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Anal. Chem. 1990, 62, 1746-1754
(22) Mantoura, R. F. C.; Dickson. A.; Riley, J. P. Estuarine Coastal Mar. SCI. 1878, 6 , 387-408. (23) Piotrowicz, S. R.; Harvey, G. R.; Springer-Young, M.; Courant, R. A,: Boran, D. A. Trace Metals In Sea Water: Wong, C. S., Ed.; NATO Conf. Ser. 4, Mar. Sci., Vol. 9; Plenum: New York, 1983; pp 699-717. (24) Bruland, K. W. Limnol. Oceancgr. 1888, 3 4 , 269-285. (25) Instruction Manual, Form 942919770, Orion Research, Inc.: Cambridge, MA, 1979. (26) Instruction Manual, No. ION-LE7501 (in Japanese), Denki Kagaku Keiki Co.: Musashino, Japan, 1986. (27) Richmond, V. L.; Denis, R . S . ; Cohen, E. Anal. Biochem. 1885, 745, 343-350. (28) Dzombak, D. A.; Fish, W.; Morel, F. M. M. Environ. Sci. Techno/. 1886, 2 0 , 669-675. (29) Ruzic, 1. Anal. Chim. Acta 1882, 740, 99-113. (30) Udenfriend, S.; Stein. S.:Dairman, E.; Leimgruber, W.: Wigeie, M. Science 1872. 778, 871-872. (31) Handa. N. J . Oceanogr. SOC.Jpn. 1886, 2 2 , 79-86. (32) Hirose, K.; Dokiya, Y.; Sugimura, Y. Mar. Chem. 1882, 7 7 , 343-354. (33) Hering, J. G.: Morel, F. M. M. Geochim. Cosmochlm. Acta 1888, 5 3 , 61 1-6 18. (34) Smith, R. M.: Marteil, A. E. Critical Stabiliw Constants; Plenum: New York, 1976; Voi. 4. (35) Turner, D. R.; Whitfieid, M.; Dickson, A. G. Geochim. Cosmochim. Acta 1881, 45, 855-881. (36) Byrne. R. H.; Kump, L. R.: Cantreli, K. J. Mar. Chem. 1888, 2 5 , 163-181. (37) Paulson, A. J.; Kester, D. R . J . Solution Chem. 1880, 9 , 269-277. (38) Gulens. J.; Leeson, P. K.; Shuin, L. Anal. Chlm. Act8 1884, 156, 19-31. (39) Kramer, C. J. M. Mar. Chem. 1988, 18. 335-349. (40) Ramamoorthy, S.; Kushner. D. J. Nature 1975, 256, 399-401. (41) Buffle, J.; Greter, F. L.; Haerdi, W. Anal. Chem. 1877, 49, 216-222. (42) Giesy, J. P.; Leversee, G. J.: Williams, D. R. Water Res. 1877, 1 7 , 10 13- 1020. (43) Buffle, J.; Greter, F. L.; Haerdi, W. Anal. Chirn. Acta 1978, 101, 339-357. (44) Tanoue, E. Unpublished work at Meterological Research Institute, Tsukuba. Japan, 1989.
(45) Brezonik, P. L.; Brauner. P. A.; Stumm, W. Water Res. 1878, 70, 605-612. (46) Greter, F. L.; Buffle, J.; Haerdi, W. J . Electroanal. Chem. 1878, 707, 2 1 1-229. (47) Ryan, D. K.; Weber, J. H. Anal. Chem. 1882, 5 4 , 986-990. (48) Stumm, W.; Morgan, J. J. Aquatic Chemistry, 2nd ed.; Wiley: New York, 1981; pp 376-378. (49) Gamble, D. S.; Schnber, M. Trace Metals in Metal-Organic Interactions in Natural Waters; Singer, P. C . , Ed.; Ann Arbor Science: Ann Arbor, MI, 1973; pp 265-302. (50) Yuchi, A.; Wada, H.; Nakagawa, G. Anal. Sci. 1885, 1 , 19-22. (51) Gamble, D. S.;Underdown, A. W.; Langford, C. H. Anal. Chem. 1880, 5 2 , 1901-1908. (52) Huhnicki, A.; Krawczynski vel Krawczyk, T.; Lewenstam, A. Anal. Chim. Acta 1984, 758, 343-355. (53) Ogura, N. Mar. BioI. 1874, 24, 305-312. (54) Wheeler, J. R. Limnol. Oceanogr. 1878, 2 7 , 846-852. (55) Andren, A. W.; Harriss, R. C. Geochim. Cosmochim. Acta 1875, 39,
1253-1 257. -.. _. (56) Maurer, L. G. Deep-sea Res. 1876, 2 3 , 1059-1064. (57) Carison, D. J.; Brann, M. L.; Mague, T. H.; Mayer, L. M. Mar. Chem. 1885. 16. 155-171. (58) Sugimura,' Y.: Suzuki, Y. Mar. Chem. 1888, 2 4 , 105-131. (59) Suzuki. Y.; Tanoue, E.; Ito, H. Deep-sea Res., in press. (60) Suzuki. Y.; Tanoue, E.; Sugimura, Y. unpublished results. (61) Suzuki, Y.; Tanoue, E. I n Ocean Margin Processes in Global Change;
Dahlem Konferenzen, Berlin, in press. Dawson, R.; Pritchard, R. G. Mar. Chem. 1878, 6 , 27-40. Lee, C.: Bada, J. L. Earth Planet. Sci. Left. 1875, 26, 61-68. Johnson, K. M.; Sieburth, J. McN. Mar. Chem. 1877, 5 , 1-13. Burney, C. M.; Johnson, K. M.; Lavoie, D. M.; Siburth, J. McN. DeepSea Res. 1878, 2 6 , 1267-1290. (66) Martell, A. E.; Smith, R. M. Critlcal Stability Constants; Plenum: New York, 1974; Voi. 1. (67) Schnitzer, M.; Khan, S . U. Humic Substances in the Environment; Marcel Dekker: New York. 1972.
(62) (63) (64) (65)
RECEIVED for review October 3,1989. Accepted May 24,1990.
Structural Analysis of Polychlorinated Biphenyls from Carbon- 13 Nuclear Magnetic Resonance Spectra Debra S . Egolf and Peter C. Jurs* Department of Chemistry, 152 Davey Laborat0r.v. T h e Pennsylvania State University, University Park, Pennsylvania 16802
The reiationshlp between 13C NMR chemical shifts and polychlorinated biphenyl (PCB) conformation Is lnvestlgated. Predictive model equatlons based on calculated structural descrlptors were developed for PCBs and were used to slmulate ''C NMR spectra for nlne conformations of each compound. For each compound, the conformatlon that produced the most accurate slmulated spectrum overall was used to develop new model equations for performing further simulations. The contlnuatlon of this iterative process allowed the conformatlone of the PCBs to be successively adlusted, based on spectral comparison resuhs. Conformatlonal proflles were developed for all 49 of the PCB compounds.
INTRODUCTION Polychlorinated biphenyl (PCB) compounds have been the subject of numerous analytical investigations because of widespread concern regarding their prevalence in the environment as residue from hazardous waste dumps. The physiological interactions of PCBs are of particular interest due to their probable toxicity. Therefore, the identification 0003-2700/90/0362-1746$02.50/0
and the structural characterization of these compounds is essential. Several researchers have addressed the topic of the conformations of biphenyl compounds using experimental methods ranging from ultraviolet ( I , 2), infrared (3-5), and Raman (6, 7) spectroscopies to electron (8-12) and X-ray (13-15) diffractions or using theoretical methods including ab initio (16-181, complete neglect of differential overlap (CNDO) (19-21), and molecular mechanics (22-25) estimations. One goal of these researchers was to determine the complete structural characteristics, including the interplanar angle, of select biphenyl compounds. While atom positions, bond lengths, and bond angles are relatively easy to assign, values derived for the interplanar angle (or twist angle or torsional angle) between the phenyl rings differ significantly. Some of these differences are related to the physical phase in which the structure was evaluated, since biphenyl compounds are thought to assume different conformations in the crystalline, solution, and gaseous phases. For most PCBs, one conformer is assumed to predominate, although two stable conformers have been observed for a few compounds. In general, the conformers assume a cis orientation ( < 9 0 O ) rather than the seemingly more sterically favorable trans orientation 0 1990 American Chemical Society
ANALYTICAL CHEMISTRY, VOL. 62, NO. 17, SEPTEMBER 1, 1990
(>goo). The lack of a comprehensive analysis of PCB conformation evidences the necessity for further research in this area. 13CNMR spectroscopy is unique as an analytical tool for the structure elucidation of organic compounds because of the direct relationship between carbon atom chemical shift and molecular structure. Information regarding both topology and geometry of a carbon atom’s local structural environment is encoded in its chemical shift. Thus, chemists seek to understand and describe this structure-shift relationship. However, the direct interpretation of complex 13C NMR spectra is hindered because this relationship is not completely understood. Therefore, alternative methods are being designed and implemented to assess the influences of basic structural properties upon chemical shift resonance. Most of these methods involve the estimation of induced substituent effects for carbon atoms in specific chemical environments. One outgrowth of this approach involves the derivation of relationships between a set of structural attributes for a particular group of carbon atoms and their associated chemical shift data. Once determined, these relationships can be used for the simulation of chemical shifts for structurally similar carbon atoms. This procedure is commonly termed the parametric approach to spectral simulation. Spectral simulation is especially useful in cases where appropriate reference spectra are unavailable for comparison with a measured spectrum of an unidentified compound or unknown. One scenario for the elucidation of the unknown compound‘s structure involves spectral simulation techniques. The chemist obtains a 13CNMR spectrum for the compound, searches a spectral library for the best matches, and assesses the plausibility of the retrieved spectra. If the matches are unacceptable, candidate structures are proposed for the unknown by using all available information as well as chemical intuition. The spectra for the proposed structures are simulated, and the simulated spectra are compared with the spectrum of the unknown to establish its identity, or additional structures are proposed for further evaluation. Spectral simulation using the parametric approach involves the development of linear model equations that relate descriptors encoding structural information (attributes) to individual chemical shift values. These equations have the form S = bo blX1 bzX2 + ... + bdXd (1) where S is the predicted chemical shift of a given carbon atom, Xiare calculated numerical descriptors, bi are the coefficients determined through multivariate regression, and d is the number of descriptors in the model. The parametric approach was first developed and applied to linear and branched alkanes (26,27). The utility of this procedure in the derivation of structure-chemical shift relationships and the subsequent simulation of 13C NMR spectra has been further demonstrated for several structural classes of compounds (28-33) through the development of an interactive computer software simulation system (34,35). This paper describes the application of multivariate methods to the 13CNMR chemical shift data of a set of PCB compounds to enable PCB identification and to explore each compound’s conformational space. A combination of computational techniques, including multiple linear regression analysis and principal components analysis, was used to evaluate the data.
+
.m4,
Table I. PCB Compounds
5
6
6’
5’
substitution
+
EXPERIMENTAL SECTION The compounds used in this study, 48 PCBs and biphenyl, are listed in Table I and are identified with both numerals and upper or lower case letters used as symbols on several plots. The lSC NMR chemical shift data were obtained from Yanagisawa et al. (36). They measured the spectra using sample concentrations
1747
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
A
49
W
B C D E F
G H I J K
L M N
0 P
Q R S
T U V
W
x Y 2
a b C
d e f g h 1
j
k 1 m n 0
P 9
r
S
t U
V
2 3 4 2,2‘ 2,4‘ 3,3‘ 4,4‘ 23 2,4 2,5 2,6 394 3,5 2,4,4’ 2,2’,5 2,3’,5 2,4‘,5 2’,3,4 2,2’,3,3’ 2.2l.3.5’ ., 2,2‘,4,4’ 2,2’,4,5’ 2.2l.5.5’ . ., 2,3’,4’,5 2,2’,6,6’ 234 2,3,6 2,4,5 2,4,6 2,2’,3,4,5’ 2,2’,3,5’,6 2,2’,3’,4,5 2,2’,4,5,5’ 2,2’,3,3’,4,4‘ 2,2‘,3,4,4’,5‘ 2,2’,3,3’,6,6’ 2,2’,4,4’,5,5’ 2,2’,4,4‘,6,6’ 2,3,4,5 2,3,5,6 2,2‘,3,4,5,5’ 2,2’,3,5,5‘,6 2,2’,3,3’,4,4’,5,5’ 2,2’,3,3‘,5,5’,6,6’ 2,3,4,5,6 2,2’,3,4,5,5’,6 2,2’,3,3’,4,4’,5,5’,6 2,2’,3,3’,4,4’,5,5’,6,6’
calc angleso
setb
29.9 35.0 29.1 29.4 47.3
r P P
r
r r
21.8 26.7 37.9 44.6 46.7 55.4 42.9 37.3
48.3
I
47.3 56.6 72.2 43.8 56.1 55.2 66.0
r P r P
r r
r r r r P P r r P r P
r
P P P P P r P r r
r 37.3 77.0 45.5 68.4 53.4 56.6 47.0 90.0 59.6 59.1
r P P
r
r P
r
r r r P
r
r P
r
The PCB compounds for which torsional angles were calculated composed the initial reference set while the remaining compounds composed the prediction set. Biphenyl was retained as a prediction compound. The reference (r) and prediction (p) compounds designated later in the investigation. a
ranging between 1.5 and 2.5 mol % in CDC13 for most of the compounds, although saturated solutions were used for some samples. All spectra were measured relative to tetramethylsilane on a JEOL GX-400 spectrometer with the following instrumental conditions: 45O flip angle, 7-s pulse repetition time, 16 000-Hz spectral width, 64K data points, and 0.01 ppm digital resolution. Although the chemical shift data were completely assigned for each compound, the investigators indicated that some of the shift assignments were uncertain. Shifts that might be interchangeable were noted for 25 of the compounds. In most cases, the shift differences were essentially negligible (C0.3 ppm difference); however, for six of the compounds (7, 16, 21, 29, 43, and 48), uncertain shifts differed by 0.39-0.68 ppm. Structural representations of the 49 compounds were entered into the computer disk files by using graphical input procedures
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ANALYTICAL CHEMISTRY, VOL. 62, NO. 17, SEPTEMBER 1, 1990
within the ADAPT software system (37-39). An interactive molecular mechanics modeling program was used to develop approximate three-dimensional atomic coordinates for the structures (40). The chemical shift data were input via a separate routine, one shift per carbon atom. All of the software used in this investigation is written in FORTRAN and is implemented on a PRIME 750 computer operating in the Department of Chemistry at The Pennsylvania State University.
RESULTS AND DISCUSSION Generation of Initial Structural Conformations. In order to develop regression equations that relate topological and geometrical features to chemical shift, accurate threedimensional structures must be generated prior to descriptor calculation. Because the absolute conformations of PCB molecules have not been established, some approximations were used to obtain initial three-dimensional coordinates for each molecule. The geometries obtained from the interactive molecular mechanics routine for the PCB compounds are considered approximate although the bond lengths and angles are, in general, reasonably accurate. The bond length for the bond connecting the rings was fixed to a value of 1.50 A for all molecules in the data set (17). Rotation about the single bond joining the phenyl rings is the only major conformational degree of freedom in the PCB structures. The torsional angle associated with each compound‘s preferred conformation has not been unequivocally established experimentally. One technique for the assignment of torsional angles of biphenyl compounds was reported by R. M. G. Roberts (41). This method utilizes the measured 13C NMR chemical shift values of the PCBs as indicators of torsional angle. According to Roberts, the 1’-and 4’-carbon atoms are particularly sensitive to changes in r-electron density and anisotropy. Both of these effects are thought to vary with the angle of twist between the phenyl rings, 6, as a function of cos2 6. Roberts derived an equation of the form
Each of the A values is equivalent to the difference between the chemical shifts of the 1‘-and 4’-carbons: AX = S(C-1’)6(C-4’). The chemical shifts are corrected for cases in which the phenyl ring is also substituted, as these substituents directly affect the chemical shifts of the 1’-and 4’-carbon atoms. Ao and Aw can be derived from compounds whose rings lie in planar and perpendicular configurations, respectively. Ao, derived from the fluorene system, was assigned a value of 14.5 ppm by Roberts. For chlorinated biphenyls, A= can be derived from a highly ortho-substituted PCB. Roberts’ method was applied to the structures of this PCB data set. Chlorine substituent effects for monosubstituted benzenes in CDC13/CC14solvent, obtained from a compilation from Ewing (42),were used for carbon shift correction. Values for chloro substitutions relative to the 1’-or 4’-carbon atoms include ipso, 6.3ppm; ortho, 0.4 ppm; meta, 1.4 ppm; and para, -1.9 ppm. The smallest value of Ax, 6.39 ppm, was calculated for 2,2‘,3,3‘,5,5‘,6,6‘-octachlorobiphenyl, 45. Therefore, this value was used as A= for this investigation. Torsional angles were only calculated for those structures that were either substituted on one phenyl ring or equivalently substituted on both rings. Table I lists the calculated angles for those 30 PCB compounds and biphenyl. Torsional angles for the remaining 18 asymetric PCBs could not be unequivocally determined. Atom List Development. The 30 PCBs with complete structural parametrization were initially designated as the reference set (or training set), and the remaining 18 PCBs and biphenyl constituted the prediction set. The atom list, composed of carbon atoms present in unique structural environ-
ments, was derived from the total set of 360 carbon atoms in the 30 reference compounds (12 carbons per compound). Topologically similar atoms within each compound were identified, and only one atom was retained in the atom list to describe each unique structural environment. A set of 222 unique carbon atoms results. Because of the exact torsional assignments, topologicall identical atoms in these compounds are not always found in identical geometrical environments when the rings remain in a fixed position (rather than assuming free rotation). Compounds in which one phenyl ring is completely unsubstituted while the other ring is unsymmetrically substituted contain topologically identical atoms on the unsubstituted ring in different static geometrical environments. The reference compounds with these characteristics are 2, 3,9-11, 13,27-29, and 40. To account for these geometrical differences, 20 carbon atoms that were originally removed as identical were returned to the atom list to give a final atom list containing 242 carbon atoms. Carbon Atom Subsetting. Chemical shifts are more accurately simulated when regression equations are generated for subsets or groups of similar carbon atoms (27,32). Two schemes for dividing the atom list into appropriate groups were apparent for this set of PCBs. Carbon atom connectivity divides the 242 carbons into 3 groups: 63 chlorine-substituted carbons (C-Cl), 46 bridging carbons (C-C), and 133 carbon atoms with an attached hydrogen (C-H). The second scheme separates carbon atoms based on their location relative to the bridging carbon atoms. Those 4 groups are 46 bridging carbons (C-C), 75 a-carbons, 75 @-carbons,and 46 y-carbons. Each carbon atom subsetting scheme has considerable merit. Even though the chemical shifts for this set of PCBs span a range of only 20 ppm, when the atoms are classed by connectivity, there is little overlap between the shift ranges of the individual classes. Thus, the immediate bonding environments of the carbon atoms in PCBs generate basic measurable shielding effects. The range of chemical shifts within a particular bonding environment provides a measure of the local topology and geometry of the carbon atoms in each group. On the other hand, the second classification scheme also proves useful for studying the relationship between torsional angle and chemical shift. Roberts (41) indicated that torsional effects would be especially significant in the bridgingand y-carbon positions. Also, the degree of chlorine substitution on the a-carbons (ortho substitution) appears to have an effect on the torsional angle. For these reasons, both subsetting schemes were used in this investigation. Descriptor Calculation. A total of 112 topological, geometrical, and electronic descriptors were calculated for the 242 carbons in the atom list. Topological descriptors encode basic structural information about the local carbon atom environment and are computed directly from the connection table representation of the molecular structure. Calculated topological descriptors included simple heteroatom and valency counts as well as functions involving connectivity indexes (43, 4 4 ) and weighted path counts (45). Geometrical descriptors useful in this study fell into two major categories. One type encodes functions of inverse throughspace distances between atoms raised to powers of 1,2,or 3. The second type encodes the count of the number of hydrogen or non-hydrogen atoms located a predetermined distance from the carbon being described. The calculated electronic or energy-type descriptors included van der Waals interaction energies (46)and extended Huckel charge values (47). The Huckel charge descriptors were developed for this investigation as an improvement over Del Re (T charge (48) descriptors. The Del Re charge descriptors are based on the topology of the molecule while extended Huckel charge descriptors are geometry dependent.
ANALYTICAL CHEMISTRY, VOL. 62, NO. 17, SEPTEMBER 1, 1990
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Table 11. Summary of Model Statistics
obsd chem shifts, ppm high mean
group
low
c-c1
130.6 132.6 124.9
135.4 144.4 132.1
132.8 138.7 128.5
132.6 124.9 126.1 127.0
144.4 135.3 135.4 135.3
138.7 129.6 130.1 130.0
SD
n
R
S
63 46 133
0.933 0.974 0.899
0.50 0.59 0.59
46 75 75 46
0.974 0.960 0.978 0.987
0.59 0.75 0.45 0.45
Connectivity Atom Groups
c-c C-H
1.3 2.4 1.3
Location Atom Groups
c-c CY
P Y
2.4 2.6 2.1 2.6
In order to minimize chance correlations, the number of descriptors submitted to the regression analysis procedure must be limited (49).The descriptors for each atom group were statistically evaluated, and those containing minimal or redundant information were discarded. Evaluation of Initial Model Equations. The remaining descriptors for each atom group were submitted to a stepwise multiple linear regression analysis procedure using the observed chemical shifts as the dependent variable. Numerous equations were generated that were evaluated by using standard statistical measures (50).Table I1 shows the statistics for the observed chemical shifts and also characterizes the best model equation generated for the six atom subsets. High, low, and mean chemical shifts as well as the standard deviation are given for the six groups (the C-C group is included twice for completeness). Model statistics shown include n, the number of carbons; d, the number of descriptors in the model; R, the multiple correlation coefficient for the regression; and s, the standard error of the regression in ppm. The equations were further evaluated to determine simple and multiple correlations among the descriptors; these were found to be within acceptable limits. The standard errors of the model equations range from 4.5% to 10.4% of the range of shift values for each group. The R values were all equal to or greater than 0.9, which indicates strong linear relationships. The types of descripton prevalent in the model equations include throughspace and throughbond counts of chlorine, carbon, or hydrogen atoms, throughspace distances between atoms, energies of van der Waals interactions, and Huckel charges. The model generated for the y-carbons provides a good, representative of the derived models: S = 4.92 (h0.28)
-
X
10.1 (11.4) X + 41.9 (h5.2) X - 2.00 (*0.59) X + 0.851 (i0.257) X - 2.07 (*0.73) X - 2.37 (h0.96) X + 127 n = 46 R = 0.987
NNCL 1 CCEL 1 HCLD 2 NTHC 1 CSTR 3 MPHC 1 AVHC 2 s = 0.45
(3)
All of the descriptors describe properties relative to the individual carbon atoms of the atom list within their respective structural environments. The first descriptor in eq 3, NNCL 1, indicates the presence or absence of an attached chlorine atom. CCEL 1 is a measure of the van der Waals energy associated with interactions between the carbon and chlorines located three or more bonds away. For the carbon atoms with an attached hydrogen atom, HCLD 2 is the sum of the cubed throughspace distances between the hydrogen and chlorines attached to adjacent @-carbons. NTHC 1 and MPHC 1 characterize the Huckel charges of attached atoms while AVHC 2 provides a measure of the average Huckel charge on atoms two bonds away. CSTR 3 quantitates the van der Waals energy of interaction with the nearest chlorine as related
to the cubed throughspace distance from the C1 to the carbon whose shift is being predicted. The descriptors present in this equation suggest that the pattern of chlorine substitution plays an important role in characterizing the observed chemical shifts for y-carbon atoms. The descriptors present in the other five equations show similar trends. The equation for the @-carbonsalso has the NNCL 1indicator descriptor first in the equation while the second descriptor provides a measure of crowding induced by chlorine substituents on neighboring carbon atoms. The remaining four descriptors encode Huckel charge information for atoms located within three bonds of the @-carbons. The first descriptor of the a-carbon model encodes throughspace distance information relative to the nearest chlorine atom. Other descriptors in the a-carbon model encode Huckel, van der Waals, and distance information. The bridging-carbon model is primarily characterized with Huckel and throughspace distance parameters. Both the a-and bridging-carbon models contain more information relative to atoms located three or more bonds from the carbon center than do the /3and y-carbon models. The remaining two models, derived for the carbons with attached chlorine atoms or attached hydrogen atoms, are each composed of nine descriptors. The model for the chlorinesubstituted carbons contains primarily Huckel charge and throughspace distance parameters, which seem to broadly characterize the complete molecule. On the other hand, the model for the carbons with attached hydrogen atoms contains Huckel, distance, and van der Waals descriptors and predominantly encodes properties of atoms located three or more bonds away. Both of these models, as well as the a-carbon model, encode significant information regarding the geometries of atoms on the attached phenyl ring. This suggests that 13C chemical shifts are dependent upon the absolute geometries of the phenyl rings as well as the positions of the chlorine substituents in these PCB compounds. Spectral Simulation. The simulated shifts calculated by using the regression models were merged to form complete simulated spectra. These spectra were evaluated by comparing them with their corresponding measured spectra. The mean spectral standard error for the 30 simulated spectra from the 3 connectivity model equations is 0.54 ppm, and for the simulated spectra derived from the 4 models based on atom location, the error is 0.59 ppm. Thus, the simulated spectra were extremely similar to the observed spectra. To further determine the quality of the simulated spectra, the library of 49 measured PCB spectra was searched for the best spectral match for each simulated spectrum. By using the squared Euclidean distance metric as a measure of spectral similarity, 28 of 30 of the spectra simulated by using the connectivity models and 29 of 30 of the spectra simulated by using the atom location models were correctly identified. The differences between the results obtained for the simulations using the two classification schemes were minimal. This identification success rate is quite remarkable considering the
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ANALYTICAL CHEMISTRY, VOL. 62, 3 .O
2.0
l'O
I a\
NO. 17, SEPTEMBER 1, 1990
3
1
t
0.0 20.0
40.0
60.0
80.0
100.0
120.0
1'
Torsional Angle,"
Figure 1. Plots of spectral error versus torsional angle for compounds 3, 28, and 20. The SOHd and dashed lines indicate piots for simulations using models derived for carbon atom groups classes by connectivity
and location. respectively. lack of information relative to molecular conformation. Torsional Angle Selection. The goal of this investigation was to address the question of the relationship between torsional angle and chemical shift for these PCBs, not to perform a complete conformational analysis for each compound. Thus, to reduce the size of the problem, only nine conformers of the PCB compounds were selected for evaluation. The initial torsional angles used for the generation of regression models span a range from 21.8 to 90.0'. A histogram of angles represented showed that six torsional angles (26, 38, 46, 56, 72, and 90') could be used to represent the torsional angle data space between 0 and 90'. Three additional angles (106,123,and 136') were chosen in order to investigate further trends in the torsional angle-chemical shift relationship. For PCBs, with a high degree of symmetry, structures with these angles should provide information similar to that obtained for the structures with 72, 56, and 46' angles. Evaluation of Torsional Angle-Chemical Shift Relationships. The structures for all 49 PCBs in the data set were copied and their torsional angles adjusted so that each compound was represented in each of the 9 different geometries. The 19 prediction compounds possess 220 unique carbon atoms in the following 6 groups: (connectivity) 83 C-Cl, 37 C-C, and 100 C-H;and (location) 73 a-carbons, 73 @-carbons, and 37 y-carbons. These atoms were combined with those of the reference compounds to give a complete atom list containing 462 carbon centers. Carbon center descriptors were calculated for all of the structures, and their spectra were simulated by using both sets of regression models previously derived. Thus, a total of 2 X 9 X 49 = 882 spectra were simulated. The standard error was determined for each simulated spectrum compared to ita corresponding observed spectrum. The variation of spectral standard error as a function of torsional angle can be plotted for each compound. Three examples are shown in Figure 1. In the plot for compound 3, as the torsional angle increases through 90°, the standard
error also increases. For compound 26, the opposite occurs. For compound 20, the standard error first decreases to a minimum at 38' and then increases through 90'. This spectral error versus torsional angle relationship is markedly similar for the two simulations using the two different atom classification schemes. These plots resemble plots of rotational energy versus torsional angle and can perhaps be similarly interpreted. Energy diagrams are presented by McKinney et al. (17)as generated by using ab initio calculations for five different PCB compounds and biphenyl. Although the plots are similar, the observed low-energy (or low-standard-error) well is not always located in the same torsional angle range. One trend is of particular note: highly ortho-chlorinated compounds show high-energy/high-spectral-errorvalues at low torsional angles in both McKinney's work and this work. While many observations can be made regarding these plots as indicative of the relationship between torsional angle and chemical shift, these calculated torsional angles were only used for initialization of the investigation. For each compound in each of the two simulations,the torsional angle 190' for which the lowest spectral error was obtained was used to fix the next geometry for the compounds in order to derive a new set of regression equations. In this way, the conformation space was iteratively explored based upon the results of the previous iteration. By using these minimal errors, mean spectral error values of 0.50 and 0.54 ppm were determined for the 30 reference compounds for the 2 simulations. The prediction compounds had mean errors of 0.65 and 0.69 ppm also based on minimal errors. The library retrieval success for the reference compounds was 30/30 for both simulations and for the prediction compounds was 14/19 and 13/19. After this procedure, the composition of the lists of reference and prediction compounds could be altered since legitimate structural representations had been generated for all 49 compounds. The original reference set did not contain any asymmetric structures. Ideally, both the reference and prediction sets should be representative of the complete set of compounds. Therefore, new reference and prediction compounds were chosen randomly, again with a 30-to-19 split, as shown in Table I. The new reference carbon atom list consisted of 264 carbons in the following groups: 84 C-Cl, 49 C-C, 131 C-H, 83 a-carbons, 83 6-carbons, and 49 y-carbons. The carbon atom list for the prediction compounds consisted of 198 atoms divided into groups as 62 C-C1,34 C-C, 102 C-H, 65 a-carbons, 65 @-carbons,and 34 y-carbons. For all further regressions, the same carbon center descriptors employed in the initial regression analyses were recalculated and resubmitted to the regression procedure. Thus, it was possible in many cases to obtain models containing the same descriptors with new coefficients. On the other hand, if significantly superior models were generated with an alternative descriptor list, those models were retained as the best models. Seven regression models were determined (the bridging carbon group (C-C) is represented by two separate models because of differing initial conformations of the compounds). As expected, models for some carbon atom groups only changed coefficients while for other groups completely new models were found and evaluated. Two sets of simulated spectra were generated for all nine conformations of the reference and prediction compounds. Mean minimal standard errors for the reference compounds were 0.54 and 0.53 ppm for the two simulations, and prediction compound errors were 0.57 and 0.61 ppm. The search retrieval success rate was 27/30 for the reference compounds for both simulations and was 19/19 and 17/19 for the prediction compounds. Iterations of this regression-simulation procedure were performed until the preferred conformations, based upon
ANALYTICAL CHEMISTRY, VOL. 62, NO. 17, SEPTEMBER 1, 1990
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Table 111. Model Statistics for All Iterations iteration 1 R
iteration 2
group
n
d
c-c1 c-c
84 49 131
9 6 9
0.908 0.956 0.936
0.51 0.67 0.53
c-c
49 83 83 49
6 7 8 7
0.956 0.968 0.975 0.984
0.67 0.62 0.48 0.48
C-H
a
P Y
d
S
iteration 3
R
S
0.914 0.956 0.939
0.49 0.67 0.52
0.956 0.974 0.975 0.985
0.67 0.56 0.48 0.46
d
R
S
6 7 8 7
0.956 0.975 0.975 0.985
0.67 0.55 0.48 0.46
Connectivity 9 6 9
Location 6 7 8 7
Table IV. Simulation and Search Statistics for Complete Spectra
low
low
high
mean
SD
search results ref pred
0.35 0.21 0.23
0.91 0.80 0.83
0.65 0.57 0.57
0.16 0.13 0.13
28 30 27 27
14 19 19
0.34 0.29 0.32 0.31
0.90 1.08 1.09 1.08
0.69 0.61 0.61 0.60
0.13 0.17 0.16 0.16
29 30 27 27 27
13 17 18 18
std err pred compd
std err ref compd high mean
SD
Connectivity original wklist
calcd 8
random wklist
min s iter 1 iter 2
original wklist
calcd 8
0.22 0.15 0.21 0.23
0.99 0.71 0.79 0.79
0.54 0.50 0.54 0.54
0.36 0.34 0.29 0.32 0.31
1.09 1.07 1.15 1.15 1.16
0.59 0.54 0.53 0.53 0.53
0.15 0.15 0.12 0.12
Location random wklist
min s iter 1 iter 2 iter 3
minimal standard error calculations, remained unchanged for all of the reference compounds. Only one additional regression-simulation iteration was necessary to obtain conformer stability for the compounds when the three connectivity atom groups were used to generate models. Two more iterations were required for the atom location subset. Model statistics for all iterations are presented in Table 111. A comparison of the data in Table I11 with that given in Table I1 for the original worklist of compounds indicates some striking differences. Although not shown in Table 111, the range of observed chemical shifts for the atom groups is essentially identical (within 0.1 ppm) with that shown in Table 11; however, the mean chemical shift values for several of the atom groups changed by 0.5 ppm or greater. The mean chemical shift for the a- and P-carbon atom groups increased by 0.5 ppm, for the y-carbon atom group by 0.6 ppm, and for the group of carbons with an attached hydrogen atom by 1.4 ppm, whereas the mean chemical shift for the bridging carbon group decreased by 0.5 ppm. These shifts in the data likely caused some of the changes evident in the model quality. The quality of models for the bridging carbons significantly degraded through changing the atom list, although substantial improvement in the models for the a-carbons and the C-H atoms WBS obtained. With each additional iteration, an overall improvement in the model statistics was observed. Simulation and search statistics for the complete spectra of both the original and new worklists of compounds are presented in Table IV. The simulation results show an increase in the average standard error for the reference compounds and a subsequent decrease for the prediction compounds for the new worklist. Similarly, the search results indicate a decrease in identification success rate for the compounds of the reference set, with a corresponding increase in correct identification of the prediction compounds for the new worklist. Generally, for a homogeneous data set, the identification success rate is somewhat greater for the reference compounds than for the prediction compounds. The behavior observed in this investigation is therefore anomalous. Unless
0.16 0.15 0.17 0.16 0.17
a more complete review of the statistics for each simulation were performed, any conclusions drawn regarding these observed trends are speculative. However, the results for a set of iterations tend to improve slightly with each iteration. Overall, the search success was 46/49 or almost 94% of the compounds were correctly identified. Ideally, the final preferred conformation (theoretically indicating the most stable conformation) of each compound in one simulation would be identical with that of the other simulation. However, the most stable conformations for these molecules may fall between the tested conformations (angles); thus, inaccurate results may have been generated for these compounds. On the other hand, the large 10-15' increments between tested conformers were necessary to make the investigation feasible to handle. This wide sampling of the conformational space should minimize the problem of falling into local minima and give a better indication of the approximate global minimum for each compound. Consideration of all the data obtained as final results for each simulation could provide a key for evaluation. Plots of the standard error versus torsional angle are shown for all 49 PCBs in Figure 2. The y axis of the plotting frame for each individual plot spans 2.0 ppm, so for compounds with extremely high values of standard error a t 26O, this point was not plotted. Similar line shapes for compounds for a given simulation should indicate similar torsional properties while comparison of the line shapes for each compound between the two simulations should help to discriminate between good and questionable results for particular compounds. If the line shapes for a compound in the two simulations are similar, but the absolute minimum is shifted slightly, that compound may be adequately described. If the line shapes differ significantly, one or both simulations for that compound have likely produced invalid results. To better evaluate line shape similarities within the plots, a principal component analysis was utilized. Each compound is represented by its 9 standard error values, so the data space for each simulation consisted of a 49 X 9 matrix of values.
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ANALYTICAL CHEMISTRY, VOL. 62, NO. 17, SEPTEMBER 1, 1990 Connectivity
Location
Location
6
66
A-
BC
Second Principal Component
n
D-
3 6
1
E
4364 $ 4
1
FG
P
n
3 l 1 1
2
H3
2
I-
J
4
5 6 4-
22
I
3
4 3 1
4461 4 44 4
2
3 3 3
K-
L
V First Principal Component
M
N
Figure 4. Principal component plot of PC2 versus PC1 with labels
n
indicating torsional angles 190' with the lowest standard error for the connectivity graphs. Labels: 1 = 26', 2 = 38', 3 = 46', 4 = 56', 5 = 72', 6 = 90'.
-0 P
L
QR -
sT U
V
Flgure 2. Plots of the standard error versus torsional angle for all 49 PCB compounds for both sets of simulations. rn
Zk Second Principal Component
E f
X
u a
G piH
D F
First Principal Component
Flgure 3. Principal component plot of PC2 versus PC1 of the standard error data for the connectivity graphs shown in Figure 2. The variance
described in this plot is 8 9 . 8 % .
Figure 3 illustrates a principal component (PC) plot of PC2 versus PC1 for the simulation using the connectivity models. The variance depicted in the plot is 89.8% of the total. A similar plot is obtained when the data from the location simulation are plotted. Both plots are roughly shaped like the letter V. Located on the left-hand branch of the V in Figure 3 are compounds G, A, C, M, H, H, D, and F. The plots in Figure 2 in the columns labeled connectivity show that all eight of these compounds are represented by curves that increase toward 90' and then decrease. Similarly, in Figure 3 points E, Z, k, and m, located at the tip of the right-hand branch, and points f, X, u, q, W, etc., also on the right branch, are indicative of curves that decrease from 26 to 90' in Figure 2. For most of these latter compounds, the standard error
at 26' is not indicated in the plots of Figure 2 because the values were so large. In general, the points located toward the tip of the V are for compounds having plots with both peaks and troughs in their curves. Figure 4 is the same plot as Figure 3 with the points labeled to indicate which of the first six conformations (1 = 26O, 2 = 38', 3 = 46', 4 = 56", 5 = 72', 6 = 90') gives the smallest standard error for the simulated spectrum for each compound. Throughout most of the plot, the numbers that are identical cluster together, although some deviations are evident. For example, one of the points labeled with a 1, corresponding to compound i, is located among 3's and 4's. Therefore, this compound's standard error plot differs greatly from those of the other compounds labeled 1. In the upper right part of the plot, compound E is represented with a 3 while other nearby points are labeled with 6's. These deviations indicate that the preferrred conformations of these compounds (and perhaps others) may not be accurately identified simply through the assignments of torsional angles based solely on minimal standard error values. However, no other examples of significant deviation from label clustering are readily apparent in the plot. A detailed examination of the measured chemical shift data indicates similar trends to that shown in the qualitative information illustrated in Figure 3. The chemical shifts a t each atom position for the set of 49 compounds were ranked. Three to six of the highest and lowest shifts for each atom position were noted. Compound 39 (m) has the highest chemical shift values, over all 49 compounds, for 6 of the atom positions and the lowest value for 2 other positions. Additionally, two other atom positions for that compound are indicated with low shifts. Likewise, compound 26 (Z) has high and low shift values indicated for eight of its atom positions. Compounds 7 (G)also has eight positions indicated as high or low shift values and compound 14 (N) has seven positions indicated. Compounds m and Z show high shifts in the a and y locations and low shifts on bridging-carbon and @-carbonatoms while for compounds G and N the opposite is true. In Figure 3, compounds m and Z and G and N are found on opposing edges of the V. In general, for the 49 PCBs of this data set, those compounds predominantly chlorinated in the @ position and having no a-chlorine substituents tend toward conformations with small (20-30') torsional angles. On the other hand, compounds highly chlorinated in the a-position, perhaps with some 7 or /3 chlorination as well, seem to have nearly perpendicular ring orientation.
ANALYTICAL CHEMISTRY, VOL. 62, NO. 17, SEPTEMBER 1, 1990 Connectivity
Conncctivity
2,7.',3,3'
CI
Location
I/
6,6'
I
Figure 5. Graphs for both simulations plotting standard error versus torsional angle for two series of compounds with successively increasing chlorination.
It is particularly interesting that several highly chlorinated compounds (e.g. w, r, and v) appear to assume intermediate conformations. Compounds with many a substituents probably tend toward large torsional angles due to steric interactions of the substituents. However, compound w has four chlorine atoms in a positions. Perhaps some electronic effect allows smaller torsional angles to be accessible or even preferable for many of the highly chlorinated compounds. Figure 5 shows the standard error versus torsional angle graphs for 10 compounds obtained for both simulations. Compounds B, I, a, n, and t are sucessively more chlorinated from 2-chlorobiphenyl to 2,3,4,5,6-pentachlorobiphenyl. Compounds E, T, i, r, and w follow the same chlorination pattern but are identically substituted on both phenyl rings. These plots are useful for illustrating the effects of additional chlorines on the shape of the standard error curves. All 10 curves for the first series of compounds have relatively similar shapes except for the two curves representing the pentachlorinated compound. The top eight curves show deep troughs for preferred conformations in the vicinity of 38-56". Addition of a chlorine at carbon 6 lowers the 90' barrier to rotation, thus allowing population of the 90' conformer. However, the conformers with smaller torsional angles are still accessible. The second series of compounds on the right side of Figure 5 are represented differently in the two simulations. In all 10 of these plots, there is a general upward sloping to the right. However, the graphs derived from the location simulation show a pronounced 90' barrier to rotation, except for compound E, which reaches a minimum at 90'. Particularly evident in the connectivity simulations for this series of compounds is the general trend of decreasing slope as substitution increases. In fact, for the most highly substituted compound of the data set, decachlorobiphenyl (w), the 90' barrier to rotation has disappeared and many conformations appear to be energetically accessible.
CONCLUSIONS The multivariate techniques of multiple linear regression analysis and principal components analysis, coupled with 13C NMR spectroscopy, are useful tools for both the identification and the structural elucidation of this set of 49 PCB compounds. Although the chemical shift data only span 20 ppm, reliable regression models were developed that relate predominantly geometrical structural properties to the observed chemical shift data. Incorporation of Huckel charge descriptors into chemical shift models proved valuable in the
1753
electronic description of this aromatic system. Accurate identification of the PCB compounds was achieved for 94% of the simulated spectra. Considering the great homogeneity of the compound list, whose only unique structural features are the topological arrangements of chlorine substituents, this proficiency in spectral identification is considered extremely good. In all cases, incorrectly identified spectra were constructed from 12 uniquely simulated chemical shifts. Of the 49 PCB spectra, 28 possess 12 carbon atoms in unique chemical environments. Therefore, slight deviations of 1or 2 simulated shifts from the optimal (observed) positions when all 12 shifts are located within 20 ppm could significantly affect the appearance of the spectrum and easily cause incorrect identification. The principal component plots of the standard error profiies for the conformational evaluation of structures provided a useful method for the evaluation of a set of diverse plots. Conformational analysis of PCBs is feasible since only one major rotational degree of freedom is present in any of the structures. The results of the analysis suggest that substantial differences exist between the accessible rotational conformations for the members of this set of compounds. The location of points within the PC plots indicate relative conformational shapes of the standard error profiles. Therefore, compounds located at opposite extremes in the plots demonstrate different conformational behavior and likely possess significantly different structural properties. One extreme appears to indicate compounds with inaccessible planar conformations but having a relatively low barrier to rotation at go', while the other extreme indicates a high barrier (although perhaps not inaccessible) at 90' whereas the planar conformation is easily accessible. Compounds with many a-chlorines compose this first group while predominantly compounds with P- and y-chlorines compose the second group. These results are especially significant because they are consistent with previously deduced conformational preferences of PCBs. McKinney and Singh (51) report that PCB physiological activity may be related to the accessability of the planar conformation. Thus, the results of this investigation provide valuable information regarding the rotational barriers encountered by PCBs in the planar conformation. The results derived from the simulations based upon different atom classification schemes contribute complementary information, as each classification method emphasizes specific structural properties as basic to each atom class. The remaining properties not used for classification are available as parameters that can be related to the differences in chemical shift resonance for the atoms within a particular atom group. Thus, unique information is gained by performing two different simulations on the same set of chemical structures. Furthermore, the individual effects on the accessible conformations of PCB molecules due to the addition of chlorine substituents can be evaluated by using the spectral error profiles. For the two series of compounds examined with respect to stepwise chlorination, the only obvious differences in conformation were noted for the most and least chlorinated compounds of each series. A method for further extraction of the voluminous information gathered through this investigation is necessary for a comprehensive evaluation. Nonetheless, the results obtained in this investigation clearly provide useful, semiquantitative information relative to PCB conformation.
LITERATURE CITED (1) (2) (3) (4)
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(8) Almenningen, A.; Bastiansen. 0.; Fernholt, L.; Cyvin, B. N.; Cyvin, S. J.: Samdal, S. J. Mol. Shuct. 1985, 728, 59-76. (9) Almenningen, A.; Bastiansen, 0.; Fernholt, L.; Gundersen, S.; KlosterJensen, E.; Cyvin. B. N.; Cyvin. S. J.; Samdal, S.:Skancke, A. J. Mol. Stfuct. 1985, 128, 77-93. (10) Almenningen, A.; Bastiansen, 0.; Gundersen, S.;Samdai, S.; Skancke, A. J. Mol. Struct. 1985, 728, 95-1 14. Samdal, S. J. Mol. Struct. 1985, 128, 115-125. (11) Bastiansen, 0.; (12) Ramming, Chr.; Seip, H. M.; Aanesen Dymo, Acta Chem. Scand. Ser. A . 1974, 2 8 , 507-514. (13) Hargreaves, A,; Rizvi, S. H. Acta Crystal/ogr. 1982, 15. 365-373. (14) Charbonneau, G.P.; Delugeard, Y. Acta Clystallcgr., Sect. 8 : Struct. Ctystallogr. Cryst. Chem. 1978. 32, 1420-1423. (15) Brock, C. P. Acta Crystallogr., Sect. 8 : Struct. Crystallogr. Cryst. Chem. 1980, 36, 968-971. (16) Aimlof, J. Chem. Fhys. 1974, 6 , 135-139. (17) McKinney, J. D.; Gottschalk, K. E.; Pedersen, L. J . Mol. Struct.: THEOCHEM 1983, 104. 445-450. (18) Hafelinger. G.; Regeimann, C. J . Comput. Chem. 1987, 8. 1057- 1065. (19) Janssen, J.; L a k e , W. J. Mol. Struct. 1979, 55, 265-281. (20) Tinland, B. Theor. Chim. Acta 1968, 1 1 , 452-454. (21) Gropen, 0.; Seip, H. M. Chem. Phys. Lett. 1971, 7 7 , 445-449. (22) Casalone, G. L.; Mariani. C.; Mugnoii, A,; Simonetta. M. Mol. phvs. 1968, 15. 339-348. (23) StQlevIk, R.; Thingstad, 0. J. Mol. Struct. 1984, 106, 333-353. (24) Lindner, H. J. Tetrahedron 1974, 30, 1127-1 132. (25) Charbonnler, S.:Beguemsi. S. T.; N'guessan, Y. T.; Legoff. D.; Proutiere. A.: Viani. R. J. Mol. Struct. 1987. 758.109-125. Grant, D. M.; Paul, E. G. J. Am. Chem. Soc. 1984, 86, 2984-2990. Lindeman, L. P.; Adams, J. Q. Anal. Chem. 1971, 43, 1245-1252. Small, G. W.; Jurs, P. C. Anal. Chem. 1983, 5 5 , 1128-1134. Small, G. W.; Jurs, P. C. Anal. Chem. 1984, 56, 2307-2314. Egolf, D. S.; Jurs, P. C. Anal. Chem. 1987, 59, 1586-1593. Egolf. D. S.;Brockett, E. 8.; Jurs. P. C. Anal. Chem. 1988. 6 0 , 2700-2706.
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RECEIVEDfor review March 14,1990. Accepted May 14,1990. This work was supported by the National Science Foundation under Grant CHE-8815785. The PRIME 750 computer was purchased with partial financial support of the National Science Foundation.
Unintensified Photodiode Array Fluorescence Detector for High-Performance Liquid Chromatography Jeff Wegrzyn, Gabor Patonay,' Michael Ford,2and Isiah Warner* Department of Chemistry, Emory Uniuersity, Atlanta, Georgia 30322
An unlntenslfled photodiode array based multichannel fluorescence detector has been developed for use In hlghperformance UquM chromatography. The detector uses a low wattage xenon capillary flashlamp as an excitatlon source s u m hlgbhtensrty radlaUon throughout the UV and VlSiMe reglon. Fluorescence emission Is monltored over a 250-nm range, provldlng on-line spectral lnformatlon of chromatographic effluent. Llnear dynamlc range covers at least 3 orders of rnagdtude, with detection lknns In the low nanogram range for several polycyclic aromatic hydrocarbons.
INTRODUCTION Fluorescence is one of the most sensitive and inherently selective methods of detection available in HPLC. However, conventional single-channel detectors, even in combination with highly efficient analytical columns (1,2),are limited in their ability to completely characterize the column effluent in one chromatographic run. Additional information about Permanent address: D e p a r t m e n t of Chemistry, Georgia State University, Atlanta, GA 30303. *Permanent address: Perkin-Elmer Limited, Beaconsfield, Bucks HPSlQA, Beaconsfield, England. * A u t h o r t o w h o m correspondence should be addressed
the components in the effluent can be gained when fluorescence excitation and/or emission spectra are obtained along with retention data. This has been the incentive for a number of innovative detectors designed to monitor multiple excitation/emission wavelengths. These detectors, when used with the enhanced separation capabilities of modern HPLC, become very powerful tools for analyzing complex mixtures. Several distinct approaches have been taken in the development of multichannel detectors. One configuration is to surround the flow cell with photomultiplier tubes (PMT). Fluorescence emission wavelengths are then selected by using interference filters placed before each PMT (3). This approach retains the sensitivity of a single-channel detector, while providing fluorescence emission intensities a t several different wavelengths to help resolve fused peaks. This design does have some limitations, since it can only accommodate a small number of PMTs. Solute characterization can be improved by fluorescence spectral detectors. An easy method to obtain a complete spectrum is to stop the HPLC flow and scan the effluent in the flow cell ( 4 ) . The technique develops problems when diffusion of components occurs during scanning. This causes a loss of chromatographic resolution and nonreproducible results. The possibility of rapid mechanical scanning (10 nm/s) without altering the flow has been discussed (5). While adequate for a number of applications, some of the spectral
0003-2700/90/0362-1754$02.50/0Q 1990 American Chemical Society
