Anal. Chem. 1987, 59, 1529-1534 (8) Lewis, G. W.; Nobes, M. J.; Carter, G.; Whitton, J. L. Nucl. Instrum. Methods 1980. 170, 363-369. (9) Boudewijn, P. R.; Akerboom, H. W. P.; Bulle-Lieuwma, C. W. T.; Haisma, J. S I A , Surf. Interface Anal. 1985, 7 , 49-52. (IO) Lewis, G. W.; KirlakMes, G.; Carter, G.; Nobes, M. J. S I A , Surf. I n terface Anat. 1982, 4 , 141-150. (11) Seah, M. P.; Jones, M. E. Thin Solid Films 1984, 115, 203-216. (12) Duncan, S.; Smith, R.; Sykes, D. E.; Wails, J. M. Vacuum 1984, 3 4 , 145- 151. (13) Andersen, H. H.; Bay, H. L. Sputtering by Partide Bombardment I ; Behrisch, R., Ed.; Springer-Verlag: Berlin, 1981; pp 145. (14) Navlnsek, 6.; Zabkar, 1. Thin SolMFilms 1978. 3 6 , 41-45. (15) Brown, J. D.; Robinson, W. H.; Shepherd, F. R.; Dzoiba, S. Secondary Ion Mass Spectrometry SIMS I V ; Benninghoven, A., Okano, J., Shimizu, R., Werner, H. W., Eds.; Springer-Veriag: Berlin, 1983; pp 296-298. (16) Whitton, J. L.; Carter, G; Nobes, M. J.; Williams, J. S. Radiat. Eff. 1977, 3 2 , 129-133. (17) Sigmund, P. J. Mater. Sci. 1973, 8 , 1545-1553. (18) Aucielio, 0.; Alstetter, C. J. Radiat. Eff. 1980, 51, 241-248. (19) Bernheim, M.; Slodzlan, 0. Int. J. Mass. Spectrom. Ion Phys. 1973, 12, 93-99. (20) Werner, ti. W. S I A , Surf. Interface Anal. 1982, 4 , 1-7.
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(21) Williams, P.; Baker, J. E. Nucl. Instrum. Methads 1981, 182/183, 15-24. (22) Phrin, J. C.; RoquesGarmes. C.; Siodzian, G. Int. J. Mass Spectrom. Ion Phys. 1978, 2 6 , 219-235. (23) Pivin. J. C.; RoquesCarmes, C.; Slodzian, 0. J . Appl. Phys. 1980, 51, 4158-4163. (24) Giber, J.; Marton, D.; Laszlo, J. J. Phys. (Les Ulls, Fr.) 1982, 45, C2: 115- 118. (25) Oechsner, H. Z . fhys. 1973, 261, 37-58.
RECEIVED for review November 17,1986. Accepted February 9, 1987. Certain commercial equipment, instruments, or materials are identified in this paper to specify adequately the experimental procedure. Such identification does not imply recommendation or endorsement by the National Bureau of Standards, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose. This work was supported by the “National Fonds voor Wetenschappelijk Onderzoek” through Grant 2.0091.85.
Multicomponent Analysis Using an Array of Piezoelectric Crystal Sensors W. Patrick Carey, Kenneth R. Beebe, and Bruce R. Kowalski* Laboratory for Chemometrics, Department of Chemistry, BG-IO, University of Washington, Seattle, Washington 98195
An array of nine piezoelectric quartz crystals, each coated with a different partially selective coating material, was constructed for multicomponent analysis of organic vapors. The usefulness of thls array was evaluated by quantitating known mixture samples in both two and three component cases using two calibration technlques, multiple linear regression (MLR) and partial least squares (PLS). With the use of microsensors, such as coated piezoelectric crystals, a high degree of collinearlty between the sensors may exist, which has an effect on the regression results. I n the two component cases, the PLS method yielded a 4- to Wold improvement In prediction capaMiity over MLR. Although the Individual sensors produce a 3 4 % relative error in response, the average relathre predktkm e m &ahred when using an array of sensors and the PLS method was 4.6% in the two component cases, while in the three component case the effect of collinearity decreases prediction capability.
One vapor sensor that has received attention in the chemical literature is the piezoelectric crystal sensor. The piezoelectric crystal, commonly referred to as the quartz microbalance, is a unique sensor due to its characteristic of changing oscillation frequency when mass is applied to the crystal surface. The use of the piezoelectric crystal as an environmental monitoring device was first described by King (1) and has since been extensively studied by Guilbault and co-workers ( 2 , 3 ) .The piezoelectric crystals commonly used consist of AT-cut quartz with a fundamental frequency of oscillation ranging between 5 and 20 MHz. The relationship between the fundamental crystal frequency, F, and the total mass change in grams, AM, of the adsorptive coating and vapor is as follows:
AF = -(2.3
X
lO-‘)F(AM/A) 0003-2700/87/0359-1529$01.50/0
where AF is the change in crystal frequency in hertz and A is the surface area of the sensitive portion of the crystal in square centimeters. The piezoelectric crystal sensor, as with most chemical sensors, is susceptible to interfering analytes, since the adsorptive coating employed is rarely totally selective for a single component. An alternative to the use of individual sensors for an analysis where interferences may be present is the multivariate approach of an array of several sensors each coated with a different partially selective coating material. The advantage of an array device of this kind is that the array’s response for each analyte corresponds to a fingerprint response pattern with component identification analogous to spectrometry. The output response of an individual sensor is scalar in form and quantitation can be performed only if the sensor is responding selectively to the analyte of interest. The data obtained from qn array coupled with multivariate calibration methods allow the analysis of a mixture of analytes as long as each responding analyte is known and calibrated. An example of the usefulness of an array of ion-selective electrodes in multivariate analysis was presented by Otto and Thomas (4).Additional applications of pattern recognition to arrays of sensors for identification of hazardous vapors have been reported in the chemical literature (5-7). Detection and quantitation of analytes in multicomponent samples with partially selective sensors requires use of multivariate calibration. The two multivariate calibration techniques used in this paper are multiple linear regression (MLR) (8) and partial least squares (PLS) (9). The representation of MLR (for direct multilinear Calibration) in matrix notation is
C=RS+E where C is an i X k matrix of k analyte concentrations in each of i samples, R is an i X j matrix of sensor responses to i samples from j sensors, S is the j x 12 matrix of regression 0 1987 American Chemical Society
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ANALYTICAL CHEMISTRY, VOL. 59, NO. 11, JUNE 1, 1987
coefficients, and E is an i X k matrix of residuals. In the calibration step, the matrices &,the measured responses, and Co, the calibration concentrations, are determined and the equation rearranged to solve for S.
(RoTRo)F1 RoTCo= S
(3)
Once estimates for S are obtained additional samples can be quantitated by substitution of the measured response vector into eq 2. The main difficulty with the use of MLR is the inversion of R when there is a high degree of collinearity (10). Collinearity is the lack of independence between the columns (sensors) of R. The less independent these columns, the more error or instability is produced when determining its inverse. PLS, on the other hand, is a technique in which the R and C matrices are represented by mutually orthogonal or linearly independent latent variables. These latent variables are linear combinations of the original variables and are calculated to describe the variance present in both the R and C matrices. For an i X j matrix R, PLS calculates latent variables one a t a time with each successive latent variable describing less of the variance than the preceding. The j latent variables combined contain all of the variance present in the original R matrix. When appropriate instrumentation is employed, variation in the response matrix from sample to sample is correlated to the variation in the concentrations of the analytes in the samples. Therefore it is the variance that represents the useful information to the analyst. Since the first latent variable describes more of the variance in the R matrix than any other latent variable, it is also the most important for analytical purposes. Likewise, since the j t h latent variable describes the smallest amount of variance, it is the least important; in fact it can be shown to adversely affect the estimation of concentrations (1I ) . These adverse latent variables are comprised mainly of random noise and therefore do not aid in describing the true relationship or model between R and C. The appropriate number of latent variables to include in the calibration model is determined by cross validation, and the real power of PLS lies in the deletion of the fraction of the latent variables that contain mostly noise. The basis for this selection process will be discussed later in this paper. The significant differences that separate PLS from MLR are that no matrix inversion is necessary, and collinearity has no direct effect in the calibration model. Once the PLS model is derived, a matrix of coefficients can be calculated that are analogous to the regression coefficients found by MLR (12). These coefficients can be studied to determine the importance of the original variables (sensors) in the PLS model. The purpose of this study was to provide evidence that when chemical sensors such as coated piezoelectric crystals are used as multivariate instruments, they can perform the task of vapor mixture analysis. This study also investigated the performance of multiple linear regression and partial least-squares modelling in the calibration and quantitation of microsensor array responses. Since the sensors used are highly nonspecific, the effect of collinearity on the ability to perform quantitative analysis was of considerable interest.
EXPERIMENTAL SECTION Instrument. The apparatus, Figure 1, consisted of nine piezoelectric quartz crystals with a fundamental frequency of 9-MHz obtained from Standard Crystal Corp. Each crystal was powered by its own oscillator circuit constructed in the University of Washington chemistry department electronics shop (13). A multiplexer controlled through a digital 1 / 0 card in an IBM PC-XT allowed for the sequential monitoring of each oscillator board output frequency by a Hewlett-Packard 5384A frequency counter. The computer then acquired the crystal frequency from the counter via a GPIB interface from National Instruments. All programs for data acquisition were written in FORTRAN with assembly language subroutines for the digital output control of
Nitrogen Purge
c
+I
Vacuum pump
I
Sample InjectionPort
1
Computer
Figure 1. Instrument diagram of piezoelectric crystal sensor array.
the multiplexer. The sensor array was mounted inside a 1.3-L Teflon chamber with dried nitrogen purge and septum port for syringe injection. Additionally the chamber temperature was controlled to 0.1 OC by the use of a resistance thermometer and heating tape. All analyses were performed between temperatures of 39.2 and 40.5 "C. Reagents. The vapors tested were rn-dichlorobenzene, 1,1,2trichloroethane, 1,2-dichloropropane, 2-methyl-2-pentano1, and water. The halocarbons were purchased as EPA high-purity standards from Alltech Assoc., Inc., while the 2-methyl-2-pentanol was 99%+ pure from Aldrich Chemical. The water was obtained in high-purity form from Burdick & Jackson Laboratories. Sensor Coatings. The adsorptive film applied to each crystal consisted of stationary-phase materials for gas chromatography. The coatings used for sensors one through nine are bis(2-ethylhexyl) sebacate, ethylene glycol phthalate, quadrol, octahexyl vinyl ether, 1,2,3-tris(2-cyanoethoxy)propane,silicone SE-54, silicone DC-710, dioctyl phthalate, and silicone OV-225, respectively. All phases were purchased from Alltech. The coating material selection was based on the application of principal component analysis to a larger group of 31 coatings, each of which was tested for its response to the analytes of interest (14). The coating of the crystals was performed by making a 5% by weight solution of the coating material in a highly volatile solvent such as methylene chloride. Each solution was brushed onto both sides of a crystal with a cotton swab until a frequency shift of approximately 3500 Hz was obtained after evaporation of the solvent. All subsequent analysis with the piezoelectric sensors was accomplished inside the Teflon chamber with an inert nitrogen atmosphere. Vapor Analysis. The calibration and quantitation steps involved injecting microliter liquid aliquots of the samples by high-precision syringe into the Teflon chamber, allowing the liquid t o vaporize, and then measuring the sensor responses. Each sample took approximately 15 min to equilibrate at the elevated temperatures. This equilibrium time was determined mainly by the time required for the samples to vaporize. The time constant of the sensors was more on the order of 30 s to 1 min. The chamber was then evacuated by a high-vacuum pump and purged with nitrogen. Evacuation and purge lasted 20 min, yielding a 35 min sample analysis. No effort was made to optimize the analysis time in the present study. In each of the experiments, the calibration concentrations of the vapors spanned the entire range of the prediction sample concentrations. Additionally, each sample was analyzed in triplicate in order to obtain an estimation of the precision of the sensor array response.
RESULTS AND DISCUSSION Two Component Mixtures. The first two sample sets chosen for this analysis were a mixture of rn-dichlorobenzene and 1,1,2-trichloroethane and a mixture of 2-methyl-2-pentanol and water. The first sample set was selected to demonstrate the usefulness of the piezoelectric crystal array in monitoring environmental hazardous vapors. With the same coatings on the crystals of the array, the second set of analytes demonstrated the wide applicability of array devices and the ability
ANALYTICAL CHEMISTRY, VOL. 59, NO. 11, JUNE 1, 1987 0 - , TCE
2500+
0-,DCB
35
b
1531
02M2P (409ppm)
AWATER ( 2 7 7 5 ~ )
t
30
0
1000
560
. 1500 - 2000 ' 2500 Vapor Concenbatnn (ppm)
3000
3500
Figure 2. Dynamic range plots for mdichlorobenzene (DCB) and 1.1,2-trIchloroethane (TCE).
e-,
4004
5 0 4 . , 0 2000
.
,
.
,
.
,
.
,
.
,
.
,
c
.
,
. 1
6000 8000 10000 12000 14000 16000 18000 Vapor Concenbatbn (ppm)
water vapor.
8
8
ODCS (100pprn)
ODCP (615ppm)
OTCE (1aOOppm)
t
60 40 20
0 1
2
3
.
. 2
.
, 3
.
, 4
.
, 5
Sew
.
,
6
. 7
,
.
,
8
. 9
Figure 5. Sensor array response patterns for water vapor and 2-
Table I. Figures of Merit for Piezoelectric Crystal Array
Flgure 3. Dynamic range plots for 2-methyld-pentanol (2M2P) and
160
1
methyl-2-pentanol (2M2P). A-,Water
2M2P
4000
oJ
4
5
6
9
Figure 4. Sensor array response patterns for mdichlorobenrene (DCB), 1,2dichloropropane (DCP), and 1,1,24richIoroethane (TCE).
to monitor a given vapor such as an alcohol in the presence of the most common environmental background, water vapor. To determine the linear dynamic range of the array, the concentration of the analytes was plotted vs. the sum of the nine individual sensor responses, Figures 2 and 3. Deviations from linearity can be noted for m-dichlorobenzene a t 2000 ppm, Figure 2, and for water vapor at approximately 12 000 ppm, Figure 3. For all analytes, the concentrations used in this study are well within the dynamic range of the sensor array. The degree of effectiveness to which multivariate regression performs in multicomponent analysis depends on the uniqueness of the sensor array response pattern of the pure analytes. The portion of an individual analyte's response pattern that is unique from all other patterns in the mixture is the part of the pattern used for identification and quantification. In Figures 4 and 5, the array response patterns for the components in each sample set are plotted to show the degree of similarity or collinearity. As the degree of collinearity increases, error is amplified in the analysis by the propagation of sensor array response error (15). Additionally, as similarity increases, the limit of determination of each component rises due to the decrease of the effective signal to noise in the response pattern. Formulas developed to express the figures of merit such as sensitivity, selectivity, and limit of determination were given by Lorber (16). From these
rn-dichlorobenzene 1,1,2-trichloroethane 2-methyl-2-pentanol water rn-dichlorobenzene 1,2-dichloropropane 1,1,2-trichloroethane
sensitivity, Hz/ppm
selectivity
0.141 0.023 0.058
0.170 0.170 0.620 0.620 0.125 0.186 0.145
0.010 0.093
0.010 0.018
expressions, the parameters for both sample sets are calculated and expressed in Table I. The sensitivity parameter is defiied as the unique portion of the response pattern divided by the analyte concentration for that response. The overall sensitivities for the array elements are lower than usual applications of piezoelectric crystals due to the increased temperature of analysis (3). The selectivity term is the ratio of the unique response pattern to the actual response pattern of the pure analyte. This term is therefore a number between zero and one. For the chlorocarbons, the selectivity term is low due to the high degree of structural similarity that gives rise to similar response patterns, while for the alcohol and water sample the selectivity is higher. The magnitude of this parameter is visually evident from comparing Figures 4 and 5. These two terms, sensitivity and selectivity, give an indication of how well the regression techniques mentioned earlier can quantitate the corresponding mixtures. The calibration samples for each trial are given in Table 11. The results of the MLR and PLS regressions for the two component cases are reported in Table 111. The calibration data for the PLS model was autoscaled (subtraction of mean and scaled by variance for each column) and only two latent variables were used. The variance retained in the model is given in Table IV. For the first sample set of rn-dichlorobenzene and 1,1,2-trichloroethane, the PLS predictions were better in all cases. The average relative error was 21.3% for MLR and 5.7% for PLS for rn-dichlorobenzeneand was 26.1% for MLR and 4.77% for PLS for 1,1,2-trichloroethane. For the second sample set of water vapor and 2-methyl-2-pentano1, the PLS results were again better in all cases. For water the average relative error improved from 35.3% to 6.3% and for 2-methyl-2-pentanol the results improved from 22.4% to 1.26% for PLS. The increase in prediction capability for PLS with the second set is due to the better selectivity of both analytes. The precision of each sensor was previously reported and c o n f i i e d in these experiments to be approximately 3 4 % relative to the magnitude of the response (4). However, since the array yields nine responses for each sample and each sample was analyzed in triplicate, the prediction results can be better than this 3-6% due to signal averaging. The propagation of error introduced by the response error affects the prediction properties of MLR more than PLS by a factor of 4-6. These results indicate the superior ability of PLS to
~
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ANALYTICAL CHEMISTRY. VOL. 59, NO. 1 1 , JUNE 1 , 1987
Table 11. Calibration Sample Concentrations set 1, ppm
set 2, ppm
set 3, ppm
sample no.
DCB
TCE
2M2P
HZO
DCB
DCP
TCE
1 2 3 4 5 6 7 8 9 10 11
85 0 85 170 510 850
0 1075 1075 1720 1505 1720
0 408 408 1141 0 652 652 571
2775 0 2775 3330 4440 0 4440 5550
85 0 0 85 170 85 85 170 170 85 170
0 615 0 615 615 1230 615 1230 615 1230 1230
0 0 645 645 645 645 1290 645 1290 1290 1290
Table 111. Multivariate Prediction for Two Component Cases Sample Set 1
1. 2. 3. 4. 5.
85 170 119 510 850
MLR predicted
P L S predicted
actual concn, PPm DCB TCE
re1 error, % -
concn, ppm
1720 1075 1505 1075 1505
concn, ppm TCE
DCB
TCE
DCB
TCE
DCB
83.3 170 112 448 784
1714 1144 1563 1168 1574
2.00 0.00 5.88 12.2 7.76 5.51
0.35 6.42 3.85 8.65 4.58
79.9 202 160 574 554
av
1746 940 1269 82 1 2662
4.77
re1 error, % DCB
TCE
6.00 18.8 34.5 12.6 34.8
1.51 12.6 15.7 23.6 76.9
21.3
26.1
Sample Set 2
1. 2. 3. 4.
MLR predicted
PLS predicted concn. Dvm
actual concn, wwm HZO 2M2P
H20
2M2P
HZO
2M2P
4440 3330 4440 3885
4362 3796 4029 3974
794 799 487 977
1.76 14.0 9.26 2.29
2.58 1.96 0.41 0.10
6.83
1.26
815 815 489 978
re1 error, %
av Table IV. Percent Variance Described by PLS Model independent variable (R) latent variable
each
dependent variable (C)
total
each
total
15.87 23.77
75.87 99.64
47.42 49.99
47.42 97.40
test set 1 DCB and TCE 1 2
99.15 0.76
99.15 99.91
test set 2 2M2P and H,O 89.90 9.96
1 2
89.90 99.59
deal with this response error and the instabilities created by collinearity.
Three Component Mixture. A third sample set composed of three components, rn-dichlorobenzene, 1,1,2-trichloroethane,
concn, ppm HzO 2M2P
re1 error, % HzO 2M2P
6161 4851 6749 4063
38.8 45.7 52.0 4.58
16.8 22.6 46.0 4.09
35.3
22.4
678 631 265 938
and 1,2-dichloropropane, was analyzed by the same array to demonstrate the effect of severe collinearity in multivariate quantitation. The response patterns for all three components, Figure 4, are similar and a high degree of collinearity exists in this sample set. When MLR was used to predict the concentrations of the analytes in the four test samples, Table V, large errors were realized for 1,2-dichloropropane. In fact, the trend in the actual concentrations of 1,2-dichloropropane in samples 1 through 4 increased while the predicted concentrations decreased. This is due to both the collinearity problem associated with MLR and the low sensitivity of 1,2-dichloropropane to the array. Improved results in this highly collinear case were obtained from PLS when each analyte was calibrated and predicted individually. This technique allows the maximum amount of response information to be used in the model. The average relative prediction error for PLS was 10.1% and for MLR was 18.6%. It must be emphasized that this is a worst case example of
Table V. Multivariate Prediction Results for the Three Component Case actual concn, ppm 1. 2. 3. 4.
av
PLS predicted concn, PPm
MLR Dredicted
re1 error, %
concn, ppm
re1 error, % DCP TCE
DCB
DCP
TCE
DCB
DCP
TCE
DCB
DCP
TCE
DCB
DCP
TCE
DCB
102 102 136 136
718 923 923 1025
968 753 860 860
113 104 119 123
804 748 841 867
860 791 907 944
10.8 1.96 12.5 9.56
12.0 19.0 8.88 15.4
11.2 5.05 5.47 9.77
120 106 140 164
1043 810 779 408
802 785 901 1017
17.7 3.92 2.94 20.6
45.3 12.2 15.6 60.2
17.2 4.25 4.77 18.3
8.71
13.8
7.87
11.3
33.3
11.1
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ANALYTICAL CHEMISTRY, VOL. 59, NO. 11, JUNE 1, 1987 40
t
Number of Components
-2.5
Flgure 6. RSS plot of water and 2-methyl-2-pentanol calibration set.
collinearity and that a limitation exists for all multivariate techniques to perform well with highly collinear data. This also provides some evidence for the necessity of selectivity in the sensors of the array. Noise Reduction. One of the features of PLS that allows greater modeling over MLR is the ability to filter noise in the calibration data by using fewer latent variables than the dimension of the matrices. The selection of these variables is performed by comparing the RSS (residual sum of squares) of the calibration concentrations for each latent variable. The optimum number of latent variables occurs when this calculated value reaches a significant break or minimum threshold value, Figure 6. When the calibration samples in these examples were analyzed by PLS, up to nine latent variables were calculated. The significance of only the first two latent variables in Table IV for the two component cases shows the amount of variance in the autoscaled data retained in the calibration model. All of the other latent variables were eliminated along with the variance that they describe. The advantage here is that these discarded latent variables represent noise. The single analyte calibration technique of PLS is based on this procedure. Instead of using the R matrix information for three components, only a single component is correlated, thus allowing much more information in the R matrix to describe that component. Figure 7 shows the correlation built by the interrelationship of PLS between latent variables in the R and C matrices for the three component chlorocarbon case when all three analytes were included in the calibration. The correlation built into the PLS model between the scores of the corresponding latent variables approaches a linear fit. Figure 7A shows the plot of the 11calibration samples where the X-axis represents the first latent variable of R and the Y-axis is the first latent variable of the C matrix. The objective of PLS is to provide a model in which a relationship between the R and C matrices exist. The higher the deviation of the samples from the line, the less accurate the model. This plot also provides useful information on the accuracy of each sample response and shows if outliers exist. If a true linear model exists for an analysis, such as in spectroscopy, the samples furthest from the regression line contain more noise or may indicate a possibility of nonlinearity at that point in the dynamic range. This is one reason for the use of calibration samples that span the entire concentration range of the prediction samples. Parts A and B of Figure 7 have good linear relationships, while in part C, the third latent variable has almost no linear relationship. The amount of scatter on the third latent variable plot indicates that there is a lack of unique chemical information to describe the full three component model. Therefore, the prediction capability for the three component case when calibrating all three components is difficult for both PLS and MLR. From the results mentioned above, it can be seen that PLS has certain advantages over MLR in multivariate calibration due to its more complex modelling attributes, such as re-
R Latent Variable 1
:::$ (u
0,b
-0.2
-0 4 -0.6 -0.8 -1.0
4
.
2
- 0 . 4 0 -0.30 -0.20 -0 10 0 . 0 0
0.10
2 0.20
0.30 0.40
R Latent Variable 2 9
0.8
3
1
; : : :J: 0
--0.6 0,b
-1.0
-1.2 -0.40
-0.30
-0.20
-0.10
0.M)
0.10
0.20
R Latent Variable 3 Figure 7. Latent variable plots of the three component PLS model containing mdichlorobenzene, 1,2-dichloropropane,and 1,1,24richloroethane: fkst latent variables (A); second latent variables (B); and third latent variables (C) for R and C.
sistance to collinearity problems and noise reduction. It should be noted that PLS is only one of several possible multivariate techniques that can be used for modelling purposes. Other techniques such as ridge regression, stepwise regression, and principal component regression have also been shown to perform well (17). The ability to combine these regression techniques with arrays of microsensors, such as the coated piezoelectric quartz oscillator crystal, provides a high-power analysis tool that may out-perform individual sensors in the same analysis. This alternative approach eliminates the need for totally selective sensors but does not imply that selectivity and sensitivity are unnecessary attributes of an array device.
ACKNOWLEDGMENT The authors express their gratitude to the Center for Process Analytical Chemistry for the use of the PLS program. LITERATURE CITED (1) King, W. H. Anal. Chem. I9S4, 3 6 , 1735-1739. (2) Guilbault, G. G. I n t . J. €nviron. Anal. Chem. 1981, 10, 89-98. (3) Suleiman, A. A,; Guilbault, G. G. Anal. Chern. 1984, 5 6 , 2964-2966. (4) Otto, M.; Thomas, J. D. R . Anal. Chem. 1985, 5 7 , 2647-2651.
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Anal. Chem. 1987, 59, 1534-1538
(5) Stetter, J. R.; Jurs, P. C.; Rose, S. L. Anal. Chem. 1988. 5 8 , 860-866. (6) Bott, B.; Jones, T. A. Sens. Actuators 1088, 9 , 19-25. (7) Muller, R.; Lange. E. Sens. Actuators 1988, 9 , 39-48. (8) Draper. N. R.; Smith, H. Applled Regression Analysis, 2nd ed.;Wiiey: New York, 1981; Chapter 4. (9) Geladi, P.; Kowalski. B. R. Anal. Chim. Acta 1988, 185, 1-17. (IO) Mandel, J. J . Res. Net/. Bur. Stand. ( U S . ) 1985, 9 0 , 465-476. (11) Naes. T.: Martens, H. Comun . Statist .-Sirnula. Compura . 1985, 1 4 , 545-576. (12) Naes. T.; Irgen, C.; Martens, H. Appl. Statist. 1988, 35, 195-206. (13) Simpson, R. L. Ph.D. Dissertation, University of Washington, Seattie, WA, 1985.
(14) Carey. W. P.; Beebe, K. R.; Kowaiski, B. R.; Iiiman, D. L.; Hirschfeid, T. Anal. Chem. 1088, 58. 149-153. (15) Carey, W. P.; Kowaiski, B. R. Anal. Chem. 1988, 58, 3077-3084. (16) Lorber, A. Anal. Chem. 1988, 58, 1167-1172. (17) Wold, S.; Ruhe, A.; Wold, H.; Dunn, W. J. SIAM J . Stat. Comput. 1984, 5 , 735-743.
RECEIVED for review October 30, 1986. Accepted February l 7 , 1987. This work was Supported in Part by the Office of Naval Research.
Effect of Fluorine Substitution on the Anodic Oxidation of Catecholamines and Amino Acids M a r g a r e t E.Rice and Bita Moghaddam
Department of Chemistry, University of Kansas, Lawrence, Kansas 66015 C y r u s R. Creveling
Laboratory of Bioorganic Chemistry, NIDDK, NIH, Bethesda, Maryland 20892 Kenneth L. Kirk*
Laboratory of Chemistry, NIDDK, N I H , Bethesda, Maryland 20892
The electrochemlcal behavior of the 2-, 5-, and Muoro analogues of dopamine (DA), noreplnephrlne (NE), and (3,4-dlhydroxypheny1)alanlne (DOPA) have been determined by cyclic voltammetry and by measuring fluorkle release during bulk oxldations. At pH 7.4, the order of lncreaslng oxldatlon potentials (€,,2) for the DA serles Is 6-FDA < DA < 5-FDA < 2-FDA; for the NE serks, &FNE < 5-FNE < 2-FNE < NE; and for the DOPA serles, 6-FWPA < 5-FDOPA = 2-FDOPA < DOPA. The 6-fkKm, analogue in each series of conpmds Is the most easlly oxldized and appears to result from a 2electron process rather than the four-electron process (the ECE pathway) for the parent catecholamines or catecholamino acld. Potentlometric measurement wlth a fluorldeton-selective electrode contkms that oxidation of the Wiuoro analogue in each series results In the release of fluorlde Ion. Molecular schemes for the rationalltationof the unlque behavior of the 6-fluor0 analogues are presented.
2-, 5-, and 6-fluoronorepinephrine (2-FNE, 5-FNE, 6-FNE) have been performed. We report here the effects of fluorine substitution on the electrochemical behavior of DA, NE, and 3,4-dihydroxyphenylalanine (DOPA). We were prompted to initiate these studies by the subjective observation that our fluorinated analogues, in particular, compounds having fluorine in the 6-position, seemed more prone to oxidative decomposition than the unsubstituted parent. Thus, initial attempts to determine the phenolic pK, of 6-FNE by measuring ultraviolet absorption spectra as a function of pH were complicated by extremely rapid oxidation at pH values greater than 9. During storage at -20 "C,sample vials containing 6-FDA were found ruptured from internal pressure. The decision to obtain a quantitative assessment of this behavior was reinforced by the belief that knowledge of the electrochemical behavior of fluorinated catecholamines and amino acids might help in understanding aspects of their biological properties.
Over the past few years, we have reported results of several studies regarding the biological properties of ring-fluorinated biogenic amines ( I ) . In addition, we and others have been pursuing actively the use of fluorinated phenolic amines and amino acids as biological tracers, in recognition of the potential of I9F NMR techniques in biological problems (2) and the importance of 18Flabeled compounds as scanning agents for positron emission transaxial tomography (3). Despite the remarkable biological effecta of fluorine substitution on certain of these biogenic amines-the adrenergic agonist properties of ring-fluorinated norepinephrines (NE) is a notable example-relatively little is known about the effect of fluorine substitution on the chemical behavior of these analogues. Fluorine substitution has the expected acid strengthening effect on phenol acidities, as we reported for fluorinated serotonins (4), dopamines (DA) (51, and NE'S (6). Calculations of the effect of fluorine substitution on the electronic charge distribution (7) and molecular electrostatic potential (8)of
EXPERIMENTAL SECTION Fluorinated analogues of DA, NE, and DOPA were prepared as previously described (5,6,9). Stock solutions of 2-FNE oxalate, 5-FNE hydrochloride, and 6-FNE oxalate were made 10 mM in 0.1 N HC104 Stock solutions of NE, DA, 6-FDA hydrobromide, and 5-FDA hydrochloride were made 20 mM in 0.1 N HC104 All electrochemical measurements were made in 0.1 M phosphate buffer, pH 7.4, with 0.9% NaCl (phosphate buffered saline, PBS) unless otherwise stated. Aliquots of stock solutions were added to a final concentration of 100 pM,unless otherwise stated. An IBM EC 225 voltammetric analyzer with a Houston Instruments Omnigraphic 200 X-Y recorder was used for the cyclic voltammetry with a large carbon-paste electrode (GP-38graphite:hexadecane pasting liquid, 2:l ratio) (IO) having a surface area of 0.21 Cm2. Hexadecane-based carbon paste can be used to make a stable electrode surface that exhibits faster electrontransfer rates than conventional Nujol paste surfaces. A platinum-gauze electrode was used for bulk oxidations. A saturated calomel electrode (SCE) was used as the reference, and the auxiliary electrode was a platinum wire. Oxidation Ellz values were determined according to the Nicholson and Shane theory (11). A fluoride-selectiveelectrode (Orion) was used to measure
This article not subject to U.S. Copyright. Published 1987 by the American Chemical Society
