according to one or more measurement principles and to a
given measurement method, based on a measurement
model and including any calculation to obtain a measure-
ment result.’’ However, this distinction is not universally
recognized, since the term ‘‘method’’ is often used as
including ‘‘procedure’’ [11], especially in chemical ana-
lytical practice [12].
The main steps of a measurement procedure in chemical
analysis include sampling, sample preparation, analysis of a
test portion and calculation of test results and reporting.
Sampling means taking at a particular time a sample/portion
(sampling target) of material, which the sample is intended to
represent. When the composition of a batch is tested, the
sampling target should have the analyte concentration close
as possible to the mean concentration value in the whole
batch. When the spatial or temporal variation of the material
composition is under study, separate sampling targets are
necessary for obtaining information about analyte concen-
trations in each specific location or time. Any sampling target
is analyzed according to the analytical/measurement proce-
dure to obtain the measurement results of the analyte
concentrations, i.e., measurand estimates and associated
uncertainty [13]. Sampling needs not necessarily be included
in a measurement. In such case, it would not contribute to
uncertainty. Whether or not sampling is included in the
measurement is reflected in the definition of the measurand.
For example, measuring ‘‘the mass concentration of chro-
mium VI in the material delivered to the laboratory’’ does not
involve sampling, whereas ‘‘the mean mass concentration of
chromium VI in Sydney Harbor’’ does.
Sample preparation includes selection of the test por-
tion, drying (or freezing, e.g., grapes), sieving, milling,
splitting, homogenization and decomposition (e.g., geo-
logical samples).
Analysis of a test portion may start from an analyte
extraction from a test portion and separation of the analyte
from other components of the extract. After that, a quali-
tative analysis is possible, including identification and
confirmation of the analyte. Then, a quantitative part of the
analysis consists of calibration of a measuring system and
measurement of the analyte property—usually concentra-
tion or mass fraction.
The measurement procedure documents human partici-
pation at each step of the analysis/testing. Detailed
examples of human error scenarios at such steps, from
sampling to reporting results, are provided in the Guide [1]
for pH measurement of groundwater, multi-residue pesti-
cide analysis of fruits and vegetables, and ICP-MS analysis
of geological samples.
In the VIM, measuring system [9–3.2] is a ‘‘set of one
or more measuring instruments and often other devices,
including any reagent and supply, assembled and adap-
tedtogiveinformationusedtogeneratemeasured
quantity values within specified intervals for quantities
of specified kinds.’’ Human beings are not included in
this definition. However, no system of this kind can
provide alone the necessary information unless it is a
part of a fully robotic laboratory. In a routine chemical
analytical laboratory, a measuring system without a
sampling inspector and/or an analyst is not complete.
Furthermore, in the case of qualitative testing (e.g.,
organoleptic testing), a measuring system for nominal
and ordinal property values [14–16]mayconsistofjust
an analyst (expert), for example, an expert for testing
coloroffreshwaterculturedpearls[17].
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Validation of measuring instrument vs method
validation
According to VIM, validation is ‘‘verification, where the
specified requirements are adequate for an intended use’’
[9–2.45], whereas verification is ‘‘provision of objective
evidence that a given item fulfills specified requirements’’
[9–2.44]. When a purchased measuring instrument has
been installed in a laboratory, an experiment should be
designed to obtain objective evidence (experimental data)
that the instrument performance meets the manufacturer
specification [12]. For example, the experiment design for
verification of a high-performance liquid chromatograph
(HPLC) intended for analysis of pesticide residues in
drinking water, includes qualification of (1) pump gradient
and precision, flow rate and online vacuum degasser; (2)
ultraviolet/visible (UV/Vis) diode array detector with hol-
mium oxide filter for automated wavelength calibration,
detector baseline noise and wavelength accuracy; (3)
autosampler with necessary number of samples, variable
volume of test portions without hardware change, needle
flush and wash to minimize sample carryover; (4) chro-
matographic column compartment and its temperature
precision; (5) instrument ability to detect leaks in each
module and to switch the pump off in the case when a leak
is detected; and (6) computer and software [18].
If the data confirm that the instrument performance is
satisfactory, it may be used in a specific procedure
according to the appropriate analytical/measurement
method. Note, a measuring instrument performance (abil-
ity) is provided by its manufacturer and does not depend on
sampling inspector and/or analyst/operator in the analytical
laboratory that purchased the instrument.
The performance characteristics for the method validation
and their limits (criteria) are set by the laboratory upon
agreement with the customer as fit for the intended use [19].
Commonly evaluated characteristics are: selectivity; limit of
detection (LOD) and limit of quantification (LOQ); working
range; analytical sensitivity; trueness (bias and recovery);
precision (repeatability, intermediate precision and repro-
ducibility); ruggedness (robustness); and measurement
uncertainty [19–21]. Their choice is a balance between costs,
risks and technical possibilities [11]. Then, evaluation of
these characteristics is performed using measurement results
obtained by a specified experiment design.
When a method prescribes human participation, it is
necessary to consider possible human errors during design
and development of the method, since further measure-
ment/analytical results may be influenced by these errors.
Therefore, mapping possible human errors at different
steps of analysis/testing should be required also as one of
the validation characteristics of the method [1].
Thus, in general, a method validation is validation of the
measurement procedure for operating a measuring system
including not only instrument(s), devices and reagents, but
human being(s) as well.
Measuring system and measurement uncertainty
The measurement result obtained with a measuring sys-
tem ‘‘…is generally expressed as a single measured
quantity value and a measurement uncertainty’’ [9–2.9].
Identifying uncertainty sources is vital for correct evalu-
ation of the uncertainty associated with the measurand
estimate. It may be useful to consider discrete operations
of the measuring system at different steps of the analysis
and to assess each operation separately to evaluate the
associated uncertainty. Then, the uncertainty contributions
of the operations are suitably summarized in the com-
bined uncertainty [22].
When human beings are involved in some of the oper-
ations, a risk of human error remains after the error
reduction by the laboratory quality system. This residual
risk is also a source of a contribution to the measurement
uncertainty. As such, it should be included in the uncer-
tainty budget and taken into account in the appropriate way
[1,2].
At the same time, for the sake of justice, one should note
that the most successful way of solving problems arising in
an analysis is human as well [23]. Therefore, it is important
that specialists in analytical chemistry and students would
be educated and trained on how to reduce human errors in a
laboratory and how to take into account the residual risk of
human error.
The reference document in the field of measurement
uncertainty, the GUM gives little attention to human errors.
According to it, ‘‘Blunders in recording or analyzing data
can introduce a significant unknown error in the result of a
measurement. Large blunders can usually be identified by a
proper review of the data; small ones could be masked by,
or even appear as, random variations. Measures of uncer-
tainty are not intended to account for such mistakes’’
[10–3.4.7]. Thus, in the GUM, only some among the pos-
sible human errors are recognized, and anyway, they are
not included as a source of uncertainty.
We think that a reliable evaluation of uncertainty should
in principle account for human errors. To this purpose, the
scope of the GUM should be broadened to include uncer-
tainties caused by human errors when appropriate, for
example, in the field of analytical chemistry. Suitable tools
are now available that can probably be adapted to and
incorporated in the procedures described in the GUM or in
its Supplements 1 and 2 [24,25].
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Conclusion
Recognizing the role of human being as a part of measuring
system in a routine chemical analytical laboratory requires:
(1) definition of human errors and their metrological
consequences in future VIM and GUM editions;
(2) considering possible human errors during design and
development of a method;
(3) mapping possible human errors as a task during
validation of a measurement procedure;
(4) teaching specialists in analytical chemistry and stu-
dents how human errors can be reduced in a laboratory
and how to take into account the residual risk of human
error.
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