Anal. Chem. IQQI, 63,767-772
767
Multivariate Prediction and Background Correction Using Local Modeling and Derivative Spectroscopy Terje V. Karstang* and Olav M. Kvalheim Department of Chemistry, University of Bergen, N-5007 Bergen, Norway
Two techniques based upon regression modeling of spectra are described for accurate estimation of the concentration of the analytes in the presence of background constituents. The results from two data sets are presented: (I) UV spectra of biphenyl with naphthalene as a contaminant and (ii) X-ray diffractograms of kaolinite in samples contaminated with smectlte. For both data sets, the novel technlques give reliable predictions, while ail the contaminated samples are detected as outUers in the conventional approach. Only after including the contaminated samples In the calibration model can the conventional approach produce predictions at the same level of precision as the novel techniques.
INTRODUCTION Routine quantitative analysis of unseparated raw samples requires techniques that can identify, estimate, and remove unmodeled background constituents from the recorded data before quantification. This can be seen as a curve resolution problem, where the spectrum of a prediction sample is fitted to the calibration model, together with a function that describes the spectral shape of the background constituents. If this shape is known, e.g., Lorentzian or Gaussian, nonlinear regression can be used to deconvolute the prediction sample
(I). Another approach is to use selective (constituent-specific) variables for prediction purposes. This can be difficult when the spectrum of the background is unknown. Some techniques add the constraint of nonnegative intensities of the background constituent spectrum. These techniques will only give reliable quantification for calibration spectra having constituent-specific variables. Such a technique is linear programming (2) and can be used for background detection and estimation for calibration spectra having such selective variables. Lawton and Sylvestre (3) proposed a self-modeling curveresolution technique. This method gives spectral bands that contain the pure constituent spectra of two-component mixtures. They made an additional assumption that the spectra had a t least one constituent-specific wavelength. This will give a unique solution and corresponds to the first estimate of the background spectrum for a one-component model in one of the methods proposed below. Constituent-specific variables may also be obtained from spectral derivatives. O’Haver and Green ( 4 ) used derivative spectrometry for the quantitative analysis of pairs of overlapping Gaussian bands. They found that derivative methods give the lowest total error in most of the cases studied as compared to the analysis of the original spectra, but they also warn against generalizing the results. Derivative spectroscopy was also used by Gerow and Rutan (5). They combined derivative spectroscopy with an adaptive Kalman filter for background correction in thin-layer fluorescence chromatography. The adaptive Kalman filter
* Corresponding author. 0003-2700/91/0363-0767$02SO/O
can be used to resolve overlapping spectral variables if the calibration model is incomplete or inaccurate, but the calibration model information must be selective for some wavelength region in the spectral response (5). Tahboub and Pardue (6) combined least-squares and derivative spectrometric methods to reduce the influence of a light-scattering component. Osten and Kowalski (7)proposed two methods, a perpendicular projection technique and an extreme-vertex projection technique, in order to obtain quantitative results from samples with a background constituent. Devaux et al. (8)removed the water spectrum from a set of near-infrared spectra of wheat by assuming all other component spectra to be orthogonal to the water spectrum. This is almost never the case, and the method cannot be used in quantitative analysis. Only in cases where the spectrum of a background constituent is orthogonal to the calibration model will it not influence predictions (9). In this work, we present two techniques that provide reliable predictions for samples containing uncalibrated interfering components. In the first method, maxima or minima of the background spectrum are found. The first derivative of these optima provides selective calibration variables. A second method assumes that the second derivative of the background spectrum can be approximated by a straight l i e within a short interval. This corresponds to fitting a third-degree polynomial to the background in that interval. Since the signal-to-noise ratio usually decreases with increasing order of derivative, it is important to smooth the spectra before differentiation. O’Haver and Begley (IO) claim that, with sufficient smoothing, high-order derivatives can be obtained with only modest signal-to-noise ratio degradation as compared to the zeroth order. Cameron and Moffatt (11) have used Fourier transforms to obtain smoothed even-order derivative spectra. They claim that they obtain even-order derivatives with a higher signal-to-noise ratio than with techniques that use convolution functions. For simplicity, the technique of Savitzky and Golay (12),which is based on convolution functions, will be used in this work. Two data sets are analyzed. The first set consists of 13 samples of biphenyl and naphthalene analyzed by UV spectroscopy. The second data set contains bulk powder X-ray diffractograms of 20 mixtures of quartz, pyrite, calcite, kaolinite, and smectite.
THEORY In this section, two methods for background correction will be given. Both methods start from an established model that describes the calibration space. Decomposition of Calibration Spectra. The predictions are based on full-spectrum calibration models. The meancentered matrix XuXwof I objects (samples) and K spectral variables can be decomposed into principal components:
X = TGPt + E
(1) where the score matrix T and the loading matrix P consist of orthonormal column vectors It,) and {pi),respectively, and EUxK)is the residual matrix. The superscript t indicates a transposed matrix or vector. The number of principal com0 1991 American Chemical Society
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ANALYTICAL CHEMISTRY, VOL. 63, NO. 8, APRIL 15, 1991
r-- )
Prediction r w l e Brckpround ap&run
n
I
Zrrc-crosainq point of
\
y
/'
WAVELENGTH
WAVELENGTH
Flgure 1. Zero-crossing point of the background spectrum in one artificial prediction sample.
ponents used is denoted by A, and the diagonal matrix G contains the length of the unnormalized score vectors. Prediction. The regression coefficients used for prediction can be estimated from any suitable regression technique, e.g., principal component regression (PCR), partial least-squares regression (PLS), etc. For PCR, the regression coefficients are given as (13)
b; = PG-'Tty;
If a sample fitted to a calibration model shows large residuals, a background correction is needed. The simplest way of testing for systematic variation in the residual spectrum is by visual inspection. If a sample xi is detected as an outlier, i.e., the residual spectrum contains systematic variations, the loadings from the decomposition must be combined with the spectrum of the contaminant ui in order to get reliable quantitative results:
t;GP' +
U:
(4)
The background spectrum ui can be a combination of several unknown constituent spectra, and it also contains the random noise el of spectrum x:. Note that ti refers to row i in the score matrix T. The prediction of sample xi with a background spectrum, eq 4 substituted into eq 3, will be iij
= (t;GPt
+
uj)bj
(5)
From eq 5, the error caused by the background spectrum is simply ujbp This error can be large if uj is strongly correlated with by The prediction is only reliable if ujb, = 0. Method 1: Background Correction, One-Component Calibration Model. Thus, if an orthogonal relationship between the background spectrum uj and the loading spectrum pi for some subset of wavelength values can be found, the calibration model will still give reliable predictions, i.e. CPlkUik
= 0 for a subset of variables
variable can be used for quantitative purposes, i.e., use the first derivatives of eq 4
(7)
(2)
where yj is a vector containing the dependent variable j (concentrations) measured for the K samples. If no background correction is performed or needed, the prediction jji; of a dependent variable j in sample i is simply i rJ. .= x!b. 1 J (3)
X: =
Flgure 2. Increased selectivity by use of third derivatives compared to first derivatives. The raw data of the prediction sample and the background spectrum are shown in A. In 6 , the first derivative of the prediction sample is shown. For this sample, the zero-crossing point of the third derivative of the background spectrum and the third derivative of the prediction sample almost coincide.
(6)
A special but common case is to identify nonoverlapping regions between the calibration constituents and the background spectrum. These regions provide selective variables. There are selective calibration variables for all zero-crossing points of the first derivative of the background spectrum, Figure 1. If the first derivative of the calibration model has a "high" amplitude a t one of the zero-crossing points, this
or dXik / d A
dUik for all wavelengths k where - = 0 dA gl dPlk/dX (8) The zero-crossing point of the background spectrum in a prediction sample is shown in Figure 1. There are two main problems involved with this technique, namely, that of finding a selective variable (duik/dX = 0) and shifts in the spectral peak position. Estimation of Peak Position. If the positions of the maxima in the background spectrum can be found, then selective calibration variables are also found. In Figure 2 the zero-crossing points of a prediction sample using the first and third derivatives are shown. As can be seen from Figure 2, selectivity increases with the order of the derivative used. Thus, an iterative procedure combining the first and third derivatives of the spectra is used to locate the positions of the maxima or minima of the background spectrum. The procedure needs a good starting estimate of the background spectrum to select the correct zero-crossing point. The following algorithm uses the spectrum of the prediction sample to get a first estimate of the background spectrum. Estimate the residual spectrum uto): ti, =
t
t
90) = xi(I -
PIP!)
(9)
The residual spectrum contains the unique part of the prediction sample xi. A spectrum with only positive elements is found in the next step of the algorithm: (i) Set U4n)
=
40)
(10)
(ii) Update ut
The subscript k indicates the variable in u),, with the lowest intensity. (iii) If there are any negative elements in u:,+,),then go to step ii.
ANALYTICAL CHEMISTRY, VOL. 63,NO. 8, APRIL 15, 1991
769
1)
Use calibration model to obtain 6rst estimate of the background SReCtnrm,eq. 9. Iterate between eq. 10 and 11 to obtain a background spectrum with
Low i n t t n a i t y of t h e firrt dtri'uitim
Estimate the thud derivate of the background at one of the m m a using the A\
Estimate the zero-crossing point at the selected maximum.
1
Estimate the first derivative at the selected maximum using the Savit7ky-Oolay vrroctdure (12).
WAVELENGTH Figure 4. Local modeling of the second derivative. (a) A resolved prediction sample. (b) The first derivative of the calibration s p e C t r " (c) The second derivatives of the calibration (cl)and the background spectrum (c2). A straight line is used to describe the background
spectrum in the region around c l .
6)
Estimate the score at the selected maximum using eq. 12.
Use calibration mcdel to find maxima of kaown constituents. Inpractice the regioar where the loadings have maximum
If the score in this loop differs significantly from the score obtained in the previous loop then estimate the background by use of eq. 11 4J
Estimate the second derivative of the prediction sample. Subtract the estimated background spectrum and use eq. 3 to obtain the concentration At the second derivative of the prediction sample to the second derivative of the calibration model and tbe second derivative of the background spechum represented as a
Figure 3. Prediction steps in the first method for background cor-
rection.
This procedure ensures nonnegative responses for all variables in ut. Using this estimate as the background provides an upper limit for the concentration of the calibration constituent. The first derivative of a spectrum a t one of its maxima or minima is zero. So if the unknown constituent spectrum ut overlaps strongly with the calibration spectrum, and if it is possible to estimate the position of one or more of the maxima or minima in ut, then the first derivative of the spectrum, dxi/dX, can be used to estimate the score, til
for variables k where dut/dX is zero. In eq 12 the dxik/dX values are selective and the zerocrossing positions are assumed to be found. The zero-crossing points are estimated from the third derivative of then the score, t("),is estimated by eq 12 and ut,,) is updated. This sequence is repeated until the difference between t(,,)and t(n+l)is negligible. The zero-crossing points are estimated by least-squares fitting of a polynomial to the estimated background spectrum. Since the zero-crossing points almost never coincide with the position of one wavelength, interpolated values must be used. This procedure corresponds to the zero-crossing approach given by O'Haver and Green (4), but since the calibration spectrum is known, i t is in this case possible to estimate the zero-crossing points. Figure 3 summarizes the method. Method 2: Background Correction, Multicomponent Model. If the first derivative of the loading spectrum has a low intensity a t all the zero-crossing points of the unknown
Flgure 5. Prediction steps in the second method for background
correction. constituent, then the second derivative of the spectra should be used, Figure 4. Regions around the maxima of the calibration spectra are selected, and the second derivative of the background spectrum is approximated by a straight line in the region. This local curve fitting of the second derivative of the spectra assumes that a polynomial of degree 3 is sufficient to describe the background spectrum in the local area. Thus, the following model is used to describe the prediction sample:
where d2uik/dX2is approximated by straight lines in at least A short intervals
for interval a and variable k . Figure 5 summarizes the method. Background Correction, Methods 1 and 2 Combined. The two procedures described above can be combined in the case of a multicomponent calibration model. However, if the spectra of the pure constituents are available, the concentration of each one can be estimated independently of the others. By looking at the first estimate of the background spectrum, obtained through the iteration procedure of eqs 10 and 11, one can decide which derivative should be used. If the
770
ANALYTICAL CHEMISTRY, VOL. 63, NO. 8, APRIL 15, 1991
Principal component for mean centered data 0.5,O.S)
P
- 3
\
I I I I
jC
0.00
0.20
0.40
0.60
(0.9,O.l) 0.80 1 10
Variable 1
Figure 8. Effect of closure and mean centering of calibration data.
For simplicity, two selective variables are chosen. The calibration model is based on three samples, (0.2,0.8), (0.5,0.5), and (0.9,O.l). For uncentered data,the two axes, variable 1 and varlable 2, describe the
concentration (calibration)space. For meancentered data,only one component is necessary to describe the calibration space. The concentration of the background corrected sample P is determined by projecting the sample onto the two axes, points B and C, for a uncentered calibration model. The renormallzed background corrected sample P,,will give predictions that sum to 1. spectrum of the unknown overlaps strongly with the calibration spectra, then the first derivative of the spectra is used; otherwise, the zero or second derivative of the spectra is used. If the first derivative of the spectra is used, the region must be located around a maximum or minimum of the unknown constituent, otherwise around maxima of the calibration spectra, Figure 4. In order to avoid matrix inversion problems, the regions should be as orthogonal to each other as possible. Closure. If a calibration model is based on mean-centered data, and the dependent variable vectors are closed, then the predicted concentrations will sum up to a constant. Furthermore, the number of components used to describe the calibration data will be reduced by one compared to the number needed by an uncentered calibration model. After removal of a background spectrum, the prediction sample must also be renormalized before fitting. This is illustrated in Figure 6. Since the prediction sample must be renormalized before fitting, only relative concentrations of the calibration constituents are obtained. In order to determine the absolute amount of the calibration constituents in the prediction sample, using a mean-centered calibration model, one has to add a known amount of one of the calibration standards and run the sample again. The concentrations of the J calibration constituents in the prediction sample before and after addition, y i and ci, respectively, together with the amount of unknown, yw+l and c ~ J + respectively, ~, sum to a constant: J+ 1
C yi,
= 1 before addition of standard
(15)
j= 1
J+ 1
C cij = 1 after addition of standard
(16)
j=1
I t can be shown that (17) cis/Clis + C U + ~= 1 Yis/Yis + YU+I= 1 where Yis and E, are the predicted concentration of constituent s after background correction, before and after addition of constituent s, respectively. The total number of calibration constituents is denoted by J. The relation between yis and cis 1s
cis = (yis + w ) / ( l
+ W)
(18)
where w is the ratio between the weight of the added constituent s and the weight of the original sample. The ratio between the amount of unknown before and after addition is (19) YiJ+l/CiJ+l = 1 + w The sample weight is assumed to be 1 before addition. By combining eqs 17-19, the concentration of the added standard constituent, y k , before addition is (20) y;s = j ; s W ( l - E;s)/(Cl& - 9,) Since = for prediction samples having only one calibration constituent, eq 20 can only be used for samples with three or more constituents. If the prediction sample only contains one calibration constituent, one has to add one of the other calibration constituents and normalize the predictions to the known amount of added constituent. The effect of closed predictions can be avoided by not centering the calibration data and by not renormalizing the prediction sample after background subtraction. Since the intensity of X-ray diffraction (XRD) data of minerals is affected by absorption effects, the data must be normalized before quantification. A similar correction can be done on spectroscopic data that contain multiplicative noise of the form (14)
where rjk is the intensity of variable k for constituent j and uj and ui are the multiplicative factors of constituent j and sample i , respectively. The concentration of constituent j is denoted by yij. Karstang and Eastgate (14) have shown that only relative multiplicative coefficients are needed. The relative coefficients both, for calibration and prediction samples, can be estimated by a proper design of the calibration set. EXPERIMENTAL SECTION UV Data. Pure biphenyl and mixtures of biphenyl and naphthalene were characterized by using a Hewlett-Packard8450 UV/vis spectrophotometer. Methanol was used as solvent and reference. Only relative concentrations of naphthalene and biphenyl are known. The intensity at each wavelength in the range 229-309 nm were transferred to a personal computer via the Rs-232C communication port. A calibration model was obtained for the five spectra of biphenyl. Eight mixtures of naphthalene and biphenyl were used for prediction. XRD Data. Eleven mixtures of quartz, calcite, pyrite, and kaolinite and nine mixtures containing smectite in addition to the four minerals already mentioned were characterized by using a Phillips 1700 automatic powder diffractometer with a monochromator and a Cu anode (A = 1.54 A) with broad focus, 12.5-mm automatic divergence slit, O.1-mm receiving slit, and fixed scatter slit. The samples were run from 1.5 to 65.0° (20) at a speed of 1.2'/min and a measurement interval of 0.05O. The recorded diffractograms were collected and stored on a PDP 11/24 computer and later transferred to a micro VAX. The data were shift corrected and normalized as described in ref 14. A calibration model for kaolinite was obtained with the diffractograms of the 11 mixtures of quartz, calcite, pyrite, and kaolinite. The diffractograms of 9 mixtures containing smectite as background constituent were used for prediction. Data Handling and Analysis. The Savitzky-Golay (12) least-squares procedures for differentiation of data were used. The calibration models were generated by the VAX version of the SIRIUSprogram (15). RESULTS A N D DISCUSSION UV Data. A calibration model for pure biphenyl was obtained by using the first principal component to describe the spectra of five samples. The procedure shown in Figure 3 was used to calculate the concentration of biphenyl in the eight prediction samples. A quadratic convolution function was
ANALYTICAL CHEMISTRY, VOL. 63, NO. 8, APRIL 15, 1991
771
Table 11. Standard Error of Prediction versus Number of Points Used in the Convolution Function
ILI
4 -0.07
1
‘Zero-croaainq
:’ ; ; !
point
,
1
’I
,
I
’J’
-0.10
I
no. of pts
SEP
no. ofpts
SEP
3 5 7
0.053 0.047 0.034
9 11
0.023 0.013 0.020
13
1
, I ,,I
n
1
-
z
r
z
o
1
a
1
n
z
-
E
r
E
o
=
a
=
n
I
+
X
r
X
Table 111. Predicted Weight Percent Kaolinite in Samples Containing Smectite as the Interfering Mineral”
WAVELENGTH nm. Flgure 7. Spectra of the fkst derivatives of naphthalene and biphenyl.
Table I. Predicted Concentrations of Biphenyl in Mixtures of Biphenyl and Naphthalene“
sample
prepared 0.10 0.20 0.40 0.50 0.60 0.33 0.80 0.25
predicted re1 concns w/backwo/background corrn max ground corrn 0.110 0.195 0.408 0.507 0.601 0.342 0.791 0.278
0.188 0.267 0.459 0.663 0.711 0.622 0.862 0.469
0.233 0.344 0.535 0.854 0.863 0.887 0.939 0.753
” The prediction error for the background-corrected samples is 0.013.
used to estimate the first derivatives and a cubic function for the third derivatives of the background (12). The standard error of prediction was reduced from 0.053 unit for a 3-point function to 0.013 unit for an 11-point function. For the first estimate of the background, the region for possible zero crossing was from 234 to 238 nm. For all samples after convergence, a minimum of the background spectrum, naphthalene, was found around 237 nm. The minimum (zerocrossing point) is seen in Figure 7 where the first derivative of the pure biphenyl spectrum and the first derivative of the estimated naphthalene spectrum are plotted. The predicted relative concentrations of the eight mixtures are given in Table I. The column named Ymaxn(Table I) contains the results using the first estimate of the background/naphthalene spectrum, i.e., jumping directly from step 2 to step 8 in the prediction procedure of Figure 3. This provides an estimate of the maximum possible concentration of biphenyl in the mixtures since the parts of the background spectrum correlating with the pure biphenyl spectrum will be included in the calculation of the concentration. The prediction using the calibration model for the pure biphenyl spectra without background correction is shown under the column “wo/background corrn” prediction. In this case, the predictions from an insufficient calibration model give results that are physically impossible. Of course, all samples were also detected as outliers by the calibration model. Table I1 gives the standard error of prediction as a function of the number of points in the convolution function. A smoothing of the spectra slightly changes the shape of the calibration spectra. This is of no concern in multivariate calibration, as long as no significant variability is lost in the process. If the derivatives have complex features in the fitting region, a too “broad” convolution function may fail to give a precise estimate of the zero-crossing positions. Too few points will also give poor predictions, caused by the methods sensitivity to random noise. This method may give large random errors in cases where the calibration spectra have low intensity a t
prepared
predicted
5.0 10.0 20.0 40.0 50.0 60.0 80.0 90.0 95.0
6.8 11.7 19.8 40.5 53.6 61.7 78.9 91.7 94.1 1.7
SEP
The technique using the second derivative of the diffractograms was used for prediction. Standard error of prediction for background corrected samples is 1.7%. the zero-crossing point of the background spectrum. For a complete error analysis using derivative techniques in quantitative analysis, see O’Haver and Green (4). XRD Data. A calibration model using 3 principal components was obtained for diffractograms of 11 mixtures of quartz, calcite, pyrite, and kaolinite. The data were shiftcorrected and variable-reduced as described in ref 14. Due to column centering, only three principal components were necessary to describe the four-component mixture. The procedure shown in Figure 5 was used for background correction and prediction of nine samples including smectite as the background constituent in addition to the four modeled constituents. An 11-point convolution function was used to estimate the second derivatives of the diffractograms. Straight lines were used to describe the diffractogram of smectite around the maximum of each of the three principal component loading spectra. In this way, the full rank of the calibration model is retained in the prediction step. Only the three points closest to the maxima were used to model the background spectrum. The predicted weight percent kaolinite is given in Table 111. The standard error of prediction for the backgroundcorrected samples is 1.7% compared to a standard error of calibration of 1.4% for a model where smectite is included in the calibration step. Since the relative amount of smectite in all samples is known, no extra additions were necessary for quantification.
CONCLUSIONS The proposed techniques give good results for the two data sets presented. The technique is general and should thus be applicable to many types of spectroscopic data where unknown interfering constituents are a major problem for quantitative analysis. As shown in this work, problems caused by spectral shifts can be cured by a use of methods such as the one described in ref 14. ACKNOWLEDGMENT Richard Eastgate, Norsk Hydro Research Center, is thanked for valuable discussions. LITERATURE CITED (1) Maddams. W. F. Appl. Spechosc. 1980, 34. 245-267. (2) Phillips, D. T.; Ravindran, A.; Solberg, J. Operations Research, Rindples and Practice; John Wiley & Sons: New York, 1976.
Anal. Chem. 1991, 63,772-775
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(3) Lawton, W. H.; Sylvestre, E. A. Technometrics 1971, 3, 617-633. (4) O'Haver, T. C.; Green, G. L. Anal. Chem. 1976,48, 312-318. (5) Gerow, D. D.; Rutan, S. C. Anal. Chlm. Acta 1988, 184, 53-64. (6) Tahboub, Y. R.; Pardue, H. L. Anal. Chem. 1985,57, 38-41. (7) Osten, D. W.: Kowalski, B. R. Anal. Chem. 1985,5 , 908-917. ( 8 ) Devaux, M. F.;Bertrand, D.; Robert, P.: Qannari, M. Appl. Spectrosc. 1988,42, 1020-1023. (9) . . Kvaiheim, 0. M.: Karstana. T. V. Chemom. Intell. Lab. Syst. 1989, 7, 39-51. (IO) O'Haver, T. C.; Begley, T. Anal. Chem. 1981,53, 1876-1878. (11) Cameron, D. G.; Moffatt, D. J. Appl. spectrosc. 1987,4 , 539-544. (12) Savitzky, A.; Golay, M. J. E. Anal. Chem. 1964,3 6 , 1627-1839.
(13) Manne, R. Chemom. Intell. Lab. Syst. 1987,2 , 187-197. (14) Karstang, T. V.; Eastgate, R. J. Chemom. Intell. Lab. Syst. 1987,2 , 209-219. (15) Kvalhelm. 0. M.; Karstang, T. V. Chemom. Intell. Lab. Syst. 1987, 2 , 235-237.
RECEIVED for review July 18,1990. Accepted January 3,1991. Norsk Hydro Ltd. is gratefully acknowledged for financial support to T.V.K.
Effect of Ascorbic Acid on Graphite Furnace Atomic Absorption Signals for Lead Shoji Imai* and Yasuhisa Hayashi Department of Chemistry, Joetsu University of Education, Joetsu, Niigata 943, J a p a n
Doublepeak atomic absorptlon dgnah for lead were observed in the presence of 1% (m/v) (mass/volume) ascorbic acld when the nonpyrolytlc coated graphlte (NPG) tube was used. The slgnal with double peaks could be separated Into two slgnais: The posltion of the first peak was In agreement wlth the posltlon of the slgnal In the absence of ascorblc acld udng the pyrolytic graphlte (PG) tube, and the posltlon of the second peak was In agreement wlth the posltlon of the signal In the absence of ascorbic acld uslng the NPG tube. A less porous and smooth surface was found by uslng scannlng electron microscopy on the NPG tube wail after pyrolyzing ascorbic acid. I t was consldered that pyroiyrlng ascorbic acid produced PG-coated sites on the NPG tube that lead to the appearance of the first peak.
INTRODUCTION Lead is one of the elements that has complex atomization mechanisms in the electrothermal graphite furnace. A signal with double peaks was observed in 1% (m/v) ascorbic acid by McLaren and Wheeler (I). They have also shown that the atomic absorption signal for lead is shifted to the lower appearance temperature (Tapp) in the presence of ascorbic acid and have suggested that signals with double peaks were caused by forming dimorphic forms of lead oxide (litharge and massicot). Salmon et al. (2) proposed that the appearance of signals with double peaks was caused by chemisorbed oxygen on the graphite. Regan and Warren (3) have reported that in the presence of 1% (m/v) ascorbic acid the temperature a t maximum absorption for lead can be reduced from 2370 to 1570 K without loss of peak height. Tominaga and Umezaki ( 4 ) have shown that the double peaks appeared in 0.05% (m/v) ascorbic acid, whereas 5% (m/v) ascorbic acid gives a single peak with a similar lowering of Tapp. Gilchrist et al. (5)have suggested that hydrogen and carbon monoxide released by the pyrolysis of ascorbic acid decrease the partial pressure of oxygen in the graphite furnace and thereby cause the equilibrium position of the reaction to shift to the right. Sturgeon and Berman (6) have proposed that pyrolyzing ascorbic acid forms active centers, which enhance the rate of scavenging of oxygen and lead to the lowering 0003-2700/9 110363-0772$02.50/0
Table I. Standard Atomization Conditions
stage
drying ashing atomizing* cleaning
temp"/OC
time/s ramp hold
120 700
30 20
0
2500 3000
0 0
2 3
5
inner gas flow/mL min-'
200 200 30 200
Values programmed in atomizer unit. If the optical temperature controller is used, the maximum current heating of 1400 K/s is carried out. of Tappof lead. They reported the Tappshift from 1045 to 955 K using the pyrolytic graphite coated graphite (PG) tube and from 1220 to 1010 K using the nonpyrolytic graphite (NPG) tube in 1% (m/v) ascorbic acid. They said that the active site provides active carbon for reduction. We found here, by scanning electron microscopy (SEM), that pyrolyzing ascorbic acid forms the pyrolytic graphite coating site on the NPG tube wall. In the present work, it was found that a lowering of Tapp for lead never appeared in 1% (m/v) ascorbic acid using the PG tube but did in 0.001 mol % / V ascorbic acid using NPG tube, and we discuss that the appearance of the double peaks was responsible for forming a pyrolytic graphite coating site on tube wall by pyrolyzing ascorbic acid. EXPERIMENTAL SECTION Apparatus. A Hitachi Model 2-8000 flame and graphite furnace atomic absorption spectrometer equipped with a Zeemann effect background corrector and an optical temperature controller system (Hitachi Model 180-0341)were used. Twenty microliters of sample solution was injected by an automatic sampler. The peak height and area were automatically printed out and displayed by using a Hitachi data processor. The analyticalwavelength and slit width were 283.3 and 1.3 nm, respectively. An Oki personal computer if-800 Model 50 was used to record the absorbance signal profiles (20-ms interval). Output data from the optical temperature controller were acquired at 4-ms intervals by the personal computer and subsequently stored on a diskette. The output data were calibrated by using the inner wall temperature monitored by a Chino Model IR-AH1S radiation thermometer and using an emissivity of one. For this thermometer, the wavelength is 960 nm and the accuracy is 0.5% to 1500 K, 1.0% to 2300 K, 2.0% to 3300 K. Reproducibility ( n = 10) of the ashing and atomizing temperatures for the NPG tube was 0.5% at 800 K and 0.3% at 0 1991 American Chemical Society
