Peak purity assessment by matrix projection for spectral line selection and background correction in inductively coupled plasma optical emission spectrometry

SPECTROCHIMICA ACTA PART B

ELSEVIER

Spectrochimica Acta Part B 50 (1995) 1263-1279

Peak purity assessment by matrix projection for spectral line selection and background correction in inductively coupled plasma optical emission spectrometry Peixun Zhang, David Littlejohn* Department of Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow G1 1XL, UK Received 21 October 1994; accepted 7 March 1995

Abstract This paper describes an improvement of the procedure, proposed previously (Spectrochim. Acta, Part B, 48 (1993) 1517), to provide information about multiplicative and spectral interferences at lines selected for the analysis of samples. The new version of the procedure only requires intensity scans across an analyte line when three solutions are aspirated: a standard solution, the sample solution and the sample spiked with the analyte standard solution. The third solution can be omitted if multiplicative interference is not of interest. Like the initial method, the present procedure does not require or assume information about the sample composition nor is the preparation of solutions of suspected interferents required. Figures of merit, namely the structured background factor, and the overall spectral interference have been devised and are used with the true detection limit at a line for line evaluation and selection. As the nonanalyte concomitant contribution to the measured spectrum is estimated, automatic background correction is possible. The proposed method not only allows selection of the spectral line, but can give a good estimate of the analyte concentration prior to a full quantitative analysis.

Keywords: Background correction; Chemometrics; ICP-OES; Interference; Line selection; Projection matrix; True detection limit

1. Introduction The high excitation temperature of an argon ICP can be both an advantage and disadvantage to an analyst. Because many electronic transitions are excited, a variety of lines are normally accessible for each element to be determined, so wavelength selection can be based on sensitivity and selectivity criteria. However, if the sample contains components which produce linerich spectra, the determination of an analyte concentration may be erroneous if the analytical line selected suffers from unknown or unexpected spectral interferences. It is desirable, therefore, to have a simple procedure which can assess a spectral line for interferences and if necessary, correct the effect. The prediction of interferences (peak purity assessment), at the lines of interest for analysis

* Corresponding author. 0584-8547/95/$09.50 © 1995 Elsevier Science B.V. All rights reserved SSDI 0584-8547(95)01333-4

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of an unknown sample, can be difficult. Common methods used to deal with spectral interferences in ICP-OES include the selection of the optimal line by reference to ICP wavelength tables [1-4], curve resolution [5], computer simulation of emission spectra [6-11], linear regression [12,13], derivative spectrometry [14], the Kalman filter method [15-22], and factor analysis [23,24]. The main disadvantage of these methods is that they require information about the sample composition (all possible interferents) prior to analysis. Some of the methods also require the preparation of solutions containing the suspected interferents. Recently, attention has been paid to the development of chemometric procedures which can be used with different analytical techniques to identify, estimate and remove the unmodelled signals of interferents before the quantification of the analyte concentration. These include, (a) the method of self-modelling curve resolution proposed by Lawton and Sylvestre [25] and a similar approach by Marstens [26], (b) perpendicular projection and extreme vertex projection by Osten and Kowalski [27], (c) matrix projection, combined with the minimum entropy principle, by Xie et al. [28], (d) the adaptive Kalman filter by Rutan and co-workers [18,19], (e) local modelling and derivative spectroscopy by Karstang and Kvalheim [29], (f) evolving factor analysis by Keller and Massart [30,31], and (g) the Gram-Schmidt orthogonalisation method by Sanchez et al. [32]. However, most of these methods are either ineffective in dealing with direct overlapping interference or incomplete in theory and hence have not been widely accepted. In addition, none of the methods has been applied in ICP-OES. In our previous paper [33], a matrix projection procedure was described which can provide information about multiplicative and spectral interferences at a number of analyte lines without the above mentioned limitations. The method requires the measurement of the peak intensities of several lines, but because the effects of multiplicative interferences are mixed with the spectral interferences, data processing becomes complex. Also, it requires that an off-peak background correction point is selected to correct the peak intensities at the analyte line centre before the interferences are assessed. This may be difficult if the spectral region near the analyte line is complex and cannot be achieved in automatic analysis. In addition, measurements at a number of wavelengths are necessary to estimate the suitability of individual lines. Perhaps the biggest limitation is that the least interfered-with line is always predicted to be interferencefree, even when all the selected lines are affected by spectral interference. In this paper, the procedure developed previously [33] is improved. The same analyte solutions are used, but the new version provides information about multiplicative and spectral interferences from spectral scans at an individual wavelength. The developed method does not require selection of a background correction point before data processing. The algorithm is used to estimate the background emission spectrum from the plasma and concomitant species in the sample (referred to hereafter as the "predicted background"). Figures of merit, known as the structured background factor and the overall spectral interference, have been devised to allow quantitative assessment of the predicted background spectrum and hence, the suitability of a line. The predicted background can also be used for spectral stripping, allowing the possibility of automatic background correction of all non-analyte emission at a line. The new method is quicker, more effective and more convenient than the multi-line procedure [33], whilst maintaining the important advantage over other approaches, i.e. prior knowledge about the identity and concentrations of the interferents is not necessary. Furthermore, the new procedure can be combined with a shortened version of the multi-line method for accurate quantitative analysis when a suitable interferent-free line is unavailable.

2. Experimental 2.1. Apparatus and operating parameters

A Unicam 7000 ICP spectrometer was used with a practical spectral bandpass and dispersion of 7.5 pm and 83 pm mm -1, respectively, at 200 nm, and 24 pm and 270 pm mm -~, respectively, at 800 nm. The free-running R.F. generator operates at a nominal frequency of 40.68 MHz with automatic impedance matching. A grid nebulizer was used and the main operating parameters are listed in Table 1.

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1265

Table l ICP operating parameters Carrier gas/lbin2 Intermediate gas/1min-~ Outer gas/l min-~ Power/kW Observation height above coil/mm Solution uptaken rate/mlmin-t

38 0 11 1.0 14 1.0

2.2. Analytes and solutions In each test, the analyte wavelengths were selected so that the most prominent line, less sensitive lines, and atomic and ionic lines were examined, if possible. The various analytes and analytical wavelengths are given in the relevant tables of results. 2.2.1. Standard solutions Single element standard solutions, in 5% (v/v) nitric acid, were used for analysis of the eight-element solution and the steel sample solution. Multi-element standard solutions (i.e. the standard solutions contained all analytes of interest) were used for analysis of the bronze reference material and the geological sample. 2.2.2. Synthetic sample solution A synthetic sample solution, hereafter called the eight-element solution, was prepared by dissolving the appropriate masses of AnalaR grade salts in 5% nitric acid to give 1000 txg ml -l of sodium, calcium, cobalt, chromium, iron, nickel, vanadium, and tungsten. The elements were selected because they are easily ionised, have strong emission lines, have line-rich spectra or are commonly encountered in samples. The analyte elements listed in Table 3 were added to the synthetic sample solution to test the developed procedure by comparison of, (a) the predicted and known figures of merit, and (b) the predicted background with the true background. The "predicted" results were those derived from application of the chemometrics procedure and the "true" and "known" results were obtained from the analyte-free spectra of the eight-element solution. 2.2.3. Sample solutions In addition to the eight-element solution, a steel sample, bronze reference material solution, and a geological sample were used to test the developed procedure. 2.3. Measurements The spectrum at each line was scanned for every solution, using 32 steps across the search window (about 70 pm wide at 200 nm and 140 pm wide at 400 nm), with an integration time of 0.1 s at each step. For the eight-element solution and steel sample solutions, five replicate measurements were made at each spectral line. However, for the geological sample and bronze reference material sample solutions, only one spectrum scan was made at each at each line. The raw intensity data were stored onto disk for processing later. 2.4. Data processing The algorithm used here is almost the same as used previously [33]. The main difference between the present and previous procedures is that the input data are raw intensities measured across a spectral line in the present procedure, rather than the net intensities at the central positions of a number of different lines. The intensities stored are transferred to the program directly. Once the predicted background spectrum is obtained from the procedure, net analyte intensities can be calculated by subtracting the background intensities from the measured intensities across the spectral scan window. The predicted background emission and the figures of

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merit, viz. the structured background factor, overall spectral interference, true detection limit and signal-to-background ratio calculated for each line are displayed for interference evaluation. A Dell 325P computer was used for all calculations and the program was written in Microsoft FORTRAN, version 5.1.

2.5. Definition of figures of merit used for spectral line evaluation 2.5.1. Structured background factor (SBF) The SBF is defined as a measurement of the variation of the predicted background spectrum within the spectral range covered by the analyte line (bandpass). A statistically meaningful value of this parameter, as defined below, indicates the presence of structured background. The greater the value of the SBF, the greater the background structure. The SBF is defined as

SBF=IO0 ~

i=g

Bi--B ~(g-t)]

B

(1)

where Bi is the predicted background emission with a mean value of B, within the analyte line bandpass (defined as beginning with the gth point and ending with the tth point). Because the variation is normalised with the mean, the SBF represents the structured characteristics of the predicted background, rather than its magnitude. This means that if two lines have the same values of SBF, they could have different mean background intensities. As the SBF is derived from measured signals which have random noise, its value has some degree of uncertainty. The uncertainty of the SBF value is estimated as SBF,n = lOO

z,

/(g-t) /

(2)

i=g where Zi is the lowest background signal predicted, Bmin, plus the standard deviation of the signals measured, Si, e.g.

Z i ~--nnain + Si]~-m

(3)

is the mean of Zi in the region defined, and m is the number of repeated spectral scans. The SBF is meaningful only when its value is significantly greater than its uncertainty of prediction and is also greater than the least value SBFmin that could be achieved (critical value in statistics). In this case, the variation of the background in the spectral region comes from structured features. Otherwise, it comes from random noise or 1/f noise at higher intensities. The critical value of the SBF is estimated as RSD~F~, SBFmi,------2.

[.L.,' 2~,n 2~Bi

~m g~tBli=g

)

(4)

where RSD is the precision (percentage) of the intensity measured at the analyte peak position, F~ is the value found from the F distribution table for the ~ level of significance, which is stored in the program. The other parameters are the same as mentioned previously.

2.5.2 Overall spectral interference (OSI) The value of the SBF is a useful indicator of the presence of structured background, but it does not give a quantitative estimate of the effect of the spectral interferences produced under particular conditions. Therefore, the "overall spectral interference" is needed, which represents the relative magnitude of the interfering signals from the sample compared to that of the analyte signal. The OSI is defined as the ratio of the integrated interfering signals (after correction of continuum emission) Y'in within the analyte line bandpass, to the integrated sample analyte signals Y'st in the same region, that is OSI = 100 Y'iJY'st where

(5)

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t

~, = ~(Yg

(6)

- Rsi )

i=g

and t

~in = ~(Rsi- Yb)

(7)

i=g

where Yb is the least sample intensity measured, plus its standard deviation.

2.5.3. True detection limit (TDL) The TDL is an important concept proposed by Boumans and Vrakking [34] to illustrate the influence of spectral interferences in ICP-OES. Unfortunately, it is very difficult to calculate the TDL for an unknown sample according to the definition, because the intensity contributed by each interferent in the sample is generally not known and is difficult to obtain. However, it is possible to calculate the TDLs of the analytes when the procedure proposed here is used. As mentioned previously, the non-analyte emission (i.e. predicted background) can be estimated without identification of the interferents. Therefore, the TDL can be estimated according to the following formula:

TDL = 2.83 ~

+ O.o6 Rs(A)s-A Rmin

(8)

where SDmi n is the standard deviation of sample intensity measured at the position of the minimum intensity; Rs(A) is the intensity of the predicted background at the analyte peak p o s i t i o n ; Rmin is the minimum intensity among the predicted background and SA is the emission sensitivity of the analyte at its central peak position obtained as SA = (Xmax- Xb)/fst

(9)

The constants 2.83 and 0.06 come from 2~/-2 and 3 times 0.02, respectively. 0.02 is the relative standard deviation assumed for the measured signals. A number of examples are given in this paper to illustrate the use of the figures of merit for spectral line evaluation and selection.

3. Results and discussion

3.1. Effect of noise on the peak purity assessment The presence of spectral interference at the analyte wavelength is the main reason for the alteration of the sample spectrum. However, noise in the measured signals may also be a contributing factor. To test how noise affects the peak purity assessment procedure, a simulated Gaussian line profile was considered with different levels (1-10% RSD) of random noise across the line (32 data points were used). The intensity (Xi) at the ith wavelength position was therefore assumed to have three contributing factors: the Gaussian line intensity, the background intensity Bi and the random noise Ni. Hence

Xi= XmaxeXp((h.i- hmax)2/(2ty2)) + Bi + Ni

i= 1. . .n

(10)

where Xm~ is the maximum intensity, hm,,x is the peak wavelength of the Gaussian line and tr is the dispersion of intensity. For the "standard spectrum", the parameters were always fixed as

Xi= 1000exp((X - 16)2/(2 x 1.7z)) + 100 +

g i

i = 1...32

(I1)

Here 1.7 is selected for the value of tr so that the value of the full width at half maximum

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[FWHM) of the simulated spectral line width is about four data points (for a Gaussian profile, FWHM = 2.3548tr) which is similar to the FWHM of the measured spectral line in this work (equivalent to about 8 pm at 200 nm and 16 pm at 400 nm). For the "sample" spectrum, the values of Xmax and B were changed so that different signalto-background ratios (SBRs) were obtained, but the spectral shape was identical to that of the standard. Also, noise levels were added without changing the spectral shape. It was observed that low level noise (1-3% RSD) does not affect the peak purity assessment even with one replicate, i.e. no difference between the standard spectrum and the sample spectrum was identified (Table 2). In such cases, the predicted "spectral interferences" are smaller than the random noise and the values of the structured background factor are smaller than the critical values. As expected, no structured features are apparent when the predicted "background spectra" are displayed. However, at a higher level of noise (5-10% RSD), differences between the standard spectrum and the sample spectrum were identified when the peak purity assessment procedure was applied with only one replicate. In such cases, the predicted "background spectra" have structured features, although the values of spectral interferences and structured background factor are smaller than the random noise and critical value, respectively. The effect of the higher level of noise was reduced when more replicates were used. In addition, a particular

Table 2 Effect of random noise on peak purity assessment and the ability of the matrix projection procedure to detect changes in the sample spectrum Condition

OSI/%a 2d

SBF/% b 0.5 a

20

SBFu, c

SBFminc

0.5 d

1 replicate, RSDe varied at 0.01 0.03 0.05 0.1

0.3 0.6 4.3 4.0

1.0 1.2 5.3 7.3

0.6 1.6 2.4 7,6

0.5 1.8 2.8 5.9

1.0 3.0 5.1 I0

2.1 6.1 10 21

0.8 1.6 0.9 2.5

1.0 1.3 1.3 2.0

0.4 0.9 1.2 3.1

0.4 1.4 2.2 4.5

0.9 2.7 4.5 9.1

1.0 3.6 8.5 16 26

0.4 0.7 1.0 3.2 10

0.9 0.9 0.9 0.9 0.9

1.9 1.9 1.9 1.9 1.9

2.0 2.3 3.5 5.3

0.8 0.9 1.0 2.0

0.9 0.9 0.9 0.9

1.8 1.8 1.8 1.8

0.8 3.7 10 17

0.4 0.4 0.9 2.5

0.9 0.9 0.9 0.9

1.8 1.8 1.8 1.8

5 replicates, RSD e varied at 0.01 0.03 0.05 0.1

0.4 1.0 0.7 2.8

5 replicates, sample peak shifted by 1/50 FWHM 1/30 FWHM 1/20 FWHM 1/10 FWHM 1/5 FWHM

0.4 3.1 11 25 63

1.1 1.1 1.0 12 122

5 replicates, sample spectrum broadened by 1/30 tr 1/20 tr 1/10~ 1/5 tr

2.0 3.6 8.8 20

0.6 0.9 1.9 9.5

5 replicates, sample spectrum narrowed by 1/30 tr 1/20 tr 1/10tr 1/5 tr

0.4 4.0 12 25

1.2 1.4 1.7 17

a Overall spectral interference, b Structured background factor. ~ See text for definitions of SBFu, and SBFmi.. d Signalto-background ratio, e Relative standard deviation.

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noise level had a greater effect at lower SBRs (compare the data when SBR = 2 with those when SBR = 0.5 in Table 2). This can be explained as follows: Because the total signal measured (Xtot) at any wavelength position is the sum of the net analyte signal (X.~t) and the background signal (Xbkg), i.e. Xto t = Xne t "[- Xbkg SO Xne t ~-- Xto t - Xbkg

The standard deviation of Xnet is calculated as

f

\~/2

,,oo,: t,,, ot

-> <,to,

The relative standard deviation (RSD.et) of Xnet is RSDnet = O'net/X,et -> trtot/X~,t= RSDtot Xtot/Xn~t

(

,

= RSDtot 1 + XbkgIX,~t SO

RSD~et > RSDtot(1 + 1/SBR ~

(12)

This shows the effect of I/SBR on the size of the net signal RSD. If the SBR is 0.5 for the measured signals, the RSD of the net signal is three times as high as the initial RSD. This indicates a way to improve the efficiency of the peak purity assessment method. If the noise level in the measured signals is low or the SBR is high, only a few replicates of measurements are required. However, the contrary conditions require that more replicates of the signal are made for accurate prediction.

3.2. Ability of the matrix projection procedure to detect changes in the sample spectrum The efficiency of the matrix projection procedure to detect an alteration of the sample spectrum was tested with simulated data. For the standard spectrum, the parameters are the same as those used before. The values of Xm~x, hmax, O" and B were changed so that the sample spectrum was altered, one parameter at a time. The test was the ability to detect the least change in the sample spectrum when the value of the predicted SBF is greater than the critical value. The results show that at a 2% RSD noise level with five replicates, the following changes in the sample spectra can be detected (see Table 2): a peak shift of 1/30 FWHM (about 0.3 pm at 200 nm and 0.6 pm at 400 nm); 1/30tr of peak broadening; and 1/20tr peak narrowing, when the sample SBR = 2. However, when the sample SBR = 0.5, the changes in the sample spectrum that can be detected become a shift of 1/10 FWHM, 1/5tr of peak broadening, and 1/5tr of peak narrowing. It means that the ability of the present procedure to detect differences is better at higher values of SBR than that at lower values of SBR, because in the case of a low SBR, the changes in the spectrum cannot be distinguished from the noise. Nevertheless, the overall performance is useful as the above spectral changes cannot be observed by viewing the standard and sample spectra.

3.3. Line evaluation and selection through the predicted figures of merit The presence of spectral interferences at the analyte lines is the main source of inaccurate results in ICP-OES. Therefore, the first criterion used to accept or reject an analytical line is that it is selective enough. This can be assessed on a quantitative basis using the figures of merit defined previously. Following the tests with simulated data, the proposed procedure was applied to ICP intensifies collected on analysis of the eight-element synthetic sample solution described in the

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Experimental section. The predicted values of the SBF and the OSI at a number of analyte lines were calculated and are listed in Table 3. Higher values of SBF and OSI indicate larger interferences. Because the composition of the synthetic sample solution was known, the true analyte-free spectrum at each line could be measured and so "known" values of the SBF and the OSI were also obtained, A comparison of the results shows that in almost all cases, the predicted SBFs agree very well with the known values, except for lines which have a very high SBR. The reason that the prediction of the SBF becomes poor at high SBR may be related to a greater effect of 1/f noise. At high SBR values, the standard deviations of the sample signals measured within the analyte bandpass predominate the background signals and cause large uncertainty in the prediction. The relation between the relative standard deviation of the predicted background and that of the sample signals measured can be deduced as below, which shows the effect of sample SBR on the size of the predicted background RSD and explains the poor prediction of the structured background factor at high values of SBR R S D B >- RSDtot{ 1 + SBR}

(13)

The spectral interferences predicted for the eight-element sample solution agree very well with the known values in the circumstances where there is partial line overlap. However, if there is almost total overlap of the interferent line with the analyte line, the magnitude of the predicted interference is lower than the true interference. This is illustrated in Figs. 2, 4 and 5, where there is only a difference of one or two spectral steps between the peak positions of the pure analyte and true background spectra. From a knowledge of the wavelength range covered by the 32 point spectral scans, these step differences are equivalent to 3 - 4 pm. It is likely that a larger number of measurement points across the spectral window will improve Table 3 Comparison of the predicted figures of merit with known values for the eight-elementsolution~ Analyte L i n e / nm

SBFp,/ %b

SBFJ %b

SBFmin/ OSlpJ %b %~

%c

OSItd

TDLpJ (~g ml-,),l

Conc./ (p.g ml-i)e

Nb(II)

269.760 18 309.418 89 313.079 11 316.340 9.7 319.498 1.8

(1.7) 22 (16) 87 (2.4) 11 (2.1) 10 (1.3) 1.7

3.6 3.0 3.2 2.1 1.0

13 148 1.8 2.4 0.4

41 226 6.0 1.6 0.4

0.2 5.2 0.04 0.09 0.03

12.0 44.0 10.4 10.1 10.2

Pr(II)

390.844 4.4 414.311 15 417.940 18 422.293 2.5 422.535 56

(1.1) 4.1 (0.7) 15 (1.1) 20 (0.8) 2.7 (0.7) 58

2.2 1.5 2.2 1.5 1.1

4.8 9.1 9.4 2.2 114

4.6 8.4 40 0.8 155

0.1 0.1 0.2 0.2 0.5

14.0 9.3 14.8 8.9 14.0

Nd(II)

386.340 10 401.225 38 406.109 1.7 410.946 32 430.358 4.1

(0.7) 9.7 (1.2) 38 (0.9) 1.6 (0.8) 31 (0.5) 5.6

1.5 2.6 1.8 1.8 l.l

4.1 36 0.5 16 3.5

26 38 0.3 15 4.6

0.2 0.02 0.09 0.1 0.1

10.3 9.0 8.2 8.0 9.3

Mo(I1)

277.540 281.615 284.823 313.259 379.825

2.2 3.8 23 6.6 15

(1.7) 1.8 (2.9) 1.8 (2.1) 21 (0.9) 12 (0.6) 15

3.4 3.8 4.4 1.9 1.2

1.0 0.2 18 4.4 5.2

0.7 0.1 19 54 5.6

0.2 0.09 0.2 0.6 0.3

9.0 9.7 10.3 21.4 10.8

238.706 3.9 240.063 6.8 263.558 27 267.590 8.4 268.517 36

(1.0) 3.9 (0.8) 7.1 (0.8) 31 (0.6) 4.4 (1.4) 44

2.0 1.6 0.9 1.3 1,7

4.7 13 36 15 37

13 14 57 31 95

0.4 0.2 0.2 0.4 0.3

10.0 9.2 12.3 11.6 26.0

Mo (I) Ta(II)

Data in parentheses are the standarddeviations,b Structuredbackgroundfactor, c Overall spectral interference,dTrue detection limit, e Analyteconcentrationderived. (The true concentrationin the solutionfor each analyteis 10 p,g ml-t.)

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Table 4 Predicted figures of merit for the steel sample Analyte

Line/nm

SBR a

SBF/% b.c

SBFm~,%b

Nb(II)

269.706 309.418 313.079 316.640 319.498

13.1 45.6 41.3 50.2 42.0

12 29 32 34 16

(17) (35) (35) (38) (42)

9.5 8.1 9.8 8.6 13

Mo(lI)

277.540 281.615 284.823 313.259 379.825

1.42 3.11 1.69 1.41 0.96

7.3 4.2 27 15 87

(1.3) (1.0) (0.6) (1.3) (1.1)

224.700 324.754 327.396 219.958 213.598

3.56 19.4 9.72 1.59 0.81

66 51 16 7.1 3.8

290.882 292.401 309.311 310.230 268.796

2.45 3.43 4.61 4.76 0.84

36 69 9.3 15 20

Mo(I) Cu(lI) Cu(I)

Cu(lI) V(II)

OSI/% b

TDL/ (l~g ml-l)b

Conc./ (txg ml-J) b'~

4.2 0.9 1.4 1.0 2.0

0.5 0.1 0.2 0.1 0.2

49 54 51 54 50

2.6 1.3 1.0 2.6 1.8

4.3 0.2 75 38 70

0.2 0.04 0.2 0.4 0.2

7.8 6.8 7.0 13 7.5

(2.5) (3.7) (2.8) (2.9) (3.5)

3.6 0.9 1.2 5.9 7.0

44 10 3.4 1.5 0.1

0.1 0.03 0.04 0.4 0.8

8.2 8.8 8.4 9.3 9.2

( 1.5) (1.3) (2.0) (1.4) (1.7)

2.5 3.0 2.4 1.7 3.5

24 18 2.7 11 32

0.03 0.01 0.02 0.01 0.2

2.3 2.1 1.9 2.0 2.8

Signal-to-background ratio. ~ See footnote to Table 3. c Data in parentheses are the standard deviations. ~ The known concentrations in the solution are 47, 6.1, 7.8 and 1.7 p~g ml -~ for Nb, Mo, Cu, and V, respectively. t h e a p p l i c a t i o n o f t h e p r o c e d u r e , as t h e r e s u l t s in T a b l e 1 f o r s i m u l a t e d l i n e s s u g g e s t t h a t p e a k d i f f e r e n c e s o f 1/20 F W H M

( 0 . 3 - 0 . 6 p m ) c a n b e d e t e c t e d . E v e n w h e n t h e s p e c t r a l o v e r l a p is

a l m o s t total, t h e p r e d i c t i o n is still u s e f u l q u a n t i t a t i v e l y a n d c a n a i d line s e l e c t i o n . F o r e x a m p l e , the predicted OSI can be compared

w i t h a t h r e s h o l d p r e c o n d i t i o n set b y t h e a n a l y s t . F o r

i n s t a n c e , i f a l i n e is p r e d i c t e d to h a v e 5 % o r l e s s s p e c t r a l i n t e r f e r e n c e , this line c a n b e c o n s i d e r e d as e f f e c t i v e l y s p e c t r a l i n t e r f e r e n c e f r e e a n d m a y b e s a f e l y u s e d in a n a n a l y s i s . I f t h e v a l u e o f t h e p r e d i c t e d O S I is b e t w e e n 5 % a n d 10%, i n d i c a t i n g s l i g h t s p e c t r a l i n t e r f e r e n c e , this Table 5 Predicted figures of merit for BCS bronze reference material 183/2 Analyte

Line/nm

SBR ~

SBF/% b.c

P(I)

213.618 214.914 253.565 203.348 255.326 215.408

2.45 5.67 2.11 1.30 0.87 0.74

214 66 2.1 12 2.3 2.0

Sb(I)

206.833 217.581 252.852 231.147

5.58 3.25 1.82 1.98

Fe(II)

238.204 259.940 234.830 263.132 234.349

5.21 1.61 0.31 0.30 1.20

OSI/% b

TDL/ (p.g ml-I) ~

Conc./ (l~g ml-I) b.d

(41) 13 (8.6) 6.6 (3.0) 6.2 (3.0) 6.2 (3.0) 6.3 (3.2) 6.4

72 64 1.1 28 0.9 2.6

2.8 2.1 1.1 3.4 2.9 3.7

137 69 26 49 29 29

11 12 3.9 1.0

(5.5) (2.9) (3.1) (3.0)

6.3 6.0 6.3 6.1

5.2 11 0.6 2.5

0.2 0.4 0.5 0.5

12 15 11 12

51

(5.4) (3.0) (2.9) (3.1) (3.1)

6.8 6.2 6.0 6.3 6.3

36 0.7 13 1.5 8.4

0.1 0.01 0.1 0.07 0.03

1.3 3.5 3.8 5.0

SBFmi,% b

6.6 0.22 0.30 0.29 0.39

a Signal-to-background ratio, b See footnote to Table 3. c Data in parentheses are the standard deviations, d The known concentrations in the solution are 26.6, 12.5 and 0.21 I~g ml-~ for P, Sb and Fe, respectively.

1272

Peixung Zhang, D. Littlejohn/Spectrochimica Acta Part B 50 (1995) 1263-1279

line could be used if the requirement in accuracy is not very strict. However, if the value of OSI is greater than 15%, the line may not be considered suitable for an analysis. All lines affected by interferences from the eight-element solution were correctly predicted (Table 3), except the Mo line at 313.259 nm. This line suffered 54% spectral interference from an unidentified line and the predicted SBF also indicated that the background spectrum had structured features. However, the spectral interference was underestimated by the OSI value, because the interfering line exhibited a high degree of overlap with the analyte line. The only other discrepancy in Table 3 is for Nd, where the results suggest that the analyte concentration was less than the target value of 10 Ixg ml -~, perhaps due to an error in solution preparation. Analytical lines can also be evaluated through the TDL and the values calculated for the different analyte lines are given in Table 3. Often, the line with the lowest T D L exhibits the least spectral interference, and hence is the best for analysis. However, the TDL cannot be used as the main criterion for line selection because the line with the lowest value is not always free of spectral interferences. This is apparent for Nd(II) 401.225 nm and Ta(II) 263.558 nm. Therefore, spectral interferences should be assessed in all circumstances. The present procedure was further tested with data obtained by analysing three real samples: a steel sample, a bronze standard reference material and a geological sample. Values of the predicted SBF, the OSI, the TDL, and the SBR at various analyte lines are listed in Table 4 for the steel sample solution, Table 5 for the bronze reference material solution and Table 6 for the geological sample solution. The best line for an analysis has the highest values of SBR and the lowest values of SBF and OSI, if possible. Otherwise, the OSI should be considered first. On this criterion, all the lines listed for determination of Nb in the steel sample can be used because they are all free of spectral interferences and are sensitive enough (Table 4). The predicted SBF values and their uncertainties are high at some lines for Nb, which may be due to the high SBR, as mentioned previously. The best lines for determination of Mo, Cu and V are 281.615 nm, 327.396 nm and 309.311 nm, respectively, because these three lines are most spectral interference free and have the lowest TDLs. Similarly, the best lines for analysis of the bronze standard reference material (Table 5) are 253.565 nm for P, 252.852 and 231.147 nm for Sb and 259.940 nm for Fe. To determine the Mn concentration in the geological sample (Table 6), all five lines listed are suitable because they are spectral interference free. The Zn lines at 213.856, 202.548 and 206.200 nm and the Cr lines at 267.720 and 284.325 nm can be used to determine these elements, from considerations of both sensitivity and selectivity. Various examples of the background emission predicted at a number of analyte lines are shown in Figs. 1-6 for Nd, Nb, Pr, Mo and Ta in the eight-element sample solution, Figs. 7 Table 6 Predicted figures of merit for a geological sample Analyte

Line/nm

SBR"

SBF/%b.c

SBFmin%b

OSI/%b

TDL/ (ixg ml t)b

(p,g ml-I)b,d

Conc./

Mn(II)

257.610 259.373 293.306 260.569 294.920

41.4 41.4 4.42 24.0 9.78

67 53 3.7 51 20

(29) (27) (4.3) (20) (9.2)

7.1 6.9 5.9 6.9 6.2

6.2 3.6 0.1 6.5 4.1

0.008 0.006 0.04 0.01 0.02

1.7 1.7 1.8 1.7 1.7

Zn(I) Zn(II)

213.856 202.548 206.200 334.502 330.259

3.77 3.50 2.53 0.03 0.03

7.6 9.3 3.1 1.9 4.5

(3.7) (3.6) (3.0) (3.0) (3.0)

6.2 6.2 6.1 6.1 6.1

5.3 5.2 0.3 -5.0

0.01 0.01 0.02 -1.6

0.49 0.50 0.55 -2.0

205.552 283.563 206.149 267.720 284.325

0.16 0.78 0.47 0.68 0.44

0.7 12 6.0 0.7 4.5

(2.9) (2.9) (3.1) (3.0) (2.9)

6.0 6.1 6.4 6.0 6.0

5.4 51 8.4 1.3 5.4

0.05 0.02 0.04 0.02 0.01

0.12 0.14 0.22 0.12 0.13

Zn(I) Cr(II)

a Signal-to-backgroundratio, b See footnote to Table 3. c Data in parentheses are the standard deviations, d The known concentrations of the three analytes in the solution were not determined.

Peixung Zhang, D. Littlejohn/Spectrochimica Acta Part B 50 (1995) 1263-1279

1273

Table 7 Comparison of the estimated analyte concentrations in some samples with and without correction of interferences predicted when none of the lines used is free from interference a Analyte

Line/nm

Concentration/(l~g ml-') Corrected b

Uncorrected

Known

11.3 126

10

Nb(II)

269.760 309.418

9.9 9.9

Pr(II)

390.844 417.940 422.535

10.3 10.4 10.4

9.5 (1,2) 9.6 (1,2) 9.6 (1,3)

9.5 (1,3) 12.7 (2,3) 12.7 (2,3)

9.8 14.0 19.3

10

Ta(II)

263.558 267.590 268.517

9.5 9.5 9.3

11.1 (1,2) 11.1 (1,2) 10.5 (1,3)

10.9 (1,3) 9.8 (2,3) 9.5 (2,3)

12.6 10.5 28.2

10

Mo(II)

277.540 284.823 313.259 379.825

5.6 5.8 5.6 5.9

277.540 284.823 313.259 379.825

6.0 5.5 6.0 6.3

Fe(II)

238.204 234.830 234.349

0.25 0.25 0.25

P(I)

213.618 214.914 203.348 215.408

Mo(I) Mo(II) MoO)

5.8 6.1 5.8 6.3

(1,2,3) (1,2,3) (1,2,3) (1,2,4)

6.1 6.4 5.3 5.6

(1,2,4) (1,2,4) (2,3,4) (2,3,4)

5.4 6.7 10.7 6.1

6.1

(1,3,4) (2,3,4) (1,3,4) (1,3,4) 7.2 0.30 0.39

27 27 27 27

0.21

128 119 33 32

26.6

a The samples are the eight-element solution for Nb, Pr and Ta, the stainless steel solution for Mo and the bronze reference material (183/2) solution for P and Fe. b The concentrations estimated with all the lines of an analyte listed, unless indicated by the line index in parentheses (for example, 1,3 means the first and the third wavelengths listed were used).

8000"

7000' 6000' 5000"E

4000 30002000"

1000

i

0

5

1'0

35

Wavelength Step +

sample

~

predicted BKG

~

standard

+

tl:ue BKG

Fig. 1. Emission and predicted background spectra at Nd 406.109 nm for the eight-element solution.

1274

Peixung Zhang, D. Littlejohn/Spectrochimica Acta Part B 50 (1995) 1263-1279

141210-

tn tE o

8=

~_o

6-

4-

20

lb

0 5

'

2'0

15 Wavelength

'

3b

35

25

Step -m- sample

t

predicted BKG

~

standard

~

true BKG

Fig. 2. Emission and predicted background spectra at Nb 269.706 nm for the eight-elementsolution. 25O

200-

100.

50-

0

5

10

15 20 Wavelength

25

30

35

Step ---m- sample

~ ' predicted BKG - - * - - standard

~

true BKG

Fig. 3. Emissionand predicted background spectra at Nb 309.418 nm for the eight-elementsolution. and 8 for P and Fe in the bronze standard reference material, Fig. 9 for Zn in the geological sample solution and in Fig. 10 and 11 for simulated data with complex spectral features. The simulated data in Figs. 10 and 11 were included as they are examples of situations that arise in ICP-OES, but were not represented by the analytical measurement made in this study. By viewing the predicted background emission and assessing the figures of merit (SBF, OSI, SBR and TDL) displayed, the analyst can estimate if the sample background emission around the analyte line is simple, or has adjacent line wings or exhibits line overlap. Therefore, lines which are suitable for analysis and lines which are not become apparent (the analyst makes the decision). For instance, it is obvious from the displays of the predicted background emission spectra that the lines in Figs. 2-8, 10 and 11 should probably be rejected because the background has structured features within the bandpass of the analyte line. If an addition of an analyte standard solution is made to the unknown sample solution, multiplicative interference effects can be estimated. The results obtained for the synthetic

Peixung Zhang, D. Littlejohn/Spectrochimica Acta Part B 50 (1995) 1263-1279

1275

18 16 14 1210-

(n

_=

o

864

2 0

1'o

0

t~

2'0

3b

is

3~

Wavelength

Step sample

i

predicted BKG

~,~ standard

Q

true BKG

Fig. 4. Emission and predicted background spectra at Pr 422.535 nm for the eight-element solution.

7000 60005000>,

ID C

40003000 200010000

o

~

1'o

1'5

~o

~;5

~o

3~

Wavelength

Step sample

,

predicted BKG

;~< standard

D

true BKG

Fig. 5. Emission and predicted background spectra at Ta 263.558 nm for the eight-element solution.

samples showed that the estimated effects of multiplicative interferences agreed very well with the known effects, but are not listed here as they were generally small and so less important compared to the spectral interferences. 3.4. Background reduction and estimation of the analyte concentration with the matrix projection procedure

Background correction by an off-peak procedure works well if the spectra are simple, but becomes problematic when the sample has a complex spectrum. In addition, manual selection of background correction points is a barrier to automatic analysis. However, these limitations can be overcome by the proposed procedure. Once the background emission has been predicted, the net analyte signal at a line can be obtained by automatically subtracting the predicted background from the measured sample spectrum. This is a more effective means of background

1276

Peixung Zhang, D. Littlejohn/Spectrochimica Acta Part B 50 (1995) 1263-1279 9000 8000-

70006000 5000-

~ 4ooo300020001000- :~ 0

0

~ ~. ~ ~ .~ ~

1'0

5

2'o

,

3'0

15 Wavelength

25

35

Step sample

~

p r e d i c t e d BKG ~

standard

,z

true BKG

Fig. 6. Emission and predicted background spectra at Mo 277.546 nm for the eight-element solution. 1000 900 800700o~ C

600"

C

500400300" 200" 100 0

5

1'0

1'5 20 Wavelength

25

3'0

35

Step --m- sample

i

predicted BKG

:

standard

Fig. 7. Emission and predicted background spectra at P 214.914 nm for the bronze reference material solution. correction, especially when sample spectra of the type shown in Figs. 2-8, 10 and 11 are encountered. In these examples, manual selection of off-peak background correction points could be difficult, particularly when there are intense line wings on both sides of the analyte line. In general, the best way to obtain an accurate estimate of the analyte concentration using the developed procedure is to examine lines individually until one that is interference-free and of adequate TDL is found. However, while line selection is being carried out, it is possible to estimate the analyte concentration from the net signal produced by background subtraction. This is illustrated by the data derived from Figs. 10 and 11 for the simulated signals, where the true analyte concentration in each case is 2 arbitrary units. As the effects of the spectral interferences have been stripped away by the developed procedure, the estimated "analyte" concentrations are reasonably accurate. In severe cases of spectral interference, when no appropriate line is interference-free, quanti-

Peixung Zhang, D. Littlejohn/Spectrochimica Acta Part B 50 (1995) 1263-1279

1277

35003000" 25002000t" t"-

1500" 1000"

5000

1'o

0

l's

~o

~s

3'0

3~

Wavelength Step sample

~

predicted BKG ~

standard

Fig. 8. Emission and predicted background spectra at Fe 238.204 nm for the bronze reference material solution.

1000 900 800 700 600 t/} r-

_=

500-

t-

4003002001000

1'o

0

1'5

~o

~'s

io

35

Wavelength

Step

sample

~

predicted BKG ~

standard

Fig. 9. Emission and predicted background spectra at Zn 206.200 nm for the geological sample solution.

tative correction of the interference effects can be achieved by combining the present single line procedure with the multiline method described elsewhere [33]. The single line procedure is applied successively to each individual line first to strip away the majority of the spectral interference effects and calculate the "net" analyte signal at the central peak positions. Then, the multiline procedure is applied to these intensities to obtain a further prediction of the residual interferences, which are subtracted from each line. Table 7 lists some examples of corrected concentrations obtained by application of the single line and multiline procedures to intensity data where none of the analyte lines used is spectral interference-free. In most cases, the effects of the interferences are quantitatively corrected.

4. Conclusions

The procedure described here was initially developed for peak purity assessment to aid line selection in ICP-OES. As the estimation of background in the unknown sample spectrum is

1278

Peixung Zhang, D. Littlejohn/Spectrochimica Acta Part B 50 (1995) 1263-1279

5000450040003500>, 30002500E 2000~ 15001000" 500" 0 ~ z ~ ~,~ ~ ~ ;~~ ~-~ 0 5 10

Pr. CONC: 2.25

~5_~~ , ~ ~ 15 20 25 30 Wavelength

35

Step sample

--+-- predicted BKG

standard

+

net

signal

Fig. 10. Sample spectrum and sloped background emission predicted for simulated data.

5000 4500 4000 3500>, 3000 2500 2000150010005000 ~,~ 0

"~

I

-r~ it/ k . ~ ~

tQ} C

~ ~ ~ ~

5

5 ~ C,.~~

10

,

/

,

15 20 Wavelength

./

~

OSl: 14% TrueCONC: 2.0 Pr. CONC: 1.96

. . . . . . . .

25

30

35

Step --!- sample

- - + - predicted BKG

-'~: standard

net signal

Fig. 11. Sample spectrum and line wings predicted for simulated data.

accurate in most instances, the procedure can also be used as an automatic method to calculate the net analyte signals. In principle, this procedure can be applied to any instrumental technique, either for interference assessment or for background reduction or for both, provided the technique produces peak signals in two or more dimensions. If the spectrometer can measure a number of spectra of an analyte simultaneously such as in ICP-OES with a CCD detector, the combination of the single line and multiline [33] procedures is particularly useful for quantitative correction of spectral interference effects in automatic analysis of a large number of samples for a larger number of elements.

Acknowledgement The authors thank Paul Neal of ATI Unicam Ltd, Cambridge for provision of the ICP-OES intensity data.

Pe&ungZhang, D. Littlejohn/Spectrochimica ActaPa~ B 50 (1995) 1263-1279

1279

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