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Trace element determination in water samples by on-line isotope dilution and inductively coupled plasma with mass spectrometry detection
Microchemical Journal 114 (2014) 99–105
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Trace element determination in water samples by on-line isotope dilution and inductively coupled plasma with mass spectrometry detection Juan Carlos Raposo a,⁎, Patricia Navarro a, Jose Ignacio Gil Felipe a, Jon Etxeandia a, Jose Antonio Carrero b, Juan Manuel Madariaga b a b
General Services of Investigation, University of the Basque Country, P.O. Box 644, E-48080 Bilbao, Spain Department of Analytical Chemistry, University of the Basque Country, P.O. Box 644, E-48080 Bilbao, Spain
a r t i c l e
i n f o
Article history: Received 8 November 2013 Received in revised form 5 December 2013 Accepted 6 December 2013 Available online 18 December 2013 Keywords: Trace metals Environmental water samples On-line isotope dilution ICP–MS Validation
a b s t r a c t This article describes the application of on-line isotope dilution mass spectrometry with inductively coupled plasma (OID–ICP–MS) to the field of trace metal analysis (B, Cd, Cr, Fe, Ni, Pb and Zn) in water samples by the certified reference material (CRM) characterization. Drinking, natural and waste water certified reference materials were analyzed. Emphasis is placed on OID–ICP–MS measurements of highest analytical quality and their validation against direct external calibration mass spectrometry with inductively coupled plasma analysis (ICP–MS). Differences in the calibration strategies such as single OID–ICP–MS versus direct external calibration ICP–MS were discussed. In general, it can be stated that OID–ICP–MS offers high accurate and precise results with small measurement uncertainties, when properly applied, compared to external calibration. Thus, OID–ICP–MS proved to be an ideal solution for routine water sample analysis, increasing sample throughput without any previous sample handling and improving the quality and reliability of the analytical results. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Water is essential for life and its quality has great impact on public health and safety. In view of the adverse health effects that may be caused by trace metals in water systems, World Health Organization (WHO) has published the Guidelines for Water Quality as the pillar of primary prevention. This point is directly related to the continuous release of trace metal contaminants into the environment from a plethora of anthropogenic sources, which can be industrial process waste streams and atmospheric emissions, the combustion of fossil fuels, mining excavations, tailings dams and urban habitation. These inputs have lead to an ongoing requirement to develop analytical methodologies for their sensitive, accurate and selective determination in many environmental sample types [1–4]. The analytical technique of choice to quantify trace metal ions in water samples has been atomic spectroscopy for many years; more specifically, inductively coupled plasma mass spectrometry (ICP–MS). Other analytical methods include flame emission spectrometry, spectrophotometric analysis, ion selective electrode potentiometry, UV–vis spectroscopy, etc. [5,6]. However, ICP–MS analysis continues to make inroads into laboratories that are requiring the lowest detection limits and
⁎ Corresponding author. Tel.: +34 946015443; fax: +34 946013500. E-mail address: [email protected] (J.C. Raposo). 0026-265X/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.microc.2013.12.004
the greatest level of productivity. The primary reasons for the growing popularity of ICP–MS can be summarized in a few points: instrument detection limits are at or below the single part per trillion (ppt) level for many of the periodic table elements, large analytical working range (nine orders of magnitude) and isotope analysis can be achieved readily [7,8]. Besides, recent developments in atomic spectrometry, particularly in ICP–MS, have opened new application fields in ultra trace, speciation and isotopic analysis, which represent new and valuable tools for the water systems characterization [9,10]. One of the advantages of mass spectrometry is the possibility of studying the isotope distribution of the elements. In the environment there is a natural occurring isotope distribution that is well established by the International Union of Pure and Applied Chemistry (IUPAC) [11]. However, there are methodologies to modify the natural isotope pattern of an element and to obtain stable and isotopically labeled/ enriched elements. The isotopically enriched element has identical properties and chemical behavior as the natural element. The use of the isotopically enriched elements for the quantification of samples is the well-known isotope dilution (ID) and it is based on the alteration of the isotope pattern of an endogenous element in the sample by adding a known quantity of an enriched isotope of the same element (spike) [12–15]. By measuring the isotope ratio of interest in the sample, in the spike and in the mixture using mass spectrometry and based on the known isotope abundances and concentration of the spike, and on the amount of sample and spike mixed, the element
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concentration in the sample can be precisely and accurately calculated [16–20]. In this case, isotope dilution mass spectrometry with inductively coupled plasma (ID–ICP–MS) is a direct method which has been identified by the Consultative Committee for Amount of Substances (CCQM) of the International Committee of Weights and Measures (CIPM) to have the potential to be a primary method [21]. As ID–ICP– MS only requires isotope ratio determination and mass measurements, the advantages compared to other methods become apparent, mainly, the methodological calibration is not needed. Although matrix-matched calibration combined with the use of the specific element signal as internal standard (more correctly: internal reference used for normalization purposes) is a suitable calibration method to avoid matrix effects and signal instabilities, ID is a more robust calibration method, resulting in a better correction for matrix effects (suppression) and other sources of change in the instrument sensitivity, thus, leading to results showing higher accuracy and precision. Mostly, ID is applied to the analysis of dissolved samples, as the addition of the spike and equilibration of the isotopes is in this case typically quite simple with little sample manipulation in an off-line mode. However, the use of ID–ICP–MS as a routine technique in testing and/ or monitoring laboratories is often judged as a costly and tedious analytical method (in “batch”). Sample preparation for conventional ID–ICP– MS is time-consuming and an important error source by isotope spiking of each sample individually. For that, the spiking of each sample can be simplified by the continuous application of an “on-line” isotope dilution (OID–ICP–MS) by means of a T-connection in a peristaltic pump system before the nebulization process. The on-line isotope dilution concept was firstly introduced by Lásztity et al. in 1989 [22] for elemental analysis. The stabilization time between the analyte and the spike is a critical term. In ICP techniques, a powerful atomization is always present and the analytical response only depends on the element amount into the plasma. However, it should be recommended to test this sensitivity in each analytical case (in here) and demonstrate that the chemical form of the elements in the plasma is not a determinant point. The main goal of this on-line system is to increase the productivity in the routine environmental analysis with a more precise and accurate methodology than ICP–MS offers. In that sense, the OID–ICP–MS was validated by comparison against a direct external calibration ICP–MS system in different reference water materials. Our laboratory was recently recognized (2013) by the National Organization of Accreditation (ENAC, Spain) [23] as reliable of the ISO 17025 [24] requirements in the trace metal analysis by ICP–MS in different water matrices. The comparison of both instrumental systems (OID–ICP–MS versus ICP–MS) was carried out by different tests and statistical treatments [25,26].
2. Materials and methods 2.1. Reagents and solutions All reagents were of analytical-reagent grade and ultrapure water (18.2 MΩ cm at 25 °C) obtained from a Milli-Q® Element A10 system (Millipore™, Bedford, USA) was employed. The volumetric glassware was grade A and was calibrated at laboratory temperature. Nitric acid (69%, Tracepur) was provided by Merck (Darmstadt, Germany). A 10 mg L−1 multi-element isotopically-enriched standard IES-WAK (B, Cd, Cr, Fe, Ga, Ni, Pb, Rh, and Zn) was obtained from ISC-Science (Oviedo, Spain). The isotopic distribution in the IES-WAK solution is shown in Table 1. Natural abundances of the elements considered are also listed in Table 1 [27]. These elements were defined by the Spanish Government Regulations (RD 140/2003) [28] as indicator parameters in drinking water quality assurance. The natural reference standard (Cst) and the calibration solutions, for external calibration, were prepared from a multi-element 1000 mg L−1 standard solution (Alfa Aesar, Specpure, Ward Hill, USA). Additionally, a 10 mg L− 1 multi-element standard solution (Sc, Y, Rh, Ho) from
Table 1 Isotopic abundances of naturally occurring elements and multi-element spike (IES-WAK) solution. Element
Isotope
a
Natural abundance, %
B
10 11 45 52 53 55 56 57 59 60 61 66 67 71 89 103 111 113 114 165 206 207 208
99.1 0.90 100 2.26 97.75 0 2.20 95.80 0 9.19 86.20 3.88 89.60 100 0 100 96.16 0.30 0.86 0 0 94.60 2.79
19.9 80.1 100 83.79 9.50 100 91.75 2.12 100 26.22 1.14 27.9 4.10 39.89 100 100 12.80 12.22 28.73 100 24.1 22.2 52.4
Sc Cr Mn Fe Co Ni Zn Ga Y Rh Cd
Ho Pb
IES-WAK abundance, %
a IES-WAK is the multi-element isotopically-enriched standard for B, Cd, Cr, Fe, Ga, Ni, Pb, Rh, and Zn.
Inorganic Ventures (Equilab, Madrid, Spain) was also used as internal standard in direct ICP–MS analysis. Different certified reference materials (CRMs) were used: natural lake water (TMDA 52.3 from UKAS Reference Materials, UK) and freshwater (NIST 1643e from the National Institute of Standards and Technology, USA). Reference materials (RMs) were also used from different proficiency testing schemes performed in 2012: drinking water (SCAB 001 from Round III scheme, Ielab, Spain; SCAB 002 from MAC 2 scheme, GSC, Spain) and surface water (SCAB 003 from AS03032 scheme from QRL Services, Spain). Apart from that, the Aquacheck scheme (LGC Standards, Spain) was performed in several Rounds during 2012 in groundwater (SCAB 004), continental (SCAB 005) and waste waters (SCAB 006). 2.2. Instrumentation Inductively coupled plasma with mass detector (7700x, Agilent Technologies, Palo Alto, USA) was used for trace metal determinations using a MicroMist micro-uptake glass concentric nebulizer (Glass Expansion, West Melbourne, Victoria, Australia). In order to reduce MO+ formation in the plasma, the spray chamber was Peltier cooled at 2 °C. A standard quartz torch with 2.5 mm internal diameter injector was used. The instrument was equipped with an Agilent I-AS integrated autosampler, and the on-line internal standard addition kit was used for the on-line addition of the multi-element isotope spike solution or internal standards in external calibration mode. Finally, standard nickel cones (sample and skimmer) were used. The optimization of the ICP–MS conditions was achieved by adjusting the torch position and tuning for reduced oxide and doubly charged ion formation with a standard tuning solution containing 1.0 μg L− 1 of 7Li, 24Mg, 59Co, 89Y, 140Ce and 205Tl in 1.0% HNO3. This equipment includes a collision cell (He gas, ORS3 system, Agilent Technologies©) for discriminate spectral interferences with high performance for all the trace metals considered in here. All elements were measured using a single set of operating conditions without switching cell gas modes. Operating conditions are shown in Table 2. The acquisition masses and integration times (Table 2) provided more
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than sufficient sensitivity to meet all certified values. In addition, EPA 6020 [29] recommendations were followed for interference overcoming such as correction equations for Pb or Cd. Measurement of Cd by ICP–MS is known to suffer from spectral interference [30]. The Cd isotopes suffer from interference by Sn at 112Cd, 114Cd and 116Cd and by molybdenum oxide (MoO+) at all Cd isotopes except 106Cd. Sn was not detectable in water and QC samples. On the other hand, the formation of Mo oxides was overcome (b 1.0%) by means of the ORS3 He collision cell. As estimation, total analysis time per sample, including wash-in and washout, was 2.5 min.
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on various QC solutions. The alternation of measurements between the sample and QC samples compensates for an error due to longterm instrumental drift. These QC samples containing the analytes were spiked solutions prepared daily for each element in the laboratory. All the study samples and QC samples were subjected to the same equilibration time and on-line analyzed alongside in the same analysis procedure. In order to perform an effective mass bias correction, an initial standard solution of known isotopic composition was analyzed at the beginning of each batch. Each sample was measured four times and final concentration was obtained from the average of the four measurements. A scheme of the analysis procedure is shown in Fig. 1.
2.3. Methodology The isotope dilution is known to reduce many systematic errors, such as mass bias, detector dead time and characterization of the spike [31]. In this case, the analyte concentrations in the water samples were re-calculated using the following simplified Eq. (1) [32], obtained from isotope dilution theory: C s ¼ C st
Rst Rn −1 Rsp −Rm Rm Rn −1 Rsp −Rst
ð1Þ
where, Cs is the concentration of the analyte in the sample, Cst the concentration of the analyte in the natural reference standard (st), Rsp the isotope ratio of spike (sp), Rn the natural isotope ratio of analyte, Rm the measured isotope ratio of the mixed sample and spike solution and Rst the measured isotope ratio of the mixed reference standard and spike solution. The concentrations of the mono-isotopic elements were simultaneously obtained by using an isotope dilution approach in which the internal standards (Ga and Rh) were used as tracer isotopes. 2.4. Sample preparation and analysis procedure Certified reference water samples were provided by the suppliers as filtered (b0.45 μm) and stabilized materials (pH b 2). No more specific pre-treatment was required for the inter-comparison samples. At least two reagent blanks were also prepared by subjecting them to all sample analysis steps to evaluate possible blank contributions. Four consecutive analyses were performed by OID–ICP–MS mode. Four sub-samples were prepared for each analysis. The analytical sequence adopts the bracketing technique which involves making measurements on each sample between measurements
Table 2 Operating parameter values used for ICP–MS and OID–ICP–MS.
3. Results and discussion The multi-element isotopic standard acts as both a calibration standard for multi isotopic elements and as an internal standard for mono isotopic ones. In isotope dilution analysis (ID–ICP–MS), the amount of spike added to the sample is usually optimized by calculating the ideal ratio using the error magnification factor. However, the spike amount is constant in the OID–ICP–MS mode, but it is preferable to over-spike the samples to yield better counting statistics and therefore less uncertainty in the isotope ratio measurements. As a result, high productivity was achieved through the combination of high sample throughput and the elimination of periodic recalibration and re-analysis. Moreover, on-line addition of the spiking solution eliminated the increased sample handling required for individual sample spiking in conventional isotope dilution analysis.
Reference standard measurement, 50 µg L-1 trace elements
Blank measurements
Initial QC
Instrumental conditions RF power (W) Plasma gas flow (L min−1) Carrier gas flow (L min−1) Helium collision flow (mL min−1) Kinetic energy discrimination (V) Spray chamber temperature (°C)
1550 15 1.05 4.5 2.0 2.0
Sample analysis
Data acquisition Mode
Spectrum
Integration time (ms) Point/mass Replicates
0.2 1 3
Periodic QC, every 10 samples
Analytical detection Isotopes
10,11
B, 45Sc, 52,53Cr, 55Mn, 56,57Fe, 59Co, 60,61Ni, Zn,71Ga, 103Rh, 111,113,114Cd, 206,207,208Pb
66,67
Fig. 1. Diagram defining the analysis procedure followed by using the OID–ICP–MS system for the water samples.
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Additionally, different water reference materials from diverse proficiency testing schemes were also used to assess the trueness of the OID–ICP–MS mode. In this case, the reference value corresponded to the consensus value obtained by the participant laboratories. Tables 4 and 5 collect the number of participants (N) and Z-score as a measure of the trueness obtained for each sample water type. This Z-score value could be obtained from the following expression defined by ISO 17043 [34]:
3.1. Analytical performance Concentration and ratios of isotope masses of the each element measured in QC solutions agreed with expected natural abundance values for these ratios, confirming the absence of any detectable polyatomic interference arising from sample matrices. The effective quantitative range of OID–ICP–MS depends on the concentration of the reference standard for each analyte. The concentration of each element in the reference standard should ideally be midway between the lower and upper quantification limit becoming into a quantification range of at least 4 orders of magnitude. As all analytes are present in the multi-element standard at the appropriate concentration level, the spike can be added on-line, automating the spike addition. The normal application ranges obtained for the tested elements were between 0.1 and 1000 μg L−1. The analytical performance of the OID–ICP–MS method was evaluated. As it can be seen, the concentrations found in the two water certified reference materials were in good agreement with the certified values for all analytes (Table 3). A t-test (Eq. (2)) was applied to the obtained results and confirmed no significant difference between the results obtained using the proposed method and the certified values at the confidence level of 95% [33].
t exp
C ref −C exp ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 s2 u C ref þ n
Z−score ¼
ð4Þ
where x and X are the experimental and consensus values, respectively. SDPA is the standard deviation for the proficiency assessment. The Z-score obtained by each participant was provided by the organizer and it should be ranged between (−2 and 2) to be defined as a reported satisfactory value. As it can be seen in Tables 4 and 5, the Z-score obtained fulfilled the proficiency testing requirements. This ideal situation (Z-score close to 0 values) means a complete concordance of the data obtained with all the participants in the proficiency testing schemes. 3.2. OID–ICP–MS versus ICP–MS
ð2Þ
Trueness was assessed using a reference method that is well known and established in our laboratory. The test samples used were different CRMs and RMs from PT (proficiency testing) schemes in order to guarantee the homogeneity, stability and similarity to routine water samples. In that sense, an accredited (under ISO 17025 requirements) external calibration ICP–MS was chosen as a reference method. As shown in Tables 3–5, it is evident that the proposed OID–ICP–MS method is superior to the external calibration method in terms of analytical accuracy and precision, for all the samples considered. For example, Z-score value obtained for OID–ICP–MS mode was closer to 0 (ideal situation) value than for ICP–MS mode. This accuracy is due to the use of on-line isotope dilution, which could efficiently eliminate the matrix effect and obtain higher analytical quality results. Although the use of multivariate statistical procedures is widely employed [25], a univariate procedure was considered sufficient in this work, because each element contents could be obtained separately and efficiently by the use of the ORS3 He collision cell. Ideally, the results obtained by using both ICP–MS and OID–ICP–MS modes should be completely correlated (equal to unity). However, the correlation could not be only interpreted in terms of accuracy and, the RSD values are only a preliminary indication, thus statistical tests (t-test, regression line and comparison of variances) should be applied to investigate whether the differences obtained are significant. The null hypothesis defines the no statistical distinction between the values obtained from both analysis modes. For that, t-student
where u(Cref) is the standard uncertainty of the reference value. The texp value calculated is compared with the two-sided t tabulated (ttab) for a 95% confidence level and the effective degrees of freedom, τeff, obtained by using the following approach [33]:
τeff
ðx−X Þ SDPA
2 2 2 u C ref þ sn ¼ 4 u C ref s4 þ τref n−1
ð3Þ
where τref is the number of freedom degrees associated with u(Cref). For all the elements, texp b ttab was obtained in both CRMs considering a 95% confidence level. The recoveries for the water materials fell within 90–100% for all elements in all the CRMs considered, demonstrating accurate measurement at different concentration levels. Experimental data obtained in both modes (ICP–MS and OID–ICP–MS) were based on 4 replicates in each case, and the measure of uncertainty as standard deviation (SD) is also listed in Table 3. The precision of the analysis showed a relative standard deviation (%RSD) of b10% for 4 different measurements of the same sample in all determinations.
Table 3 Water CRM concentrations (μg L−1) obtained using the OID–ICP–MS and external ICP–MS calibration. Element
B Co Cr Cd Fe Mn Ni Pb Zn
Lake water, TMDA 52.3 (n = 4)
Freshwater, NIST 1643 e (n = 4)
OID–ICP–MS
ICP–MS
11.0 140 167 91.8 406 201 286 365 255
11.7 145 163 92.4 428 210 300 365 275
± ± ± ± ± ± ± ± ±
0.9 1 1 0.2 1 1 2 1 2
± ± ± ± ± ± ± ± ±
1.0 6 4 0.4 2 5 20 2 9
Certified
OID–ICP–MS
ICP–MS
10.7 136 165 90.9 412 198 274 358 263
166 ± 26.9 ± 20.6 ± 6.56 ± 97.9 ± 39.0 ± 63.4 ± 19.4 ± 76.8 ±
170 25.1 19.8 6.58 97.8 38.5 58.0 19.7 74.4
± ± ± ± ± ± ± ± ±
1.2 6 5 4.0 19 7 10 14 12
6 0.2 0.2 0.08 0.1 0.2 0.3 0.1 2
± ± ± ± ± ± ± ± ±
Certified 18 0.8 0.4 0.11 0.5 0.8 1.0 0.1 2
158 27.1 20.4 6.57 98.1 39.0 62.4 19.6 78.5
± ± ± ± ± ± ± ± ±
2 0.2 0.1 0.04 0.7 0.2 0.4 0.1 1.1
J.C. Raposo et al. / Microchemical Journal 114 (2014) 99–105 Table 4 Z-score values obtained in the proficiency testing schemes for water samples using the OID–ICP–MS and external ICP–MS calibration. Element
B Cr Cd Fe Mn Ni Pb Zn
SCAB 001 (N = 99)
SCAB 002 (N = 46)
SCAB 003 (N = 11)
OID–ICP–MS
OID–ICP–MS
OID–ICP–MS
ICP–MS
0.20
ICP–MS
ICP–MS
0.10
0.15
0.10 0.20 −0.07
0.10 0.24 0.03
−0.02 0.15 0.20
−0.10 −0.72 −0.51
0.23
0.51
−0.04 0.08
0.06 −0.71
0.31
−0.08 0.58
0.10 0.10
parameter for a significance level of 95% was calculated taking into account the SD values obtained [25]: ðX ICPMS −X OID−ICPMS Þ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi t exp ¼ 1 1 σ ðX tot Þ þ NICPMS NOID−ICPMS
ð5Þ
a change in slope, thus these differences between slopes gave an estimation of the proportional error. A constant systematic error shows up in a value of the intercept different from zero [25]. The fit test equation can be expressed as: y ¼ a þ bx
200
ðNICPMS −1Þσ ðX ICPMS Þ2 þ ðN OID−ICPMS −1Þσ ðX OID−ICPMS Þ2 : ð6Þ NICPMS þ N OID−ICPMS −2
Cr
150
OID-ICPMS
2
ð8Þ
where y and x are the experimental results obtained by both analysis modes from the CRM samples. In order to be statistically correct, orthogonal regression takes into account errors in x and y. However, the usual least-squares regression is often employed in practice. This calculation requires two steps: 1) the determination of a and b from the experimental data and 2) a test to investigate whether a and b significantly differed from 0 and 1, respectively. As an example, Fig. 2 shows the linear regression obtained for water CRM and RM samples for Cd, Cr and Pb. As it can be seen in Fig. 2, the multi-isotopic-elements were close to those assumptions for all the CRMs considered and not significant difference of the slope and ordinate were obtained from 1 and 0 values. Moreover, the experimental results obtained for all elements are in good agreement with the certified values for all the CRMs considered. The same tendencies were
where X are the mean value of the element obtained by both modes, N the number of determinations and σ the total SD obtained from the expression: σ ðX tot Þ ¼
103
If the null hypothesis is true, the ratio of the variances should not differ much from the unity. The Fexp parameter defines the corresponding ratio of variances as:
100
y = 1.0214x- 0.1773 R² = 0.9998
50
0 0
2
σ ðX ICPMS Þ : σ ðX OID−ICPMS Þ2
50
It is usual to calculate the F ratio by dividing the largest variance by the smallest in order to obtain a value equal to or larger than unity. The data obtained in this work showed this concordance and different experimental parameters (Fexp) were obtained from Eq. (7) for each experimental determination. This critical parameter depends on the significance level and the number of the determinations (Fcrit = 9.277, [25], for a 95% significance level and 4 determinations). As Fcrit N Fexp was obtained for all the elements, it indicated the correctness of the null hypothesis and that the procedures are not significantly different in precision. Additionally, both analysis modes were compared by plotting in a X– Y graph the obtained values for the CRMs. A straight regression line should be obtained with a slope (b) of exactly 1 and an intercept on the ordinate (a) of zero. Moreover, all the experimental data should fall on the corresponding line. A proportional systematic error leads to
100
B Co Cr Cd Fe Mn Ni Pb Zn
SCAB 004 (N = 28)
SCAB 005 (N = 30)
SCAB 006 (N = 50)
OID–ICP–MS
OID–ICP–MS
OID–ICP–MS
ICP–MS
0.05
−0.17
0.12 0.26 0.21 0.10 0.03 0.23 −0.02
ICP–MS −0.64 −1.29 −0.77 −0.83 −0.60 −0.40 −0.70
0.15 0.12
−0.06 −0.13
ICP–MS
200
Cd
y = 0.9955x + 0.1801 R² = 0.9999
50
0 0
20
40
60
80
100
ICPMS 400
OID-ICPMS
Table 5 Z-score values obtained in the Aquacheck PT scheme using the OID–ICP–MS and external ICP–MS calibration.
150
ICPMS
Pb
350
Element
100
ð7Þ
OID-ICPMS
F exp ¼
300 250 y = 1.0032x + 0.0367 R² = 0.9999
200 150 100 50
−0.20 −1.23
0 0.23 0.15
0.80 0.92
0.11
−0.28
0.25 −0.52
0
100
200
300
400
ICPMS Fig. 2. Linear regressions obtained for the OID–ICP–MS versus ICP–MS analysis of CRM and RM water samples. • CRM and ◦ RM data in μg L−1.
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obtained for the other multi- (B, Fe and Ni) and monoisotopic elements studied (Co and Mn). The results obtained from both analysis modes were confirmed by paired Student's test. This test requires a normal distribution of the difference of the values obtained using both analytical procedures. The calculated t-value was compared with the theoretical one for the 95% significance level. In all cases analyzed, tcalculated b ttheoretical, thus no difference between the both modes was obtained. Additionally, plots of the residual values were performed. These residuals were calculated by subtracting the model estimates (certified data for CRMs and consensus for RMs) from the experimental OID–ICP–MS data.
Table 6 Uncertainty (U, %) obtained for the OID–ICP–MS and external ICP–MS calibration.
e ¼ xexp −a−bxcert
The uncertainty estimation (U) for each analyte was found to be fit for purpose as the relative expanded uncertainty (Table 6) for each analyte was less than 10% (k = 2) for OID–ICP–MS mode:
ð9Þ
where a and b are respectively the intercept and the slope of the straight lines. Fig. 3 collects the randomly distribution of the residuals obtained for Cd, Cr and Pb elements.
Element
B Cr Cd Fe Ni Pb Zn
U ¼ 2u ¼
Lake water, TMDA 52.3
Freshwater, NIST 1643 e
OID–ICP–MS
ICP–MS
OID–ICP–MS
ICP–MS
8 6 8 9 8 8 10
10 10 9 10 9 8 13
7 4 5 1 2 6 8
10 8 7 2 10 9 10
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u2r þ u2h þ u2rv :
ð13Þ
3.3. Calculation of the method uncertainty The major contributors in the overall uncertainty budget for each analyte are method recovery, biases in results due to the choice of analysis mode, method precision and preparation of calibration standard. The uncertainty for the mean value (ur) could be calculated from the 4 measurements performed for each CRM sample and considering the standard deviation of those 4 repetitions. As it was pointed out before, excellent precision was observed in all analysis. This method precision also captured any biases by repeatability of the experiment since the results obtained were performed by the same analyst on different days (n = 4): ur ¼ w
SD : n
ð10Þ
The uncertainty due to the sample heterogeneity was calculated by taking repeatability and the repetition numbers of each sample (n = 1): SD uh ¼ w pffiffiffi : n
ð11Þ
The uncertainty of isotope ratios and instrumental drift can be assumed to be captured within method precision and no further uncertainty term was needed. As it can be seen in Table 6, the uncertainty obtained by OID–ICP–MS was significantly lower than that obtained by ICP–MS. 4. Conclusion The analysis of trace metals in environmental samples often generates data with large uncertainty values and poor repeatability. This kind of samples have often variable composition and high levels of matrix elements. This fact increases the unpredictable matrix-based polyatomic interferences that affect commonly to required analytes. OID– ICP–MS was proven to be less prone to matrix effects. OID–ICP–MS offered the possibility to determine trace concentrations of elements in virtually any environmental water matrix, with superior accuracy and precision compared to external calibration. This combination has the potential to be an ideal solution for high matrix sample analysis, providing increased sample throughput and improved productivity, as well as better accuracy, absolute quantification, and reduced uncertainty. Acknowledgments
In Eqs. (10) and (11), w is the WECC factor based on the number of measurements performed (typically, w = 1.7 for n = 4). Finally, the uncertainties (urv) for CRM samples were calculated by combining the uncertainties of the primary certified standards as (K = 2): urv ¼
U cert : K
ð12Þ
(yOID-ICPMS)-(yestimated)
3 2 Cd
1
Pb Cr
0 -1 -2
Fig. 3. Distribution of the residuals obtained in the linear regression for the OID–ICP–MS versus ICP–MS analysis (CRM data in black).
This work has been technically supported by Innovate Solutions in Chemistry S.L. (Oviedo, Spain). The authors acknowledge the technical and commercial relationship with J. A. Rodriguez, G. Centineo and S. García (ISC specialists). References [1] X. Gao, F. Zhou, C.T.A. Chen, Pollution status of the Bohai Sea: an overview of the environmental quality assessment related trace metals, Environ. Int. 62 (2014) 12–30. [2] I.D. Marsden, B.D. Smith, P.S. Rainbow, Effects of environmental and physiological variables on the accumulated concentrations of trace metals in the New Zealand cockle Austrovenus stutchburyi, Sci. Total Environ. 470 (2014) 324–339. [3] Z. Zhu, G. Sun, X. Bi, Z. Li, G. Yu, Identification of trace metal pollution in urban dust from kindergartens using magnetic, geochemical and lead isotopic analyses, Atmos. Environ. 77 (2013) 9–15. [4] D. Varrica, E. Tamburo, G. Dongarrà, F. Sposito, Trace elements in scalp hair of children chronically exposed to volcanic activity (Mt. Etna, Italy), Sci. Total Environ. 470 (2014) 117–126. [5] S.E. Bufflap, H.E. Allen, Sediment pore water collection methods for trace metal analysis: a review, Water Res. 29 (1995) 165–177. [6] M. Pesavento, G. Alberti, R. Biesuz, Analytical methods for determination of free metal ion concentration, labile species fraction and metal complexation capacity of environmental waters: a review, Anal. Chim. Acta. 631 (2009) 129–141. [7] M.R. Cave, O. Butler, J.M. Cook, M.S. Cresser, L.M. Garden, D.L. Miles, Environmental analysis, J. Anal. At. Spectrom. 15 (2000) 181–235. [8] M.R. Cave, O. Butler, R.N. Chenery, J.M. Cook, M.S. Cresser, D.L. Miles, Atomic spectrometry update. Environmental analysis, J. Anal. At. Spectrom. 16 (2001) 194–235. [9] W. Maher, F. Krikowa, M. Ellwood, S. Foster, R. Jagtap, G. Raber, Overview of hyphenated techniques using an ICP–MS detector with an emphasis on extraction
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[22] A. Lásztity, M. Viczián, X. Wang, R.M. Barnes, Sample analysis by on‐line isotope dilution inductively coupled plasma mass spectrometry, J. Anal. At. Spectrom. 4 (1989) 761–779. [23] http://www.enac.es. [24] ISO, ISO/IEC 17025 General Requirements for the Competence of Calibration and Testing Laboratories, 2005. [25] J.N. Miller, J.C. Miller, Statistic and Chemometry for Analytical Chemistry, Prentice Hall, Madrid, Spain, 2002. [26] W. Funk, V. Dammann, G. Donnevert, Quality Assurance in Analytical Chemistry, Wiley-VCH, Weinheim, Germany, 2007. [27] International Union of Pure and Applied Chemistry (IUPAC), http://www.iupac.org. [28] Spanish Government (Ministry of Presidency), Sanitary Quality Criteria for Drinking Water RD 140/2003, 2003. 7228–7245. [29] EPA 6020A, Inductively Coupled Plasma Mass Spectrometry, Environmental pollution agency, USA, 2007. [30] E. Vassileva, C.R. Quétel, I. Petrov, Certification of the Cu and Cd amount contents in artificial food digest using isotope dilution inductively coupled plasma mass spectrometry for Pilot Study 13 of the Comité Consultatif pour la Quantité de Matière, Spectrochim. Acta B 58 (2003) 1553–1565. [31] A. Henrion, Reduction of systematic errors in quantitative analysis by isotope dilution mass spectrometry (IDMS): an iterative method, Fresenius J. Anal. Chem. 350 (1994) 657–658. [32] G. Centineo, J.A. Rodriguez Castrillón, On-line Isotope Dilution Analysis ICP–MS: Technical Overview, ISC-Science Publication, Spain, 2011. (http://www.isc-science. com). [33] R. Boque, A. Maroto, J. Riu, F.X. Rius, Validation of analytical methods, Oils Greases 53 (2002) 128–143. [34] ISO, 17043 proficiency assessments, General Requirement for the Proficiency Testing Schemes2010.
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Microchemical Journal journal homepage: www.elsevier.com/locate/microc
Trace element determination in water samples by on-line isotope dilution and inductively coupled plasma with mass spectrometry detection Juan Carlos Raposo a,⁎, Patricia Navarro a, Jose Ignacio Gil Felipe a, Jon Etxeandia a, Jose Antonio Carrero b, Juan Manuel Madariaga b a b
General Services of Investigation, University of the Basque Country, P.O. Box 644, E-48080 Bilbao, Spain Department of Analytical Chemistry, University of the Basque Country, P.O. Box 644, E-48080 Bilbao, Spain
a r t i c l e
i n f o
Article history: Received 8 November 2013 Received in revised form 5 December 2013 Accepted 6 December 2013 Available online 18 December 2013 Keywords: Trace metals Environmental water samples On-line isotope dilution ICP–MS Validation
a b s t r a c t This article describes the application of on-line isotope dilution mass spectrometry with inductively coupled plasma (OID–ICP–MS) to the field of trace metal analysis (B, Cd, Cr, Fe, Ni, Pb and Zn) in water samples by the certified reference material (CRM) characterization. Drinking, natural and waste water certified reference materials were analyzed. Emphasis is placed on OID–ICP–MS measurements of highest analytical quality and their validation against direct external calibration mass spectrometry with inductively coupled plasma analysis (ICP–MS). Differences in the calibration strategies such as single OID–ICP–MS versus direct external calibration ICP–MS were discussed. In general, it can be stated that OID–ICP–MS offers high accurate and precise results with small measurement uncertainties, when properly applied, compared to external calibration. Thus, OID–ICP–MS proved to be an ideal solution for routine water sample analysis, increasing sample throughput without any previous sample handling and improving the quality and reliability of the analytical results. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Water is essential for life and its quality has great impact on public health and safety. In view of the adverse health effects that may be caused by trace metals in water systems, World Health Organization (WHO) has published the Guidelines for Water Quality as the pillar of primary prevention. This point is directly related to the continuous release of trace metal contaminants into the environment from a plethora of anthropogenic sources, which can be industrial process waste streams and atmospheric emissions, the combustion of fossil fuels, mining excavations, tailings dams and urban habitation. These inputs have lead to an ongoing requirement to develop analytical methodologies for their sensitive, accurate and selective determination in many environmental sample types [1–4]. The analytical technique of choice to quantify trace metal ions in water samples has been atomic spectroscopy for many years; more specifically, inductively coupled plasma mass spectrometry (ICP–MS). Other analytical methods include flame emission spectrometry, spectrophotometric analysis, ion selective electrode potentiometry, UV–vis spectroscopy, etc. [5,6]. However, ICP–MS analysis continues to make inroads into laboratories that are requiring the lowest detection limits and
⁎ Corresponding author. Tel.: +34 946015443; fax: +34 946013500. E-mail address: [email protected] (J.C. Raposo). 0026-265X/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.microc.2013.12.004
the greatest level of productivity. The primary reasons for the growing popularity of ICP–MS can be summarized in a few points: instrument detection limits are at or below the single part per trillion (ppt) level for many of the periodic table elements, large analytical working range (nine orders of magnitude) and isotope analysis can be achieved readily [7,8]. Besides, recent developments in atomic spectrometry, particularly in ICP–MS, have opened new application fields in ultra trace, speciation and isotopic analysis, which represent new and valuable tools for the water systems characterization [9,10]. One of the advantages of mass spectrometry is the possibility of studying the isotope distribution of the elements. In the environment there is a natural occurring isotope distribution that is well established by the International Union of Pure and Applied Chemistry (IUPAC) [11]. However, there are methodologies to modify the natural isotope pattern of an element and to obtain stable and isotopically labeled/ enriched elements. The isotopically enriched element has identical properties and chemical behavior as the natural element. The use of the isotopically enriched elements for the quantification of samples is the well-known isotope dilution (ID) and it is based on the alteration of the isotope pattern of an endogenous element in the sample by adding a known quantity of an enriched isotope of the same element (spike) [12–15]. By measuring the isotope ratio of interest in the sample, in the spike and in the mixture using mass spectrometry and based on the known isotope abundances and concentration of the spike, and on the amount of sample and spike mixed, the element
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concentration in the sample can be precisely and accurately calculated [16–20]. In this case, isotope dilution mass spectrometry with inductively coupled plasma (ID–ICP–MS) is a direct method which has been identified by the Consultative Committee for Amount of Substances (CCQM) of the International Committee of Weights and Measures (CIPM) to have the potential to be a primary method [21]. As ID–ICP– MS only requires isotope ratio determination and mass measurements, the advantages compared to other methods become apparent, mainly, the methodological calibration is not needed. Although matrix-matched calibration combined with the use of the specific element signal as internal standard (more correctly: internal reference used for normalization purposes) is a suitable calibration method to avoid matrix effects and signal instabilities, ID is a more robust calibration method, resulting in a better correction for matrix effects (suppression) and other sources of change in the instrument sensitivity, thus, leading to results showing higher accuracy and precision. Mostly, ID is applied to the analysis of dissolved samples, as the addition of the spike and equilibration of the isotopes is in this case typically quite simple with little sample manipulation in an off-line mode. However, the use of ID–ICP–MS as a routine technique in testing and/ or monitoring laboratories is often judged as a costly and tedious analytical method (in “batch”). Sample preparation for conventional ID–ICP– MS is time-consuming and an important error source by isotope spiking of each sample individually. For that, the spiking of each sample can be simplified by the continuous application of an “on-line” isotope dilution (OID–ICP–MS) by means of a T-connection in a peristaltic pump system before the nebulization process. The on-line isotope dilution concept was firstly introduced by Lásztity et al. in 1989 [22] for elemental analysis. The stabilization time between the analyte and the spike is a critical term. In ICP techniques, a powerful atomization is always present and the analytical response only depends on the element amount into the plasma. However, it should be recommended to test this sensitivity in each analytical case (in here) and demonstrate that the chemical form of the elements in the plasma is not a determinant point. The main goal of this on-line system is to increase the productivity in the routine environmental analysis with a more precise and accurate methodology than ICP–MS offers. In that sense, the OID–ICP–MS was validated by comparison against a direct external calibration ICP–MS system in different reference water materials. Our laboratory was recently recognized (2013) by the National Organization of Accreditation (ENAC, Spain) [23] as reliable of the ISO 17025 [24] requirements in the trace metal analysis by ICP–MS in different water matrices. The comparison of both instrumental systems (OID–ICP–MS versus ICP–MS) was carried out by different tests and statistical treatments [25,26].
2. Materials and methods 2.1. Reagents and solutions All reagents were of analytical-reagent grade and ultrapure water (18.2 MΩ cm at 25 °C) obtained from a Milli-Q® Element A10 system (Millipore™, Bedford, USA) was employed. The volumetric glassware was grade A and was calibrated at laboratory temperature. Nitric acid (69%, Tracepur) was provided by Merck (Darmstadt, Germany). A 10 mg L−1 multi-element isotopically-enriched standard IES-WAK (B, Cd, Cr, Fe, Ga, Ni, Pb, Rh, and Zn) was obtained from ISC-Science (Oviedo, Spain). The isotopic distribution in the IES-WAK solution is shown in Table 1. Natural abundances of the elements considered are also listed in Table 1 [27]. These elements were defined by the Spanish Government Regulations (RD 140/2003) [28] as indicator parameters in drinking water quality assurance. The natural reference standard (Cst) and the calibration solutions, for external calibration, were prepared from a multi-element 1000 mg L−1 standard solution (Alfa Aesar, Specpure, Ward Hill, USA). Additionally, a 10 mg L− 1 multi-element standard solution (Sc, Y, Rh, Ho) from
Table 1 Isotopic abundances of naturally occurring elements and multi-element spike (IES-WAK) solution. Element
Isotope
a
Natural abundance, %
B
10 11 45 52 53 55 56 57 59 60 61 66 67 71 89 103 111 113 114 165 206 207 208
99.1 0.90 100 2.26 97.75 0 2.20 95.80 0 9.19 86.20 3.88 89.60 100 0 100 96.16 0.30 0.86 0 0 94.60 2.79
19.9 80.1 100 83.79 9.50 100 91.75 2.12 100 26.22 1.14 27.9 4.10 39.89 100 100 12.80 12.22 28.73 100 24.1 22.2 52.4
Sc Cr Mn Fe Co Ni Zn Ga Y Rh Cd
Ho Pb
IES-WAK abundance, %
a IES-WAK is the multi-element isotopically-enriched standard for B, Cd, Cr, Fe, Ga, Ni, Pb, Rh, and Zn.
Inorganic Ventures (Equilab, Madrid, Spain) was also used as internal standard in direct ICP–MS analysis. Different certified reference materials (CRMs) were used: natural lake water (TMDA 52.3 from UKAS Reference Materials, UK) and freshwater (NIST 1643e from the National Institute of Standards and Technology, USA). Reference materials (RMs) were also used from different proficiency testing schemes performed in 2012: drinking water (SCAB 001 from Round III scheme, Ielab, Spain; SCAB 002 from MAC 2 scheme, GSC, Spain) and surface water (SCAB 003 from AS03032 scheme from QRL Services, Spain). Apart from that, the Aquacheck scheme (LGC Standards, Spain) was performed in several Rounds during 2012 in groundwater (SCAB 004), continental (SCAB 005) and waste waters (SCAB 006). 2.2. Instrumentation Inductively coupled plasma with mass detector (7700x, Agilent Technologies, Palo Alto, USA) was used for trace metal determinations using a MicroMist micro-uptake glass concentric nebulizer (Glass Expansion, West Melbourne, Victoria, Australia). In order to reduce MO+ formation in the plasma, the spray chamber was Peltier cooled at 2 °C. A standard quartz torch with 2.5 mm internal diameter injector was used. The instrument was equipped with an Agilent I-AS integrated autosampler, and the on-line internal standard addition kit was used for the on-line addition of the multi-element isotope spike solution or internal standards in external calibration mode. Finally, standard nickel cones (sample and skimmer) were used. The optimization of the ICP–MS conditions was achieved by adjusting the torch position and tuning for reduced oxide and doubly charged ion formation with a standard tuning solution containing 1.0 μg L− 1 of 7Li, 24Mg, 59Co, 89Y, 140Ce and 205Tl in 1.0% HNO3. This equipment includes a collision cell (He gas, ORS3 system, Agilent Technologies©) for discriminate spectral interferences with high performance for all the trace metals considered in here. All elements were measured using a single set of operating conditions without switching cell gas modes. Operating conditions are shown in Table 2. The acquisition masses and integration times (Table 2) provided more
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than sufficient sensitivity to meet all certified values. In addition, EPA 6020 [29] recommendations were followed for interference overcoming such as correction equations for Pb or Cd. Measurement of Cd by ICP–MS is known to suffer from spectral interference [30]. The Cd isotopes suffer from interference by Sn at 112Cd, 114Cd and 116Cd and by molybdenum oxide (MoO+) at all Cd isotopes except 106Cd. Sn was not detectable in water and QC samples. On the other hand, the formation of Mo oxides was overcome (b 1.0%) by means of the ORS3 He collision cell. As estimation, total analysis time per sample, including wash-in and washout, was 2.5 min.
101
on various QC solutions. The alternation of measurements between the sample and QC samples compensates for an error due to longterm instrumental drift. These QC samples containing the analytes were spiked solutions prepared daily for each element in the laboratory. All the study samples and QC samples were subjected to the same equilibration time and on-line analyzed alongside in the same analysis procedure. In order to perform an effective mass bias correction, an initial standard solution of known isotopic composition was analyzed at the beginning of each batch. Each sample was measured four times and final concentration was obtained from the average of the four measurements. A scheme of the analysis procedure is shown in Fig. 1.
2.3. Methodology The isotope dilution is known to reduce many systematic errors, such as mass bias, detector dead time and characterization of the spike [31]. In this case, the analyte concentrations in the water samples were re-calculated using the following simplified Eq. (1) [32], obtained from isotope dilution theory: C s ¼ C st
Rst Rn −1 Rsp −Rm Rm Rn −1 Rsp −Rst
ð1Þ
where, Cs is the concentration of the analyte in the sample, Cst the concentration of the analyte in the natural reference standard (st), Rsp the isotope ratio of spike (sp), Rn the natural isotope ratio of analyte, Rm the measured isotope ratio of the mixed sample and spike solution and Rst the measured isotope ratio of the mixed reference standard and spike solution. The concentrations of the mono-isotopic elements were simultaneously obtained by using an isotope dilution approach in which the internal standards (Ga and Rh) were used as tracer isotopes. 2.4. Sample preparation and analysis procedure Certified reference water samples were provided by the suppliers as filtered (b0.45 μm) and stabilized materials (pH b 2). No more specific pre-treatment was required for the inter-comparison samples. At least two reagent blanks were also prepared by subjecting them to all sample analysis steps to evaluate possible blank contributions. Four consecutive analyses were performed by OID–ICP–MS mode. Four sub-samples were prepared for each analysis. The analytical sequence adopts the bracketing technique which involves making measurements on each sample between measurements
Table 2 Operating parameter values used for ICP–MS and OID–ICP–MS.
3. Results and discussion The multi-element isotopic standard acts as both a calibration standard for multi isotopic elements and as an internal standard for mono isotopic ones. In isotope dilution analysis (ID–ICP–MS), the amount of spike added to the sample is usually optimized by calculating the ideal ratio using the error magnification factor. However, the spike amount is constant in the OID–ICP–MS mode, but it is preferable to over-spike the samples to yield better counting statistics and therefore less uncertainty in the isotope ratio measurements. As a result, high productivity was achieved through the combination of high sample throughput and the elimination of periodic recalibration and re-analysis. Moreover, on-line addition of the spiking solution eliminated the increased sample handling required for individual sample spiking in conventional isotope dilution analysis.
Reference standard measurement, 50 µg L-1 trace elements
Blank measurements
Initial QC
Instrumental conditions RF power (W) Plasma gas flow (L min−1) Carrier gas flow (L min−1) Helium collision flow (mL min−1) Kinetic energy discrimination (V) Spray chamber temperature (°C)
1550 15 1.05 4.5 2.0 2.0
Sample analysis
Data acquisition Mode
Spectrum
Integration time (ms) Point/mass Replicates
0.2 1 3
Periodic QC, every 10 samples
Analytical detection Isotopes
10,11
B, 45Sc, 52,53Cr, 55Mn, 56,57Fe, 59Co, 60,61Ni, Zn,71Ga, 103Rh, 111,113,114Cd, 206,207,208Pb
66,67
Fig. 1. Diagram defining the analysis procedure followed by using the OID–ICP–MS system for the water samples.
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Additionally, different water reference materials from diverse proficiency testing schemes were also used to assess the trueness of the OID–ICP–MS mode. In this case, the reference value corresponded to the consensus value obtained by the participant laboratories. Tables 4 and 5 collect the number of participants (N) and Z-score as a measure of the trueness obtained for each sample water type. This Z-score value could be obtained from the following expression defined by ISO 17043 [34]:
3.1. Analytical performance Concentration and ratios of isotope masses of the each element measured in QC solutions agreed with expected natural abundance values for these ratios, confirming the absence of any detectable polyatomic interference arising from sample matrices. The effective quantitative range of OID–ICP–MS depends on the concentration of the reference standard for each analyte. The concentration of each element in the reference standard should ideally be midway between the lower and upper quantification limit becoming into a quantification range of at least 4 orders of magnitude. As all analytes are present in the multi-element standard at the appropriate concentration level, the spike can be added on-line, automating the spike addition. The normal application ranges obtained for the tested elements were between 0.1 and 1000 μg L−1. The analytical performance of the OID–ICP–MS method was evaluated. As it can be seen, the concentrations found in the two water certified reference materials were in good agreement with the certified values for all analytes (Table 3). A t-test (Eq. (2)) was applied to the obtained results and confirmed no significant difference between the results obtained using the proposed method and the certified values at the confidence level of 95% [33].
t exp
C ref −C exp ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 s2 u C ref þ n
Z−score ¼
ð4Þ
where x and X are the experimental and consensus values, respectively. SDPA is the standard deviation for the proficiency assessment. The Z-score obtained by each participant was provided by the organizer and it should be ranged between (−2 and 2) to be defined as a reported satisfactory value. As it can be seen in Tables 4 and 5, the Z-score obtained fulfilled the proficiency testing requirements. This ideal situation (Z-score close to 0 values) means a complete concordance of the data obtained with all the participants in the proficiency testing schemes. 3.2. OID–ICP–MS versus ICP–MS
ð2Þ
Trueness was assessed using a reference method that is well known and established in our laboratory. The test samples used were different CRMs and RMs from PT (proficiency testing) schemes in order to guarantee the homogeneity, stability and similarity to routine water samples. In that sense, an accredited (under ISO 17025 requirements) external calibration ICP–MS was chosen as a reference method. As shown in Tables 3–5, it is evident that the proposed OID–ICP–MS method is superior to the external calibration method in terms of analytical accuracy and precision, for all the samples considered. For example, Z-score value obtained for OID–ICP–MS mode was closer to 0 (ideal situation) value than for ICP–MS mode. This accuracy is due to the use of on-line isotope dilution, which could efficiently eliminate the matrix effect and obtain higher analytical quality results. Although the use of multivariate statistical procedures is widely employed [25], a univariate procedure was considered sufficient in this work, because each element contents could be obtained separately and efficiently by the use of the ORS3 He collision cell. Ideally, the results obtained by using both ICP–MS and OID–ICP–MS modes should be completely correlated (equal to unity). However, the correlation could not be only interpreted in terms of accuracy and, the RSD values are only a preliminary indication, thus statistical tests (t-test, regression line and comparison of variances) should be applied to investigate whether the differences obtained are significant. The null hypothesis defines the no statistical distinction between the values obtained from both analysis modes. For that, t-student
where u(Cref) is the standard uncertainty of the reference value. The texp value calculated is compared with the two-sided t tabulated (ttab) for a 95% confidence level and the effective degrees of freedom, τeff, obtained by using the following approach [33]:
τeff
ðx−X Þ SDPA
2 2 2 u C ref þ sn ¼ 4 u C ref s4 þ τref n−1
ð3Þ
where τref is the number of freedom degrees associated with u(Cref). For all the elements, texp b ttab was obtained in both CRMs considering a 95% confidence level. The recoveries for the water materials fell within 90–100% for all elements in all the CRMs considered, demonstrating accurate measurement at different concentration levels. Experimental data obtained in both modes (ICP–MS and OID–ICP–MS) were based on 4 replicates in each case, and the measure of uncertainty as standard deviation (SD) is also listed in Table 3. The precision of the analysis showed a relative standard deviation (%RSD) of b10% for 4 different measurements of the same sample in all determinations.
Table 3 Water CRM concentrations (μg L−1) obtained using the OID–ICP–MS and external ICP–MS calibration. Element
B Co Cr Cd Fe Mn Ni Pb Zn
Lake water, TMDA 52.3 (n = 4)
Freshwater, NIST 1643 e (n = 4)
OID–ICP–MS
ICP–MS
11.0 140 167 91.8 406 201 286 365 255
11.7 145 163 92.4 428 210 300 365 275
± ± ± ± ± ± ± ± ±
0.9 1 1 0.2 1 1 2 1 2
± ± ± ± ± ± ± ± ±
1.0 6 4 0.4 2 5 20 2 9
Certified
OID–ICP–MS
ICP–MS
10.7 136 165 90.9 412 198 274 358 263
166 ± 26.9 ± 20.6 ± 6.56 ± 97.9 ± 39.0 ± 63.4 ± 19.4 ± 76.8 ±
170 25.1 19.8 6.58 97.8 38.5 58.0 19.7 74.4
± ± ± ± ± ± ± ± ±
1.2 6 5 4.0 19 7 10 14 12
6 0.2 0.2 0.08 0.1 0.2 0.3 0.1 2
± ± ± ± ± ± ± ± ±
Certified 18 0.8 0.4 0.11 0.5 0.8 1.0 0.1 2
158 27.1 20.4 6.57 98.1 39.0 62.4 19.6 78.5
± ± ± ± ± ± ± ± ±
2 0.2 0.1 0.04 0.7 0.2 0.4 0.1 1.1
J.C. Raposo et al. / Microchemical Journal 114 (2014) 99–105 Table 4 Z-score values obtained in the proficiency testing schemes for water samples using the OID–ICP–MS and external ICP–MS calibration. Element
B Cr Cd Fe Mn Ni Pb Zn
SCAB 001 (N = 99)
SCAB 002 (N = 46)
SCAB 003 (N = 11)
OID–ICP–MS
OID–ICP–MS
OID–ICP–MS
ICP–MS
0.20
ICP–MS
ICP–MS
0.10
0.15
0.10 0.20 −0.07
0.10 0.24 0.03
−0.02 0.15 0.20
−0.10 −0.72 −0.51
0.23
0.51
−0.04 0.08
0.06 −0.71
0.31
−0.08 0.58
0.10 0.10
parameter for a significance level of 95% was calculated taking into account the SD values obtained [25]: ðX ICPMS −X OID−ICPMS Þ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi t exp ¼ 1 1 σ ðX tot Þ þ NICPMS NOID−ICPMS
ð5Þ
a change in slope, thus these differences between slopes gave an estimation of the proportional error. A constant systematic error shows up in a value of the intercept different from zero [25]. The fit test equation can be expressed as: y ¼ a þ bx
200
ðNICPMS −1Þσ ðX ICPMS Þ2 þ ðN OID−ICPMS −1Þσ ðX OID−ICPMS Þ2 : ð6Þ NICPMS þ N OID−ICPMS −2
Cr
150
OID-ICPMS
2
ð8Þ
where y and x are the experimental results obtained by both analysis modes from the CRM samples. In order to be statistically correct, orthogonal regression takes into account errors in x and y. However, the usual least-squares regression is often employed in practice. This calculation requires two steps: 1) the determination of a and b from the experimental data and 2) a test to investigate whether a and b significantly differed from 0 and 1, respectively. As an example, Fig. 2 shows the linear regression obtained for water CRM and RM samples for Cd, Cr and Pb. As it can be seen in Fig. 2, the multi-isotopic-elements were close to those assumptions for all the CRMs considered and not significant difference of the slope and ordinate were obtained from 1 and 0 values. Moreover, the experimental results obtained for all elements are in good agreement with the certified values for all the CRMs considered. The same tendencies were
where X are the mean value of the element obtained by both modes, N the number of determinations and σ the total SD obtained from the expression: σ ðX tot Þ ¼
103
If the null hypothesis is true, the ratio of the variances should not differ much from the unity. The Fexp parameter defines the corresponding ratio of variances as:
100
y = 1.0214x- 0.1773 R² = 0.9998
50
0 0
2
σ ðX ICPMS Þ : σ ðX OID−ICPMS Þ2
50
It is usual to calculate the F ratio by dividing the largest variance by the smallest in order to obtain a value equal to or larger than unity. The data obtained in this work showed this concordance and different experimental parameters (Fexp) were obtained from Eq. (7) for each experimental determination. This critical parameter depends on the significance level and the number of the determinations (Fcrit = 9.277, [25], for a 95% significance level and 4 determinations). As Fcrit N Fexp was obtained for all the elements, it indicated the correctness of the null hypothesis and that the procedures are not significantly different in precision. Additionally, both analysis modes were compared by plotting in a X– Y graph the obtained values for the CRMs. A straight regression line should be obtained with a slope (b) of exactly 1 and an intercept on the ordinate (a) of zero. Moreover, all the experimental data should fall on the corresponding line. A proportional systematic error leads to
100
B Co Cr Cd Fe Mn Ni Pb Zn
SCAB 004 (N = 28)
SCAB 005 (N = 30)
SCAB 006 (N = 50)
OID–ICP–MS
OID–ICP–MS
OID–ICP–MS
ICP–MS
0.05
−0.17
0.12 0.26 0.21 0.10 0.03 0.23 −0.02
ICP–MS −0.64 −1.29 −0.77 −0.83 −0.60 −0.40 −0.70
0.15 0.12
−0.06 −0.13
ICP–MS
200
Cd
y = 0.9955x + 0.1801 R² = 0.9999
50
0 0
20
40
60
80
100
ICPMS 400
OID-ICPMS
Table 5 Z-score values obtained in the Aquacheck PT scheme using the OID–ICP–MS and external ICP–MS calibration.
150
ICPMS
Pb
350
Element
100
ð7Þ
OID-ICPMS
F exp ¼
300 250 y = 1.0032x + 0.0367 R² = 0.9999
200 150 100 50
−0.20 −1.23
0 0.23 0.15
0.80 0.92
0.11
−0.28
0.25 −0.52
0
100
200
300
400
ICPMS Fig. 2. Linear regressions obtained for the OID–ICP–MS versus ICP–MS analysis of CRM and RM water samples. • CRM and ◦ RM data in μg L−1.
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obtained for the other multi- (B, Fe and Ni) and monoisotopic elements studied (Co and Mn). The results obtained from both analysis modes were confirmed by paired Student's test. This test requires a normal distribution of the difference of the values obtained using both analytical procedures. The calculated t-value was compared with the theoretical one for the 95% significance level. In all cases analyzed, tcalculated b ttheoretical, thus no difference between the both modes was obtained. Additionally, plots of the residual values were performed. These residuals were calculated by subtracting the model estimates (certified data for CRMs and consensus for RMs) from the experimental OID–ICP–MS data.
Table 6 Uncertainty (U, %) obtained for the OID–ICP–MS and external ICP–MS calibration.
e ¼ xexp −a−bxcert
The uncertainty estimation (U) for each analyte was found to be fit for purpose as the relative expanded uncertainty (Table 6) for each analyte was less than 10% (k = 2) for OID–ICP–MS mode:
ð9Þ
where a and b are respectively the intercept and the slope of the straight lines. Fig. 3 collects the randomly distribution of the residuals obtained for Cd, Cr and Pb elements.
Element
B Cr Cd Fe Ni Pb Zn
U ¼ 2u ¼
Lake water, TMDA 52.3
Freshwater, NIST 1643 e
OID–ICP–MS
ICP–MS
OID–ICP–MS
ICP–MS
8 6 8 9 8 8 10
10 10 9 10 9 8 13
7 4 5 1 2 6 8
10 8 7 2 10 9 10
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u2r þ u2h þ u2rv :
ð13Þ
3.3. Calculation of the method uncertainty The major contributors in the overall uncertainty budget for each analyte are method recovery, biases in results due to the choice of analysis mode, method precision and preparation of calibration standard. The uncertainty for the mean value (ur) could be calculated from the 4 measurements performed for each CRM sample and considering the standard deviation of those 4 repetitions. As it was pointed out before, excellent precision was observed in all analysis. This method precision also captured any biases by repeatability of the experiment since the results obtained were performed by the same analyst on different days (n = 4): ur ¼ w
SD : n
ð10Þ
The uncertainty due to the sample heterogeneity was calculated by taking repeatability and the repetition numbers of each sample (n = 1): SD uh ¼ w pffiffiffi : n
ð11Þ
The uncertainty of isotope ratios and instrumental drift can be assumed to be captured within method precision and no further uncertainty term was needed. As it can be seen in Table 6, the uncertainty obtained by OID–ICP–MS was significantly lower than that obtained by ICP–MS. 4. Conclusion The analysis of trace metals in environmental samples often generates data with large uncertainty values and poor repeatability. This kind of samples have often variable composition and high levels of matrix elements. This fact increases the unpredictable matrix-based polyatomic interferences that affect commonly to required analytes. OID– ICP–MS was proven to be less prone to matrix effects. OID–ICP–MS offered the possibility to determine trace concentrations of elements in virtually any environmental water matrix, with superior accuracy and precision compared to external calibration. This combination has the potential to be an ideal solution for high matrix sample analysis, providing increased sample throughput and improved productivity, as well as better accuracy, absolute quantification, and reduced uncertainty. Acknowledgments
In Eqs. (10) and (11), w is the WECC factor based on the number of measurements performed (typically, w = 1.7 for n = 4). Finally, the uncertainties (urv) for CRM samples were calculated by combining the uncertainties of the primary certified standards as (K = 2): urv ¼
U cert : K
ð12Þ
(yOID-ICPMS)-(yestimated)
3 2 Cd
1
Pb Cr
0 -1 -2
Fig. 3. Distribution of the residuals obtained in the linear regression for the OID–ICP–MS versus ICP–MS analysis (CRM data in black).
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