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Experimental approaches for the estimation of uncertainty in analysis of trace inorganic contaminants in foodstuffs by ICP-MS
Food Chemistry 141 (2013) 604–611
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Analytical Methods
Experimental approaches for the estimation of uncertainty in analysis of trace inorganic contaminants in foodstuffs by ICP-MS Inês Coelho a, Sandra Gueifão a, Ana Sofia Matos b, Mark Roe c, Isabel Castanheira a,⇑ a
Department of Food Safety and Nutrition, National Institute of Health Dr. Ricardo Jorge (INSA), Av. Padre Cruz, 1649-016 Lisbon, Portugal UNIDEMI, Departamento de Engenharia Mecânica e Industrial, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal c Institute of Food Research (IFR), Norwich NR4 7UA, United Kingdom b
a r t i c l e
i n f o
Article history: Received 22 March 2012 Received in revised form 9 November 2012 Accepted 13 March 2013 Available online 21 March 2013 Keywords: Uncertainty evaluation Experimental approaches Food consumption Comparability of data Food chemical contaminants
a b s t r a c t Total Diet Studies to estimate dietary exposure to food contaminants need to evaluate laboratory measurements data variance. In this process it is critical that data from analytical methods are reliable to correctly scrutinize and compare values over time and between countries. In Europe it is widely recognized that the evaluation of measurement uncertainty is an important parameter when assessing the sources of analytical data variability. Two approaches are considered to estimate uncertainty in analytical measurement. Arsenic, Lead, Chromium and Cadmium, content in several food matrix determined by Inductively Coupled Mass Spectrometry (ICP-MS) microwave digestion assisted, are used as examples. The aim of the present research work is to compare both approaches accepted by Eurolab and GUM: Mathematical modeling to assess uncertainty components based on a classical model (bottom up) and an empirical method (top down), based on either experimental data obtained from a single laboratory validation data or interlaboratory data from Proficiency Testing schemes. Relative expanded uncertainty calculated by both approaches agree when U (%) <20%. These values are concordant with RSDR reported in collaborative studies of EN 15763 (2009), which were assumed as target uncertainty. The top down approach described is simple and easy to use when compared with the mathematical modeling approach providing considerable benefits to those who assess data produced by several laboratories. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Total Diet Studies (TDS) are valuable tools to monitor risk assessment for food contaminants population exposure. Since 2005 three complementary TDS methods are recommended by WHO: (a) market basket studies consisting of representative key foods (raw and processed); (b) analysis of duplicate food portions in representative meals and (c) analysis of individual food items (EFSA, 2011; Egan, Tao, Pennington, & Bolger, 2002; FDA, 2010; Leblanc et al., 2005; Pennington, 2004; Sirot et al., 2009). The market basket study is used in many countries to assess pesticide residues, industrial chemicals, radionuclides and heavy metals dietary intakes. The data from analysis combined with information from food consumption surveys allows estimation of dietary exposure to food toxicants at an individual level as well as at population group level. TDS is also used to estimate toxicological thresholds for risk assessment (Egan et al., 2002; Sirot et al., 2009; Thomson, Vannoort, & Haslemore, 2008). Errors associated with analytical procedure and sampling strategies contribute to overall error associated with measurement of dietary exposure (Tsukakoshi, 2011). Additional factors may come ⇑ Corresponding author. Tel.: +351 21 751 92 88; fax: +351 21 750 81 53. E-mail address: [email protected] (I. Castanheira). 0308-8146/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2013.03.040
into play reinforcing differences in exposure assessment results between countries, when food consumption habits, levels of food contamination and differing procedures for data generation are considered. In Europe several studies pointed out the need of developing an harmonization approach to contribute to reduction of artefactual differences in the process of calculating and comparing dietary contaminant exposure based on data from different sources (EFSA, 2011). High quality scientific data is essential to enable European legislation to establish maximum levels of contaminants in foods supported by robust scientific data (Gibney & Sandström, 2001). The quality framework includes a methodology to evaluate quality of data produced by laboratories (Millour et al., 2011). In several countries, complementary methods are used to monitor the intake of nutrients against Recommended Daily Intake (RDI). In these countries the quality system is based on existing data quality already in place for nutrients contained in foods and published in national Food Composition Data Banks (Sirot et al., 2009; Thomson et al., 2008). The Quality Systems include requirements improving and standardizing the quality of factors such as food description, component identification, sampling plan, number of samples, sample handling, analytical method and laboratory performance. Assessment of analytical method and laboratory performance is based on the use of standard analytical methods
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(e.g. CEN, AOAC, ISO), certified reference materials and participation in proficiency testing (PT) schemes. This methodology encompasses a set of metrological principles and practices to maximize the reliability of the results during the study and the compatibility of data over time and across countries. Therefore Metrology (the science of measurement) plays a crucial role to guarantee the foundation of the measurement system which should act as a bridge across analytical food measurements and mathematical approaches when evaluating population dietary exposure to food chemical contaminants. Addressing these issues is one of the aims of the recently created committee of IMEKO Metrology in Food and Nutrition (IMEKO, 2012; Iyengar, 2007). One of the committee goals is further enhancing metrological best practices, including the use of traceability chains, use of appropriate reference materials and realistic approaches to estimate the measurement uncertainty associated with analytical data. This is crucial to provide representative and accurate value ranges for the estimation of exposure thresholds for toxic elements as well as to identify specific improvements in the measurement procedure. The estimation of measurement uncertainty includes at least two sources: sampling and analytical procedure (Castanheira et al., 2009). These contributions can be estimated by modeling individual sources of uncertainty that are evaluated and combined (sum of square roots of their variances) which is a ‘‘bottom-up’’ approach. A practical estimation taking into account the information from daily quality control measurements has now been adopted in many cases and is known as the ‘‘top-down’’ approach (Dabalus et al., 2008; Medina-Pastor et al., 2011; Rozet et al., 2011). Data from in–house quality control measurements or from interlaboratory comparison, either in collaborative studies or in Proficiency Test schemes have been used in many studies in environmental and biological analysis (Companyó et al., 2008; Fisicaro, Amarouche, Lalere, Labarraque, & Priel, 2010; Lecomte et al., 2012). The main objective of this work is to compare the estimation of measurement uncertainty using two approaches: classical GUM approach and experimental approach. The alternative to GUM approach was based on data from single laboratory validation data and Proficiency Test schemes. The approaches were applied to chemical contaminants in food, determined by ICP-MS, as part of Portuguese Total Diet Study.
2. Materials and methods
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unit. Between homogeneity characteristic studies two test portions from five units were analyzed. 2.2. Reagents, standards and Labware Standard preparation steps were carried out in clean room facilities. All solutions were prepared using ultrapure water (18.2 MX cm) (Q-POD Millipore, Interface, Portugal) and nitric acid pro analysis (65% v/v) (Merck, VWR, Portugal), which was purified by sub-boiling distillation using a SubPur apparatus (Milestone, Unicam, Portugal). Two different batches of multi element standard solutions containing 100 mg/ L of As, Cd, Cr, Pb, Ni, Zn were purchased in highest purity from Merck Multi XVI (VWR, Portugal) and were used to prepare calibration standards. Working standards used to prepare calibration curves and internal quality standards were daily prepared in HNO3 6% (v/v). Rhodium and Yttrium were used as internal standard at the concentration of 15 lg/L diluted from a solution of 1000 lg/L prepared from a 1000 mg/ L mono-elemental stock solutions purchased from Merck. ICP-MS tuning was performed on a daily basis with a 10 lg/ L multi-element solution high purity grade purchased from Analytika, (UNICAM, Portugal). All labware such as PFA or PP bottles, microwave vessels or volumetric flasks were decontaminated either with an automatic cleaning device for ultrapure chemistry (Milestone, Traceclean) or using a 20% (v/v) solution of purified nitric acid. 2.3. Traceability Equipment used during experiments was calibrated according to approved calibration procedures and external standards traceable to national measurement standards. Calibration values for the analytical balance, refrigerators, and thermometers were registered and corrections were made when necessary. 2.4. Certified reference materials Certified reference material (CRM) DORM-3 (Fish protein), from the National Research Council of Canada (CNRC), was all purchased from the producer. All samples were used as provided without further grinding. Moisture content was determined following the instructions for use as described in the certificates of analysis.
2.1. Sample collection and preparation 2.5. Strategies for sample digestion Fish, vegetables species and edible oil were collect from national supermarket chains in city of Lisbon during winter and summer of 2010. Rice samples were obtained from local farms located at most significant regions of cultivation in Portugal. Surface water, was taken from a domestic farm as a representative of water feeding family cultivars. Individual food samples were pooled to obtain laboratory samples for analysis. Samples were grinded in a grindomix using titanium blades (Retasch Grindomix GM200, Germany) to avoid contamination. Homogenization was then carefully proceed accordingly with texture. Material was then dispensed as units of 50 g (approximately) in plastic sachets. Afterwards units were frozen and subsequently freeze-dried. Each unit was vacuum sealed and kept at –20 °C until required for analysis. Prior to the microwave digestion (Milestone, Ethos 1), samples were allowed to reach room temperature and then sub-sampled. For homogeneity studies random sachets were collect during first middle and last third of dispensing process. To verify homogeneity within and between units a lay-out plan was used. Within homogeneity characteristic was tested taking five test portions from one
Food samples weighing 0.25 g (Fish) /0.500 g (Other foods) were digested in a closed vessel microwave digestor (Ethos 1, Milestone). To optimize the matrix microwave assisted digestion procedure and element yield several mineralization conditions HNO3:H2O2 ratios (4/2; 4/3; 7/1) and different time and temperature (max 20 min 180 °C) conditions were studied. The optimized irradiation program was applied: 850 W, 5 min at 0 W, 6 min at 1100 W, 5 min at 0 W and 6 min at 650 W in moderate mineralization conditions. One vessel was filled with reagents and considered as blank in order to monitor the quality of the digestion process. After cooling down to room temperature, the content of each PTFE vessel was transferred into pre-cleaned 100 ml PFA volumetric flasks and stored in a refrigerator at 4 °C, if necessary, until required for analysis. Prior to the ICP-MS analysis samples were diluted 10 times. The appropriate conditions were settle when recovery was within acceptable range 80 > AC > 120, obtained as an average of three replicate experiments for each matrix compared with expected concentration for a 100% recovery.
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2.6. Instrumentation Measurements were performed using a quadrupole inductivelycoupled plasma mass spectrometry (ICP-MS; Thermo Elemental, X-series 2, UK). Instrumental operation conditions were setting according to manufacture guidelines. Since arsenic is a monoisotopic element only mass 75 is available for monitoring. For the other 3 elements the following isotopes were chosen: 53Cr, 111Cd, 206Pb, 207Pb and 208Pb. 2.7. Internal quality control Standards were prepared fresh every day. Calibration curves were drawn based on six points ranging from 0.25 to 20 lg/ L. Only calibration curves with a coefficient of determination R2 P 0.9995 were accepted. Two independent quality control standards, 0.25 and 10 lg/L, and a blank, were read before and after each batch of samples. Also each sample was prepared in duplicate and a duplicate of the first sample was read at the end of each batch. Samples were diluted based on its expected element concentration. After digestion program optimization, vegetables, fish and cereals samples, were studied in order to evaluate method repeatability and reproducibility. For accuracy assessment reference materials (DORM-3) and spiked samples were used. Day to day variability was monitoring with CRM with certified values within the range of interest. 2.8. Method performance A full single laboratory validation was conducted using the following parameters: evaluation of method precision (repeatability and reproducibility), accuracy, linearity, selectivity, matrix effects, working range, limits of detection and recovery rates considered as quantification of fortified samples matrices. 2.9. Participation in Proficiency Test Schemes Laboratory Performance for determination of inorganic contaminants in foodstuffs by ICP-MS was evaluated by participation in Proficiency Test Schemes launched by Accredited Proficiency Testing Providers (FAPAS). Participation covers test runs for food matrix within the concentration of interest. The spread of results obtained by the laboratory in test rounds over the three last years was used to check uncertainty measurement results. 2.10. Evaluation of uncertainty Before any approach was carried out the following requirements were step up: 1. A clear definition of measurand (e.g.: total element in fish); 2. A comprehensive description of the measurement procedure (e.g. the flowchart for TDS); 3. An exhaustive identification of the causes that influence the measurement results. 3. Results and discussion
presents different approaches founded on the modeling of the process as described in the GUM and empirical approaches founded on the exploitation of intra-laboratory data of method validation or on exploitation of proficiency testing according to the ISO/IEC 5725-6 (1994). According with the experimental data available an appropriate approach is chosen. The obtained value is then verified by another approach and compared with target value for measurement uncertainty. Target uncertainty was considered as RSDR published in European standard EN 15763 (2009) for similar matrix in the case of fish samples was selected fish homogenate. The final results are multiplied by a coverage factor of 2 to obtain the expanded uncertainty (U), so that the reported result is estimated to have a level of confidence of 95%. In this work we compare data obtained in alternative approaches with data from modeling. This methodology is in accordance with Eurolab document (Eurolab Technical report 1/2007, 2007) and has been discussed in other areas like, pesticides, environmental and biomedical testing analysis (Fisicaro et al., 2010; Medina-Pastor et al., 2011). 3.2. Identification of sources of uncertainty associated to modeling approach Modeling is the reference approach for calculation uncertainty in accordance with EURACHEM: 2000. In the Table 1 uncertainty sources are presented for the determination on contaminants in food by ICP-MS. The main contributions were grouped in two major sources: sample preparation (including microwave digestion) and operations with analytical instrumentation. The analytical procedure involved sources which the model is well established and sources which contribution is difficult to quantified due to the lack of mathematical models. This was obtained after the traceability chain and critical control points were identified. Sources are classified as Type A or Type B. Type A correspond to a measurable standard deviation obtained during laboratory analysis. Type B uncertainty is derived from a fixed value, often a tolerance limit defined by manufacturer’s instructions of certificate of analysis in the case of chemical standards (Ruth, 2004). The contributions were then grouped by hierarchical contributions and the most significant were analyzed. All uncertainty components were considered uncorrelated. The combined standard uncertainty (ucomb) was obtained by law of propagation of uncertainty according with equation 1, considering the groups of contributors reported in Table 1.
ucomb ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi AS2 þ CS2 þ MC 2 þ CRM 2 þ Rec2 :
ð1Þ
The methodology has received much attention in our laboratory in the past, for calculation of associated measureament uncertainty to determination of minerals and trace elements by different instrumental methods of analysis (Castanheira et al., 2009). In the case of ICP-MS analysis approach has been applied over 13 elements in different foods. The relative expanded uncertainty (U%) k = 2 is < 20% In all historical data sources associated to matrix (MC) were the major contributors to combined standard uncertainty. The second major contributor has been uncertainty associated to Calibration solutions (CS). It has advantages over experimental approaches because make possible to identify specific improvements of analytical procedure. However received some criticism because it is sometimes difficult to acquire all the sources of uncertainty, in particular those related with sample preparation (Priel, Amarouche, & Fisicaro, 2009).
3.1. A generic flowchart for uncertainty evaluation To calculate uncertainty a generic flowchart was created and presented in Fig. 1. This diagram was based on Eurolab document (Eurolab Technical report 1/2007). It is supported by ISO/IEC 17025 (2005), considered a key document in TDS. This methodology
3.3. Uncertainty evaluation using data from single laboratory validation data Method validation results and estimated uncertainty based on in house validation are presented in Table 2.
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607
Fig. 1. Flowchart to calculate uncertainty.
Combined standard uncertainty was estimated as a square root of data on precision and uncertainty on bias arising from method and laboratory, according with following equations:
jxCRM xj t cal ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 u2CRM þ sn uTrac ¼
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s2 u2CRM þ n
uProc ¼ s ucomb
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s2 ¼ u2Trac þ u2Proc ¼ ðu2CRM þ Þ þ s2 n
ð2Þ
ð3Þ ð4Þ ð5Þ
where uTrac is the traceability standard uncertainty, obtained by Eq. (3) and considering a non-significant bias (Eurolab Technical report 1/2007). Analytical procedure standard uncertainty (uProc) is given by s (standard deviation of test results). One of the problems with the use of a single laboratory validation approach is defining how criteria such as method accuracy and precision shall be evaluated to produce realistic uncertainty estimations. In our study the method validation parameters were
quantified covering all analytical procedures (including sample digestion) in order to guarantee that uncertainty estimations encompass contributions from critical steps of the measurement process, monitored under internal quality control conditions. Parameters quantification was obtained by measuring a reference standard containing a certified or validated amount of the analyte in a similar matrix to the one corresponding to the sample under test. Accuracy was quantified using DORM 3, a fish protein certified reference material which contains a similar level of the element when comparing to the level expected in the test samples. Calibration curve reproducibility obtained on 7 different days (over a period of a month) by using different chemical standard solutions was also subject to analysis. After linear regression analysis, correlation coefficient (r) for the straight lines was better than 0.999 and slope RSDR was equal to 1.1%. Linearity is appropriated and in compliance with other results published in literature (Millour et al., 2011). Limit of Detection (LoD) and Limit of Quantification (LoQ) was estimated according with Eurachem guidelines (EURACHEM 2000) and expressed in lg of each element per kg of sample weight when samples were submitted to optimized digestion procedure. As and Cd present lowest limit of quantification whereas the highest value was found for Zn. Method performance achieved adequate detection resolution regarding all elements under study
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Table 1 Sources of variability in modeling approach for estimate associate uncertainty to measurement value in determination of total arsenic in fish by ICP-MS. Type A parameters are determined statistically, while Type B parameters are determined by other means. Uncertainty Component
Model parameter Type A
Analytical signal (AS)
Calibration solution (CS)
Analytical signal for analyte in sample Analytical signal for internal std. in sample Internal standard concentration in sample Analytical signal for analyte in blank Conc. in calibration std. stock solution (new) purity Volume of stock solution
U U U U
Water volume in dilution to 2nd stock solution
U
Internal standard concentration in cal. std. Dry matter correction Weight of sample
U U U
U U U U
Matrix contribution (MC)
CRM Rec
Determination
Type B
Pipetting
U U U
Sub-sampling bias Instrument drift Matrix interference Contamination/losses Spectral interferences CRM Recovery
U U U U U U
Repeated measurements on the sample (N = 5) Repeated measurements on the sample (N = 5) Control chart Control chart Manufacturer’s information certificate of analysis Control chart Pipette tolerance worst condition Control chart Pipette tolerance worst condition Control chart Control chart Control chart Balance tolerance Worst condition from literature Pipette tolerance Control chart for duplicate samples Tolerance for drift Estimated from literature Estimated from literature Estimated from literature Certificate Control chart from same matrix
Table 2 In-house validation data used to estimate uncertainty for the determination of metals and metaloides in fish samples. Quantification of the parameters was obtained from n > 10 replicates. Element
Cd Pb Cr Ni Zn As
DORM 3
Linearity
Certified ± U (mg/kg)
r
WRange (lg/kg)
0.290 ± 0.020 0.395 ± 0.050 1.890 ± 0.17 1.280 ± 0.24 51.300 ± 3.1 6.880 ± 0.30
>0.9995 >0.9995 >0.9995 >0.9995 >0.9995 >0.9995
0.25–50 0.5–30 0.5–50 0.5–30 2.5–30 0.25–30
LoD (lg/kg)
LoQ (lg/kg)
0.009 0.018 0.019 0.018 0.062 0.010
0.100 0.250 0.400 0.400 1.250 0.100
Intermediate precision Srw (%)
SRw (%)
2.3 5 5 6 7 3
2.2 9 11 9 9 4
because LoQ were about 100 times lower than the concentration found either in CRM or real samples (results not shown). For all elements trueness was under acceptance criteria (<10%). For this parameter it was considered as reference value DORM 3 certificate and observed value in the laboratory was obtained was mean of 20 replicates. Data on intermediate precision of measurement procedure was quantified by: (a) standard deviation obtained under repeatability conditions carried out by same operator in same equipment and short-time repetition ranging from 2.3% (Cd) up to 7% (Zn) and (b) within-laboratory reproducibility standard deviation, achieved by different operators in the same equipment varies between 2.2% (Cd) and 11% (Cr). Ferrão et al. (2011) reported similar values for intermediate precision. The first largest contribution to the combined standard uncertainty comes from withinlaboratory reproducibility. The primary contributions to this source came from uncertainties associated to sample preparation, operators and instrument drift. Accuracy expressed as z-score was satisfactory for all elements. For each analysis expanded uncertainty was obtained by multiplying the combined standard uncertainty by a coverage factor of 2 providing confidence that 95% of values are dispersed within the range. Each estimated uncertainty and respective target uncertainty were compatible when the target value of uncertainty was selected from EN 15763 (2009) (Cd, Pb, As) or taken from collaborative studies (Cr, Ni, Zn).
Truness (%)
101.4 103.0 108.2 93.4 98.4 97.5
Accuracy (z- score)
<2Z <2Z <2Z <2Z <2Z <2Z
Uncertainty uproced (lg/kg)
utrac (lg/kg)
U (%)
6.59 29.21 177.86 92.22 1909.71 209.93
10.1 25.83 93.84 121.75 4989.00 157.17
8.3 19.7 21.3 23.9 20.8 7.6
3.4. Exploitation of results from PT schemes Proficiency Test schemes is a favorable mean to demonstrate competence to achieve ISO 17025 (2005) accreditation and a prerequisite for those who generate data for TDS. Therefore the so called proficiency testing approach could be an easy and convenient method to estimate uncertainty. In Table 3 is presented data obtained on PT schemes used to estimate uncertainty. Uncertainty values were derived from Eqs. (6) and (7).
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U ¼ k:u ¼ k: uðRwÞ2 þ uðbiasÞ2
ð6Þ
where U is the expanded uncertainty, k is the coverage factor, u is the combined standard uncertainty, u(Rw) is the within-laboratory reproducibility standard deviation obtained from QC data and u(bias) is the uncertainty component arising from method and laboratory bias, expressed below by Eq. (7).
uðbiasÞ ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RMS2Bias þ uðC Ref Þ2 :
ð7Þ
For all rounds z-cores are between 2 and + 2 z-score and are considered satisfactory and appropriated to estimate uncertainty (EUROLAB technical report 1/2007, 2007). Over six rounds of testing, bias were below internal requirements meaning that laboratory performed satisfactorily either for cadmium or arsenic in a
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Table 3 Data of PT schemes for rounds span three years participation for heavy metals in food matrix using the same microwave digestion procedure and ICP-MS operating conditions. PT round
Analyte
Reference value (lg/kg)
Analyse value (lg/kg)
BIAS (%)
SR (%) (from report)
No of participants
z-score
Canned fish Canned crab meat Canned fish Canned crab meat Canned crab meat Canned fish Canned crab meat Canned fish Canned crab meat Canned crab meat Infant cereal
As As As As As Cd Cd Cd Cd Cd Cd As As Cd Cd
2550 11,500 1354 11,200 12,000 16.6 5760 9.68 4670 5.6 56.6
2593 10,000 1131 10,950 12,500 17.6 5900 11.1 4613 5.2 51.8
1.7 13.0 16.5 2.2 4.2 6.0 2.4 14.7 1.2 7.1 8.5 5.2 9.7 1.0 8.0
13.9 11.0 15.3 11.1 11.0 21.9 12.3 22.0 1.3 12.4 22.1 12.5
56 64 73 43 79 73 84 88 61 103 77 63
0.1 1.1 1.1 0.2 0.4 0.3 0.2 0.7 0.1 0.6 0.4
15.3
82
Average RMS bias Average RMS bias
Table 4 Comparison of results achieved by different approaches to calculate uncertainty for the overall measurement of arsenic in foods and in water by ICP-MS. Target measurement uncertainty was taken from the RSDR obtained in an appropriated interlaboratory method validation study. Matrix
Mean value (lg/kg)
Purpose
Uncertainty approach
Combined standard uncertainty uc (lg/kg)
Salmon
816 816 816 1600 155 155 193.4 0.83 0.83 6.9
Estimation Estimation Verification Target Estimation Verification Target Estimation Verification Target
PTs Single Laboratory Validation Data modeling:modelling Method performance study Single laboratory Validation data modeling:modelling Method performance study Single laboratory validation data modeling:modelling Method performance study
63.65 60.38 66.10 140.80 11.47 12.87 14.31 0.06 0.08 0.95
Fish homogenate Rice
Surface water
Relative expanded uncertainty U (%)
Reference for the approach Eurolab.2007 ISO 5725–6 ISO-GUM EN 15763 ISO 5725–6 ISO-GUM Llorente.2012 ISO 5725–6 ISO-GUM ISO 17294–2
16.20 17.60 14.8 14.8 18.6 27.7
Table 5 Uncertainty and regulatory limits: Uncertainty approach used to assess the suitability of ICP-MS to measure contaminants in foods. Analyte
Cd Cd Cd Pb Pb Pb a b c
Foodstuff
Potato Canned fish Quinoa Tomato Infant cereal Edible oil
LoD (mg/kg)
Content (mg/kg)
Obtained
Maximum limit
0.000009 0.000009 0.000009 0.000018 0.000018 0.000018
0.01 0.01 0.02 0.01 0.02 0.01
a
Standard uncertainty (mg/kg)
Determined
Maximum level
0.03 0.01 0.0137 0.03 0.07 0.171
0.1 0.1 0.2 0.1 0.02 0.1
b
Calculated
Maximum limitc
a
0.005996 0.00246 0.00274 0.0058 0.01305 0.03078
0.00781 0.00557 0.0057 0.00766 0.01398 0.03118
0.2 0.2 0.2 0.2 0.18 0.18
Directive 2001/22/EC (2001). EC regulation 466/2001. Directive 2005/4/EC (2005).
consistent way. Therefore, in day to day analysis, bias was considered negligible. However the root mean square of bias result (RMSbias) is an uncertainty component and analysis of the distribution of individual z scores, for both testing materials, showed the same level of performance, when estimated uncertainty was calculated by Eq. (6). An example of evaluation of uncertainty by the PT approach calculated according to Eqs. (6) and (7) is presented in Table 4. The determined mean value and associated measurement uncertainty for arsenic content in salmon, as consumed in country was obtained applying the steps describe in Fig. 1. The compliance of the method with EU regulation is presented in Table 5. There was no significant effect of either sample size or food matrix on the analytical results, reinforcing the idea that our PT laboratory data could be used to estimate uncertainty. Our results are in the same line of Companyó et al. (2008). According with this experi-
ence the participation in PT schemes constitutes a preferable means to evaluate laboratory performance to demonstrate that laboratory results are consistent and accurate (Fisicaro et al., 2010). 3.5. Uncertainty evaluation and verification using several approaches Arsenic content in foods with associated measurement uncertainties, are presented in Table 4. The metalloid content was evaluated in real samples consumed in country and considered major contributors to arsenic intake. The arsenic in foodstuffs was analyzed by ICP-MS assisted by microwave assisted digestion (Salmon and Rice) or acidified with 1% (by volume) concentrated nitric acid (surface water). The analytical procedure was previous validated and quality control was in place for each assay. Associated measurement uncertainty was calculated using different approaches.
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Estimated uncertainty was derived from Eqs. (5) and (6); respectively single laboratory validation data and PTs data. Combined standard uncertainty calculated by modeling was derived from equation 1and was used to verify if the experimental approaches are valid for the analytical procedure overtime. Modeling which is the classical approach was chosen for comparison since laboratory has a historical background. This approach was previously a prerogative for accredited laboratory. Uncertainty ranges estimated by different approaches are not significantly different so we could assume that both can be applied by laboratory. Although, in detail uncertainty is slightly underestimated when reported by single laboratory validation data. In the assay for determination of arsenic in fish the different to modeling is more accentuated, may be high fat content of salmon and the binding of arsenic to phospholipids could explain the slight difference. However we face situations where uncertainties ranges do not agree as estimated by different approaches (results not found) it happened when not all sources were quantified or when matrix-matching CRM are not available. The same was discussed by Priel et al. (2009). The combination of several approaches, is recommended and reported by several authors (Medina-Pastor et al., 2011; EUROLAB technical report 1/2007, 2007). Aligned with literature target measurement uncertainty is defined as measurement uncertainty specified as an upper limit and decided on the basis of the intended use of measurement results. In our study it was derived from RSDR found in matrix matching collaborative studies. The acceptance criteria is defined as U (%) <2RSDR. In the analyzed samples calculated U(%) are in agreement with literature.
4. Conclusions The evaluation and verification of uncertainty, in analysis of inorganic contaminants in food by ICP-MS, is a complex process. In this work two approaches were compared, one of them based on a mathematical measurement model and the other based on alternative approaches supported by experimental data. The classical approach, based on mathematical modeling, can face many difficulties to identify each source and its corresponding contribution to combined uncertainty. This is due to interferences that can have different origins, from sample handling to instrumental determination, and in some cases could be impossible to identify as an individual component of uncertainty budget. The experimental approach is an easier method to apply due to the use of quality tools with which laboratories are quite familiar. The modeling approach requires a mathematical background to apply the law of propagation of uncertainty and it is difficult to highlight the advantages of such complex calculations. Furthermore experimental approaches presented in this work are in accordance with IMEKO (IMEKO, 2012) principles and will facilitate the integration of metrological tools among the TDS community. Acknowledgments This work was completed on behalf of Project GOODFISH PTDC/ SAU-ESA/103825/2008. The authors acknowledge Marina Saraiva for technical assistance. References
3.6. Uncertainty estimation and compliance with European Legislation In Table 5 is presented an overview of results and their compliance with European Legislation. The maximum standard uncertainty is designated by uncertainty function approach and calculated using the following equation.
Uf ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðLoD=2Þ2 þ ðaCÞ2
ð8Þ
where Uf is the maximum standard uncertainty, LoD is the limit of detection of the method, C is the concentration of interest and a is a numeric factor to be used depending on the value of C . The results showing data on foods under study to determine the cadmium and lead content by ICP-MS and its associated uncertainty. This feature has relevance and is required to assess and interpreted the adequacy of a method of analysis and instrumental procedure to measure small amounts of cadmium and lead eventually present in foods. However few results are published in literature. The LoD values were obtained for blank and matrix and made under repeatability conditions. Mean values with associated Uf showed content values below those settle in the EC Regulation. For Pb uncertainty range from 0.030 (edible oil) to 0.005 (tomato) and for cadmium results are also below the maximum limits lay down in 2005/4/EC. For arsenic no maximum level is yet established. Nevertheless anticipating future legislation expanded uncertainty (U) expressed by U = kUf when k = 2 and Uf estimated by equation 8 adapted from Directive 2005/4/EC (2005) was calculated. LoD data was obtained from determination of arsenic in water, rice and fish as the foodstuffs that most contribute to arsenic exposure (EFSA, 2011). The results are below 2005/4/EC criteria and agree with data published in literature (LlorenteMirandes et al., 2012) and Ferrão et al. 2011). The analytical method and instrumental procedure are suitable and comply with legislation.
Castanheira, I., Figueiredo, C., André, C., Coelho, I., Silva, A. T., Santiago, S., et al. (2009). Food Chemistry, 113(2), 621–628. Companyó, R., Rubio, R., Sahuquillo, A., Boqué, R., Maroto, A., & Riu, J. (2008). Uncertainty estimation in organic elemental analysis using information from proficiency tests. Analytical and Bioanalytical Chemistry, 392(7–8), 1497–1505. Dabalus, I. M., Schweikert, T. M., & Cannavan, A. (2008). A comparison of methods for the estimation of measurement uncertainty for an analytical method for sulphonamides. Food Additives and Contaminants. Part A, Chemistry, Analysis, Control, Exposure and Risk Assessment, 25(12), 1439–1450. Directive 2001/22/EC. (2001). 8 March 2001 laying down the sampling methods and the methods of analysis for the official control of the levels of lead, cadmium, mercury and 3-MCPD in foodstuffs. European Commission. Official Journal of the European Union, L 77/14. Directive 2005/4/EC. (2005). amending Directive 2001/22/EC laying down the sampling methods and the methods of analysis for the official control of the levels of lead, cadmium, mercury and 3-MCPD in foodstuffs. European Commission. Official Journal of the European Union, L 19/50, 19 January. EFSA. (2011). FAO AND WHO towards a harmonized Total Diet Study approach: A guidance document. http://www.efsa.europa.eu/en/efsajournal/doc/2450.pdf Retrieved 15-03-2012. Egan, S. K., Tao, S. S-H., Pennington, J. A. T., & Bolger, P. M. (2002). U.S. Food and Drug Administration’s total diet study: Intake of nutritional and toxic elements, 1991–96. Food Additives and Contaminants 19(2),103–125. EN 15763. (2009). Foodstuffs – Determination of trace elements – Determination of arsenic, cadmium, mercury and lead in foodstuffs by inductively coupled plasma mass spectrometry (ICP-MS) after pressure digestion. Austrian Standards Institute. EURACHEM. (2000). EURACHEM/CITAC, quantifying Uncertainty in analytical measurement (2nd ed.). EURACHEM/CITAC Guide CG4, UK/Switzerland. Eurolab technical report no. 1/2007. (2007). Measurement uncertainty revisited: Alternative approaches to uncertainty evaluation. Eurolab. FDA (2010). U.S. Food and Drug Administration. Available from: www.fda.gov/ downloads/Food/FoodSafety/FoodContaminantsAdulteration/TotalDietStudy/ UCM184301.pdf. Ferrão, M. F., Godoy, S. C., Gerbase, A. E., Mello, C., Furtado, J. C., Petzhold, C. L., et al. (2011). Method validation for determination of arsenic, cadmium, chromium and lead in milk by means of dynamic reaction cell inductively coupled plasma mass spectrometry. Analytica Chimica Acta, 595(2), 209–218. Fisicaro, P., Amarouche, S., Lalere, B., Labarraque, G., & Priel, M. (2010). Approaches to uncertainty evaluation based on proficiency testing schemes in chemical measurements. Accreditation and Quality Assurance, 13(7), 361–366. Gibney, M., & Sandström, B. (2001). A framework for food-based dietary guidelines in the European Union. Public Health Nutrition, 4(2A), 293–305. IMEKO. (2012). International measurement confederation. http://www.imeko.org./ Retrieved 15-03-2012.
I. Coelho et al. / Food Chemistry 141 (2013) 604–611 ISO 5725-6. (1994). Accuracy (trueness and precision) of measurement methods and results – Part 6: Use in practice of accuracy values. Geneva: ISO. ISO/IEC 17025, (2005). General requirements for the competence of testing and calibration laboratories. Geneva: ISO. Iyengar, V. (2007). Metrological concepts for enhancing the reliability of food and nutritional measurements. Journal of Food Composition and Analysis, 20(6), 449–450. Leblanc, J. C., Guérin, T., Noël, L., Calamassi-Tran, G., Volatier, J. L., & Verger, P. (2005). Dietary exposure estimates of 18 elements from the 1st French total diet study. Food Additives and Contaminants, 22(7), 624–641. Lecomte, F., Hubert, C., Demarche, S., De Bleye, C., Dispas, A., Jost, M., et al. (2012). Comparison of the quantitative performances and measurement uncertainty estimates obtained during method validation versus routine applications of a novel hydrophilic interaction chromatography method for the determination of cidofovir in human plasma. Journal of Pharmaceutical and Biomedical Analysis, 57, 153–165. Llorente-Mirandes, T.; Calderon, J., Lopez-Sanchez, Jose F.; Centrich, F.; Rubio, R. (2012). A fully validated method for the determination of arsenic species in rice and infant cereal products. Pure and Applied Chemistry, 84 (2), 225–238. Medina-Pastor, P., Valverde, A., Pihlstrom, T., Masselter, S., Gamon, M., Mezcua, M., et al. (2011). Comparative study of the main top-down approaches for the estimation of measurement uncertainty in multiresidue analysis of pesticides in fruits and vegetables. Journal of Agricultural and Food Chemistry, 59(14), 7609–7619.
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Millour, S., Noël, L., Kadar, A., Chekri, R., Vastel, C., & Guérin, T. (2011). Simultaneous analysis of 21 elements in foodstuffs by ICP–MS after closed-vessel microwave digestion: Method validation. Journal of Food Composition and Analysis, 24(1), 111–120. Pennington, J. (2004). Proceedings of the workshop on food-consumption surveys in developing countries: General approaches to estimation of dietary exposure to contaminants. Food and Nutrition Bulletin, 25(4), 420–427. Priel, M., Amarouche, S., & Fisicaro, P. (2009). Metrological traceability is not always a straight line. Accreditation and Quality Assurance, 14(11), 593–599. Rozet, E., Rudaz, S., Marini, R. D., Ziémons, E., Boulanger, B., & Hubert, P. (2011). Models to estimate overall analytical measurements uncertainty: Assumptions, comparisons and applications. Analytica Chimica Acta, 702(2), 160–171. Ruth, T. (2004). A model for the evaluation of uncertainty in routine multi-element analysis. Accreditation and Quality Assurance, 9(6), 349–354. Sirot, V., Volatier, J. L., Calamassi-Tran, G., Dubuisson, C., Menard, C., Dufour, A., et al. (2009). Core food of the French food supply: Second total diet study. Food Additives and Contaminants: Part A: Chemistry, Analysis, Control, Exposure and Risk Assessment, 26(5), 623–639. Thomson, B. M., Vannoort, R. W., & Haslemore, R. M. (2008). Dietary exposure and trends of exposure to nutrient elements iodine, iron, selenium and sodium from the 2003–4. New Zealand total diet survey. British Journal of Nutrition, 99(3), 614–625. Tsukakoshi, Y. (2011). Sampling variability and uncertainty in total diet studies. Analyst, 136(3), 533–539.
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Food Chemistry journal homepage: www.elsevier.com/locate/foodchem
Analytical Methods
Experimental approaches for the estimation of uncertainty in analysis of trace inorganic contaminants in foodstuffs by ICP-MS Inês Coelho a, Sandra Gueifão a, Ana Sofia Matos b, Mark Roe c, Isabel Castanheira a,⇑ a
Department of Food Safety and Nutrition, National Institute of Health Dr. Ricardo Jorge (INSA), Av. Padre Cruz, 1649-016 Lisbon, Portugal UNIDEMI, Departamento de Engenharia Mecânica e Industrial, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal c Institute of Food Research (IFR), Norwich NR4 7UA, United Kingdom b
a r t i c l e
i n f o
Article history: Received 22 March 2012 Received in revised form 9 November 2012 Accepted 13 March 2013 Available online 21 March 2013 Keywords: Uncertainty evaluation Experimental approaches Food consumption Comparability of data Food chemical contaminants
a b s t r a c t Total Diet Studies to estimate dietary exposure to food contaminants need to evaluate laboratory measurements data variance. In this process it is critical that data from analytical methods are reliable to correctly scrutinize and compare values over time and between countries. In Europe it is widely recognized that the evaluation of measurement uncertainty is an important parameter when assessing the sources of analytical data variability. Two approaches are considered to estimate uncertainty in analytical measurement. Arsenic, Lead, Chromium and Cadmium, content in several food matrix determined by Inductively Coupled Mass Spectrometry (ICP-MS) microwave digestion assisted, are used as examples. The aim of the present research work is to compare both approaches accepted by Eurolab and GUM: Mathematical modeling to assess uncertainty components based on a classical model (bottom up) and an empirical method (top down), based on either experimental data obtained from a single laboratory validation data or interlaboratory data from Proficiency Testing schemes. Relative expanded uncertainty calculated by both approaches agree when U (%) <20%. These values are concordant with RSDR reported in collaborative studies of EN 15763 (2009), which were assumed as target uncertainty. The top down approach described is simple and easy to use when compared with the mathematical modeling approach providing considerable benefits to those who assess data produced by several laboratories. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Total Diet Studies (TDS) are valuable tools to monitor risk assessment for food contaminants population exposure. Since 2005 three complementary TDS methods are recommended by WHO: (a) market basket studies consisting of representative key foods (raw and processed); (b) analysis of duplicate food portions in representative meals and (c) analysis of individual food items (EFSA, 2011; Egan, Tao, Pennington, & Bolger, 2002; FDA, 2010; Leblanc et al., 2005; Pennington, 2004; Sirot et al., 2009). The market basket study is used in many countries to assess pesticide residues, industrial chemicals, radionuclides and heavy metals dietary intakes. The data from analysis combined with information from food consumption surveys allows estimation of dietary exposure to food toxicants at an individual level as well as at population group level. TDS is also used to estimate toxicological thresholds for risk assessment (Egan et al., 2002; Sirot et al., 2009; Thomson, Vannoort, & Haslemore, 2008). Errors associated with analytical procedure and sampling strategies contribute to overall error associated with measurement of dietary exposure (Tsukakoshi, 2011). Additional factors may come ⇑ Corresponding author. Tel.: +351 21 751 92 88; fax: +351 21 750 81 53. E-mail address: [email protected] (I. Castanheira). 0308-8146/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2013.03.040
into play reinforcing differences in exposure assessment results between countries, when food consumption habits, levels of food contamination and differing procedures for data generation are considered. In Europe several studies pointed out the need of developing an harmonization approach to contribute to reduction of artefactual differences in the process of calculating and comparing dietary contaminant exposure based on data from different sources (EFSA, 2011). High quality scientific data is essential to enable European legislation to establish maximum levels of contaminants in foods supported by robust scientific data (Gibney & Sandström, 2001). The quality framework includes a methodology to evaluate quality of data produced by laboratories (Millour et al., 2011). In several countries, complementary methods are used to monitor the intake of nutrients against Recommended Daily Intake (RDI). In these countries the quality system is based on existing data quality already in place for nutrients contained in foods and published in national Food Composition Data Banks (Sirot et al., 2009; Thomson et al., 2008). The Quality Systems include requirements improving and standardizing the quality of factors such as food description, component identification, sampling plan, number of samples, sample handling, analytical method and laboratory performance. Assessment of analytical method and laboratory performance is based on the use of standard analytical methods
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(e.g. CEN, AOAC, ISO), certified reference materials and participation in proficiency testing (PT) schemes. This methodology encompasses a set of metrological principles and practices to maximize the reliability of the results during the study and the compatibility of data over time and across countries. Therefore Metrology (the science of measurement) plays a crucial role to guarantee the foundation of the measurement system which should act as a bridge across analytical food measurements and mathematical approaches when evaluating population dietary exposure to food chemical contaminants. Addressing these issues is one of the aims of the recently created committee of IMEKO Metrology in Food and Nutrition (IMEKO, 2012; Iyengar, 2007). One of the committee goals is further enhancing metrological best practices, including the use of traceability chains, use of appropriate reference materials and realistic approaches to estimate the measurement uncertainty associated with analytical data. This is crucial to provide representative and accurate value ranges for the estimation of exposure thresholds for toxic elements as well as to identify specific improvements in the measurement procedure. The estimation of measurement uncertainty includes at least two sources: sampling and analytical procedure (Castanheira et al., 2009). These contributions can be estimated by modeling individual sources of uncertainty that are evaluated and combined (sum of square roots of their variances) which is a ‘‘bottom-up’’ approach. A practical estimation taking into account the information from daily quality control measurements has now been adopted in many cases and is known as the ‘‘top-down’’ approach (Dabalus et al., 2008; Medina-Pastor et al., 2011; Rozet et al., 2011). Data from in–house quality control measurements or from interlaboratory comparison, either in collaborative studies or in Proficiency Test schemes have been used in many studies in environmental and biological analysis (Companyó et al., 2008; Fisicaro, Amarouche, Lalere, Labarraque, & Priel, 2010; Lecomte et al., 2012). The main objective of this work is to compare the estimation of measurement uncertainty using two approaches: classical GUM approach and experimental approach. The alternative to GUM approach was based on data from single laboratory validation data and Proficiency Test schemes. The approaches were applied to chemical contaminants in food, determined by ICP-MS, as part of Portuguese Total Diet Study.
2. Materials and methods
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unit. Between homogeneity characteristic studies two test portions from five units were analyzed. 2.2. Reagents, standards and Labware Standard preparation steps were carried out in clean room facilities. All solutions were prepared using ultrapure water (18.2 MX cm) (Q-POD Millipore, Interface, Portugal) and nitric acid pro analysis (65% v/v) (Merck, VWR, Portugal), which was purified by sub-boiling distillation using a SubPur apparatus (Milestone, Unicam, Portugal). Two different batches of multi element standard solutions containing 100 mg/ L of As, Cd, Cr, Pb, Ni, Zn were purchased in highest purity from Merck Multi XVI (VWR, Portugal) and were used to prepare calibration standards. Working standards used to prepare calibration curves and internal quality standards were daily prepared in HNO3 6% (v/v). Rhodium and Yttrium were used as internal standard at the concentration of 15 lg/L diluted from a solution of 1000 lg/L prepared from a 1000 mg/ L mono-elemental stock solutions purchased from Merck. ICP-MS tuning was performed on a daily basis with a 10 lg/ L multi-element solution high purity grade purchased from Analytika, (UNICAM, Portugal). All labware such as PFA or PP bottles, microwave vessels or volumetric flasks were decontaminated either with an automatic cleaning device for ultrapure chemistry (Milestone, Traceclean) or using a 20% (v/v) solution of purified nitric acid. 2.3. Traceability Equipment used during experiments was calibrated according to approved calibration procedures and external standards traceable to national measurement standards. Calibration values for the analytical balance, refrigerators, and thermometers were registered and corrections were made when necessary. 2.4. Certified reference materials Certified reference material (CRM) DORM-3 (Fish protein), from the National Research Council of Canada (CNRC), was all purchased from the producer. All samples were used as provided without further grinding. Moisture content was determined following the instructions for use as described in the certificates of analysis.
2.1. Sample collection and preparation 2.5. Strategies for sample digestion Fish, vegetables species and edible oil were collect from national supermarket chains in city of Lisbon during winter and summer of 2010. Rice samples were obtained from local farms located at most significant regions of cultivation in Portugal. Surface water, was taken from a domestic farm as a representative of water feeding family cultivars. Individual food samples were pooled to obtain laboratory samples for analysis. Samples were grinded in a grindomix using titanium blades (Retasch Grindomix GM200, Germany) to avoid contamination. Homogenization was then carefully proceed accordingly with texture. Material was then dispensed as units of 50 g (approximately) in plastic sachets. Afterwards units were frozen and subsequently freeze-dried. Each unit was vacuum sealed and kept at –20 °C until required for analysis. Prior to the microwave digestion (Milestone, Ethos 1), samples were allowed to reach room temperature and then sub-sampled. For homogeneity studies random sachets were collect during first middle and last third of dispensing process. To verify homogeneity within and between units a lay-out plan was used. Within homogeneity characteristic was tested taking five test portions from one
Food samples weighing 0.25 g (Fish) /0.500 g (Other foods) were digested in a closed vessel microwave digestor (Ethos 1, Milestone). To optimize the matrix microwave assisted digestion procedure and element yield several mineralization conditions HNO3:H2O2 ratios (4/2; 4/3; 7/1) and different time and temperature (max 20 min 180 °C) conditions were studied. The optimized irradiation program was applied: 850 W, 5 min at 0 W, 6 min at 1100 W, 5 min at 0 W and 6 min at 650 W in moderate mineralization conditions. One vessel was filled with reagents and considered as blank in order to monitor the quality of the digestion process. After cooling down to room temperature, the content of each PTFE vessel was transferred into pre-cleaned 100 ml PFA volumetric flasks and stored in a refrigerator at 4 °C, if necessary, until required for analysis. Prior to the ICP-MS analysis samples were diluted 10 times. The appropriate conditions were settle when recovery was within acceptable range 80 > AC > 120, obtained as an average of three replicate experiments for each matrix compared with expected concentration for a 100% recovery.
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2.6. Instrumentation Measurements were performed using a quadrupole inductivelycoupled plasma mass spectrometry (ICP-MS; Thermo Elemental, X-series 2, UK). Instrumental operation conditions were setting according to manufacture guidelines. Since arsenic is a monoisotopic element only mass 75 is available for monitoring. For the other 3 elements the following isotopes were chosen: 53Cr, 111Cd, 206Pb, 207Pb and 208Pb. 2.7. Internal quality control Standards were prepared fresh every day. Calibration curves were drawn based on six points ranging from 0.25 to 20 lg/ L. Only calibration curves with a coefficient of determination R2 P 0.9995 were accepted. Two independent quality control standards, 0.25 and 10 lg/L, and a blank, were read before and after each batch of samples. Also each sample was prepared in duplicate and a duplicate of the first sample was read at the end of each batch. Samples were diluted based on its expected element concentration. After digestion program optimization, vegetables, fish and cereals samples, were studied in order to evaluate method repeatability and reproducibility. For accuracy assessment reference materials (DORM-3) and spiked samples were used. Day to day variability was monitoring with CRM with certified values within the range of interest. 2.8. Method performance A full single laboratory validation was conducted using the following parameters: evaluation of method precision (repeatability and reproducibility), accuracy, linearity, selectivity, matrix effects, working range, limits of detection and recovery rates considered as quantification of fortified samples matrices. 2.9. Participation in Proficiency Test Schemes Laboratory Performance for determination of inorganic contaminants in foodstuffs by ICP-MS was evaluated by participation in Proficiency Test Schemes launched by Accredited Proficiency Testing Providers (FAPAS). Participation covers test runs for food matrix within the concentration of interest. The spread of results obtained by the laboratory in test rounds over the three last years was used to check uncertainty measurement results. 2.10. Evaluation of uncertainty Before any approach was carried out the following requirements were step up: 1. A clear definition of measurand (e.g.: total element in fish); 2. A comprehensive description of the measurement procedure (e.g. the flowchart for TDS); 3. An exhaustive identification of the causes that influence the measurement results. 3. Results and discussion
presents different approaches founded on the modeling of the process as described in the GUM and empirical approaches founded on the exploitation of intra-laboratory data of method validation or on exploitation of proficiency testing according to the ISO/IEC 5725-6 (1994). According with the experimental data available an appropriate approach is chosen. The obtained value is then verified by another approach and compared with target value for measurement uncertainty. Target uncertainty was considered as RSDR published in European standard EN 15763 (2009) for similar matrix in the case of fish samples was selected fish homogenate. The final results are multiplied by a coverage factor of 2 to obtain the expanded uncertainty (U), so that the reported result is estimated to have a level of confidence of 95%. In this work we compare data obtained in alternative approaches with data from modeling. This methodology is in accordance with Eurolab document (Eurolab Technical report 1/2007, 2007) and has been discussed in other areas like, pesticides, environmental and biomedical testing analysis (Fisicaro et al., 2010; Medina-Pastor et al., 2011). 3.2. Identification of sources of uncertainty associated to modeling approach Modeling is the reference approach for calculation uncertainty in accordance with EURACHEM: 2000. In the Table 1 uncertainty sources are presented for the determination on contaminants in food by ICP-MS. The main contributions were grouped in two major sources: sample preparation (including microwave digestion) and operations with analytical instrumentation. The analytical procedure involved sources which the model is well established and sources which contribution is difficult to quantified due to the lack of mathematical models. This was obtained after the traceability chain and critical control points were identified. Sources are classified as Type A or Type B. Type A correspond to a measurable standard deviation obtained during laboratory analysis. Type B uncertainty is derived from a fixed value, often a tolerance limit defined by manufacturer’s instructions of certificate of analysis in the case of chemical standards (Ruth, 2004). The contributions were then grouped by hierarchical contributions and the most significant were analyzed. All uncertainty components were considered uncorrelated. The combined standard uncertainty (ucomb) was obtained by law of propagation of uncertainty according with equation 1, considering the groups of contributors reported in Table 1.
ucomb ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi AS2 þ CS2 þ MC 2 þ CRM 2 þ Rec2 :
ð1Þ
The methodology has received much attention in our laboratory in the past, for calculation of associated measureament uncertainty to determination of minerals and trace elements by different instrumental methods of analysis (Castanheira et al., 2009). In the case of ICP-MS analysis approach has been applied over 13 elements in different foods. The relative expanded uncertainty (U%) k = 2 is < 20% In all historical data sources associated to matrix (MC) were the major contributors to combined standard uncertainty. The second major contributor has been uncertainty associated to Calibration solutions (CS). It has advantages over experimental approaches because make possible to identify specific improvements of analytical procedure. However received some criticism because it is sometimes difficult to acquire all the sources of uncertainty, in particular those related with sample preparation (Priel, Amarouche, & Fisicaro, 2009).
3.1. A generic flowchart for uncertainty evaluation To calculate uncertainty a generic flowchart was created and presented in Fig. 1. This diagram was based on Eurolab document (Eurolab Technical report 1/2007). It is supported by ISO/IEC 17025 (2005), considered a key document in TDS. This methodology
3.3. Uncertainty evaluation using data from single laboratory validation data Method validation results and estimated uncertainty based on in house validation are presented in Table 2.
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607
Fig. 1. Flowchart to calculate uncertainty.
Combined standard uncertainty was estimated as a square root of data on precision and uncertainty on bias arising from method and laboratory, according with following equations:
jxCRM xj t cal ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 u2CRM þ sn uTrac ¼
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s2 u2CRM þ n
uProc ¼ s ucomb
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s2 ¼ u2Trac þ u2Proc ¼ ðu2CRM þ Þ þ s2 n
ð2Þ
ð3Þ ð4Þ ð5Þ
where uTrac is the traceability standard uncertainty, obtained by Eq. (3) and considering a non-significant bias (Eurolab Technical report 1/2007). Analytical procedure standard uncertainty (uProc) is given by s (standard deviation of test results). One of the problems with the use of a single laboratory validation approach is defining how criteria such as method accuracy and precision shall be evaluated to produce realistic uncertainty estimations. In our study the method validation parameters were
quantified covering all analytical procedures (including sample digestion) in order to guarantee that uncertainty estimations encompass contributions from critical steps of the measurement process, monitored under internal quality control conditions. Parameters quantification was obtained by measuring a reference standard containing a certified or validated amount of the analyte in a similar matrix to the one corresponding to the sample under test. Accuracy was quantified using DORM 3, a fish protein certified reference material which contains a similar level of the element when comparing to the level expected in the test samples. Calibration curve reproducibility obtained on 7 different days (over a period of a month) by using different chemical standard solutions was also subject to analysis. After linear regression analysis, correlation coefficient (r) for the straight lines was better than 0.999 and slope RSDR was equal to 1.1%. Linearity is appropriated and in compliance with other results published in literature (Millour et al., 2011). Limit of Detection (LoD) and Limit of Quantification (LoQ) was estimated according with Eurachem guidelines (EURACHEM 2000) and expressed in lg of each element per kg of sample weight when samples were submitted to optimized digestion procedure. As and Cd present lowest limit of quantification whereas the highest value was found for Zn. Method performance achieved adequate detection resolution regarding all elements under study
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Table 1 Sources of variability in modeling approach for estimate associate uncertainty to measurement value in determination of total arsenic in fish by ICP-MS. Type A parameters are determined statistically, while Type B parameters are determined by other means. Uncertainty Component
Model parameter Type A
Analytical signal (AS)
Calibration solution (CS)
Analytical signal for analyte in sample Analytical signal for internal std. in sample Internal standard concentration in sample Analytical signal for analyte in blank Conc. in calibration std. stock solution (new) purity Volume of stock solution
U U U U
Water volume in dilution to 2nd stock solution
U
Internal standard concentration in cal. std. Dry matter correction Weight of sample
U U U
U U U U
Matrix contribution (MC)
CRM Rec
Determination
Type B
Pipetting
U U U
Sub-sampling bias Instrument drift Matrix interference Contamination/losses Spectral interferences CRM Recovery
U U U U U U
Repeated measurements on the sample (N = 5) Repeated measurements on the sample (N = 5) Control chart Control chart Manufacturer’s information certificate of analysis Control chart Pipette tolerance worst condition Control chart Pipette tolerance worst condition Control chart Control chart Control chart Balance tolerance Worst condition from literature Pipette tolerance Control chart for duplicate samples Tolerance for drift Estimated from literature Estimated from literature Estimated from literature Certificate Control chart from same matrix
Table 2 In-house validation data used to estimate uncertainty for the determination of metals and metaloides in fish samples. Quantification of the parameters was obtained from n > 10 replicates. Element
Cd Pb Cr Ni Zn As
DORM 3
Linearity
Certified ± U (mg/kg)
r
WRange (lg/kg)
0.290 ± 0.020 0.395 ± 0.050 1.890 ± 0.17 1.280 ± 0.24 51.300 ± 3.1 6.880 ± 0.30
>0.9995 >0.9995 >0.9995 >0.9995 >0.9995 >0.9995
0.25–50 0.5–30 0.5–50 0.5–30 2.5–30 0.25–30
LoD (lg/kg)
LoQ (lg/kg)
0.009 0.018 0.019 0.018 0.062 0.010
0.100 0.250 0.400 0.400 1.250 0.100
Intermediate precision Srw (%)
SRw (%)
2.3 5 5 6 7 3
2.2 9 11 9 9 4
because LoQ were about 100 times lower than the concentration found either in CRM or real samples (results not shown). For all elements trueness was under acceptance criteria (<10%). For this parameter it was considered as reference value DORM 3 certificate and observed value in the laboratory was obtained was mean of 20 replicates. Data on intermediate precision of measurement procedure was quantified by: (a) standard deviation obtained under repeatability conditions carried out by same operator in same equipment and short-time repetition ranging from 2.3% (Cd) up to 7% (Zn) and (b) within-laboratory reproducibility standard deviation, achieved by different operators in the same equipment varies between 2.2% (Cd) and 11% (Cr). Ferrão et al. (2011) reported similar values for intermediate precision. The first largest contribution to the combined standard uncertainty comes from withinlaboratory reproducibility. The primary contributions to this source came from uncertainties associated to sample preparation, operators and instrument drift. Accuracy expressed as z-score was satisfactory for all elements. For each analysis expanded uncertainty was obtained by multiplying the combined standard uncertainty by a coverage factor of 2 providing confidence that 95% of values are dispersed within the range. Each estimated uncertainty and respective target uncertainty were compatible when the target value of uncertainty was selected from EN 15763 (2009) (Cd, Pb, As) or taken from collaborative studies (Cr, Ni, Zn).
Truness (%)
101.4 103.0 108.2 93.4 98.4 97.5
Accuracy (z- score)
<2Z <2Z <2Z <2Z <2Z <2Z
Uncertainty uproced (lg/kg)
utrac (lg/kg)
U (%)
6.59 29.21 177.86 92.22 1909.71 209.93
10.1 25.83 93.84 121.75 4989.00 157.17
8.3 19.7 21.3 23.9 20.8 7.6
3.4. Exploitation of results from PT schemes Proficiency Test schemes is a favorable mean to demonstrate competence to achieve ISO 17025 (2005) accreditation and a prerequisite for those who generate data for TDS. Therefore the so called proficiency testing approach could be an easy and convenient method to estimate uncertainty. In Table 3 is presented data obtained on PT schemes used to estimate uncertainty. Uncertainty values were derived from Eqs. (6) and (7).
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U ¼ k:u ¼ k: uðRwÞ2 þ uðbiasÞ2
ð6Þ
where U is the expanded uncertainty, k is the coverage factor, u is the combined standard uncertainty, u(Rw) is the within-laboratory reproducibility standard deviation obtained from QC data and u(bias) is the uncertainty component arising from method and laboratory bias, expressed below by Eq. (7).
uðbiasÞ ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RMS2Bias þ uðC Ref Þ2 :
ð7Þ
For all rounds z-cores are between 2 and + 2 z-score and are considered satisfactory and appropriated to estimate uncertainty (EUROLAB technical report 1/2007, 2007). Over six rounds of testing, bias were below internal requirements meaning that laboratory performed satisfactorily either for cadmium or arsenic in a
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Table 3 Data of PT schemes for rounds span three years participation for heavy metals in food matrix using the same microwave digestion procedure and ICP-MS operating conditions. PT round
Analyte
Reference value (lg/kg)
Analyse value (lg/kg)
BIAS (%)
SR (%) (from report)
No of participants
z-score
Canned fish Canned crab meat Canned fish Canned crab meat Canned crab meat Canned fish Canned crab meat Canned fish Canned crab meat Canned crab meat Infant cereal
As As As As As Cd Cd Cd Cd Cd Cd As As Cd Cd
2550 11,500 1354 11,200 12,000 16.6 5760 9.68 4670 5.6 56.6
2593 10,000 1131 10,950 12,500 17.6 5900 11.1 4613 5.2 51.8
1.7 13.0 16.5 2.2 4.2 6.0 2.4 14.7 1.2 7.1 8.5 5.2 9.7 1.0 8.0
13.9 11.0 15.3 11.1 11.0 21.9 12.3 22.0 1.3 12.4 22.1 12.5
56 64 73 43 79 73 84 88 61 103 77 63
0.1 1.1 1.1 0.2 0.4 0.3 0.2 0.7 0.1 0.6 0.4
15.3
82
Average RMS bias Average RMS bias
Table 4 Comparison of results achieved by different approaches to calculate uncertainty for the overall measurement of arsenic in foods and in water by ICP-MS. Target measurement uncertainty was taken from the RSDR obtained in an appropriated interlaboratory method validation study. Matrix
Mean value (lg/kg)
Purpose
Uncertainty approach
Combined standard uncertainty uc (lg/kg)
Salmon
816 816 816 1600 155 155 193.4 0.83 0.83 6.9
Estimation Estimation Verification Target Estimation Verification Target Estimation Verification Target
PTs Single Laboratory Validation Data modeling:modelling Method performance study Single laboratory Validation data modeling:modelling Method performance study Single laboratory validation data modeling:modelling Method performance study
63.65 60.38 66.10 140.80 11.47 12.87 14.31 0.06 0.08 0.95
Fish homogenate Rice
Surface water
Relative expanded uncertainty U (%)
Reference for the approach Eurolab.2007 ISO 5725–6 ISO-GUM EN 15763 ISO 5725–6 ISO-GUM Llorente.2012 ISO 5725–6 ISO-GUM ISO 17294–2
16.20 17.60 14.8 14.8 18.6 27.7
Table 5 Uncertainty and regulatory limits: Uncertainty approach used to assess the suitability of ICP-MS to measure contaminants in foods. Analyte
Cd Cd Cd Pb Pb Pb a b c
Foodstuff
Potato Canned fish Quinoa Tomato Infant cereal Edible oil
LoD (mg/kg)
Content (mg/kg)
Obtained
Maximum limit
0.000009 0.000009 0.000009 0.000018 0.000018 0.000018
0.01 0.01 0.02 0.01 0.02 0.01
a
Standard uncertainty (mg/kg)
Determined
Maximum level
0.03 0.01 0.0137 0.03 0.07 0.171
0.1 0.1 0.2 0.1 0.02 0.1
b
Calculated
Maximum limitc
a
0.005996 0.00246 0.00274 0.0058 0.01305 0.03078
0.00781 0.00557 0.0057 0.00766 0.01398 0.03118
0.2 0.2 0.2 0.2 0.18 0.18
Directive 2001/22/EC (2001). EC regulation 466/2001. Directive 2005/4/EC (2005).
consistent way. Therefore, in day to day analysis, bias was considered negligible. However the root mean square of bias result (RMSbias) is an uncertainty component and analysis of the distribution of individual z scores, for both testing materials, showed the same level of performance, when estimated uncertainty was calculated by Eq. (6). An example of evaluation of uncertainty by the PT approach calculated according to Eqs. (6) and (7) is presented in Table 4. The determined mean value and associated measurement uncertainty for arsenic content in salmon, as consumed in country was obtained applying the steps describe in Fig. 1. The compliance of the method with EU regulation is presented in Table 5. There was no significant effect of either sample size or food matrix on the analytical results, reinforcing the idea that our PT laboratory data could be used to estimate uncertainty. Our results are in the same line of Companyó et al. (2008). According with this experi-
ence the participation in PT schemes constitutes a preferable means to evaluate laboratory performance to demonstrate that laboratory results are consistent and accurate (Fisicaro et al., 2010). 3.5. Uncertainty evaluation and verification using several approaches Arsenic content in foods with associated measurement uncertainties, are presented in Table 4. The metalloid content was evaluated in real samples consumed in country and considered major contributors to arsenic intake. The arsenic in foodstuffs was analyzed by ICP-MS assisted by microwave assisted digestion (Salmon and Rice) or acidified with 1% (by volume) concentrated nitric acid (surface water). The analytical procedure was previous validated and quality control was in place for each assay. Associated measurement uncertainty was calculated using different approaches.
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Estimated uncertainty was derived from Eqs. (5) and (6); respectively single laboratory validation data and PTs data. Combined standard uncertainty calculated by modeling was derived from equation 1and was used to verify if the experimental approaches are valid for the analytical procedure overtime. Modeling which is the classical approach was chosen for comparison since laboratory has a historical background. This approach was previously a prerogative for accredited laboratory. Uncertainty ranges estimated by different approaches are not significantly different so we could assume that both can be applied by laboratory. Although, in detail uncertainty is slightly underestimated when reported by single laboratory validation data. In the assay for determination of arsenic in fish the different to modeling is more accentuated, may be high fat content of salmon and the binding of arsenic to phospholipids could explain the slight difference. However we face situations where uncertainties ranges do not agree as estimated by different approaches (results not found) it happened when not all sources were quantified or when matrix-matching CRM are not available. The same was discussed by Priel et al. (2009). The combination of several approaches, is recommended and reported by several authors (Medina-Pastor et al., 2011; EUROLAB technical report 1/2007, 2007). Aligned with literature target measurement uncertainty is defined as measurement uncertainty specified as an upper limit and decided on the basis of the intended use of measurement results. In our study it was derived from RSDR found in matrix matching collaborative studies. The acceptance criteria is defined as U (%) <2RSDR. In the analyzed samples calculated U(%) are in agreement with literature.
4. Conclusions The evaluation and verification of uncertainty, in analysis of inorganic contaminants in food by ICP-MS, is a complex process. In this work two approaches were compared, one of them based on a mathematical measurement model and the other based on alternative approaches supported by experimental data. The classical approach, based on mathematical modeling, can face many difficulties to identify each source and its corresponding contribution to combined uncertainty. This is due to interferences that can have different origins, from sample handling to instrumental determination, and in some cases could be impossible to identify as an individual component of uncertainty budget. The experimental approach is an easier method to apply due to the use of quality tools with which laboratories are quite familiar. The modeling approach requires a mathematical background to apply the law of propagation of uncertainty and it is difficult to highlight the advantages of such complex calculations. Furthermore experimental approaches presented in this work are in accordance with IMEKO (IMEKO, 2012) principles and will facilitate the integration of metrological tools among the TDS community. Acknowledgments This work was completed on behalf of Project GOODFISH PTDC/ SAU-ESA/103825/2008. The authors acknowledge Marina Saraiva for technical assistance. References
3.6. Uncertainty estimation and compliance with European Legislation In Table 5 is presented an overview of results and their compliance with European Legislation. The maximum standard uncertainty is designated by uncertainty function approach and calculated using the following equation.
Uf ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðLoD=2Þ2 þ ðaCÞ2
ð8Þ
where Uf is the maximum standard uncertainty, LoD is the limit of detection of the method, C is the concentration of interest and a is a numeric factor to be used depending on the value of C . The results showing data on foods under study to determine the cadmium and lead content by ICP-MS and its associated uncertainty. This feature has relevance and is required to assess and interpreted the adequacy of a method of analysis and instrumental procedure to measure small amounts of cadmium and lead eventually present in foods. However few results are published in literature. The LoD values were obtained for blank and matrix and made under repeatability conditions. Mean values with associated Uf showed content values below those settle in the EC Regulation. For Pb uncertainty range from 0.030 (edible oil) to 0.005 (tomato) and for cadmium results are also below the maximum limits lay down in 2005/4/EC. For arsenic no maximum level is yet established. Nevertheless anticipating future legislation expanded uncertainty (U) expressed by U = kUf when k = 2 and Uf estimated by equation 8 adapted from Directive 2005/4/EC (2005) was calculated. LoD data was obtained from determination of arsenic in water, rice and fish as the foodstuffs that most contribute to arsenic exposure (EFSA, 2011). The results are below 2005/4/EC criteria and agree with data published in literature (LlorenteMirandes et al., 2012) and Ferrão et al. 2011). The analytical method and instrumental procedure are suitable and comply with legislation.
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