Trends in quality in the analytical
laboratory. II. Analytical method
validation and quality assurance
Isabel Taverniers, Marc De Loose, Erik Van Bockstaele1
It is internationally recognized that validation is necessary in analytical
laboratories. The use of validated methods is important for an analytical
laboratory to show its qualification and competency. In this update on
analytical quality, we place validation of analytical methodologies in the
broader context of quality assurance (QA). We discuss different approaches
to validation, giving attention to the different characteristics of method
performance. We deal with the concepts of single-laboratory or in-house
validation, inter-laboratory or collaborative study, standardization, internal
quality control (IQC), proficiency testing (PT), accreditation and, finally,
analytical QA (AQA).
This article provides a good, complete, up-to-date collation of relevant
information in the fields of analytical method validation and QA. It describes
the different aspects of method validation in the framework of QA. It offers
insight and direct help to anyone involved in any analytical methodologies,
whether they are an academic researcher or in the industrial sector.
ª 2004 Elsevier Ltd. All rights reserved.
Keywords: Accreditation; Analytical method validation; Performance parameter; Quality
assurance; Quality control
Abbreviations: AOAC, Association of Official Analytical
Chemists; AQA, Analytical quality assurance;
ASTM, American Society for Testing and Material; c,
Concentration of measurand; CCMAS, Codex Committee
on Methods of Analysis and Sampling; CCa,
Decision limit; CCb, Detection capability; CEN,
European Committee for Normalization; CITAC,
Cooperation on International Traceability in Analytical
Chemistry; CRM, Certified reference material;
CV, Coefficient of variation ( ¼ % RSD); EA, European
Cooperation for Accreditation; EC, European
Community; EN, European Norm; EPA,
Environmental Protection Agency; EQC, External
quality control; EU, European Union; FAO, Food
and Agricultural Organization; FDA, Food and Drug
Administration; FSA, Food Standards Association;
GLP, Good Laboratory Practices; GMP, Good Manufacturing
Processes; HACCP, Hazard Analysis Critical
Control Points; ICH, International Conference
on Harmonization; IEC, International Electrotechnical
Commission; ILAC, International Laboratory
Accreditation Cooperation; IQC, Internal quality
control; ISO, International Standardization Organization;
IUPAC, International Union of Pure and
Applied Chemistry; k, Numerical factor used in
formulae for LOD and LOQ; LIMS, Laboratory
information management system; LOD, Limit of
detection; LOQ, Limit of quantification; MRL,
Maximum residue level ( ¼ PL); MRPL, Minimum
required performance limit; MU, Measurement
uncertainty; PL, Permitted limit ( ¼ MRL); PT, PTS,
Proficiency Testing (Scheme); q, Quantity of
measurand; QA, Quality assurance; QC, Quality
control; r, Repeatability value or limit; R, Reproducibility
value or limit; RM, Reference material;
RSD, Relative standard deviation; SD (s), Standard
deviation(s); SOP, Standard operating procedure;
USP, United States Pharmacopeia; WHO,
World Health Organization; x, Measured response
or signal
1. The role of method
validation in AQA
The terms validation and quality assurance
(QA) are widely used. However, a lot
of analysts and laboratories do not know
the exact meaning neither the difference
nor the relationship between the two
terms. Validating a method is
investigating whether the analytical purpose
of the method is achieved, which is
obtaining analytical results with an acceptable
uncertainty level [1]. Analytical
method validation forms the first level of
QA in the laboratory (Fig. 1). AQA is the
complete set of measures a laboratory
must undertake to ensure that it can always
achieve high-quality data. Besides
the use of validation and/or standardized
methods, these measures are: effective IQC
procedures (use of reference materials
(RMs), control charts, etc.); participation
in PT schemes; and, accreditation to an
international standard, normally ISO/IEC
17025 [1–3].
Isabel Taverniers*,
Marc De Loose,
Erik Van Bockstaele
Department for Plant Genetics
and Breeding (DvP),
Centre for Agricultural
Research (CLO),
Ministry of the Flemish
Community,
Caritasstraat 21, B-9090 Melle,
Belgium
*Corresponding author:
Tel.: +32-9-272-2876;
Fax: +32-9-272-2901;
E-mail: i.taverniers@clo.fgov.be
1
Department for Plant Production,
Ghent University, Coupure
Links 653, B-9000 Gent,
Belgium.
0165-9936/$ - see front matter ª 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.trac.2004.04.001 535
Trends in Analytical Chemistry, Vol. 23, No. 8, 2004 Trends
The different levels in Fig. 1 represent the different
measures a laboratory must undertake to ensure that it
is qualified and competent to perform analytical
measurements that satisfy their agreed requirements. A
laboratory must be capable of providing analytical data
of the required quality. The ‘agreed requirement’ of an
analytical method and the ‘required quality’ of an analytical
result refer to the ‘fitness for purpose’ of the
method [1,4,5].
The ISO definition of validation is ‘confirmation by
examination and provision of objective evidence that the
particular requirements of a specified intended use are
fulfilled’ [4,6]. Method validation is needed to ‘confirm
the fitness for purpose of a particular analytical method’,
i.e. to demonstrate that ‘a defined method protocol,
applicable to a specified type of test material and to a
defined concentration rate of the analyte’ – the whole is
called the ‘‘analytical system’’ – ‘is fit for a particular
analytical purpose’ [1]. This analytical purpose reflects
the achievement of analytical results with an acceptable
standard of accuracy. An analytical result must always
be accompanied by an uncertainty statement, which
determines the interpretation of the result (Fig. 1). In
other words, the interpretation and the use of any
measurement fully depend on the uncertainty (at
a stated level of confidence) associated with it [5].
Validation is thus the tool used to demonstrate that a
specific analytical method actually measures what it is
intended to measure, and thus is suitable for its intended
purpose [6–8].
In the first place, validation is required for any new
method. As the definition says, validation always
concerns a particular ‘analytical system’. This means
that, for a particular type of material and a particular
operating range of concentrations, the method must be
able to solve a particular analytical problem [1]. As a
consequence, ‘revalidation’ is needed whenever any
component of the analytical system is changed or if there
are indications that the established method does not
perform adequately any more [4,8,9].
Method validation is closely related to method
development. When a new method is being developed,
some parameters are already being evaluated
during the ‘development stage’ while in fact this forms
part of the ‘validation stage’ [4]. However, a validation
study may indicate that a change in the method protocol
is necessary, and that may then require revalidation
[10].
Before any method validation is started, the scope of
validation must be fixed, comprising both the ‘‘analytical
system’’ and the ‘‘analytical requirement’’. A
description of the analytical system includes the purpose
and the type of method, the type and the concentration
range of analyte(s) being measured, the types of
material or matrices for which the method is applied,
and a method protocol. The basis of a good analysis
rests on a clear specification of the analytical
requirement. The latter reflects the minimum fitnessfor-purpose
criteria or the different performance criteria
that the method must meet in order to solve the
particular problem. For example, a minimum precision
(RSD, see later) of 5% may be required, or a limit of
detection (LOD) of 0.1% (w/w) [1,2,4,10]. The established
criteria for performance characteristics form the
basis of the final acceptability of analytical data and of
the validated method [10].
Validation of a new analytical method is typically
done at two levels. The first is pre-validation, aimed at
fixing the scope of the validation. The second is an
extensive, ‘‘full’’ validation performed through a collaborative
trial or inter-laboratory study. The objective of
full validation, involving a minimum number of
laboratories, is to demonstrate that the method performs
as was stated after the pre-validation.
2. Guidelines and guidance on AQA
As shown in Fig. 1, using validated methods is the first
level of QA, required within a system of IQC. The latter is
needed for participation in PT schemes, which, in turn,
form a prerequisite for accreditation [1].
For the different levels of QA presented in Fig. 1,
guidelines and requirements are well described in detail
by several regulatory bodies, standardization agencies
accreditation
proficiency testing
IQC
analytical
system
analytical
result
measurement
uncertainty
validation
fitness for purpose accuracy
performance
characteristics
interpretation
- method protocol
- type of matix
- concentration range
of analyte
Figure 1. Different levels of ‘QA’ measurements for analytical
chemistry and food laboratories [1,4,5].
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and working groups or committees. Just as for traceability
and measurement uncertainty (MU) (Part I of this
review, Trends Anal. Chem. 23 (2004)), relevant
guidelines are given in Table 1. Eurachem guides are
published on quality in the laboratory in general [2],
method validation [4] and PT [11]. A guideline on PT
from the joint Eurachem-Eurolab-EA group is also
available [12]. On the European level, there is also the
CEN, that is working through different technical
committees and working groups on standardization of
analytical methods for all sectors [13].
On the international level, we distinguish IUPAC, ISO
and AOAC International. All three bodies develop validation
and standardization frameworks for analytical
chemistry. AOAC International introduced the ‘AOAC
Peer Verified Methods Program’ [14]. IUPAC, ISO and
the AOAC International together developed different
harmonized guidelines and protocols [1,5,15–19], in
addition to a number of ISO standards [20–23]. The US
FDA, USP and ICH developed guidelines specifically
for pharmaceutical and biotechnological methods
[7,24–26].
The international Codex Alimentarius Commission
within the United Nations FAO/WHO Food Standards
Program has a Codex Committee on Methods of Analysis
and Sampling (CCMAS). CCMAS works out criteria for
evaluating the acceptability of methods of analysis as
well as guidelines on single-laboratory and interlaboratory
validation of methods [27–31]. For singlelaboratory
validation, CCMAS defends the harmonized
IUPAC guidelines [1]. On the international level, also
ILAC provides guidelines on PT [2] and on accreditation
[33,34] (Table 1).
3. Approaches for evaluating acceptable
methods of analysis
The purpose of an analytical method is the delivery of a
qualitative and/or quantitative result with an acceptable
uncertainty level, so, theoretically speaking, ‘validation’
boils down to ‘measuring uncertainty’. In
practice, method validation is done by evaluating a
series of method-performance characteristics, such as
precision, trueness, selectivity/specificity, linearity,
operating range, recovery, limit of detection (LOD), limit
of quantification (LOQ), sensitivity, ruggedness/robustness
and applicability. Calibration and traceability have
been mentioned also as performance characteristics of a
method [1,2]. To these performance parameters, MU
can be added, although MU is a key indicator for both
fitness-for-purpose of a method and constant reliability
of analytical results achieved in a laboratory (IQC). MU
is a comprehensive parameter covering all sources of
error and thus more than method validation alone. In
practice, data from method validation and collaborative
studies form the basis for, but are only a part of, MU
estimation. MU is thus more than just a ‘‘methodperformance
parameter’’, as described extensively in
Part I of this review. Over the years, the concept of MU
has won attention in all analytical areas and this has
led to two different approaches currently accepted and
used for analytical method validation. The traditional
‘criteria approach’ is to identify specific performance
parameters and to assign numeric values to these. These
numeric values represent cut-off or threshold values
that the method parameters must meet, in order for the
method to be acceptable. The alternative approach is
Table 1. Overview of European and international regulatory bodies and their guidelines and standards on different aspects of AQA
Body Full name Guidance on References
Eurachem A Focus for Analytical Chemistry in Europe • Method validation [2,4,11,12]
CITAC Cooperation of International Traceability in Analytical • Proficiency testing
Chemistry • Quality Assurance
EA European Cooperation for Accreditation • Accreditation
CEN European Committee for Normalization • Standardization [13]
IUPAC International Union of Pure and Applied Chemistry • Method validation [1,5,14–23]
ISO International Standardization Organisation • Standardization
AOAC Association of Official Analytical Chemists • Internal quality control
International • Proficiency testing
• Accreditation
FDA United States Food and Drug Administration • Method validation [7,24–26]
USP United States Pharmacopeia
ICH International Conference on Harmonization
FAO/WHO: Food and Agricultural Organization/World Health • Method validation [27–31]
Codex/CCMAS Organisation: Codex Committee on Methods of
Analysis and Sampling
ILAC International Laboratory Accreditation Cooperation • Proficiency testing [32–34]
• Accreditation
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focused on fitness-for-purpose and MU. In this ‘fitnessfor-purpose
approach’, the overall MU is estimated as a
function of the analyte concentration (see Part I of this
review).
Generally, the criteria approach is used for rational
methods, i.e. methods where the measurement result
can be obtained independently of the method used.
Opposite to rational methods, there are empirical
methods, in which the measured value depends on the
method used. For empirical methods, the criteria
approach cannot simply be applied. Instead, precision
data from collaborative studies are normally used as
basis for MU estimation and validation [27,29].
Validation is needed to demonstrate that the
analytical method complies with established criteria for
different performance characteristics [35]. When these
different characteristics are being evaluated individually,
this is generally done for the analytical method as
such – where the input is the purified or isolated
analyte and the output is the analytical result. However,
MU covers the whole analytical procedure, starting
from the original sample lot. The assessment of MU (see
Part I) is in line with the so-called ‘modular validation
approach’. Modular validation refers to the ‘‘modularity’’
of an analytical procedure, divided up into several
sequential steps needed to analyze the material.
These may be sample preparation, analyte extraction
and analyte determination (Fig. 2). Each step in the
procedure can be seen as an ‘‘analytical system’’ and
can thus be validated separately and combined later on
with other ‘‘modules’’ in a flexible way. Modular validation
is thus a stepwise validation of a whole procedure,
taking into consideration all possible difficulties or
uncertainty factors at each level in the procedure. The
concept of modular validation originates from the
domain of predictive microbiology and is now being
proposed for methods of analysis for genetically modified
organisms (GMOs) [36]. We show the relationship
between the three approaches to validation described
above in Fig. 2.
4. Method-performance characteristics
and the ‘criteria approach’
4.1. The extent of validation depends on the type
of method
On the one hand, the extent of validation and the choice
of performance parameters to be evaluated depend on
the status and experience of the analytical method. On
the other hand, the validation plan is determined by the
analytical requirement(s), as defined on the basis of
customer needs or as laid down in regulations.
When the method has been fully validated previously
according to an international protocol [15,20], the
laboratory does not need to conduct extensive in-house
validation studies. It must verify only that it can achieve
the same performance characteristics as outlined in the
collaborative study.
As a minimum, precision, bias, linearity and ruggedness
studies should be undertaken. Similarly, limited
validation is required in cases where it concerns a fully
validated method applied to a new matrix, a wellestablished
but non-collaboratively studied method and
a scientifically published method with characteristics
given. More profound validation is needed for methods
published in the literature as validation methods, without
any characteristic given, and for methods developed
in-house [37].
Which performance criteria have to be evaluated
depends also on the purpose of the method. Different
ICH/USP guidelines are set up for: (1) identification tests;
SAMPLE PREPARATION
ANALYTE EXTRACTION
ANALYTICAL METHOD
sample lot
laboratory sample
test sample
analytical sample
analytical result
SAMPLING
ANALYTE DETERMINATION
CRITERIA APPROACH
- precision
- trueness
- selectivity/specificity
- linearity & range
- LOD & LOQ
- recovery
- robustness/ruggedness
ANALYTICAL PROCEDURE
FITNESS-FOR-PURPOSE
APPROACH
MU = f (conc)
PROCEDURE consisting of differe
MODULAR VALIDATION APP
uncertainty (module 1)
uncertainty (module 2)
uncertainty (module 3)
uncertainty (module 4)
g of different MODULES
TION APPROACH
Figure 2. Schematic representation of the ‘analytical method’ within the ‘analytical procedure’, and of different approaches for validation.
MU ¼ measurement uncertainty, f ¼ function (of), conc ¼ concentration, LOD ¼ limit of detection, and LOQ ¼ limit of quantification.
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(2) impurity tests; and, (3) assay tests. An identification
test ensures the identity of an analyte in a sample, by
comparing it to a known RM. An impurity test is
intended to confirm the identity of (limit impurity test) or
to accurately quantify (quantitative impurity test) an
impurity, defined as an entity ‘which may normally not
be present’. An assay test finally applies to the major
component or active ingredient in a sample and quantifies
the drug substance as such, as a whole, or the drug
substance in a drug product.
For an assay test, where the major component or
active ingredient is supposed to be present at high levels,
other criteria than for an impurity test should be
investigated. The same is valid for quantitative tests
versus identification and limit impurity tests (Table 2)
[7,8,24,38].
The literature gives a wide range of practical guidelines
for the evaluation of method-performance characteristics
[10]. Besides the diversity of approaches, the
terminology and the way of reporting results vary
widely. Differences may occur depending on the purpose
and the application field of the method, and validation
studies may become more difficult as the complexity of
the analysis increases [39]. In what follows, terms and
formulae are taken from the accepted IUPAC ‘Nomenclature
for the presentation of results of chemical analysis’
[18]. For each validation parameter, we set out in
Table 3 definitions, ways of expression, determination
guidelines and acceptance criteria.
4.2. Accuracy
4.2.1. Precision and bias studies. Precision and bias
studies, which form a part of the MU estimate, are the
most important validation criteria.
Precision measures are divided into:
1. repeatability precision measures s or SD ( sr or SDr)
and RSD (RSDr);
2. intra-laboratory reproducibility precision or ‘intermediate
precision’ measures, SD and RSD; and,
3. inter-laboratory reproducibility precision s or SD
(sR or SDR) and RSD (RSDR) [18].
Besides standard deviations and coefficients of variation,
repeatability/reproducibility values or limits (r,R)
are additional parameters of high value in the assessment
of precision (for formulae, see Table 3. These
criteria mean that the absolute variation between two
independent results – obtained within the same laboratory
respectively between different laboratories – may
exceed the value of r respectively R in a maximum of 5%
of the cases [2]. Another measure of precision is the
confidence interval, in which all measurements fall with
a certain probability or confidence level 1-a (a is often
0.05, giving a probability here of 95%) [18].
Calculated repeatability, intermediate precision and
reproducibility values can be compared with those of
existing methods. If there are no methods with which to
compare the precision parameters, theoretical relative
reproducibility and repeatability standard deviations can
be calculated from the Horwitz equation and the Horrat
value (Table 3). Horwitz RSD values are reported in
Table 4. Higher variability is expected as the analyte
levels approach the LOD (see below). Next to the Horwitz
equation, the AOAC’s Peer Verified Program proposes its
own levels of acceptability of %RSD, as function of
analyte concentration level [8,24].
Precision data can be documented in bar charts or
control charts, such as Shewhart control charts (see also
Section 5.3 on IQC). Bar charts plot %RSD values with
their corresponding confidence interval. Control charts
plot the individual measurement results respectively the
means of sets of measurements with their confidence
level (or with horizontal lines representing ‘limits’, see
further), as a function of the measurement number
respectively the run number [4,7,8,10,24,38].
Precision relates to the random error of a measurement
system (see Fig. 3) and is a component of MU (see
Part I) [2].
4.2.2. Trueness. Trueness is expressed in terms of bias or
percentages of error. Bias is the difference between the
mean value determined for the analyte of interest and
the accepted true value or known level actually present
[40]. It represents the systematic deviation of the
Table 2. Criteria to establish for different categories of methods of analysis [8]
Method-performance parameter Identification test Impurity test Assay
test
Limit impurity test Quantitative impurity test
Precision )a
) + +
Trueness ) )a
+ +
Specificity + + + +
LOD )a
+ ) )
LOQ )a
) + )
Linearity )a
) + +
Range )a
)a
+ +
Ruggedness + + + +
a
May be performed.
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Table 3. Summary of method-performance parameters: definitions, ways of expression, requirements or acceptance criteria and guidelines for practical assessment (For more details, see text) [1,4,7,8,24,27–29]. (*) tp,m is the Student factor corresponding to
the confidence level 1 ) a and v degrees of freedom. The symbol p represents the percentile or percentage point of the t-distribution. For 1-sided intervals, p ¼ 1 À a; for 2-sided intervals, p ¼ 1 À a=2. Values of t can be found in the IUPAC Nomenclature
(t ¼ 2.776 for n ¼ 5 and t ¼ 3.182 for n ¼ 4 at p ¼ 0.95) [19]. ðÃÃÞ
X is the mean determined value and n is the number of measurements for which the SD was calculated. If standard deviation data of the certified RMs are not available, 95% confidence limits may
be used as an estimate of CRM standard deviation (see second form of formula for the z-score) [28]. ðÃÃÃÞ
xbl is the mean of the blank measurements, sbl is the standard deviation (SD) on the blank measurements and S is the sensitivity of the method or the slope
of the calibration function. The calibration function is the relationship between the measured response xL and the concentration cL or amount qL [8,24,49,50]
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Table3(continued)
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measured result from the true result. Method trueness is
also an indicator of utility and applicability of that
method with real samples [41].
Different sources of systematic errors contribute to the
overall bias (Fig. 3). Thompson and Wood [5] describe
‘persistent bias’ as the bias affecting all data of the
analytical system, over longer periods of time and being
relatively small but continuously present. Different components
contribute to the persistent bias, such as laboratory
bias, method bias and the matrix-variation effect.
Next to persistent bias, the larger ‘run effect’ is the bias of
the analytical system during a particular run [1,4,5].
One or more of these bias components are encountered
when analyzing RMs. In general, RMs are divided
into certified RMs (CRMs, either pure substances/solutions
or matrix CRMs) and (non-certified) laboratory
RMs (LRMs), also called quality control (QC) samples
[42]. CRMs can address all aspects of bias (method,
laboratory and run bias); they are defined with a statement
of uncertainty and traceable to international
standards. CRMs are therefore considered as useful tools
to achieve traceability in analytical measurements, to
calibrate equipment and methods (in certain cases), to
monitor laboratory performance, to validate methods
and to allow comparison of methods [1,4,43]. However,
the use of CRMs does not necessarily guarantee trueness
of the results. The best way to assess bias practically is
indeed by replicate analysis of samples with known
concentrations, such as RMs (see also Part I of this
article). The ideal RM is a matrix CRM, as this is very
similar to the samples of interest (the latter is called
‘matrix matching’). However, a correct result obtained
with a matrix CRM does not guarantee that the results of
unknown samples with other matrix compositions will
be correct [1,42].
The usefulness of CRMs for validation (in particular
for trueness assessment) and traceability purposes has
been debated for years. This is illustrated by the
enormous number of papers published on this topic. We
mention here only some interesting references
[42,44,45]. Examples of the use of pure substance RMs,
matrix CRMs or LRMs can be found in Special Issues of
Accreditation and Quality Assurance (Volume 9, 2004) and
Analytical and Bioanalytical Chemistry (Volume 278,
2004) on ‘Biological and Environmental Reference
Materials’ and the Special Issue of TrAC (volume 23,
2004) on ‘Challenges for achieving traceability of
environmental measurements’.
If no such (certified) RMs are available, a blank sample
matrix of interest can be ‘spiked’ with a known amount of
a pure and stable in-house material, called the ‘spike’ or
‘surrogate’. Recovery is then calculated as the percentage
of the measured spike of the matrix sample relative to the
measured spike of the blank control or the amount of
spike added to the sample. The smaller the recovery %,
the larger the bias that is affecting the method and thus
the lower the trueness [1,8,24,46,47].
An indication of trueness can also be obtained by
comparing the method with a second, well-characterized
reference method, under condition that the precision of
the established reference method is known. Results from
the two methods, performed on the same sample or set of
samples, are compared. The samples may be CRMs,
in-house standards or just typical samples [4]. A comparison
between the three ways of establishing bias, is
also given in Part I of this article. It should be clear that
the use of recovery estimates and comparing methods,
are alternative ways which encompass serious limitations.
They can give an idea about data comparability;
however, trueness cannot be assured [42].
Trueness or exactness of an analytical method can be
documented in a control chart. Either the difference
between the mean and the true value of an analyzed
(C)RM together with confidence limits, or the percentage
recovery of the known, added amount can be plotted
[8,14]. Here again, special caution should be taken
concerning the reference used. Control charts may be
useful to achieve trueness only if a CRM, which is in
principle traceable to SI units, is used. All other types of
references only allow traceability to a ‘consensus’ value,
Table 4. Horwitz function as an empirical relationship between the precision of an analytical method and the concentration of the analyte
regardless of the nature of the analyte, matrix and the method used. Acceptable RSDR and RSDr values according to [27] and to AOAC International
[8,14] (PVM ¼ Peer Verified Methods (Program))
Analyte % Analyte ratio Unit Horwitz
%RSD
AOAC PVM
%RSD
100 1 100% 2 1.3
10 1.00E ) 01 10% 2.8 2.8
1 1.00E ) 02 1% 4 2.7
0.1 1.00E ) 03 0.10% 5.7 3.7
0.01 1.00E ) 04 100 ppm 8 5.3
0.001 1.00E ) 05 10 ppm 11.3 7.3
0.0001 1.00E ) 06 1 ppm 16 11
0.00001 1.00E ) 07 100 ppb 22.6 15
0.000001 1.00E ) 08 10 ppb 32 21
0.0000001 1.00E ) 09 1 ppb 45.3 30
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which is assumed to be equal to the ‘true’ value, although
this is not necessarily the case [42]. The expected trueness
or recovery % values depend on the analyte concentration.
Trueness should therefore be estimated for at
least three different concentrations. If recovery is measured,
values should be compared to acceptable recovery
rates, as outlined by the AOAC Peer Verified Methods
program (Table 5) [8,14]. Besides bias and % recovery,
another measure for the trueness is the z-score (Table 3).
It is important to note that a considerable component of
the overall MU will be attributed to MU on the bias of a
system, including uncertainties on RMs (Fig. 3) [2].
4.2.3. Recovery. Recovery is often treated as a separate
validation parameter (Table 3). Analytical methods aim
to estimate the true value of the analyte concentration
with an uncertainty that is fit for purpose. However, in
such analytical methods, the analyte is transferred from
the complex matrix to a simpler solution, whereby there
is a loss of analyte. As a consequence, the measured
value will be lower than the true concentration present
in the original matrix. Therefore, assessing the efficiency
of the method in detecting all of the analyte present is a
part of the validation process. Eurachem, IUPAC, ISO
and AOAC International state that recovery values
TRUENESS PRECISIONindicator for difference between
expected value and true value
indicator for difference between
result and expeced value
analysis of CRMs +
statistical control
duplicate analysis
random
bias
minimally needed in method validation
single-laboratory validation
matrix variation
effect
method
bias
laboratory
bias
run
bias
persistent bias run effect
variations within the whole analytical
system, over longer periods
variations during a
particular run
true value
analytical result
error
inaccuracy
difference between
analytical result and
true value
bias
systematic error
difference between
expected value and
true value
expected value
(limiting mean)
imprecision
random error
difference between
analytical result and
expeted mean value
intermediate
precision
reproducibility repeatability
inter-assay precision=
variability over a short
time interval, under the
same conditions
within-lab variation due to
random effects= variability
over a longer period of time,
under different conditions
inter-laboratory
variation, tested by
collaborative studies
Figure 3. Composition of the error of an analytical result, related to the accuracy of the analytical method [1,5].
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should always be established as a part of method
validation. Recovery or spiking studies should be performed
for different types of matrices, several examples of
each matrix type and for each matrix type at different
levels of analyte concentration [1,2,4].
4.3. Specificity and selectivity
Specificity and selectivity both give an idea of the
reliability of the analytical method (for definitions, see
Table 3). Some authors give different definitions for both
terms while, for others, they are identical. The term
‘specific’ generally refers to a method that produces a
response for a single analyte only, while the term ‘selective’
is used for a method producing responses for different
chemical entities or analytes which can be distinguished
from each other. A method is called ‘selective’ if the
response is distinguished from all other responses. In this
case, the method is perfectly able to measure accurately
an analyte in the presence of interferences [8,48].
According to Eurachem, specificity and selectivity essentially
reflect the same characteristic and are related very
closely to each other in such a way that specificity means
100% selectivity. In other words, a method can only be
specific if it is for 100% selective. Another related term is
‘confirmation of identity’, which is the proof that ‘the
measurement signal, which has been attributed to the
analyte, is only due to the analyte and not to the presence
of something chemically or physically similar or arising as
coincidence’ [4]. A method must first show high specificity
before true quantification can be performed [40].
There is no single expression for specificity. It is rather
something that must be demonstrated. The way that this
is done depends on the objective and the type of analytical
method (see also below). For identification tests, the goal
is to ensure the identity of an analyte. Specificity is here
the ability to discriminate between compounds of closely
related structures that can be present.
For impurity tests (limit impurity test; quantitative
impurity test) and assay tests, the accent is on the ability
to determine or to discriminate for the analyte in the
presence of other interferants. Selectivity can be assessed
by spiking samples with possible interferants (degradation
products,Á Á Á) [7,8,24].
4.4. LOD
There is no analytical term or parameter for which there
is a greater variety of terminology and formulations than
for LOD and quantification. The limits of detection, or
detection limit, is the terminology most widely used, as
accepted by Eurachem. ISO uses ‘minimum detectable net
concentration’, while IUPAC prefers ‘minimum detectable
(true) value’ [4]. However, all official organizations
refer to the same definition: ‘the lowest amount of an
analyte in a sample which can be detected but not necessarily
quantified as an exact value’. In general, the LOD
is expressed as a concentration cL or a quantity qL, derived
from the smallest signal xL which can be detected with
reasonable certainty for a given analytical procedure. The
lowest signal xL is the signal that lies k times SDblank above
the mean blank value, whereby k is a numerical factor
chosen according to the level of confidence required
[8,24,49–51].
The larger the value of k, the larger the confidence
level. Eurachem and IUPAC recommend a value of 3 for
k, meaning that the chance that a signal more than 3s
above the sample blank value is originating from the
blank is less than 1%. The LOD is thus the concentration
or amount corresponding to a measurement level
(response, signal) three sbl units above the value for zero
analyte (Table 3). At the concentration or amount three
times the sbl, the relative standard deviation or coefficient
of variation on the measured signal is 33% (measure for
uncertainty) [1,2,4,27,49,52]. According to USP/ICH,
the LOD corresponds to that signal where the ‘signalto-noise
ratio’ is 2:1 or 3:1 [24,38].
It is not true – as is often thought – that detection or
quantification is impossible below the determination
limit; but, at these lower levels, the uncertainty of the
detection/quantification measurement is higher than the
actual value itself [28]. In this context, Huber [8] also
defines the LOD as the point at which a measured value
is larger than the uncertainty associated with it.
According to Krull and Swartz [41], the LOD is a concentration
point where only the qualitative identification
is possible but not accurate and precise quantification.
For qualitative methods, the LOD is defined as the
‘threshold concentration at which the test becomes
Table 5. Acceptable recovery percentages as a function of the analyte concentration [8]
Analyte% Analyte ratio Unit Mean recovery (%)
100 1 100% 98–102
10 1.00E ) 01 10% 98–102
1 1.00E ) 02 1% 97–103
0.1 1.00E ) 03 0.10% 95–105
0.01 1.00E ) 04 100 ppm 90–107
0.001 1.00E ) 05 10 ppm 80–110
0.0001 1.00E ) 06 1 ppm 80–110
0.00001 1.00E ) 07 100 ppb 80–110
0.000001 1.00E ) 08 10 ppb 60–115
0.0000001 1.00E ) 09 1 ppb 40–120
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unreliable’. Each of a series of blank samples, spiked with
different concentrations of the analyte, is analyzed at least
10 times. The threshold or ‘cut-off’ concentration is determined
visually based on a response curve, plotting the %
positive results versus the concentration. In this respect,
the LOD is also defined as the concentration at which 95%
of the experiments give a clearly positive signal [4].
The LOD may not be confused with the sensitivity of
the method. The latter is ‘the capability of the method to
discriminate small differences in concentration or mass
of the test analyte’ and is equal to the slope of the
calibration curve (see below) [8].
4.5. LOQ
For the LOQ or ‘limit of determination’, definitions and
formulas are very similar to those of LOD, except that for
LOQ, k is taken to be 5, 6 or even 10 [1,2,4,8,24,50]. A
value of 10 for k means that the relative standard deviation
(%RSD) at the LOQ is 10%. The LOQ thus corresponds
to that concentration or amount of analyte, quantifiable
with a variation coefficient not higher than 10% [52].
The LOQ is always higher than the LOD and is often taken
as a fixed multiple (typically 2) of the LOD [1]. Also, the
determination limit is referred to as the signal 10 times
above the noise or background signal, corresponding to a
‘signal-to-noise ratio’ of 10:1 [24,38].
In practice, the LOQ can be calculated analogously to
the LOD, as indicated in Table 3. An alternative way of
practically assessing the LOD and LOQ is the following. In
a first step, 10 independent sample blanks are each
measured once, the blank standard deviation sbl is
calculated and the lowest signals corresponding to both
the LOD and the LOQ are calculated as xLOD ¼ xbl + 3sbl
respectively as xLOQ ¼ xbl + 10 sbl. In a second step, sample
blanks are spiked with various analyte concentrations
(e.g., 6) close to the LOD. Per concentration, 10 independent
replicates are measured and the standard deviation
of the measured signals calculated. These standard
deviations s (or the relative standard deviations %RSD)
are then plotted against the concentration. LOD and LOQ
values are those concentrations of analyte corresponding
to %RSD values of 33% and 10%, respectively [4,28].
As was said for LOD, it is not true that at and below
the LOQ, quantification becomes impossible. Quantification
is possible, but it becomes unreliable as the uncertainty
associated with it at these lower levels is higher
than the measurement value itself. Quantification
becomes reliable as soon as the MU is lower than the
value measured [28].
4.6. Decision limit and detection capability:
for specific sectors only
In the context of analytical method validation, the terms
decision limit (CCa) and detection capability (CCb) as well
as minimum required performance limits (MRPLs) are
often used and need some clarification. These terms are
applicable for the measurement of organic residues,
contaminants and chemical elements in live animals and
animal products, as regulated within the EU by the
Council Directives 96/23/EC [53], 2002/657/EC [35]
and 2003/181/EC [54]. The Commission distinguishes
‘Group A substances’, for which no permitted limit (PL)
(maximum residue level, MRL) has been established, and
‘Group B substances’ having a fixed PL.
CCa is the limit at and above which it can be
concluded with an error probability of a that a sample is
non-compliant. If a PL has been established for a
substance (Group B or the regulated compounds), the
result of a sample is non-compliant if the decision limit is
exceeded (CCa ¼ xPL + 1.64 sMRL). If no PL has been
established (Group A), the decision limit is the lowest
concentration level at which the method can discriminate
with a statistical certainty of 1-a that the particular
analyte is present (CCa ¼ xbl + 2.33 ssample). CCb is the
smallest content of the substance that may be detected,
identified and/or quantified in a sample with an error
probability of b (CCb ¼ CCa + 1.65 ssample).
MRPLs have been established for substances for which
no PL has been fixed and in particular for those
substances the use of which is not authorized or even
prohibited within the EU (Group A). A MRPL is the
minimum content of an analyte in a sample, which at
least has to be detected and confirmed. A few MRPLs for
residues of certain veterinary drugs have been published
so far in Directive 2003/181/EC.
For Group A substances (no PL established), CCa and
CCb are comparable with LOD and LOQ, respectively, as
their concentrations correspond to measured signals
laying y times above the blank signal. For substances
having a PL (Group B), CCa and CCb are not related to
LOD and LOQ but are expressed in relation to this PL. It
is important to note that these terms apply specifically to
inspection of animals and fresh meat for the presence of
residues of veterinary drugs and specific contaminants
and are therefore different from LOD and LOQ
[35,53–56].
4.7. Linearity and range
For assessment of the linearity of an analytical method,
linear regression calculations are not sufficient. In
addition, residual values should be calculated (Table 3).
The latter represent the differences between the actual y
value and the y value predicted from the regression
curve, for each x value. If residual values, calculated by
simple linear regression, are randomly distributed about
the regression line, linearity is confirmed, while
systematic trends indicate non-linearity. If such a trend
or pattern is observed, this suggests that the data are
best treated by weighted regression. For either simple or
weighted linear regression, linearity supposes that the
intercept is not significantly different from zero
[1,4,27,28].
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An alternative approach to establishing linearity is to
divide the response by the respective concentrations
and to plot these ‘relative responses’ as a function of
the concentration, on a log scale. The line obtained
should be horizontal over the full linear range, with a
positive deviation at low concentrations and a negative
deviation at high concentrations. By drawing parallel
horizontal lines, corresponding to, e.g., 95% and 105%
of the horizontal relative response line, the intersection
points can be derived where the method becomes
non-linear [8].
It is important that a linear curve is repeatable from
day to day. However, linear ranges may be different for
different matrices. The reason for this is a possible effect
of interferences inherent to the matrix. A test for general
matrix effect can be performed by means of ‘standard
additions’ or the method of analyte additions. For a set of
samples, obtained by adding different concentrations of
analyte to a certain matrix, the slope of the calibration
curve is compared with the slope of the usual calibration
function. A lack of significance (curves are parallel)
means that there is no matrix effect [27,28].
4.8. Ruggedness and robustness
Although the terms ruggedness and robustness are often
treated as the same and used interchangeably, separate
definitions exist for each, as indicated in Table 3.
To have an idea about the ruggedness, Eurachem
recommends introducing deliberate variations to the
method, such as different days, analysts, instruments,
reagents, variations in sample preparation or sample
material used. Changes should be made separately and
the effect evaluated of each set of experimental conditions
on the precision and trueness [1,4,38]. To examine
the effects of different factors, a ‘‘factorial design’’
methodology can be applied, as described by von Holst
et al. [57]. By combining changes in conditions and
performing a set of experiments, one can determine
which factors have a significant or even critical influence
on the analytical results.
In ICH/USP guidelines, ruggedness is not defined
separately but treated under the same denominator as
reproducibility precision: it is ‘the degree of reproducibility
of the results obtained under a variety of conditions,
expressed as %RSD’ [8,38].
Robustness is a term introduced by USP/ICH [41].
Although Eurachem has included the term robustness in
its official list of definitions, the term is not used by
official organizations other than USP/ICH. According to
Eurachem, both parameters do present the same and are
thus synonyms [4,24,38].
4.9. Sensitivity
The sensitivity of a method is the gradient of the
response curve. In practical terms, sensitivity refers to
the slope of the calibration curve. Sensitivity is often
used together with LODs and LOQs. Indeed, the slope of
the calibration curve is used for the calculation of LODs
and LOQs. A method is called sensitive if a small change
in concentration or amount of analyte causes a large
change in the measured signal [1,4,28]. Sensitivity is
not always mentioned as a validation parameter in
official guidelines. According to Thompson et al. [1], it is
not useful in validation because it is usually arbitrary,
depending on instrument settings. USP/ICH does not
mention sensitivity at all.
PREVALIDATION
SINGLE-LABORATORY OPTIMIZATION
description of analytical system:
- purpose of the method?
- type of analyte?
- type of method?
(FULL) VALIDATION
INTERLABORATORY or
COLLABORATIVE TRIAL:
applicability/ intended use of method:
- type(s) of material/ matrix(matrices)
- concentration rate of analyte
writing a SOP
(standard operating procedure)
fixing the analytical requirement
evaluation of method performance
characteristics
1. ISO 5725 (1994)
2. IUPAC: Horwitz (1995)
precision data must be given
in terms of RSD or CV (%)
minimum of 5 materials
minimum of 8 laboratories
both repeatability and reproducibility
precision data must be given
precision data must be documented
both without and with outliers
(Cochran test; Grubbs test)
STANDARDIZATION
ADOPTION by INTERNATIONALLY
RECOGNIZED STANDARDIZATION BODY
precision: calculated values of RSD must be
in compliance with Horwitz (Horrat) values
method has been validated collaboratively
(ISO 5725 or Horwitz, 1995)
evaluation of precision and other statistical data
by an accepted method of statistical analysis
(Cochran, Grubbs)
precision: not more than 1 of the 5 sets of data
give more than 20% statistically outying results
mandatory standard format for text and
presentation of results
Figure 4. Hierarchy of relationship between and objectives and requirements for prevalidation [61], validation [14,15,20] and standardization
of analytical methods [14,15,19,20,27,37]. RSD ¼ relative standard deviation; CV ¼ coefficient of variation.
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5. AQA
QA is the complete organizational infrastructure that
forms the basis for all reliable analytical measurements
[5]. It stands for all the planned and systematic activities
and measures implemented within the quality system
[2,58]. A quality system has a quality plan, which
emphasizes the implementation of Good Laboratory
Practice (GLP). GLP is comparable to the Good Manufacturing
Process (GMP) and the larger HACCP (Hazard
Analysis Critical Control Point) quality systems of foodproduction
factories. Attention goes to all aspects of
quality management in the laboratory organization,
including staff training, the maintenance and calibration
of all equipment used, the laboratory environment,
safety measures, the system of sample identification,
record keeping and storage – the latter may be simplified
by the use of laboratory information management
systems (LIMS), the use of validated and standardized
methods and the documentation of these methods and of
all information concerning the followed procedures
(standard operating procedures, SOP).
QA embraces both QC and ‘quality assessment’. QC is
defined as the mechanism or the practical activities
undertaken to control errors, while quality assessment is
‘the mechanism to verify that the system is operating
within acceptable limits’. Quality assessment and QC
measures are in place to ensure that the measurement
process is stable and under control [2,5].
Within QC, we distinguish between internal and
external QC. In general, QA comprises the following
topics, as also schematized in Fig. 1.
5.1. The use of validated methods: in-house versus
inter-laboratory validation
Wherever possible or practically achievable, a laboratory
should use methods that have been ‘‘fully validated’’
through a collaborative trial, also called inter-laboratory
study or method-performance study. Validation in
collaborative studies is required for any new analytical
method before it can be published as a standard method
(see below). However, single-laboratory validation is a
valuable source of data usable to demonstrate the fitnessfor-purpose
of an analytical method. In-house validation
is of particular interest in cases where it is inconvenient
or impossible for a laboratory to enter into or to organize
itself a collaborative study [1,59].
On the one hand, even if an in-house validated method
shows good performance and reliable accuracy, such a
method cannot be adopted as a standard method.
In-house validated methods need to be compared
between at least eight laboratories in a collaborative trial.
On the other hand, a collaborative study should not
be conducted with an un-optimized method [10]. Interlaboratory
studies are restricted to precision and trueness
while other important performance characteristics,
such as specificity and LOD, are not addressed [60]. For
these reasons, single-laboratory validation and interlaboratory
validation studies do not exclude each other
but must be seen as two necessary and complementary
stages in a process, as presented in Fig. 4. The added
value of single-laboratory validation is that it simplifies
the next step – inter-laboratory validation – and
thereby minimizes the gap between internally (validated
or not) developed methods and the status of interlaboratory
validation. By optimizing the method first
within the laboratory, as a kind of preliminary work,
an enormous amount of collaborators time and money
is saved [10].
The importance of conducting such a single-laboratory
preliminary validation step is increasingly highlighted
by international standardization agencies. IUPAC
and AOAC International include a ‘Preliminary Work’
paragraph in their guidelines for collaborative studies,
Table 6. Mandatory text format for standardized methods according to ISO Guide 78/2 [2,10]
1 Scope States briefly what the method determines
2 Definitions Precise definition of the analyte or parameter determined by the method
3 Fields of application Type of materal(s)/matrix(ces) to which the method is applicable
4 Principle Basic steps involved in the procedure
5 Apparatus Specific apparatuses required for the determination are listed
6 Reagents Analytical reagent-grade reagents needed for the determination are listed
7 Sampling Description of the sampling procedure
8 Procedure Divided into numbered paragraphs or sub-clauses; includes a ‘preparation of test sample’
step and a reference to ’quality assurance’ procedures
9 Calculation and expression
of results
Indication of how the final results are calculated and of units in which the results are to be
expressed
10 Notes Additional information as to the procedure; may be in the form of notes, placed here or in
the body of the text
Annex Includes all information on analytical quality control, such as precision clauses (repeatability
and reproducibility data), table of statistical data outlining the accuracy (trueness and
precision) of the method
References References to the report published on the collaborative study carried out prior to
standardization of the method
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stating that within-laboratory testing data are required
on precision, bias, recovery, applicability. Additionally, a
clear description of the method including statements on
the purpose of the method, the type of method and the
probable use of the method is required within this preliminary
work [14,15]. However, it is not only in the
harmonized guidelines for collaborative trials (see below)
that the link between a single-laboratory pre-validation
step and the collaborative trial is emphasized. Separate
guidelines for single-laboratory validation of methods of
analysis have recently been published by IUPAC, ISO
and AOAC International [1]. The IUPAC guidelines have
also been considered and accepted by the Codex Committee
on Methods of Analysis and Sampling (CCMAS)
[30]. In addition, specific, individual working groups or
scientists are presenting their own framework for ‘pre-,
single-laboratory validation’ of methods of analysis. The
latter do not concern official, published guidelines but
can be found on the internet (e.g., [10,38]) or are distributed
through national or international specific
working groups. The objectives of a single-laboratory or
in-house validation process are depicted in Fig. 4.
Depending on the type of method (Table 2), data can be
obtained for all criteria except for the reproducibility
(inter-laboratory) precision. However, it is this ‘amonglaboratories
variability’ or reproducibility which is the
dominating error component in analytical measurement
and which underlies the need for inter-laboratory
validation [61].
Inter-laboratory or collaborative validation studies
can be organized by any laboratory, institute or organization,
but should preferably be conducted according
to one of the following recognized protocols:
(1) ISO 5725 on Accuracy (Trueness and Precision)
of Measurement Methods and Results [20]; or,
(2) IUPAC Protocol for the Design, Conduct and
Interpretation of Method-Performance Studies [15].
The latter revised, harmonized guidelines have been
adopted also by AOAC International as the guidelines for
the AOAC Official Methods Program [14]. The main
requirements for collaborative studies outlined in these
guidelines are shown in Fig. 4.
Precision plays a central role in collaborative studies.
Wood [37] defines a collaborative trial as ‘a procedure
whereby the precision of a method of analysis may be
assessed and quantified’. Precision is the objective of
inter-laboratory validation studies, and not trueness or
any other method-performance parameter. Evaluation of
the acceptability of precision data is important for the
standardization of methods (see hereafter).
5.2. The use of standardized methods
The first level of AQA is the use of validated or standardized
methods. The terms validated and standardized
here refer to the fact that the method-performance
characteristics have been evaluated and have proven to
meet certain requirements. At least, precision data are
documented, giving an idea of the uncertainty and thus
of the error of the analytical result. In both validated and
standardized methods, the performance of the method is
known.
Validated methods can be developed by the laboratory
itself, or by a standardization organization after interlaboratory
studies. Standardized methods are developed
by organizations such as the AOAC International, ISO,
Table 7. Differences between method-performance studies and proficiency testing (PT) schemes [5,31,62]
Characteristic Collaborative/interlaboratory studies
(method performance studies)
Proficiency testing schemes
Main objective Validation of new methods Competency check of analytical laboratories
Application • new methods • routinely used (validated and/or standardized
methods)
• required for full validation and standardization • recommended within IQC and QA system
• first prerequisite for IQC and QA
Results aimed at Precision: multiple results, both repeatability and
reproducibility ! % RSD is compared to theoretical
Horwitz and Horrat values
Trueness: 1 single result per test material !
calculation of Z-score as measure for the bias
Method & protocol used 1 single, prescribed method for which SOP must
strictly be followed
Multiplicity of methods; participants have free choice
of (validated and/or standardized) method
Test materials • minimum of five different materials • no minimum; often less than 5 test samples per
round
• no stipulations for homogeneity and stability of test
samples
• homogeneity and stability of materials must be
assured
Participating laboratories • minimum of eight • no minimum; variety in participants is possible
throughout 1 scheme (different rounds)
• are assumed to be equally competent • not assumed to have equal competency (will be
tested)
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USP (see Table 1), US Environmental Protection Agency
(EPA), American Society for Testing and Material
(ASTM) or Food Standards Association (FSA) [8]. This is
exactly where the difference lies between a validated and
a standardized method: an analytical method can only
be standardized after it has been validated through interlaboratory
comparisons. The main prerequisite for a
standards organization is that a method has been
adequately studied and its precision shown to meet a
required standard, as summarized in Fig. 4. The format
of a standard method, as outlined in ISO Guide 78/2 [10]
[10], as shown in Table 6 [2]. A specific IUPAC Protocol
[19] describes in detail how to present AQA data, such as
the performance characteristics.
5.3. Effective IQC
In the IUPAC Harmonized Guidelines for IQC, Thompson
and Wood [5] define IQC as a ‘set of procedures undertaken
by the laboratory staff for the continuous monitoring
of operation and the results of measurements in
order to decide whether results are reliable enough to be
released’. IQC guarantees that methods of analysis are fit
for their intended purpose, meaning the continuous
achievement of analytical results with the required
standard of accuracy. The objective of IQC is the elongation
of method validation: continuously checking the
accuracy of analytical data obtained from day to day in
the laboratory. In this respect, both systematic errors,
leading to bias, as well as random errors, leading to
imprecision, are monitored. In order to be able to monitor
these errors, they should remain constant. Within
the laboratory, such constant conditions are typically
achieved in one analytical run. The word ‘internal’ in
IQC implicates that repeatability conditions are achieved.
Thus, monitoring the precision as an objective of IQC
does not concern reproducibility or inter-laboratory
precision, but only repeatability or intra-laboratory
precision. The monitoring of accuracy of an analytical
method in IQC can be translated into the monitoring of
the analytical system [5].
Two aspects are important for IQC:
(1) the analysis of ‘control materials’, such as RMs or
spiked samples, to monitor trueness; and,
(2) replication of analysis to monitor precision.
Also of high value in IQC are blank samples and blind
samples. Both IQC aspects form a part of statistical
control, a tool for monitoring the accuracy of an analytical
system. In a control chart, such as a ‘Shewhart
control chart’, measured values of repeated analyses of
an RM are plotted against the run number. Based on the
data in a control chart, a method is defined either as an
‘analytical system under control’ or as an ‘analytical
system out of control’. This interpretation is possible by
drawing horizontal lines on the chart: x(mean value),
x + s(SD) and x ) s, x + 2s (upper warning limit) and
x ) 2s (lower warning limit), x + 3s (upper action or
control limit) and x ) 3s (lower action or control limit).
An analytical system is under control if no more than
5% of the measured values exceeds the warning limits
[2,3,38].
5.4. Participation in proficiency testing (PT) schemes
PT is the periodic assessment of the competency or the
analytical performance of individual participating laboratories
[11]. An independent coordinator distributes
individual test portions of a typical uniform test material.
The participating laboratories analyze the materials by
their method of choice and return the results to the
coordinator. Test results obtained by different laboratories
are subsequently compared with each other and the
performance of each participant evaluated based on a
single competency score [16,62]. International harmonized
protocols exist for the organization of PT schemes
[11,12,16,21,32].
Participation in PTs is not a prerequisite or an absolute
substitute for IQC measures, or vice versa. However,
participation in PT is meaningless without a well developed
IQC system. IQC underlies participation in PT
schemes, while IQC and participation in PT schemes are
both important substitutes of AQA (Fig. 1). It is shown
that laboratories with the strongest QC procedures score
significantly better in PT schemes [5,62]. Participation in
PT can to a certain extent improve laboratory performance.
However, unsatisfactory performance in schemes
(up to 30% of all participants) has been reported. This
means that there is no correlation between good
analytical performance and participation in PT [63].
However, PT has a significant educational function, as it
helps a laboratory to demonstrate competency to an
accreditation body or another third party [12].
The terms ‘PT schemes’ and ‘collaborative trials’ are
often confused with each other, as, in both QA measures,
a number of different laboratories is involved. However,
there is a clear distinction between both. The main
differences with respect to objective and application,
results, used method, test materials and participating
laboratories are summarized in Table 7. It is important to
note also that the results obtained from PT schemes, as
well as those from collaborative performance studies,
can be used for assessing the MU (see Part I).
5.5. External QC (EQC) and accreditation
Participation in PT schemes is an objective means of
evaluating the reliability of the data produced by a laboratory.
Another form of external assessment of laboratory
performance is the physical inspection of the
laboratory to ensure that it complies with externally
imposed standards. Accreditation of the laboratory
indicates that it is applying the required QA principles.
The ‘golden standard’ ISO/IEC 17025 [23], which is the
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revised version of ISO Guide 25 [22], describes the
general requirements for the competence of calibration
and testing laboratories. In Europe, the accreditation
criteria have been formalized in European Standard
EN45001 [64]. Participation in PT schemes forms the
basis for accreditation, because PT is a powerful tool for
a laboratory to demonstrate its competency. Accreditation
guides use the information obtained by PT schemes
[2,12,16,23].
Accreditation is formal recognition that a laboratory
is competent to carry out specific (types of) calibrations
or tests [2]. After the use of validated and standardized
methods, the introduction and use of appropriate IQC
procedures and the participation in PT schemes,
accreditation to ISO/IEC 17025 is the fourth basic
principle related to laboratory QA in general [1].
Guidelines on the implementation of ISO/IEC 17025,
including the estimation of MU (see also Part I), are
published in the literature and by official accreditation
bodies, such as Eurachem, CITAC, EA, Eurolab and ILAC
(see Table 1) [2,12,33,34,65]. It is worthwhile to
mention that accreditation, just like participation in PT
schemes, does not necessarily indicate that the laboratory
has a good performance [63].
6. Summary
Together with the fast development of analytical methodologies,
nowadays great importance is attached to the
quality of the measurement data. Besides the necessary
reporting of any result with its MU and traceability of the
results to stated standards or references (Part I of this
review), a third crucial aspect of analytical methods of
any type is their validation status. It is internationally
recognized that validation is necessary in analytical
laboratories. However, less is known about what
validation is and what should be validated, why
validation is important, when and by whom validation is
performed and, finally, how it is carried out practically.
This article tries to answer these questions.
We define method validation in detail and describe
different approaches to evaluating the acceptability of
analytical methods. We attach great importance to the
different method-performance parameters, their definitions,
ways of expression and approaches for practical
assessment.
Validation of analytical methodologies is placed in the
broader context of QA. The topics of standardization,
internal and external QC and accreditation are discussed,
as well as the links between these different aspects.
Because validation and QA apply to a specific analytical
method, it is important to approach each method on a
case-by-case basis. An analytical method is a complex,
multi-step process, starting with sampling and ending
with the generation of a result. Although every method
has its specific scope, application and analytical
requirement, the basic principles of validation and QA
are the same, regardless the type of method or sector of
application. The information in this work is mainly
taken from the analytical chemistry, although it applies
to other sectors as well.
This second part on quality in the analytical laboratory
provides a good, complete, up-to-date collation of
relevant information in the fields of analytical method
validation and QA. It is useful for the completely
inexperienced scientist as well as for those involved in
this topic for a long time, but having somewhere lost
their way in the labyrinth, looking for more explanation
on a particular aspect, or longing for deeper insight and
knowledge.
Acknowledgements
The authors thank Simon Kay for preliminary discussions
on the topic, Janna Puumalainen for reading and
commenting on early versions of this article, Andrew
Damant for giving suggestions, Arne Holst-Jensen for
refreshing ideas and Friedle Vanhee for many helpful
discussions, for reading and assistance throughout the
writing process of this article.
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Isabel Taverniers graduated in Agricultural and Applied Biological
Sciences from the University of Gent, Belgium, in 1999. Until April
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2001, she worked at AgriFing, a joint spin-off laboratory of Gent
University, Hogeschool Gent and the Department for Plant Genetics and
Breeding (DvP); she specialized in DNA fingerprinting technologies. She
is now preparing a Ph.D. thesis in the Laboratory of Applied Plant
Biotechnology of the Department for Plant Genetics and Breeding (CLO,
Flemish Community).
Erik Van Bockstaele is Head of the Department for Plant Genetics
and Breeding and Professor at the Faculty of Agricultural and Applied
Biological Sciences of the University of Gent.
Marc De Loose is Head of the Section of Applied Plant Biotechnology
at the Department for Plant Genetics and Breeding.
Trends Trends in Analytical Chemistry, Vol. 23, No. 8, 2004
552 http://www.elsevier.com/locate/trac