Anal. Chem.
Table VIII. Quantitative Analysis of tert-Butyl Alcphol, tert-Butyl Hydroperoxide and tert-Butyl Peroxide Mixture %
889
1981, 5 3 , 889-893
%
% found found theoretical (a C!) (P C) Acetone-d, Solvent tert-butyl alcohol 29.9 29.7 29.ga tert-butyl 46.4 45.3 44.3 hydroperoxide tert- butyl 23.6 25.0 25.8’ per oxide CIICI, Solvent tert-butyl alcohol 48.8 50.8 49.9 tert-butyl 31.0 31.8 28.6 hydroperoxide tert- butyl 20.2 18.0’ 21.5 peroxide Solvent peak resonance is abutting the peak from which the value was calculated.
carbon
to the peroxide linkage.
ACKNOWLEDGMENT The peroxides were prepared by with the capable assistance of W. R. Loder, Jr., and W. M. Wheatley, Jr.
LITERATURE CITED
(12)
briefly. Raman spectrometry has a strong polarized oxygenoxygen stretch mode in the 700-900-~m-~ region. But caution should be exercised in its use because this mode may be highly coupled and therefore not a reliable group frequency. In addition, carbon skeletal modes often seveirely interfere in this region, making unambiguous assignment of this band very difficult. Infrared spectrometry shows no usable group frequency for the peroxy linkage. Proton NMR spectrometry provides the most information. There is a characteristic chemical shift for protons on carbon a to the peroxide, 0.4-0.5 ppm downfietld from analogous ethers. Use of a Shoolery additivity constant of 2.90 for the peroxide group enables one to calculate rehable chemical shifts for the methylene group. The only observed exception is if the methylene CY to the peroxy group has an oxygen substituent. Shift reagents complex with the peroxide oxygen in preference to the ether oxygen in the same molecule. 13CNMR spectrometry promises to be a useful technique for peroxide characterization for peroxides having no hydrogen. There is a characteristic 13C NMR, chemical shift for
CY
(13) (14) (15) (16) . (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28)
Sheppard, N. Faraday Discuss. Chem. SOC. 1950, 9 , 322. Minkoff, 0. J. Faraday Discuss. Chem. SOC. 1950, 9 , 320. Shreve, 0. D.; Heether, E. I.; Knight, H. B.; Swern, D. Anal. Chem. 1951, 2 3 , 282. Williams, H. R.; Mosher, H. S. Anal. Chem. 1955, 2 7 , 517. Hawklns, E. G. E. “Organic Peroxides: Thelr Formation and Reactions”; Van Nostrand: Princeton, NJ, 1961; p 339. Swern, D.; Silbert, L. S. Anal. Chem. 1963 35, 880. McKean, D. C.; Duncan, J. L.; Hay, R. K. M. Spectrochim. Acta, Part A 1967, 23A, 605. Philpotts, A. R.; Thain, W. Anal. Chem. 1952, 2 4 , 638. Bellamy, L. S. “The Infrared Spectra of Comblex Molecules”, 3rd ed., Wlley: New York, 1975; p 135. Colthup, N. B.; Daly, L. H.; Wlberley, S. E. “Introductlon to Infrared and Raman Spectroscopy“; Academlc Press: New York, 1964; p 276. Milas. N. A.: Maaeli, 0. L.; Golubovic, A.; Arndt, R. W.; Ho, J. C. J. J . Am. Chem. So;. 1963, 8 5 , 222. Makoveeva, N. P.; Bitman, G. L.; Karasev, Yu. 2.; Eiyashberg, M. E.; Shapet’ka, N. N.; Bogachev, Yu. S . J. Gen. Chem. USSR (Engl. Trans/.) 1974, 43, 1760. Dollish, F. R.; Fately, W. G.; Bentley, F. F. “Characteristic Raman Frequencies of Organic Compounds”; Wiley: New York, 1974; p 33. Christe, K. 0. Spectrochim. Acta, PartA 1971 27A, 463. Melveger, A. J.; Anderson, L. R.; Ratcliffe, C. T.; Fox, W. 8. Appl. Spectrosc. 1972 2 6 , 381. Fuiiwara, S.; Katayama, M.; Kamio, S. Bull. Chem. SOC. Jpn. 1959, 32, 657. Ward, G. A.; Mair, R. D. Anal. Chem. 1969, 41, 538. Swern, D.; Clements, A. H.; Luong, T. M. Anal. Chem. 1969, 41, 412. Kropf, H.; Bernert, C. R.; Lutjers, J.; Pavicic, V.; Weiss, T. Tetrahedron 1970, 2 6 , 347. Kropf, H.; Bernert, C. R.; Dahlenberg, L. Tetrahedron 1970 2 6 , 3269. Razumovskii, S. D.; Yurev, Y. N. Neftekhimiya 1966, 6, 737. Chem. Abstr. I967 66, 2 8 2 3 2 ~ . Renard, J.; Fliszar, S. Can. J . Chem. 1969 47, 3333. Keaveney, W. B.; Berger, M. G.; Pappas, J. J. J. Org. Chem. 1967, 3 2 , 1537. Bricker, C. E.; Johnson, H. R. Ind. Eng. Chem., Anal. Ed. 1945, 17, 400. Horner, L.; Juergens, E. Angew. Chem. 1956, 70, 266. Bell, M. E. B.; Laane, J. Spectrochim. Acta, Part A 28A, 2239. Simons, W. W.; Zanger, M. ”The Sadtler Guide to NMR Spectra”; Sadier Research Laboratorles, Inc.: Philadelphia, PA, 1972. Kropf, H.; Bernert, C. R. Z . Anal. Chem. 1969, 248, 35.
RECEIVED for review May 5, 1980. Resubmitted January 13, 1981. Accepted January 29, 1981.
Infrared Spectral Search System for Gas Chromatography/Fourier Transform Infrared Spectrometry Stephen R. Lowry* and David A. Hulppler Nicolet Instrument Corporiition, 5225 Verona Road, Madison, Wisconsin 5 3 7 1 1
A number of gas chromiatograph/Fourler transform Infrared (GC/FT-IR) experlments were performed In an effort to evaluate the capabillties of some new spectral search software. The software has been designed to work as an integral part of our GC/FT-IR instrument. The speclflc experlments were chosen to examine problems such as dlfferentlatlon among closely matchinlg spectra, effects of spectral noise, effects of sloping base lines, and searching spectra from nonresolved GC peaks. The search llbrary Is a compressed version of the vapor-phase Infrared spectral file supplied by Sadtler laboratories to the Envlronmental Protection Agency. The results demonstrate that excellent search results are obtainable when hlgh-quality digital spectra are used as reference spectra.
Although the direct coupling of a gas chromatograph with a Fourier transform infrared spectrometer (GC/FT-IR) is rapidly being accepted as a reliable analytical tool, the large amounts of data produced by even a simple experiment can be overwhelming. Methods of reducing this information mountain are essential if GC/FT-IR is to become a completely practical technique. Software specifically designed to handle the data produced by the GC/FT-IR experiment can alleviate much of the routine analysis. Automated information processing is particularly appealing, in this case, because a computer is already present in the FT-IR system. Previous work in this laboratory resulted in the development of software which allows “on-the-fly” plotting of five chromatograms based on the integrated absorbances in five
0003-27O0/81/0353-0889$01.25/00 1981 American Chemlcal Soclety
890
ANALYTICAL CHEMISTRY, VOL. 53, NO. 6 , MAY 1981
user-defined IR spectral regions ( I ) . By careful choice of the five infrared windows, the five chromatograms produced by the spectrometer can be used to identify various functional groups found in the different components of the sample. Frequently, this information can be used to identify the GC peaks that might be significant without actually examining the infrared spectra. While this procedure may substantially reduce the number of spectra that must be interpreted, the identification of the remaining spectra can still be time-consuming. One solution to this problem is a computerized interpretation system which can rapidly identify the spectra acquired during the GC run. The simplest method of doing this is a mathematical comparison of the unknown spectra to members of a reference file. Although this search concept is simple and numerous examples have been reported in the literature (2-5), the lack of high-quality digital spectra has limited the usefulness of many search systems. This problem has been reduced in the area of GC/FT-IR by the availability of gas-phase infrared spectra in digital format. These spectra were produced by Sadtler Research Laboratories for the Environmental Protection Agency and were provided by L. V. Azarraga (EPA Water Laboratory, Athens, GA). The high quality of the spectra in this file provides an excellent base for the development of GC/IR search systems, and one application of this data base has recently appeared in the literature (6). This paper will describe a unified GC/FT-IR search system which we have developed in this laboratory. We will also report the results of several experiments which were specifically designed to test various aspects of the systems performance. These experiments were chosen to examine problems such as differentiation of compounds with similar spectra, effects of spectral base line slope, effect of spectral noise, and searching spectra from nonresolved GC peaks. A comparison of several search algorithms will also be discussed.
EXPERIMENTAL SECTION All the results reported in this work were performed on a Nicolet (Madison, WI) 7199 FT-IR spectrometer. The FT-IR is equipped with a 42 cm long, 3 mm diameter gold-coated light pipe of the type developed by Azmraga (7). This light pipe is connected to a Varian (Palo Alto, CA) 3700 gas chromatograph by a 40-cm glass-coated transfer line. Both the light pipe and transfer line are thermostated and may be heated to 350 "C. The detector for the system is a short-range (5000-700 cm-'), liquid-nitrogen-cooled HgCdTe detector. All GC data were acquired with a 1.82 m by 3 mm column with a 5% loading of OV-17. Both isothermal and temperature programming were employed with a gas flow rate of about 15 mL/min. Most of the samples used in this work were common chemicals found in the laboratory. All software development was carried out on a Nicolet 1180 computer system equipped with a disk drive, line printer, and nine-track magnetic tape drive. SYSTEM DESIGN A number of constraints are imposed on the search software because of the need to function as an integral part of the present GC/FT-IR system. A detailed description of the overall system and its operation has been described elsewhere (I). In order to operate efficiently in this environment, we designed the system to search reference spectra that were compatible with the present plot and display software and yet required minimal disk storage space. The software was also designed to run on an interrupt basis with the present software so that both processor time and the peripherals can be shared optimally with other FT-IR routines. The basic operation of the search system can be broken into three steps. The first step requires that the spectra acquired by the GC/FT-IR software be converted into a searchable format. This format is an absorbance spectrum normalized to one absorbance unit and reduced to 16 cm-' resolution. The
original spectrum is not destroyed by the formation of the search spectrum. The second step involves the actual comparison of the sample spectrum with each member of the search file. The search can be controlled by three userspecified parameters. The first parameter allows the user to choose the matching algorithm to be used in the search. Four different search algorithms are presently implemented in the system. The first involves computing the absolute difference between the sample spectrum (S)and each member of the reference file (R). Equation 1 is shown where si and ri are the 4000 MAB
= C Isj - riJ i=460
(1)
normalized absorbance values at each resolution element in the sample and reference spectra. A lower value of Mm indicates a better match. The second search algorithm uses a least-squares approach. The match value is calculated as AlMll _"-~ V I ~= ~ C (si - rJ2
i=450
The least-squares metric tends to a weight large differences in the spectrum more than the absolute value calculation. In other words, one large difference between the sample and a reference spectrum is worse than an equivalent number of small ones. The last two search algorithms utilize the first derivative spectra of the sample and reference file. The derivative calculation is available with both the absolute value and the least-squares metrics. Equations 3 and 4 are shown where Asi = s, - si-l and Ari 4000
MAD = C Ipsi - Aril i=450
(3)
4000
MsD= C (Asi - ArJ2 i=460
(4)
The most obvious advantages of the derivative searches are to minimize the effects of sloping base line and broad nonspecific spectral features on the match value. To choose a search algorithm, the user simply enters the two-letter identification (AB, SQ, AD, or SD) into the parameter SRA. A second user-defiied parameter is SRM. This parameter allows the different reference libraries to be searched. This parameter is specifically designed to allow the formation of subset or user-created search files. The last parameter defies the number of best matches that are retained and printed out. From 1to 32 best matches may be stored. A typical output would list the ten best hits. The first line for each entry contains the identification number and the name of the compound. The second line contains the similarity value and the Wiswesser line notation. The output can be assigned either to the printer or to the plotter where it can appear in conjunction with the spectrum. The final step in the search process is the retrieval of the reference spectrum and a visual comparison for final identification or rejection. The parameters in this operation are the identification number of the desired spectrum and the search file number. The search file is automatically retrieved along with the name and Wiswesser, and the data are converted into a normal FT-IR spectral file. Figure 1shows an overlay plot of the spectrum of acetone from the GC peak and the retrieved data from the nearest match. The name and Wiswesser notation were automatically printed on the spectrum by a macro command. In cases where reliable compound identification is required, the visual comparison provided by the overlay plots can provide final confirmation. Three external routines were designed along with the search software. The first converts the original data supplied by
ANALYTICAL CHEMISTRY, VOL. 53,
NO.6, MAY 1981 891
c
POSSIBLE HITS I BUTYRIC ACIO la ov9 5116 PENTANOIC ACID 120 OVI, 665 BUTYRIC ACIO, 9 ETHYL-. 169 OVlY
.SRR
Print-out of search results from solvent peak in experiment. Flgure 1.
AD
1
GC-FT-IR
Azarraga on magnetic tape into a format compatible with the search system. Three separate files have been formed from the original data. The major file contains the compressed spectral data. Each absorbance spectrum was reduced from 1842 to 460 points by applying a 17-point smooth and then saving every fourth point. The spectra were then normalized so that the intensity of the largest peak could be represented by 10 bits. These compression techniques allow an infrared spectrum from 4000 to 4b0 cm-l to be stored in 230 words of memory, and yet sufficient resolution is retained to provide accurate spectral representation. The other two files contain the names and Wiswesser notation for the compounds. The Wiswesser line notation is a code for representing the molecular structure in a computer-accessible Iformat. We chose to retain this information because of ambiguities in the names of several compounds. These problems were usually encountered when a common name was used rather than the Chemical Abstracts version. For the user generated search files, any text information can be retained in place of the Wiswesser notation. Some useful information might be catalog number, batch number, or even physical properties of the compounds. The total storage requirement for the three files is less than 600000 words. This can be stored in about an eighth of the total disk space available on the system. A second external routine was designed to allow the user to form his own search files and to add spectra to an already existing file. This routine takes spectra acquired on the system and converts them into the three-file format. This routine provides the capability of searching spectra that are not gas phase or of developing smrdl search files applicable to a specific problem. A third routine is also being designed to output spectral information on magnetic tape in a standardized format that can be read by other systems. This will allow the exchange of information among users of different systems. RESULTS AND DISCUSSION Four experiments were performed on the search system software. While three of these involve data obtained in the standard GC/FT-IR operation, one of the experiments involves a comparison of different members of the reference library. Although the discovery of this result was somewhat accidental, it is an excellent example of a problem where the derivative search can provide an essential alternative. The first experiment was designed to test the ability of the search
Figure 2. Effects of base line on search results: (A) spectrum of butyric acid retrieved from search file: (B) same spectrum after base line adjustment to create sloping base line: (C) spectrum of best match using absolute value search.
system to differentiate between the spectra of similar compounds. In an effort to evaluate a “worst case situation” (very few spectral features and no unique peaks), samples of hexane, heptane, cyclohexane, and isooctane were obtained by GC/ FT-IR. The resulting spectra were searched against the reference file and the best ten hits reported. These results were obtained with the absolute value matching algorithm (AB). Although all the alkanes gave very close matches, in each case the best match was the correct one. The results are particularly impressive for the heptane and hexane samples. Not only does the search differentiate between two compounds differing by a single methylene group but the next hits are also the next normal alkanes in the series. The alkanes were chosen as test compounds for two reasons. First, the spectra are very similar and contain no unique features. This makes the search considerably more difficult. Second, hydrocarbon chains are present in most compounds, and the ability to differentiate among isomers is very important to any search system. A second problem which is not as important with gas-phase spectra but must be considered in a general search system is the effect of sloping base line. To investigate this problem, we took a spectrum from the fiie and introduced a sloping base line with the base line correction software. The original spectrum and the adjusted one are shown in Figure 2A,B. Ideally, the search should have no difficulty with this minor change. However, when the absolute value algorithm was used, the results were confusing. Figure 2C shows the spectrum of the best match. This compound is suberic acid, and the ability to form intramolecular hydrogen bonds is indicated by the presence of a broad band at 3100 cm-l. The base line slope has apparently added sufficient absorbance in this region that this similarity contributes heavily to the match. The Wiswesser line notion quickly shows that suberic acid has a structure double that of butyric acid. This suggests that the relative absorbances of the peaks in the two spectra should be similar, and the small amount of hydrogen bonding becomes a dominant feature of the difference spectrum. The two-derivative algorithms were included specifically for these types of spectra. The weighting of broad spectral features is significantly reduced when these algorithms are employed,
ANALYTICAL CHEMISTRY, VOL. 53, NO. 6, MAY 1981
892
POSSIBLE HITS 249 METHACRYLIC ACID. 24BO ’IOVYUI 568 METHRCRYLIC ACID, 2506 1YBlOVYUl 655 METMACRYLIC ACID, 2506 lYBUVQ2NlBl le5 METHACRYLIC ACID. 2520 ozavyui
?!
0
BUTYL ESTER ISOBUTYL ESTER 2-DIMETHYLAMINO 2-HYDROXYETHYL
9
88 20 .zoo
#k =8
-.os0
.I37
9
1750
W
0
-
1680
a075 .012
9
Q
0
2060
1
iebo
iebo
irbo
izbo
UAVENUIIBERS
lobo
ebo
ebo
Flgure 3. Spectrum and search results from low slgnalhoise data for isobutyl methacrylate. and the butyric acid spectrum matches correctly. Another important problem in automated searching is the effect of noise upon the search performance. To investigate this problem, we ran a series of isobutyl methacrylate solutions on the GC-IR. The amount of isobutyl methacrylate varied from about 50 ng down to about 10 ng. Searches were performed on the samples using all four algorithms. The square derivative (SD) performed significantly better than the others. Figure 3 shows the spectrum and search results for the smallest sample. The better performance for the derivative search can be explained by the normalization routine used to standardize the spectrum to one absorbance unit. This computation greatly amplifies the noise and base line drift. In Figure 3, this will contribute significantly to the match values computed using absolute value metric but has a smaller effect on the derivative algorithms. These results demonstrate that the search system can identify spectra even when the signal to noise ratio is quite small. This experiment provides a good example of one major advantage of the full spectral search technique. The computation used in searching is very similar to a least-squares fit. Since the noise in the spectrum is random, the contribution to the match value from the noise should be independent of the reference spectrum. For spectra with very high noise levels, this contribution may be significant, but the search will still differentiate on the peak shape and intensity information which is not random. The match values shown in Figure 3 show that the first four matches differ by less than 80 units even though there is a contribution of over 2000 units from the noise. In general, the search algorithm tends to perform consistently until the noise reaches a certain level and then the results seem to have no relation to the spectral data. Although this point where the search collapses is somewhat independent of the spectral data, spectra with unique strong peaks tend to yield better results in high noise situations. The final experiment was designed to study the effects of searching on spectra from nonresolved GC peaks. The five real time integrated absorbance plots shown in Figure 4 were obtained by injecting a solution of dimethyl fumarate, methyl salicylate, and nitrotoluene in acetone into the gas chromatograph. The search easily identified the solvent as acetone (Figure 1)but had difficulty with the dimethyl fumarate. The results for this search are shown in Figure 5. This is an excellent example of search performances when a spectrum is not in the search library. In a full spectral search, the best matches should correspond to compounds with functional groups similar to the unknown since the vibrational
-*OSO
!A
TIME. SECONDS
Figure 4. Real-time integrated absorbance chromatographs from a sample of dimethyl fumarate, methyl salicylate, and o-nitrotoluene In acetone. POSSIBLE HITS
1019 FUMARIC ACID. DIALLYL ESTER 260 IUZOVI ZU 1’113 CINNAMIC ACID. METHYL ESTER 267 IOVIUIR 2283 3-PYRIOINEACRYLIC ACID, METHYL E S
267 T8NJ ClUIVOI 1512 MALEIC ACID. OIMETHYL ESTER 270 lOV1UIVOI 1010 FUMARIC ACID. DIISOBUTYL ESTER 280 IYBlOVl 2u 1515 MALEIC ACID. DIETHYL ESTER 289 20v1u1v02 951 PHTHALIC ACID. DIMETHYL ESTER 296 lOVR BVO1 1753 FUMARIC ACID. DIBUTYL ESTER
Flgure 5. Search results from the dimethyl fumarate peak.
modes corresponding to the various functional groups provide the significant differences in infrared spectra. If a spectrum is not in the library, but representative ones are, the search system tends to identify compounds with some of the functional groups as was seen in this example. In this search algorithm, the no peak areas may provide as much discriminative information as a strong peak by rejecting reference spectra with strong peaks not found in the sample. The other two components of the mixture were not completely resolved by the chromatograph. This can be seen in the five integrated absorbance plots shown in Figure 4. The plot of the 1560-1520-~m-~ region shows that one component is slightly offset from the other. A search was performed on a spectra from the leading edge of the nonresolved peak and the search results indicate that this spectrum corresponds to methyl salicylate. A search on a spectrum from the other side of the chromatographic peak indicates that a second component is present and that it is most likely an aromatic nitro
ANALYTICAL CHEMISTRY, VOL. 53, NO. 6, MAY 1981 TEN = 2 RTN = 693 TOLUENE, 0-NITRO-, WNR B 336.99 SECONOS INTO RUN 1.55 SEC. HERS. TIME
C
robo
h ssbo sobo
zobo
WAVENUMBERS
I
I
!,i o
zsbo
893
iobo
sbo
Spectra from nonresolved GC peak: (A) spectrum from trailing edge of GC peak; (B)l spectrum from the leading edge, methyl salicylate; (C) difference spectrum obtained by wbtractlng spectrum B from spectrum A. Figure 6.
Table I. Search Results from Spectra in Figure 6 A POSSIBLE HITS 1960 SALICYLIC ACID, 4-NIT190-, METHYL 3903 WNH CO DVOI 576 BETULA OIL /SYNTHETIC/ OR BVOl 3732 1539 SALICYLIC ACID. ETHYL ESTER OR BV02 C477 1218 SALICVLIC ACID, BUTYL ESTER 4569 OR BVO9 B POSSIBLE HITS BETULA OIL. /SYNTHETIC/
576is3
OR BVO:,
1539 SALICYLIC ACID, ETHYL ESTER 1069 OR BVOi! 1218 SALICYLIC ACID, BUTYL ESTER 1925 OR BVOLIi 705 SPLICYLIC ACID, ISOPENTYL ESTER 1649 OR BVOE!Y C
POSSIBLE HITS 899 TOLUENE, IFNITRO-, WNR B 1676 BENZENE, I-ETHYL-2-NITRO-, 170 WNR B2 TOLUENE, I+NITRO-, '365 WNR C 1109 BIPHENYL. 2-NITRO-, 453 WNR BR
compound. Frequently, spectral subtraction techniques can be employed to obtain pure spectra from incomplete chromatographic separations. Figure 6 shows the result of subtracting the methyl salicylate spectrum from the mixture spectrum. In this exampl'e,the carbonyl pe,ak of the ester was used to zero out the spectral contributions of the methyl salicylate producing a simplified difference spectrum. The search results from the two original spectra and the difference spectrum are shown in Table I. The search results from the difference spectrum indicate that the second component is
Figure 7. Results of automatic search and retrieval software for the difference spectrum.
o-nitrotoluene. A visual comparison of the unknown spectrum and the spectra from the possible match is particularly important when the working with difference spectra. Figure 7 shows a plot of the difference spectrum and the reference spectrum of the best match. The search and the overlay plot were automatically produced with a single command. This result provides solid evidence that the second component is the o-nitrotoluene. The results obtained in each of these experiments have provided us with a better understanding of how different spectral parameters can affect the results of a search and retrieval system. Our results indicate that many of the problems encountered in early attempts to automate infrared spectral searching were caused by poor data bases. The use of high-quality digital spectral data and full spectrum search algorithms allows the use of refined matching algorithms with minimal decision-making requirements. Although the development of high-quality infrared spectral files is both time-consuming and expensive, such files appear to be essential for good search results.
LITERATURE CITED (1) Coffey, P. J.; Mattson, D. R.; Wright, J. C. Am. Lab. (Fairfield, Conn.) 1978, 10, 126. (2) Erley, D. S. Anal. Chem. W88, 40, 894. (3) Rasmussen, G. T.; Isenhour, T. L. Appl. Spectrosc. 1979, 33, 371. (4) Powell, L. A.; Hieftje, G. M. Anal. Cbim. Acta 1978, 100, 313. (5) Small, C. W.;Rasmussen, G. T.; Isenhour, T. L. Appl. Spectrosc. 1979, 33, 444. (6) Hanna, A.; Marshall, J. C.; Isenhour, T. L. J. Chromatogr. Sci. 1979, 17, 434. (7) Azarraga, L. V. presented at the 5th Annual Symposium on Recent Advances In Analytlcal Chemistry of Pollutants, Jekyll Island, GA, May 1975.
RECEIVED for review September 11,1980. Accepted February 20, 1981.
