The contribution of lot-to-lot variation to the measurement uncertainty of an LC-MS-based multi-mycotoxin assay
The contribution of lot-to-lot variation to the measurement uncertainty of an LC-MS-based multi-mycotoxin assay
David Stadler 0
Michael Sulyok 0
Rainer Schuhmacher 0
Franz Berthiller 0
Rudolf Krska 0
0 Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences , Vienna (BOKU), Konrad-Lorenz-Straße 20, 3430 Tulln , Austria
1 Michael Sulyok
Multi-mycotoxin determination by LC-MS is commonly based on external solvent-based or matrix-matched calibration and, if necessary, the correction for the method bias. In everyday practice, the method bias (expressed as apparent recovery RA), which may be caused by losses during the recovery process and/or signal/suppression enhancement, is evaluated by replicate analysis of a single spiked lot of a matrix. However, RA may vary for different lots of the same matrix, i.e., lot-to-lot variation, which can result in a higher relative expanded measurement uncertainty (Ur). We applied a straightforward procedure for the calculation of Ur from the within-laboratory reproducibility, which is also called intermediate precision, and the uncertainty of RA (ur,RA). To estimate the contribution of the lot-to-lot variation to Ur, the measurement results of one replicate of seven different lots of figs and maize and seven replicates of a single lot of these matrices, respectively, were used to calculate Ur. The lot-to-lot variation was contributing to ur,RA and thus to Ur for the majority of the 66 evaluated analytes in both figs and maize. The major contributions of the lot-to-lot variation to ur,RA were differences in analyte recovery in figs and relative matrix effects in maize. Ur was estimated from long-term participation in proficiency test schemes with 58%. Provided proper validation, a fit-forpurpose Ur of 50% was proposed for measurement results obtained by an LC-MS-based multi-mycotoxin assay, independent of the concentration of the analytes.
Relative matrix effect; Uncertainty budget; Recovery; Matrix mismatch; Proficiency test; Fit for purpose
Mycotoxins are toxic secondary metabolites produced by
fungi on agricultural commodities and processed foods
]. They have adverse effects on humans and animals
that result in illnesses and economic losses. To protect
the consumer from harmful effects, regulations limit
maximum levels of mycotoxins in food and feed [
Reliable analytical methods are needed for compliance
testing and to monitor mycotoxin contamination.
Multi-mycotoxin analytical methods allow timely
routine screening for a multitude of analytes with a broad
spectrum of physiochemical properties [
Multimycotoxin methods are based on liquid chromatography
electrospray ionisation tandem mass spectrometry
] in combination with an extraction
procedure that recovers a broad range of analytes [
In most cases, raw extracts are diluted and injected with
] or even no sample clean-up [
4, 5, 10
Bdilute and shoot^, as clean-up steps would remove
some of the analytes for further analysis. For
multimycotoxin assays, quantification is commonly based on
external solvent-based or matrix-matched calibration. In
essence, the response of the analyte is compared to the
calibration curve and, if necessary, is corrected for the
method bias. The method bias, expressed as apparent
recovery (RA), may be caused by losses during the
rec o v e r y p r o c e d u r e ( R E ) o r s i g n a l s u p p r e s s i o n /
enhancement (SSE) [
]. Compensation of RA could
be achieved by using a stable isotope-labelled internal
s t a n d a r d . H o w e v e r, t h i s a p p r o a c h i s l i m i t e d t o
mycotoxins where 13C-labelled internal standards are
available and may not be feasible in economic terms.
A measurement result is only complete when it is
accompanied by a statement of the associated
uncertainty. Although various guideline documents on uncertainty
estimation are available [
], an uncertainty
statement is missing in most validation studies of
multimycotoxin methods. For multi-mycotoxin methods, the
relative expanded measurement uncertainty (Ur) may be
estimated from the uncertainty associated with the
precision and the uncertainty associated with the method
bias (if a bias correction is applied).
Especially when a bias correction is performed (e.g.
analysis of patulin and aflatoxins in foodstuffs [
the uncertainty of RA may be underestimated since the
uncertainty contribution originating from bias is usually
not considered. In everyday practice, RA is evaluated
based on replicate analysis of a single lot of a matrix
4, 5, 8–10
]. Due to the heterogeneous nature of food
matrices, RA may vary for different lots (quantity of
material known to have uniform characteristics such as
origin and variety) of the same matrix resulting in
Blotto-lot variation^ . This so-called matrix-mismatch
phenomenon would contribute to the uncertainty of
RA and thus to Ur [
]. Matuszewski et al. first
described differences in SSE for an analyte in plasma
samples from different sources, which is referred to as
relative matrix effect [
]. Also for mycotoxins,
large differences in SSE have been observed for
different varieties of sorghum and rice [
]. However, in
multi-mycotoxin analysis, the lot-to-lot variation is often
neglected during method validation. According to the
US Food and Drug Administration (US-FDA)
guidelines, the lot-to-lot variation needs to be evaluated from
replicates of at least five different lots of the same
] for bioanalytical assays and three different
lots of the same matrix for mycotoxin assays . In
the relevant regulation on mycotoxin determination of
the European Union (EU), the matrix-mismatch
phenomenon is considered a potential component of
uncertainty, but it is not required to validate the assay based
on different lots of a matrix [
We hypothesise that neglecting the lot-to-lot variation
during method validation can lead to an underestimation
of Ur. The objectives of this study were to (i) apply a
practical procedure for the realistic estimation of Ur for
measurement results obtained by an LC-MS-based
multimycotoxin assay and to (ii) determine the contribution of
the lot-to-lot variation to Ur. This study presents the first
calculation of Ur for the determination of mycotoxins in
food and feed considering the lot-to-lot variation, and
differs significantly from studies which evaluated Ur under
repeatability conditions of a single lot of a matrix.
Materials and methods
Chemicals and reagents LC gradient grade methanol and
acetonitrile as well as MS grade ammonium acetate and
glacial acetic acid (p.a.) were purchased from
SigmaAldrich (Vienna, Austria). A Purelab Ultra system (ELGA
LabWater, Celle, Germany) was used for further
purification of reverse osmosis water.
Standards were obtained as gifts either from various
research groups or from commercial sources. Stock solutions
of each analyte were prepared. Combined working solutions
were prepared by mixing the stock solutions of the
corresponding analytes for easier handling and were stored at −
20 °C. The multi-analyte standard solution, which contained
66 compounds, was freshly prepared prior to spiking
experiments by mixing of the combined working solutions.
LC-ESI-MS/MS The measurement method used in this study
was described in detail elsewhere [
]. Briefly, the analysis
was carried out with a QTrap 5500 MS/MS system (Sciex,
Foster City, CA, USA) equipped with a TurboV electrospray
ionisation (ESI) source and a 1290 series UHPLC system
( A g i l e n t Te c h n o l o g i e s , Wa l d b r o n n , G e r m a n y ) .
Chromatographic separation was performed at 25 °C on a
Gemini C18-column, 150 × 4.6 mm i.d., 5 μm particle size,
equipped with a C18 security guard cartridge, 4 × 3 mm i.d.
(both Phenomenex, Torrance, CA, USA). Elution was carried
out in binary gradient mode with a flow rate of 1000 μl/min.
Both mobile phases contained 5 mM ammonium acetate and
were composed of methanol/water/acetic acid 10:89:1 (v/v/v;
eluent A) and 97:2:1 (v/v/v; eluent B), respectively. After an
initial time of 2 min at 100% A, the proportion of B was
increased linearly to 50% within 3 min. Further linear increase
of B to 100% within 9 min was followed by a hold time of
4 min at 100% B and 2.5 min column re-equilibration at 100%
A. ESI-MS/MS was performed in scheduled multiple reaction
monitoring (sMRM) mode both in positive and in negative
polarity in two successive chromatographic runs respectively.
Confirmation of positive analyte identification is obtained by
the acquisition of two sMRM transitions per analyte. The
retention time, lowest calibration level, and the spiking
concentration for every analyte is given in Table S1 of the Electronic
Supplementary Material (ESM).
Calibration and quantitation External neat solvent
calibration was prepared by dilution of appropriate amounts of
multi-analyte standard solution with acetonitrile/water/
acetic acid (49.5/49.5/1, v/v/v) to obtain relative
concentration levels 1:3:10:30:100:300. To check the linearity of
the response, linear, 1/x weighted calibration curves were
constructed for the neat solvent standards. The
construction of calibration curves and peak integration were
performed using MultiQuant™2.0.2 software (Sciex, Foster
City, CA, USA). Further data evaluation, such as the
calculation of the method performance parameter and the
associated uncertainties, was carried out in Microsoft
Excel 2013 and RStudio Version 1.0.143.
Representative set of analytes The described method covers
550 analytes, for which standards with certified purity are
available. As representative analytes, 66 fungal metabolites,
including all regulated mycotoxins, were chosen. The analytes
were evenly distributed over the whole chromatogram
(Table S1, ESM) covering both ESI polarities and differences
in physiochemical properties (e.g. hydrophobicity, acidity,
functional groups). As representative matrices, two matrices
from different commodity groups according to [
chosen. Figs were taken from the commodity group dried
fruits, which are characterised by a high sugar and low water
content. Maize was selected as representative matrix of cereal
grains, a matrix with high starch and protein and low in water
and fat content.
Spiked samples An appropriate amount of multi-analyte
standard solution (166 μL) was added to 0.25 g of
homogenised blank sample. The samples were placed in
darkness to avoid analyte degradation (e.g. ergot
alkaloids) and stored overnight at room temperature to allow
the evaporation of the solvent and to establish an
equilibration between analytes and matrix. After this period, the
samples were extracted with 1 mL of extraction solvent
(acetonitrile/water/acetic acid 79:20:1, v/v/v) and shaken
using a rotary shaker (GFL 3017, GFL; Burgwedel,
Germany) for 90 min in a horizontal position. In the
routine analysis of naturally contaminated material, a higher
amount of sample (5 g) is extracted with 20 mL of
extraction solvent. As demonstrated before [
4, 5, 10, 30
is sufficient to use only a small amount of blank sample
for spiking experiments, which allows for the economical
use of mycotoxin standards. The supernatant (300 μL)
was transferred into HPLC vials and diluted with the same
volume of dilution solvent (acetonitrile/water/acetic acid
20:79:1, v/v/v). After appropriate mixing, 5 μL of the
diluted extract was injected into the LC-MS/MS system
without further pre-treatment.
Spiked extracts Five grams of sample was extracted with
20 mL of extraction solvent. The supernatant was
fortified with an appropriate amount of multi-analyte
standard, diluted and injected into the LC-MS/MS system
as described above.
Calculation of RE, SSE and RA The performance characteristics
RA, RE and SSE were calculated according to:
RE ¼ areaspiked extract
The RA, RE and SSE values were calculated as the average
of two injections from the same vial.
Samples In order to maximise the differences between the
individual lots of a matrix, seven lots were collected. For
maize, seven lots of different origin and variety (where
specified) were obtained as a ground powder from other research
groups (Table 1).
Figs were bought in local supermarkets and originated,
where specified, from Turkey (Table 2). They differed in
specification; however, the variety was not specified. To
homogenise the samples, figs where cut in small pieces, frozen in liquid
nitrogen and ground using an Osterizer blender (Sunbeam
Oster Household Products, Fort Lauderdale, FL, USA).
Description of the seven lots of maize used to determine the lot-to-lot variation
The following abbreviations (Table 3) were used in the
manuscript and were based on the abbreviations used in [
Method bias The method bias was estimated by spiking a
known amount of the analyte to the homogenised
sample aliquots prior to extraction and measuring sample
and standard. The method bias was expressed as RA.
The relative standard uncertainty of RA (ur, RA) was
calculated as the relative standard deviation (RSD) of
the individual RA values. ur, RA was evaluated from
seven aliquots of the same lot (ur;RAsingle lot ) and one
aliquot of seven different lots (ur;RAlot−to−lot ) under
repeatability conditions (within one analytical sequence).
Precision The within-laboratory reproducibility, also
referred to as intermediate precision, was used as an
estimate for the random variation of the results generated
by applying the method. The relative standard
uncertainty of the within-laboratory reproducibility (ur, wL) was
determined as the RSD of replicate analysis (seven
replicates) of the RA value of spiked samples of lot 1. To
cover the whole variation that can occur during the
sample preparation and measurement, spiked replicates
were prepared from aliquots of the same lot directly
before the analysis. For the within-laboratory
characterisation of reproducibility, the following conditions were
used: the same measurement procedure, laboratory and
equipment, different operators and repetitions over a
long time interval (7 weeks).
Statement of the result The measurand is defined as the mass
fraction w (e.g. μg/kg) and its corresponding expanded
measurement uncertainty (U) of a mycotoxin in a certain
commodity. The range ±U around w describes the concentration range
where the true concentration of the analyte can be found with a
probability of approximately 95% (k = 2).
w was calculated by comparing the peak area of the sample to
the peak area of the standard in neat solvent and the correction
w ¼ areastandard
U was calculated from Ur:
U ¼ w
Ur was calculated from the relative combined uncertainty (ur,
c) and the coverage factor (k) [
U r ¼ k
ur;c with k ¼ 2
ur,c was calculated from estimates of precision and trueness
ur2;wL þ ur2;RA
As individual components of ur,c combine as squares,
small contributions can be neglected. The uncertainty
associated with the mass concentration of the standard and
the LOQ was considered negligible, since commercially
available standards with certified purity were spiked at
concentration levels far above the LOQ. The contribution
of sampling to the uncertainty was not considered since,
for mycotoxins, compliance with the maximum limits is
established on the basis of the levels determined in the
laboratory sample [
]. As Ur was calculated on one
concentration level, the assumption that ur,RA and ur,wL do not
change significantly over the calibration range was made.
Determination of the contribution
of the lot-to-lot variation to Ur
In order to estimate the contribution of the lot-to-lot variation
to Ur, two different estimates of Ur were calculated. Ur, single lot
was calculated based on seven replicates of a single lot of a
matrix and does not account for the lot-to-lot variation. Ur, lot −
to − lot was calculated based on seven different lots of a matrix
and accounts for the lot-to-lot variation.
U r;single lot ¼ 2 qffiuffiffiffir2ffi;ffiwffiffiLffiffiffiþffiffiffiffiffiuffiffir2ffiffi;ffiRffiffiAffiffisffiinffigffiffileffiffilffiot
U r;lot‐to‐lot ¼ 2
ur2;wL þ ur2;RAlot‐to‐lot
Any increase of Ur, lot − to − lot compared to Ur, single lot was
ascribed to the lot-to-lot variation.
Estimation of Ur from proficiency test results
To establish Ur based on inter-laboratory validation data, the
results our laboratory has achieved in proficiency test (PT)
schemes, provided by BIPEA, were evaluated. During the last
4 years, 594 results were submitted in 60 PT rounds for
aflatoxins, fumonisins, T-2 toxin, HT-2 toxin, deoxynivalenol,
nivalenol, ochratoxin and zearalenone in various food and
feed matrices. To calculate Ur based on PT results (Ur, PT),
the relative bias value ( biasPT) of the result submitted by our
laboratory (x) to the assigned value of the PT (X) was
calculated according to:
Ur, PT may be estimated from ur,wL, the root mean square of
the biasPT values (RMSbias,PT) and the relative standard
uncertainty of X (ur,X) as described in [
U ¼ k
ur2;wL þ RMS2bias;PT þ ur2;X
As the biasPT values were taken from 60 PT rounds carried
out over 4 years, we reasoned that ur,wL was already included
in RMS2bias;PT . To avoid double counting of an uncertainty
component, ur,wL was not considered a separate contribution
in the calculation of Ur, PT. The average ur,X was 6% and was
negligible compared to RMSbias,PT. Therefore, Ur, PT was
estimated according to:
U PT ¼ k
with k ¼ 2
Contribution of the lot-to-lot variation to Ur
In multi-mycotoxin analysis, an uncertainty statement of the
measurement result is often missing. Therefore, we employed
a practical procedure to estimate Ur for 66 analytes in figs and
maize. Multi-mycotoxin methods are commonly validated
based on a single lot of a matrix. Neglecting the lot-to-lot
variation may lead to an overoptimistic estimate of Ur. For
the first time, Ur was evaluated based on different lots of a
matrix and thus accounts for the lot-to-lot variation.
Ur was calculated from the relative combined uncertainty
(ur, c). ur, c was calculated from estimates of precision (Ur,wL)
and trueness (Ur,RA) (Fig. 1). For the visualisation of the
contribution of the lot-to-lot variation to the individual Ur
components, the median value of the representative analytes,
represented as blue points, was indicated in red. In general, the
trend that was observed by comparing the median values of
the 66 analytes could also be observed for the individual
analytes. The values for the individual analytes are listed in
Tables S2 and S3 of the ESM. The median value was
only used for illustrative purposes and was not used
for the estimation of Ur.
When ur,RA was calculated from replicates of a single lot of
a matrix, its contribution to ur, c was negligible (Fig. 1, top).
Therefore, ur,wL could be used to estimate ur, if the lot-to-lot
variation does not need to be taken into account. When the
lotto-lot variation was taken into account, ur,RA contributed
significantly to ur, c (Fig. 1, bottom) which led to an increase in
Ur. To estimate the contribution of the lot-to-lot variation to
Ur, Ur evaluated based on seven different lots of a matrix was
compared to Ur evaluated based on one lot of a matrix (Fig. 2).
When the lot-to-lot variation was not considered, as is the
common practice, the median Ur of the 66 analytes was 24%
in figs and 21% in maize. When the lot-to-lot variation was
considered, the median Ur of the 66 analytes increased to 31%
in both figs and maize. This corresponds to a relative increase
of the median Ur of 30% in figs and 50% in maize.
Contribution of the lot-to-lot variation to the uncertainty of the signal suppression/enhancement and analyte recovery
Different RA values for individual lots of a matrix contributed
to ur,RA and thus ur. The RA value shows the combined effect
of RE and SSE. In order to reveal which of the two processes,
differences in RE or SSE, contributed to ur,RA, we have
determined the contribution of the lot-to-lot variation to the relative
standard uncertainty of RE (ur,RE) and SSE (ur,SSE) (Fig. 3).
Details can be found in Tables S4 and S5 of the ESM.
ur,RA single lot was considered to be a measure of the
repeatability of the recovery process (spiking, extraction, dilution)
and measurement. Therefore, any increase of ur,RA lot–to–lot
compared to ur,RA single lot would be caused by the lot-to-lot variation.
Due to the lot-to-lot variation, the median ur,RA of the 66
analytes increased from 3 to 9% in figs and 4 to 10% in maize.
As RA shows the combined effect of RE and SSE, ur,RA can be
seen as a combination of ur,RE and ur,SSE. In figs, the increase of
ur,RA lot–to–lot compared to ur,RA single lot was due to an increase of
ur,RE lot–to–lot which was the result of differences in analyte
recovery. As ur,SSE lot–to–lot was similar to ur,RA single lot, relative
matrix effects did not contribute to ur,SSE and ur,RA, respectively.
For most compounds in maize, ur,RE lot–to–lot was similar to ur,RA
single lot. For most analytes, differences in analyte recovery were
not contributing to ur,RE and ur,RA. The increase of ur,RA lot–to–lot
compared to ur,RA single lot was due to an increase of ur,SSE lot–to–
lot due to differences in SSE. Therefore, for most of the analytes,
relative matrix effects contributed to ur,SSE and ur,RA,
respectively. In summary, the major contributions of the lot-to-lot
variation to ur,RA were differences in analyte recovery in figs and
relative matrix effects in maize.
Measurement uncertainty for the determination of the regulated mycotoxins in figs and maize
To visualise the contribution of lot-to-lot variation to Ur for
the regulated mycotoxins in figs and maize, ur,single lot was
compared to ur,lot–to–lot (Fig. 4).
The trend which was observed by the comparison of the
median values of the representative sets of analytes was also
observed for the individual regulated mycotoxins in both
matrices. Ur increased due to the lot-to-lot variation. For the
aflatoxins in maize, the contribution was especially high and
could be ascribed to relative matrix effects.
Estimation of the measurement uncertainty based on the results of PT schemes
The spread of the biasPT values, which describe the deviation
of the submitted result to the assigned value, was used to
estimate Ur, PT. Upon visual inspection, the biasPT values were
unbiased and normally distributed (Fig. 5).
For unbiased results, the RMS equals the standard
deviation of the biasPT values which was determined with
29% and corresponds to Ur, PT = 58%. From 2013 to
2017, 95% of the submitted concentration values were
within ± 58% of the assigned concentration.
Fig. 5 Histogram of the relative bias values of the results submitted by
our laboratory to the assigned value of the proficiency test scheme
achieved from 2013 to 2017 for aflatoxins, fumonisins, T2, HT2,
deoxynivalenol, nivalenol, ochratoxin and zearalenone in various food
and feed matrices. A normal distribution curve with mean μ and
standard deviation σ is fitted to the histogram in red
Choosing an adequate procedure for the calculation of the uncertainty budget of a multi-mycotoxin method
Ur for the multi-mycotoxin method was calculated from
intra-laboratory validation data for 66 analytes in figs and
maize and from the participation in PT schemes for the
regulated mycotoxins (except patulin) and nivalenol, T-2
toxin and HT-2 toxin in various food and feed matrices.
The following paragraph lists common approaches for the
calculation of Ur and aims to discuss their applicability to
calculate Ur for a multi-mycotoxin method.
The Bbottom-up^ approach: The measurement uncertainty
can be calculated based on the identification, quantification
and combination of all individual components of
measurement uncertainty (i.e. bottom-up approach). A rigorous
bottom-up approach, as it is proposed in the BGuide to the
Expression of Uncertainty in Measurement^ [
considered impractical for multi-analyte methods, covering several
hundred substances, as the calculation of the individual
uncertainty components for each analyte-matrix combination is too
]. Furthermore, effects which were not
considered as potential error sources may lead to an
underestimation of the measurement uncertainty.
The Btop-down^ approach: By a top-down approach, as
proposed by ISO\TS 21748 [
], the performance of a
welldefined measurement method is evaluated by an
interlaboratory comparison (ILC) study. The reproducibility
standard deviation (RSDR) is then used to calculate Ur. ILC studies
are only available for methods covering a limited number of
analytes in a few matrices [
]. No inter-laboratory
comparison studies or certified reference materials (CRM) are
available that cover the scope of the described multi-mycotoxin
assay. Incomplete variation of certain influences (e.g. lot-to-lot
variation) in the highly homogenous CRMs may lead to an
underestimation of the measurement uncertainty.
Proficiency tests: Ur may also be estimated from
results achieved during the participation in PT schemes. In
contrast to ILC studies, laboratories can employ their
own test method. Although in multi-residue analysis
different methods (GC-MS and LC-MS) were used, it has
been verified that the performance of the methods in the
PTs does not depend strongly on the extraction method
or the detection techniques [
]. Therefore, the RSD of
the PT was used to estimate Ur of the individual
methods which successfully participated in PT schemes.
The mean RSD of EU-based PT studies for pesticides in
fruit and vegetables has ranged at approximately 25%
]. Therefore the EU member states have adopted
a default value of Ur = 50% for pesticide residues in
food consignment entering the EU .
The performance of the presented method has been
evaluated by the participation in PT schemes provided by BIPEA.
The methods used in PTs for mycotoxins included LC-MS,
GC-MS, LC-UV, LC-FLD and ELISA. As it is unsure
whether the individual methods perform similar in PTs, the RSD
value of the PT scheme cannot be used for the estimation of
Ur. Therefore, Ur, PT was calculated from the spread of
submitted results of the laboratory to the assigned values. As the
raw material and the concentration of the analyte change for
every PT round, the influence of the lot-to-lot variation and
different concentration ranges were considered as potential
uncertainty components. Ur, PT for regulated mycotoxins in
food and feed was estimated with 58% similar to the average
Ur of about 50% achieved in pesticide analysis [
]. Ur, PT
might overestimate Ur since the criteria for the homogeneity
of the samples are not as strict as for CRMs.
Intra-laboratory validation: Intra-laboratory validation can
be carried out by estimating the uncertainty of precision and
trueness based on measurements of spiked samples [
]. Ur was calculated for a representative set of analyte/
matrix combinations and not for each individual analyte/
matrix combination as recommended for multi-residue
]. We considered Ur, determined for a representative set
of analyte and matrices, a valid estimator for Ur for all analytes
included in the assay. Due to the limited availability of CRMs
of mycotoxins in food and feed, we evaluated ur,wL and ur,RA
using spiked samples. A similar approach has been used
previously for multi-mycotoxin methods in sorghum and feed
where Ur was calculated from ur,wL (measurements were
carried out at 3 different days) and ur,RA (from replicates of the
same lot of a matrix) [
]. Our approach differed in
calculating ur,RA from seven different lots of the matrix and thus
accounting for the lot-to-lot variation. Furthermore, we
calculated ur,wL from one replicate at 7 different days distributed
over a longer time period (7 weeks). This should provide a
more realistic measure of ur,wL and ur,RA.
Method performance in regard to the official EC decisions and regulations on mycotoxin determination
Performance criteria for compliance testing according to
European Commission Decision EC 1881/2006 [
] are set in
European Commission Decision EC 401/2006 [
which cannot be validated by ILC studies, like the described
multi-mycotoxin assay, may be validated by in-house studies.
For the analytes under investigation, the achieved ur, c needs to
comply with the maximum standard uncertainty (k = 1) defined
by the fitness-for-purpose function (uf). The maximum relative
standard uncertainty ur, f converges to approximately 20% at
concentration levels below 500 μg/kg, which corresponds to a
maximum Ur of 40%. As the EU legislation requires that Ur is
evaluated based on a single lot, compliance to this criterion is
achieved by Ur, single lot < 40%. All regulated mycotoxin-matrix
combinations showed acceptable Ur values. Therefore, we see
the method suitable for compliance testing according to
European Commission Decision EC 1881/2006.
Fit-for-purpose U for a LC-MS-based multi-mycotoxin method
For LC-MS-based multi-mycotoxin determination, we propose
one fit-for-purpose Ur for all analyte/matrix combinations
independent of the concentration level of the analyte. In our view, a
fit-for-purpose Ur should provide maximum utility by
minimising the effort in validation and at the same time
providing a realistic estimation for Ur. Similar to Stroka and Maragos
], we favour a fixed over a concentration-dependent Ur as all
analytes were subjected to the same measurement principle.
During intra-laboratory validation, we found that the main
contribution factors to Ur were the within-laboratory precision and
the lot-to-lot variation, which we consider to be independent of
the concentration of the analyte. For the described
multimycotoxin assay, we propose a fit-for-purpose Ur of 50%. It
applies to the measurement procedure (i.e. extraction, dilution
and mass spectrometric determination) for analytes where a
standard with certified purity was available and does not
include uncertainty components arising from sampling or
concentration levels close to the limit of quantitation. Intra-laboratory
validation showed that respectively 90 and 80% of the 66
representative analytes in figs and maize had an associated Ur, lot −
to − lot < 50%. Ur derived from long-term participation in PT
schemes for regulated mycotoxins in various food and feed
matrices was estimated with 58%. The proposed
fit-forpurpose Ur of 50% is also in line with the default value of
Ur = 50% that was set for LC-MS-based multi-residue analysis,
which depends on the same detection principle and covers
analytes in the same concentration range as LC-MS-based
multi-mycotoxin analysis. Clearly, this approach cannot replace
method validation. The latter is necessary to prove that the
method fulfils other requirements (e.g. recovery, matrix effects),
pointed out in EC 401/2006. Only if those requirements are
passed, a default Ur of 50% might be assumed.
This study presents the first calculation of the relative
expanded measurement uncertainty (Ur) of measurement results
obtained by an LC-MS-based multi-mycotoxin assay in food and
feed matrices accounting for the lot-to-lot variation. We have
shown that neglecting the lot-to-lot variation during method
validation can lead to an underestimation of Ur. We applied a
straightforward procedure for the estimation of Ur and
determined the contribution of the lot-to-lot variation to Ur. Ur was
estimated based on intra-laboratory validation data for a
representative set of analytes from the uncertainty of the
within-laboratory precision, ur,wL, (seven replicates of the
same lot of a matrix distributed over a time frame of seven
weeks) and the uncertainty of the method bias, ur,RA, (seven
replicates measured under repeatability conditions). The
contribution of the lot-to-lot variation to Ur was estimated by
taking the replicates for the determination of ur,RA from seven
different lots instead of single lot a matrix as is the common
practice. The lot-to-lot variation contributed to ur,RA which
resulted in an increase of Ur for most analytes in figs and
maize. The major contributions of the lot-to-lot variation to
ur,RA were differences in analyte recovery in figs and relative
matrix effects in maize. Ur was also estimated from the
longterm participation in PT schemes with 58%. Accounting for
the lot-to-lot variation leads to a more realistic estimate of Ur
and should be required by the official guidelines on
mycotoxin determination. Provided proper validation, a fit-for-purpose
Ur of 50% was proposed for measurement results obtained by
a LC-MS/MS-based multi-mycotoxin assay, independent of
the concentration of the analytes.
Funding information Open access funding provided by University of
Natural Resources and Life Sciences Vienna (BOKU). This project has
received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No. 678012 for the
Compliance with ethical standards
Conflict of interests The authors declare that they have no conflict of
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1. Zain ME . Impact of mycotoxins on humans and animals . J Saudi Chem Soc . 2011 ; 15 : 129 - 44 . https://doi.org/10.1016/j. jscs. 2010 . 06 .006.
2. European Commission . Commission Regulation (EC) No 1881 / 2006 of 19 December 2006 setting maximum levels for certain contaminants in foodstuffs . Off. J Eur Union L. 2006 ; 364 : 5 - 24 .
3. Van Egmond HP , Schothorst RC , Jonker MA . Regulations relating to mycotoxins in food: perspectives in a global and European context . Anal Bioanal Chem . 2007 ; 389 : 147 - 57 . https://doi.org/10. 1007/s00216-007-1317-9.
4. Sulyok M , Berthiller F , Krska R , Schuhmacher R . Development and validation of a liquid chromatography/ tandem mass spectrometric method for the determination of 39 mycotoxins in wheat and maize . Rapid Commun Mass Spectrom . 2006 ; 20 : 2649 - 59 . https:// doi.org/10.1002/rcm.2640.
5. Malachová A , Sulyok M , Beltrán E , Berthiller F , Krska R. O p t i m i z a t i o n a n d v a l i d a t i o n o f a q u a n t i t a t i v e l i q u i d chromatography-tandem mass spectrometric method covering 295 bacterial and fungal metabolites including all regulated mycotoxins in four model food matrices . J Chromatogr A . 2014 ; 1362 : 145 - 56 . https://doi.org/10.1016/j.chroma. 2014 . 08 .037.
6. Habler K , Gotthardt M , Schüler J , Rychlik M. Multi-mycotoxin stable isotope dilution LC-MS/MS method for Fusarium toxins in beer . Food Chem . 2017 ; 218 : 447 - 54 . https://doi.org/10.1016/j. foodchem. 2016 . 09 .100.
7. Di Mavungu JD , Monbaliu S , Scippo M-L , Maghuin-Rogister G , Schneider Y-J , Larondelle Y , et al. LC-MS/MS multi-analyte method for mycotoxin determination in food supplements . Food Addit Contam Part A . 2009 ; 26 : 885 - 95 . https://doi.org/10.1080/ 02652030902774649.
8. Mol HGJ , Plaza-Bolaños P , Zomer P , Rijk TC De , Stolker A a. M, Mulder PPJ ( 2008 ) Toward a generic extraction method for simultaneous determination of pesticides, mycotoxins, plant toxis, and veterinary drugs in feed and foof matrix Anal Chem 80 : 9450 - 9459 . doi: https://doi.org/10.1021/ac801557f.
9. Rasmussen RR , Storm IMLD , Rasmussen PH , Smedsgaard J , Nielsen KF Multi-mycotoxin analysis of maize silage by LC-MS/ MS . doi: https://doi.org/10.1007/s00216-010-3545-7.
10. Sulyok M , Krska R , Schuhmacher R . Application of a liquid chromatography-tandem mass spectrometric method to multimycotoxin determination in raw cereals and evaluation of matrix effects . Food Addit Contam . 2007 ; 24 : 1184 - 95 . https://doi.org/10. 1080/02652030701510004.
11. Burns DT , Danzer K , Townshend A . Use of the terms Brecovery^ and Bapparent recovery^ in analytical procedures . Pure Appl Chem . 2002 ; 74 : 2201 - 5 . https://doi.org/10.1351/pac200274112201.
12. In Metrology JCFG. Evaluation of measurement data-guide to the expression of uncertainty in measurement . JCGM . 2008 ; 100
13. Magnusson , B ; Örnemark U ( 2014 ) Eurachem guide: the fitness for purpose of analytical methods-a laboratory guide to method validation and related topics .
14. Eurolab ( 2006 ) EUROLAB Technical Report 1/2006 Guide to the evaluation of measurement uncertainty for quantitative test results .
15. Codex Alimentarius Commission ( 2006 ) Guidelines on estimation of uncertainty of results (CAC/GL 59- 2006 ).
16. ISO/TS21748 ( 2004 ) Guidance for the use of repeatability, reproducibility and trueness estimates in measurement uncertainty estimation .
1 7 . H ä s s e l b a r t h W. B A M - L e i t f a d e n z u r E r m i t t l u n g v o n Messunsicherheiten bei quantitativen Prüfergebnissen: 1 . Fassung vom. 2004;(11. März 2004 )
18. European Commission ( 2003 ) COMMISSION DIRECTIVE 2003 /78/EC of 11 August 2003 laying down the sampling methods and the methods of analysis for the official control of the levels of patulin in foodstuffs . Off J Eur Union L203 : 40 - 44 .
19. European Commission ( 2004 ) COMMISSION DIRECTIVE 2004 /43/EC of 13 April 2004 amending Directive 98 /53/EC and Directive 2002 / 26/EC as regards sampling methods and methods of analysis for the official control of the levels of aflatoxin and ochratoxin A in food for infants and young children . Off J Eur Union L113 : 14 - 16 .
20. Thompson M. Towards a unified model of errors in analytical measurement . Analyst . 2000 ; 125 : 2020 - 5 . https://doi.org/10.1039/ b006376m.
21. European Commission ( 2004 ) Report of the relationship between analytical results, measurement uncertainty, recovery factors and the provision of EU food and feed legislation . https://ec.europa. eu/food/sites/food/files/safety/docs/cs_contaminants_sampling _ analysis-report_2004_en.pdf. Accessed 10 Jan 2018 .
22. Matuszewski BK , Constanzer ML , Chavez-Eng CM . Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS . Anal Chem. 2003 ; 75 : 3019 - 30 . https://doi.org/10.1021/ac020361s.
23. Matuszewski BK . Standard line slopes as a measure of a relative matrix effect in quantitative HPLC-MS bioanalysis . J Chromatogr B Anal Technol Biomed Life Sci . 2006 ; 830 : 293 - 300 . https://doi. org/10.1016/j.jchromb. 2005 . 11 .009.
24. Njumbe Ediage E , Van Poucke C , De Saeger S. A multi-analyte LCMS/MS method for the analysis of 23 mycotoxins in different sorghum varieties: the forgotten sample matrix . Food Chem . 2015 ; 177 : 397 - 404 . https://doi.org/10.1016/j.foodchem. 2015 . 01 .060.
25. Food and Drud Administration (FDA). Guidance for industry . In: Bioanalytical method validation; 2001 .
26. Viswanathan CT , Bansal S , Booth B , DeStefano AJ , Rose MJ , Sailstad J , et al. Quantitative bioanalytical methods validation and implementation: best practices for chromatographic and ligand binding assays . Pharm Res . 2007 ; 24 : 1962 - 73 . https://doi.org/10. 1007/s11095-007-9291-7.
27. Food and Drud Administration (FDA). Guidelines for the validation of chemical methods for the FDA FVM program . In: 2nd edition; 2015 .
28. European Commission ( 2014 ) Commission Regulation (EC) No 401/2006 of 23 February 2006 laying down the methods of sampling and analysis for the official control of the levels of mycotoxins in foodstuffs . L 70/12 and L 142/29. Off. J. Eur . Union.
29. European Commission ( 2015 ) Guidance document on analytical quality control and method validation procedures for pesticides residues analysis in food and feed (SANTE/11945/ 2015 ).
30. Sulyok M , Beed F , Boni S , Abass A , Mukunzi A , Krska R . Quantitation of multiple mycotoxins and cyanogenic glucosides in cassava samples from Tanzania and Rwanda by an LC-MS/MS-based multi-toxin method . Food Addit Contam Part A . 2015 ; 32 : 488 - 502 . https://doi.org/10.1080/19440049. 2014 . 975752 .
31. Magnusson B , Teemu N , Havard H , Krysell M ( 2012 ) Calculation of measurement uncertainty in environmental laboratories .
32. Valverde A , Aguilera A , Valvedere-Monterreal A . Practical and valid guidelines for realistic estimation of measurement uncertainty in multi-residue analysis of pesticides . Food Control . 2017 ; 71 : 1 - 9 . https://doi.org/10.1016/j.foodcont. 2016 . 06 .017.
33. Breidbach A. A greener, quick and comprehensive extraction approach for LC-MS of multiple mycotoxins . Toxins (Basel) . 2017 ; 9 : 1 - 14 . https://doi.org/10.3390/toxins9030091.
34. Breidbach A , Bouten K , Kroeger-Negiota K , Stroka J , Ulberth F ( 2013 ) LC-MS based method of analysis for the simultaneous determination of four mycotoxins in cereals and feed . doi: https://doi. org/10.2787/77845.
35. Medina-Pastor P , Valverde A , Pihlström T , Masselter S , Gamon M , Mezcua M , et al. Comparative study of the main top-down approaches for the estimation of measurement uncertainty in multiresidue analysis of pesticides in fruits and vegetables . J Agric Food Chem . 2011 ; 59 : 7609 - 19 . https://doi.org/10.1021/ jf104060h.
36. Alder L , Korth W , Patey AL , van der Schee HA , Schoeneweiss S. Estimation of measurement uncertainty in pesticide residue analysis . J AOAC Int . 2001 ; 84 : 1569 - 78 .
37. Monbaliu S , Van Poucke C , Detavernier C , Dumoulin F , Van De Velde M , Schoeters E , et al. Occurrence of mycotoxins in feed as analyzed by a multi-mycotoxin LC-MS/MS method . J Agric Food Chem . 2010 ; 58 : 66 - 71 . https://doi.org/10.1021/jf903859z.
38. Stroka J , Maragos CM ( 2016 ) Challenges in the analysis of multiple mycotoxins . 9 : 1 - 15 . doi: https://doi.org/10.3920/WMJ2016. 2038 .