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Anal. Chern. 1986, 58, 149-153
Selection of Adsorbates for Chemical Sensor Arrays by Pattern Recognition W. Patrick Carey, Kenneth R. Beebe, and Bruce R. Kowalski* Laboratory for Chemometrics, University of Washington, Seattle, Washington 98195
Deborah L. Illman Center for Process Analytical Chemistry, University of Washington, Seattle, Washington 98195
Tomas Hirschfeld Lawrence Livermore Laboratory, P.O. Box 808 L-322, Livermore, California 94550
Pattern recognttion techniques were appiled to frequency shlfl data obtalned from 27 piezoelectric sensors1 formed by coating quartz crystals with 27 dtfferent GC stationary phases, tested using 14 analytes. The objective was to determine whether or not the 27 original coatlngs could be reduced to a subset of coatings without a significant loss of anaiyte identificationInformation. The amount of information loss with a given decrease in dimensionality and the identity of the coatings in the “best” subset of coatings was sought. The first seven prlnclpal components described almost 95 % of the variance In the original data set of 27 coatings. A generally useful procedure is proposed and tested herein that allowed the selection of seven coatings that provided the maximum level of analyte discriminating ability. This procedure allows other factors (cost, reilabliity, etc.) to enter Into the selection process while maintaining maximum analytical power with the fewest sensors.
In recent years, process analytical chemists have been investigating the use of a variety of chemical sensing devices coated with adsorbates. Analytes interacting with these adsorbates are identified and quantitated via chemical or physical changes in the adsorbates detected by the devices. Examples include hydrogen gas sensors that use palladium gate field effect transistors (1)and moisture detecting piezobalance detectors with hydrophilic coatings (2). While the sensor coatings that interact with the analytes and the sensing phenomena vary widely, the predominant goal has been to build a device that is fully selective for a particular analyte. While fully selective sensing is indeed a noble goal, in practice, it is not often or easily reached. An alternative to searching for sensor coatings that can select one analyte from many is to employ an array of sensors each coated with a sensitive, but partially selective, coating. Provided the coatings are selected with care, the pattern of responses measured from the full array of sensors can be used to identify the analyte. One need not search beyond the typical analytical laboratory to find a useful analogy to explain the advantages of this alternate approach. A computerized low-resolution mass spectrometer with a mass range of only 600 amu can be used to identify orders of magnitude more pure compounds than the 600 allowed if each m/e were fully selective for one anal@. Actually, sensor arrays based on spectral regions are at a disadvantage as the spectrum of a molecule is more constrained by nature than an array of sensors, each selected to provide an optical discrimination for a collection of analytes. 0003-2700/86/0358-0149$01.50/0
The goal of the present work is to present a systematic method that can be used to provide such a collection. Piezoelectric crystals have long been used in electronic equipment for controlling frequency. Their introduction as sorption detectors by King occurred in 1964 (3). Since then many applications of the piezoelectric crystal sensor have been developed, including detection of air pollutants such as SOz, NH3,H2S,CO, and organophosphorus compounds (4-6), use as detectors for gas chromatography (3, 7), and use as a monitor of deposited film thickness on surfaces (8). The use of these piezoelectric crystals as sorption sensors is made possible by their ability to detect mass changes on their surfaces by the change in frequency of the mechanically vibrating quartz crystal (thickness shear mode). By application of specific gas chromatographic stationary phases to quartz surfaces, partially selective adsorption for certain gaseous analytes can be achieved with an estimated detection limit of approximately g. Guilbault has used materials such as triethanolamine and quadrol to detect SO2 and a cobalt complex of isonitrobenzoylacetone to detect organophosphorus pesticides (4). The methods useful to select the coating materials are taken from a collection of methods under the heading of pattern recognition first introduced as a general tool in chemistry in 1972 by Kowalski and Bender (9, 10). By application of principal component analysis (PCA) and hierarchical cluster analysis to a data set containing piezoelectric crystal frequency shifts measured in response to analytes adsorbing on specific candidate coating materials, a reduced set of nearly orthogonal coatings can be selected to provide maximum discriminating information for all analytes of interest. Another way of thinking about the resulting selection of coatings is that they span the space of all coatings using the fewest number of individual coatings while retaining analyte discrimination. Examples of these two techniques include the use of PCA by Wold and Andersson (11)to model the McReynolds retention index matrix and a recent paper by Huber and Reich (12) in which cluster analysis is used to characterize gas chromatographic stationary phases.
EXPERIMENTAL SECTION The data used in this experiment were derived from research done by Olness and Hirschfeld of Lawrence Livermore National Laboratory (13). Fourteen analytes were introduced to 27 separate coating materials on quartz crystals (Table I). The coatings were selected from a larger list on the basis of stability, reversibility, selectivity, and hydrophobic nature. The primary data used in this experiment were the frequency shifts observed for each analyte on each coating. The data set was analyzed by a series of pattern recognition methods included in the program ARTHUR (14). The data set of 0 1985 American Chemical Society
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ANALYTICAL CHEMISTRY, VOL. 58, NO. 1, JANUARY 1986
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frequency shifts was first entered as a matrix with the 27 coating materials as variables and the 14 analytes as samples. The correlation matrix and their covariance matrix were then calculated for later examination. The data were autoscaled to convert the variables to features with mean zero and constant variance. Principal component analysis (PCA) was used to extract the eigenvectors and eigenvalues from the covariance matrix. These eigenvectors are made up of linear combinations of the variables (or columns) of the covariance matrix. The first eigenvector is selected so that it describes as much variation in the data as possible. For example, if the data set forms an ellipse in twodimensional space, the first eigenvector would correspond to the major axis joining the two focal points. Since the covariance matrix is used, the subsequent eigenvectors will be mutually orthogonal and represent the axes of decreasing variance in the data. From this description, it can be seen that more of the variance, and ultimately more information, can be described by eigenvectors than by the original axes. Next the eigenvectors were subjected to varimax rotation based on the method of Kaiser (15). This method further rotates the axes such that the projections of the samples in the eigenvector space (the space defined using the eigenvectors as the axes) lie either near the extremes or near the origin of these new axes. These new vectors are similar to the eigenvectors with additional freedom to represent the variables. The data were also subjected to a hierarchical cluster analysis of the samples (16). Since the sample-variable (coating-analyte, if the transpose of data matrix is used) interactions determine a sample’s position in feature space, one would expect chemically similar samples to lie close together and therefore have high similarity values. Therefore, hierarchical clustering gives an indication of the classes of interactions that are occurring.
RESULTS AND DISCUSSION The data analysis was performed with two objectives in mind. The first objective was to find a subset of coatings that described a t least 95% of the information or, in statistical terms variance, contained in the original complete data set. Since eigenvectors by definition describe maximum variance, they were used for this purpose. No consideration was given to the sensitivity of an individual stationary phase. As sensitivity is often a major consideration in the selection of coatings, it is important to study both the range and standard deviation of responses for each coating to determine the “bestn subset. This can be done after the most selective coatings have been chosen. For this reason, the data set was autoscaled to give each variable (coating) zero mean and constant variance. The analysis of the eigenvectors of the data set, Table 11, shows seven eigenvectors, which represent 95% of the total variance in the original data set. Choosing 95% as a cutoff limit in this analysis was done assuming an approximate 5 % noise in the data. The data set was judged too small for cross validation or other tests. This then would require a t least seven sensors to span the subspace. The coating material for each sensor can be selected from Table 11. The first coating in each eigenvector would be the optimal selection. If it is not available or otherwise undesirable, that coating should be eliminated from the data matrix and the PCA repeated. In this case, seven crystals could have been coated with poly(capro1actone) triol, poly(butadiene methacrylate), polybutadiene hydroxy terminated, poly(viny1 isobutyl ether), poly(p-vinylphenol), poly(methy1 methacrylate), and poly(vinyl chloride). At this point, coating sensitivity can be considered if desired. In the present case, collodion was by far the most sensitive coating (collodion had the largest variance in the data set) and could be included for that reason alone. As a final means of selecting sensors, one might also wish to include sensitive materials since this method will not select these due to the autoscaling procedure. The second goal was to determine the types of chemical interactions that governed selectivity. The varimax rotated eigenvectors were used for this analysis since they are often
151
Table 11. Eigenvectors and Eigenvector Components Eigenvectors and Eigenvalues Determined by PCA
eigenvector
eigenvalue
1 2 3 4 5 6
11.75 4.12 3.94 2.22 1.48 1.19 0.86
I
variance preserved each total 43.5 15.3 14.6 8.2 5.5 4.4 3.1
43.5 58.8 73.4 81.6 87.1 91.5 94.1
Eigenvector Components 70
ID no.
component
contribution
22 1 20 27 24 21
poly(capro1actone)triol phenoxy resin carnuba wax poly(capro1actone)
7.1 5.9 5.4 5.3 5.3 5.3
vector 2
3 8 14 25 10
poly(butadiene methacrylate) methyl vinyl ether poly(vinylcarbazo1e) abietic acid polystyrene
9.7 9.1 8.4 7.9 7.9
vector 3
4 9
vector 1
poly(butadieneacrylonitri1e) poly(ethy1ene glycol methyl ether)
polybutadiene hydroxy terminated octadecyl vinyl ether/maleic anhydride polybutadiene hydroxy terminated liquid poly-1-butadiene
11.0 8.8
11 13 23 18
poly(viny1 isobutyl ether)
20.6 19.3 15.9 9.5
2 14 15
poly (p-vinylphenol)
poly(vinylcarbazo1e) co11odion
13.6 12.1 10.4
vector 6
17 19 21
poly(methy1 methacrylate) ethylcellulose poly(capro1actone)
16.3 10.9 9.7
vector 7
12 25 8
poly(viny1 chloride) abietic acid methyl vinyl ether
47.4 7.3 6.0
7 6
vector 4
vector 5
poly-1-butene poly(capro1actone)triol 2X polyethylene
8.2 7.3
more chemically descriptive. The analysis of the varimax rotated vectors from PCA, Table 111, shows interesting groupings of the coating materials. The striking features in this table are that the vectors differ markedly from each other in composition and each is characterized by a different coating functional group. For example, in the first varimax rotated vector, the first four coatings (numbers 3,6,4, and 7) account for 22.7, 19.5, 14.5, and 9.6% of that vector (or a total of 71.3%) and were all polybutadiene polymers. The varimax rotated vectors from largest to smallest variance can be described by the following chemical types: (1) polybutadiene groups [3, 6, 4, 71; (2) cellulose and ring 24, 19,251; (3) polyvinyl groups [14, 16, 21; (4) structures [E, polymer ring structures [19, 21, 261; (5) acrylic polymers [17, 11; (6) poly(viny1 chloride) [12]; (7) poly(viny1 isobutyl ether) [ l l ] ; (8) poly-1-butene [13]. The high degree of similarity of the coating materials in each of the above groupings provides useful information about the nature of the interactions that occur between vapors and stationary phases. The number of interactions of this type
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ANALYTICAL CHEMISTRY, VOL. 58, NO. 1, JANUARY 1986
Table 111. Varimax Rotated Vectors and Varimax Rotated Vector Component Varimax Rotated Vectors and Eigenvalues Determined by PCA
varimax vector
eigenvalue
variance preserved each total
r
i
21
I
L14-
2.53 2.38 2.19 1.84 1.31 0.75 0.66 0.65
19.5 18.3 16.8 14.1
10.1 6.1 5.0 5.0
19.5 37.8 54.5 68.6 78.7 84.8 89.8 94.9
7
D[:i; 15 24
.jn: 23
70
3 6
component
contribution
poly(butadiene methacrylate) poly-1-butadiene polybutadiene hydroxy terminated polybutadiene hydroxy terminated liquid
27.7
collodion carnuba wax ethyl cellulose abietic acid
25.0 18.4 10.4 10.3
poly(vinylcarbazo1e) poly(vinylbutyra1) poly(pvinylpheno1) polystyrene
31.5
ethylcellulose poly(capro1actone) GE DC 11
34.1
17 1
poly(methy1 methacrylate) poly(butadieneacrylonitri1e)
39.9 9.9
vector 6
12
poly(viny1 chloride)
66.5
vector 7
11
poly(viny1 isobutyl ether)
84.9
vector 8
13
poly-1-butene
75.1
vector 1
4 7
vector 2
15 24
19 25 vector 3
14 16 2
10 vector 4
19 21
26 vector 5
h 1
112‘
Varimax Rotated Vector Components
ID no.
I
19.5 14.5 9.6
19.1
17.0 13.6 23.9 18.4
has been considered to be small in number. These interactions include such forces as Lewis acidity, van der Waals interactions, and polarity. These forces can be thought of as being vectors in feature space. Each varimax vector is a linear combination of these “force” vectors. Therefore, one cannot assume that any single interaction such as van der Waal forces is solely responsible for the groupings. Since structure determines the type of chemical interaction for each coating, the observed groupings of similar structure within an individual varimax rotated vector is expected. The hierarchical dendrogram plot of the coating materials, Figure 1,can also be used to provide information on chemical behavior and to verify the results obtained by the varimax rotated vectors of PCA. The interactions between the analytes and the coatings result in data points which lie close together for similar coatings and far apart for dissimilar coatings. Therefore, chemically similar coatings should be grouped within the same cluster. On comparison of the hierarchical cluster analysis results with those obtained from PCA, a good correlation can be seen between coatings in the dendrogram clusters and in the varimax rotated eigenvectors. If the dendrogram (Figure 1)is separated into six groups A-F, this correlation can be readily seen. Group A consists primarily of the first seven materials, which include the polybutadiene polymers with octadecyl vinyl ether/maleic anhydride co-
F C
;;
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Anal. Chem. 1986, 58, 153-157 (8) Heislng, R. A. “Quartz Crystals for Electrical Circults”; Van Nostrand: New York, 1946; p 24. (9) Kowalski, B. R.; Bender, C. F. J. Am. Chem. SOC. 1972, 9 4 , 5632. (IO) Kowalskl, B. R.; Bender, C. F. J. Am. Chem. SOC.1974, 96, 916. (11) Wold, S.; Andersson, K. J. Chromatogr. 1973, BO, 43. (12) Huber, J. F. K.; Relch, G. J,; ChrOmatOgf‘. 1984, 294, 15. (13) Hirschfeld, T.; Oiness, D. Sorption Detector System for Chemical Agents Detection and Recognition”; US Army Chemical Research and October DeVelODment Center Technical R e ~ ~#r CRCD-TR-84086, t 1984. (14) ARTHUR, Laboratory of Chemometrlcs, Department of chemistry, BG10, University of Washington, Seattle, WA.
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(15) Horst, P. “Factor Analysis of Data Matrices”, Holt, Rinehart and Winston: New York, 1965. (16) Kowaiskl, B. R. in “Computers In Chemical and Blochemlcal Research”; Klopfenstein, C. E., Wilkins, C. L., Eds.; Academic Press: New York, 1974; Vol. 2.
RECEIVED for review June 7,1985. Accepted August 19,1985. This work was supported by a grant from the Center for Process Analytical Chemistry.
Indirect Chromatography Using a Membrane Polarographic Detector Andrew Mills* and Susan L. Giddings
Department of Chemistry, University College of Swansea, Singleton Park, Swansea SA2 BPP, United Kingdom
A hydrogen membrane polarographlc detector (H,-MPD) has been used to detect a varlety of simple, volatlle, organic compounds (malniy Isomers of pentane and hexane), separated by gas chromatography, using H2 as the carrler gas. The H,-MPD acted as an indlrect detector inasmuch as It was able to monltor only the partial pressure of the H2carrier gas leaving the column, and thls quantity was reduced (albeit transitory) as any of the organic compounds used was eluted from the column. The recorded negative peak heights and areas were found to be directly related to the quantitles present, for >2 orders of magnltude (Le., from 1 pL down to < I O nL). As predlcted by the theory developed far this detector, the molar peak area (Le., peak areahumber of moles Injected) was found to be Independent of the nature of the sample injected. Although this latter effect is a marked advantage over the tradltional thermal conductlvlty detector (TCD), the former detector was not found to be without problems, including low sensitivity (ca., 3 nL), due to signal noise and drlft, and short detector llfetlme (2-3 days), due tO deactlvatlon of the platinized platinum working electrode. Despite these problems, the H,-MPD represents a simple, cheap, nondestructlve, and rugged detector for gas chromatography.
Membrane polarographic detectors (MPD’s) are sensitive, robust, and inexpensive devices for specific gas detection in the atmosphere or in solution. Developed originally by Clark for analysis of dissolved Oz in blood (1)the OZ-MPD(or “Clark electrode” as it is more commonly known) has proved invaluable in a large number of areas, including medicine, agriculture, brewing, and environmental studies (2). From the 0,-MPD there have followed many other MPD’s capable of detecting gases other than 02,(such as C12 ( 3 ) ,NOz ( 3 ) ,SOz (3),NzO (4), or COz ( 5 ) ) . In all the MPD’s mentioned (3-5), the gas under analysis is reduced a t the working electrode; however there exists another type of MPD (such as those for CO (6) or Hz (6,7))in which the gas is oxidized at the working electrode. In either type, the reaction at the working electrode (be it a reduction or an oxidation) is often nonspecific (8,9); thus other gases can interfere in measurements made with a MPD. For example, an 0,-MPD will respond not only to Oz 0003-2700/86/0358-0153$01.50/0
but also to many other easily reduced gases (e.g., Clz, NO2, SO2 (9)). This lack of specificity represents an important limitation on the part of most MPD’s, since it prevents their use in the analysis of mixtures of gases with similar electrochemical characteristics. This problem may be obviated if the gases are separated prior to analysis. Indeed, Bergman et al. (6) were able to show that mixtures of CO and Hz could be separated by gas chromatography (GC) and subsequently detected by using a metalized membrane electrode, and Blurton and Stetter (10) have used a PTFE-bonded diffusion electrode in the GC analysis of H2S, NO, CO, SOz, and NOz. A second important limitation of all MPD’s is their basic requirement that the gas under detection be electroactive (9); i.e., it must be able to (a) permeate through the protective membrane covering the sensing electrodes and (b) react at the working electrode. It is worthwhile to note at this point that a gas will appear “nonelectroactive” if either (a) or (b) is not achieved. (The significance of this will become apparent later on.) In a recent paper (9) we demonstrated that nonelectroactive gases can be detected (albeit indirectly) by using a MPD, provided that an electroactive gas is present. The system used was comprised of an 0,-MPD coupled to a flow system with air as the carrier gas. Following injection of a nonelectroactive gas (such as COz, Nz,Ar, He, and CH4) into the flow system, the O,-MPD registered a transitory drop in oxygen partial pressure and a negative peak was recorded. Both experiment and theory showed that the area and height of the peak were related directly to the amount of nonelectroactive gas injected (9). In this paper we extend the idea of indirect detection using a MPD to the gas chromatography of mixtures of volatile organic compounds. Throughout this work a thermal conductivity detector (TCD), as well as a MPD, was used to help in the assessment of the properties and potential of the MPD in GC analysis.
EXPERIMENTAL SECTION Apparatus. H2-MPD. As shown in Figure 1, the hydrogen membrane polarographic detector (H2-MPD)consisted of a plastic base (1)containing both the Pt working electrode (2) (platinized according to the procedure recommended by Calzaferri et al. (11)) and the Ag/AgCl counter/reference electrode (3) (prepared from the bare Ag metal using the method outlined by Janz and Ives (12)). Over these electrodes was placed a thin, circular, papertissue spacer (ca., 2 cm diameter) with a 1-mm hole at its center, 0 1985 American Chemical Society
