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Anal. Chem. 1981, 53, 2184-2187
problem if the exact enantiomeric purity of the derivatizing reagent is not available.
ACKNOWLEDGMENT The authors thank the Research Resources Center, Univerity Of Illinoisat the MedicalCenter, and the USEPA Regional Laboratory, Chicago, IL, for the use of their GC/MS facilities.
LITERATURE CITED State v. McNeal, 288 N.W. 2d 874 (Wis. App. 1980). Liu, J H.; Tsav. J. T.. unoublished results. Liu, J. H.; Ramesh, S.;Tsay, J. T.; Ku, W. W.; Fitzgerald, M. P.; Ange10% S. A.; Lins, C. L. K. J . Forensic Sci. 1981, 26, 656-663. Gelsomino, R.; Raney, J. K. Microgram 1979, 12, 222-230. Kroll, J . A. d . Forensic Sci. 1979, 24, 303-306. Raban, M.; Mislow, K. I n "Topics in Stereochemistry"; Eliel, E. L., Allinger, N. L., Eds.; Interscience: New York, 1967; Voi. 2; pp 199-230. Vitt, S.V.; Saporawskaya, M. B.; Gudkova, I.P.; Belikov, V. M. Tetrahedron Lett. 1985, 2575-2580. GiI-Av, E.; Charles-Sigier, R.; Fischer, G.; Nurok, D. J. J. Gas Chromatogr. 1966,4 , 51-58. Lochmulier, C.H.; Souter, R. W. J . Chromatogr. 1975, 113, 283-302. Karger, B. I.; Stern, R. L; Keane, W.; Halpern, B.; Westley, J. W. Anal. Chem. 1967, 39,228-230. Matin, S.B.; Wan, S.H.; Knight, J. B. Biomed. Mass Spectrom. 1977, 4 , 118-121. Gal, J. Biomed. Mass Spectrom. 1978, 5, 32-37. Gal, J. J. Pharm. Sci. 1977, 66, 169-172. Gilbert, M. T.; Brooks, G. J. W. Biomed. Mass Spectrom. 1977, 4 , 226-231. Gilbert, M. T.; Gilbert, J. 0.; Brooks, C. J. W. Biomed. Mass Specfrom. 1974, I , 274-280. Pohi. L. R. J . Med. Chem. 1973, 16, 475-479. Gunne, L. M. Biochem. Pharmacol. 1967, 16, 663-869.
(18) Gordis, E. Biochem. Pharrnacol. 1988, 15, 2124-2126. (19) Souter, R. W. J . Chromatogr. 1975, 108, 265-274. (20) Horiba, M.; Kitahara, H.; Yamamoto, S.;Oi, N. Agric. Biol. Chem. 1980, 44, 2987-2988, (21) Bonner, W. A. J . Chromatogr. Sci. 1972, 10, 159-164. (22) Frank, H.; Nicholson, G. J.; Bayer, E. J . Chromatogr. Sci. 1977, 15, 174- 176. (23) Frank, H.; Nicholson, G. J; Bayer, E. J . Chromatogr. 1978, 146, 197-206. (24) Frank, H.; Rettenmeier, A,; Weicker, H.; Nicholson, G. J.; Bayer, E. Clin. Chim. Acta 1980, 105, 201-211. (25) Frank, H.; Woiwode, W.; Nicholson, G. J.; Bayer, E. I n "Stable Isotopes: Proceedings of the Thlrd International Conference"; Klein, E. R., Klein, P. O.,Eds.; Academic Press: New York, 1979; pp 165-172. (26) Saeed, T.; Sandra, P.; Verzele, M. J. Chromarogr. 1979, 166, 611-618. (27) Konig, W. A. Chromatographia 1976, 9,72-73. (28) GiI-Av, E.; Feibush, 8.; Charles-Sigler, R. I n "Gas Chromatography 1966"; Littlewood, A. B. Ed.; Institute of Petroleum: London. 1972:. .DD . 227-239. (29) Koenig, W. A.; Parr, W.; Lichtenstein, H. A,; Bayer, E.; Ore, J. J. Chromatogr. Sci 1970, 6,183-186. (30) Koenig, W. A.; Nicholson, G. J. Anal. Chem. 1975, 47, 951-952. (31) Koenig, W. A.; Stoelting, K.; Kruse, K. Chromatographia 1977, 70, 444-448. (32) Corbin, J. A.; Rhoad, J. E.; Rogers, L. B. Anal. Chem. 1971, 43, 327-33 1. (33) Feibush, B.; Gil-Av, E. Tetrahedron 1970, 26, 1361-1368. (34) Parr, W.; Howard, P. Y. J . Chromatogr. 1972, 71, 193-201. (35) Parr, W.; Howard, P. Y. Anal. Chem. 1973, 45, 711-720. (36) Frank, H.; Nicholson, G. J.; Bayer, E. Angew. Chem., Int. Ed. Engl. 1978, 17, 363-365. (37) Westley, J. W.; Halpern, B. I n "Gas Chromatography, 1968"; Harbourne, S. L. A,, Ed.; Institute of Petroleum: London, 1969; pp 119-128.
RECEIVED for review April 29, 1981. Accepted July 24, 1981.
alculation of Linear Temperature Programmed Capillary Gas hromatographic Retention Indices of Polycyclic Aromatic m(ssunds Erik K. Whalen-Pedersen and Peter C. Jurs" DepatWnent of Chemistry, The Pennsylvania State University, 152 Davey Laboratory, University Park, Pennsylvania 16802
Capillary column gas chromatographlc retention indices of polycyclic aromatic compounds (PAC) can be calculated by using descriptors derived from the structures of the solute molecules. The retention indices of 231 PAC from the literature were regressed against a set of calculated molecular structure descriptors, and a four-variable equation was found with a multiple correlation coefflcient of 0.990. The variables included In the model are consistent with qualltative observations made during the experimental measurements of the retention indices.
Polycyclic aromatic compounds (PAC) comprise the largest class of known chemical carcinogens. Their widespread presence throughout our environment as products of incomplete combustion of organic materials make their identification and determination an important analytical problem. Capillary column gas chromatography is a technique commonly used for the analysis of mixtures containing PAC ( I ) . A retention index system for linear temperature programmed gas chromatography (GC) of PAC has recently been described in the literature ( 2 ) along with reported retention index data for a large number of PAC ( 3 , 4 ) .Retention indices are widely used
as a standardized method for reporting retention data. Retention indices can be used as an aid for tentative identification of chromatographic peaks within one laboratory as well as interlaboratory comparisons where pure reference materials may not be available. Chromatographic retention for capillary column gas chromatography is the measured quantity which represents the interactions between gas-phase solute molecules and the stationary liquid phase of the chromatographic system. The strength of the interactions, and ultimately the retention index, depends upon the identities of the solute molecules. The retention of the homologous series of alkanes is known to increase in an exponential relationship with the number of carbon atoms comprising the alkyl chain. This series of hydrocarbons is used as the internal standards for measuring Kovats retention indices. The relationships between molecular structure and chromatographic retention are not always known. Studies of quantitative structure-activity relationships (QSAR) investigate the relationships between the molecular structures of organic compounds and their biological activities. Structure-activity studies are based on the premise that relationships exist between numerical quantities (descriptors) used to represent molecular features and the biological ac-
0003-2700/81/0353-2184$01,25/0 0 1981 American Chemical Society
ANALYTICAL CHEMISTRY, VOL. 53, NO. 14, DECEMBER 1981
tivities o,r physical properties of those molecules. T h e techniques used in QSAR studies have been developed over may years and include vai~iousmathematical and statistical methods (5-7). The development of structure-activity reiationshipe (SAR) depenlds upon the ability of the researcher t o represent molecular features using calculated molecular structure descriptors. SAR studies have been applied t o a variety of classes of biologically active molecules including pesticides, pharmaceuticals, olfactory stimulants, mutagens, and chemical carcinogens. The work described here involves the development of quantitative structure-retention index relationships for linear temperature programmed capillary GC of PAC. Simple moleculu structure descriptors have been used as independent variables for multiple linear regression analysis. A linear equation has been developed by regressing the calculated molecular structure descriptors against the experimentally measured retention indices. This equation can be used t o calculate the retention indices of other PAC not in the original data set. E X P E R I M E N T A L SECTION In order to investigate the relationships between molecular structure and retention index, one must have a series of compounds whose chemical structures are known and for which the retention indices have been experimentally measured. For the purposes of this study the structures must be represented in a computer.compat,ible form for storage which can then be utilized by computer software to calculate molecular structure descriptors. These molecular structure descriptors will subsequently be used as the independent variables for regression against the experimentally measured retention indices. The structure entry and storage, molecular structure descriptor generation, and multiple linear regression analysis capabilities are all contained in the ADAPT computer software system (8, 9) which was used for this study. The ADAPT system has been implemented on the Department of Chemistry’s PRIME 750 computer which was used for this study. Data Set. Experimentally measured retention indices for 207 PAC were’ taken from Lee et al. (3) and an additional 24 sulfur-containing PAC were taken from Willey et al. ( 4 ) . The identities of the compounds comprising the data set can be found in Table I1 of ref 3 and Table I1 of ref 4. Excluded from consideration are those comlpounds from ref 4 already included in ref 3 and B pair of cis/trans isomers from ref 3. These isomers are indistinguishable with this set of calculated molecular structure descriptors. The data set contains polycyclic aromatic hydrocarbons (PAH), sulfur, oxygen, and nitrogen heterocycles in combination with various aryl and alkyl substitutions. The retention indices were experimentally measured under linear temperature plrogrammed conditions using a 12-20 m glass capillary column coated with SE-ij2 (5% phenyl) methylphenylsilicone stationary liquid phase. The temperature was linearly programmed from 50 to 250 “C a t a rate of 2 “C min+. The retontion indices were calculated by using a linear retention index scale for linear temperature programmed GC suggested by van Den 1)ool and Kratz (10). This retention index scale was applied to PAC analysis by Lee et al. (3) for calculating the retention index (a) of a substance and is given in eq 1. This is TRisubstance)
-
TR(Cz)
z = loo-- TR(Cz+l) - TR(Cz)
+ 1002
(1)
a linear retention index scale for linear temperature programmed GC of PAC where T R ( c Z ) and T R ( c Z + l ) are the retention times of the PAC internal reference standards bracketing the compound of interest and z is the number of rings in the PAC internal standard eluting immediately prior to the compound of interest. The PAC used as internal reference standards were naphthalene, phenanthrene, chrysene, and picene with two, three, four, and five rings, respectively. This linear retention index scale has been found to be superior to Kovats retention index for reporting retention data for PAC with regard to reproducibility and insensitivity to stationary phase
*
2185
film thickness and experimental conditions (1-3). The retention indices are computed on a linear retention scale. This uses the nearly linear increase in retention for the PAC internal standards with the addition of a ring. Kovats retention indices utilize a logarithmic retention scale. Another difference between the two approaches is the use of PAC internal standards rather than the alkanes which are used for measuring Kovats indices. Descriptor Generation. The structures of the 231 PAC comprising the data set were stored in computer disk files after entering them with the aid of a computer graphics terminal. The structures were stored as atom types, bond types, and bond connections. The following types of molecular structure descriptors were calculated from the stored molecular topologies. Molecular Volume. The molecular volume was calculated by summing contributions from spheres of van der Waals radii for each atom in the molecule. A fraction of the van der Waals radius (0.75) was used to minimize duplicate contributions arising from overlap of spheres. Standard bond lengths and angles were used for these calculations. Three-dimensional atomic coordinates can be used for the molecular volume calculations when available. The ADAPT system has the capability to calculate three-dimensional models of the structures using molecular mechanics. Fragment Descriptors. The fragment descriptors are atomic and molecular fragments. The following fragments were calculated the number of atoms, the number of atoms of each type (e.g., carbon), the number of bonds, the number of bonds of each type (e.g., single), the number of basis rings (the smallest set of smallest rings), the number of ring atoms, and the molecular weight. Molecular Connectivity Descriptors. The molecular connectivity index was introduced by Randic (11) as a numerical measure of the degree of branching of a molecule. It is a graph-theoretical index which treats molecular structures as graphs with the bonds representing edges and the atoms representing vertices. Molecular connectivity has been shown to correlate with a variety of biological activities and physicochemical properties (12). The path-one molecular connectivity is the sum of contributions from all bonds, which are paths of length one, in the molecule. Higher order molecular connectivities can be calculated in a similar manner. The molecular connectivities for paths one through four as well as path-cluster three (isobutane graph) and path-cluster four (isopentane graph) were also calculated. A total of 25 molecular structure descriptors were used in a stepwise multiple linear regression program as independent variables and were regressed against the experimentally measured retention indices. The variables were prescreened to eliminate from consideration those descriptors found to be involved in multicollinear relationships with other descriptors. This was accomplished by successively considering each descriptor as the dependent variable and regressing it against the remaining descriptor pool. Those which were found to be linear combinations of other descriptors, as well as those with high multiple correlation coefficients, were eliminated from consideration. A final pool of 17 descriptors was used in the stepwise multiple linear regression program to generate a linear equation which could be used to calculate retention indices. In addition to stepwise multiple linear regression, leaps and bounds regression was used to identify a number of “best” subsets of descriptors. These subsets correspond to the “best” linear regression equations for the dependent variable.
RESULTS A N D D I S C U S S I O N There are many possible regression equations which could be generated and the “best” is given in eq 2. The independent I = 32.33 (f1.52) [no. of basis rings] 10.84 (h0.48) [mol vol] 103.78 (h6.28) [ p a t h 1 molecular connectivity] 17.04 (*2.07) [no. of nitrogen atoms] - 57.28 (2)
+
+
n = 231
S = 12.05 r = 0.990 F(4,231) = 2766
variables are listed in the order of their selection by the stepwise multiple linear regression program and the values
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ANALYTICAL CHEMISTRY, VOL. 53, NO. 14, DECEMBER 1981
Table I. Example Calculation of Retention Index for Benzo[a]acridine descriptor name
value
coefficient
no. of basis rings molecular vol path 1 molecular connectivity no. of nitrogen atoms
4 74.13 4.740
32.33 10.84 -103.78
VN-
contribution 129.3 803.6 -491.9
525
455
t I
385 315
1 I
1
17.04
17.04
I 245
constant calcd retention index obsd retention index residual
1
-57.28 480.8
398.7 2.1
175
245
315
Experimsnto
385 Reren:
455
525
or- Index
Figure 1. Plot of calculated retention indices vs. experimental retention indices for 231 PAC.
in parentheses are the 95% confidence limits for the regression equation coefficients. The standard error for the calculated retention indices is 12.05 retention units. This represents a relative standard error of 4% of the experimental retention indices range (196-504). The multiple correlation coefficient for the equation is 0.990 and the overall F value for the equation is 2766. This regression equation represents an excellent fit of the calculated retention indices to the observed values. The F value, a statistical measure of goodness of fit, indicates that the equation has a great degree of statistical significance as the probability associated with this F value is less than 1.0 E-6. An additional test of the equation’s validity was performed by dividing the data set in half, developing an equation on each half, and using the remaining compounds as a test of the equation. The resulting equations were capable of producing calculated values for the remaining compounds with correlation coefficients of 0.98 and virtually identical regression coefficients. Many regression equations could be generated by using this pool of molecular structure descriptors. The equation reported here is developed from stepwise multiple linear regression and appears as the best regression subset in Leaps and Bounds regression subset for four variables. The variables included in this equation consistently appear in the “best” subsets selected in the Leaps and Bounds regression procedure. In addition, the F value reaches maximum with four variables in the equation and the multiple correlation coefficient will only increase to a maximum of 0.992 with the addition of all available descriptors. The calculation of retention index using this regression equation is illustrated in Table I for benzo[a]acridine. Calculated retention indices are plotted against the experimentally measured retention indices in Figure 1. The line represents how this plot would look if the retention indices could be exactly calculated. This figure illustrates the degree to which the calculated values fit the calculated values. Figure 2 shows the residuals, the difference between the calculated retention indices and the experimental values, plotted against the calculated retention index values. The residuals show no apparent systematic tendencies and a frequency histogram shows them to be well distributed. Simple correlations between a single variable and retention indices have been reported in the literature (13). The correlations were reported for homologous series of PAC and for a much smaller number of observations. The correlations were only successful for planar PAH and not for heterocycles or partially saturated molecules. These correlations included molecular connectivit,y (14,15),boiling point, and a molecular shape parameter (16). The work presented here includes a large set of PAC including PAH, heterocycles, and partially saturated PAC. The regression equation was developed by using easily calculable
7
- 1-58 16
1 175
245 315 385 455 talcuiated Retention I n d e x
825
Flgure 2. Plot of residuals vs. calculated retention indices for 231 PAC.
molecular structure descriptors. The equation obtained shows an excellent fit using only four independent variables and with a great degree of statistical significance. Empirical observations for retention trends for a series of PAC were reported by Lee and Wright ( I ) . They observed that retention tended to increase along with both molecular weight and degree of alkyl substitution. For a homologous series of PAH with the same number of rings, the retention tended to increase as the molecules become more “extended” (Le., benzo[c]phenanthrene < chrysene < naphthacene). For a series of PAC with the same PAH skeleton, the compounds eluted in the following order: oxygen heterocycles < sulfur heterocycles < PAH < nitrogen heterocycles. The stepwise multiple linear regression program selected independent variables from a pool and chose variables which seem to quantify the molecular features in agreement with these observed tendencies. Molecular volume and the number of basis rings, both included in the regression equation, are related to the size of the molecules. These quantities will also tend to increase along with the molecular weight. Molecular connectivity was developed to represent the “branching” of a molecule and as such the “extent” of a molecule will tend to be encoded in this molecular structure descriptor. The benzo[c]phenanthrene, chrysene, and naphthacene series illustrates and the branching can be seen to be different on inspection. The path 1 molecular connectivity included in the equation is corrected for both heteroatom valences and the presence of rings. Thus these molecular features will simultaneously be reflected in the path 1 molecular connectivity. The degree of alkyl substitution will be reflected in the molecular connectivity as well. Finally the number of nitrogen atoms will directly adjust the calculated retention index for a nitrogen PAC with relation to the corresponding PAH. In addition to producing a good equation, this method of SAR study has also been shown to parallel the empirical observations of the experiment.
Anal. Chem. 1981, 53,2187-2189
ACKNOWLEDGMENT The authors wish to thank M. L. Lee and C. M. White for their assistance and the use of their data.
LITElRATURE CITED
2187
Society: Washington, DC, 1979; pp 103-129. (9) Stuper, A. J.; Brugger, W. E.; Jurs, P. C. "Computer Assisted Studies of Chemical Structure and Biological Function", Wiley-Interscience: New York, 1979. (IO) van Den Dool, H.; Kratz, P. Dec. J . Chromatogr. 1983, 11, 463-471. (11) Randic, M. J . Am. Chem. Soc. 1975, 9 7 , 6609-6615. (12) Kier. L. B.: Hall. L. H. "Molecular Connectivitv in Chemistrv and Druo Research"; Academic Press: New York, 19f6. (13) Bartle, K. D.; Lee, M. L.; Wise, S. A. Chromatographla 1981, 14, 69-72. (14) Kler, L. B.; Hall, L. H. J . Pharm. Sci. 1979, 68, 120-121. (15) Kaliszan, R.; Lamparczyk, H. J . Chromatogr. Sci. 1978, 16, 246-248. (16) Radecki, A.; Lamparczyk, H.; Kaliszan, R. J . Chromatogr. Sci. 1979, 12, 595-599. 0
~I
Lee, M. L.; \Nright, R. N. J . Chromatogr. Sci. 1980, 18, 345-358. While, C. M.; Sharkey, A. G.; Lee, M. L.; Vassilaros, D. L., I n Polynuclear Aromatic Hydrocarbons"; Jones, P. W., Leber, P., Eds.; Ann Arbor Science Publishers: Ann Arbor, MI, 1979; pp 261-275. Lee, M. L.; Vassilaros, D. L.; White, C. M.; Novotny, M. Anal. Chem. 197!), 51, 768-773. Willey, C.; Iwao, M.; Castle, R. N.; Lee, M. L. Anal. Chem. 1981, 53, 400-407. Gould, R. F., Ed. "Biological Correlations-The Hansch Approach"; American Chemical Society: Washington, DC, 1972. Kowalski, B. R., Ed. "Chemometrics: Theory and Application"; American Chemical Society: Washington, DC, 1977. Franke, R., Oehme, P., Eds. "Quantltative Structure-Activity Analysis"; Acadamie-Vwlag: Berlin, 1970. Jurs, P. C.; Chou, J. T.; Yuan, M. I n "Computer Assisted Drug Desilgn"; Olsen, E. C., Christoffersen, R. E.,Eds.; American Chemical
RECEIVED for review June 3, 1981. Accepted September 8, 1981. This work was supported by the National Cancer Institute through Contract NO1 CP 75926. The PRIME 750 computer was purchased with partial financial support from the National Science Foundation.
Ion Chromatographic Determination of Anions in Wastewater Precipitate L. W. Green" and J. R. Woods Chalk River Nuclear Laboratories, Chalk River, Ontario, Canada KOJ 1JO
A method for the treatment of water-insoluble materials tor anion analysis by Ion chromatography is descrlbed. Sodium carbonate fusion was used to decompose the sample; the sodium carbonate matrix subsequently was removed by passing the dissolved flux through a hydrogen-form cationexchange preparatory column and heating the eluate. For F-, CI-, PO:-', and $042- recoveries were 92, 91, 95, and 96%, relative standard deviations were 4, 1, 3, and 5 % , and detection limits were 8, 4, 5, and 10 pg, respectively. The method was applied to 'the analysis of a wastewater precipitate.
Radionuclides in low-activity wastewater at Chalk River Nuclear Laboratories are concentrated by reverse osmosis to reduce the disposal volumes. During the concentration process the membrane may clog due to the buildup of a precipitate on the surface of the membrane. Analyses of the water and precipitate are necessary to determine the elements and SOlution conditions responsible for the formation of the precipitate. Inorganic anions have a strong influence on the solubility of solution constituents and thus must be included in the analyses. The anions in the wastewater can readily be determined by ion chnomatography (l-3),but those in the precipitate must f i s t be released by a suitable sample-decomposition technique. The decomposition technique must employ a reagent which either does not interfere with the chromatography or can be removed prior to injection. Popular sample-decomposition techniques, such as wet ashing with strong acids, are not suitable because the anioin which is introduced into the sample at high concentration severely interferes with the chromatographic process ( 4 ) .
Three sample-decomposition methods for the determination of inorganic anions in water-insoluble materials have been reported. For the determination of C1- in silicate rocks Evans and Moore (5) employed an oxygen furnace coupled to a sodium carbonate scrubber. Their results for rock standards compared well in many cases with results obtained by neutron activation and X-ray fluorescence analyses. However, the method is not applicable to many anions. For the analysis of oyster tissue for C1- and Sod2- Koch ( 4 ) employed the Schoniger-oxygen-flask technique and for the analysis of crankcase oil for C1- and Br- he used the sodium alcoholate method. Neither of these latter techniques is applicable to inorganic material; however in his paper Koch demonstrated the usefulness of a hydrogen-form cation-exchange preparatory column for the treatment of samples for ion chromatography. This paper describes a method which uses a sodium carbonate fusion to decompose the solid material. After fusion, the sodium carbonate matrix is removed by passing the dissolved flux through a hydrogen-form cation-exchange preparatory column and heating the eluate. The preparatory column converts Na2C03(aq)to C02(aq)which is volatized by heating. The method is applicable to many of the anions normally determined by ion chromatography.
EXPERIMENTAL SECTION Apparatus. Cellulose acetate membrane R.0.320C (Electrohome Ltd., Kitchener, Ontario) rated for 95% salt retention was used in the reverse osmosis apparatus. The membrane plus precipitate were fused in 30-mL platinum crucibles heated t o -900 "C in a muffle furnace. The preparatory column was a 100-mLNalgene buret packed with AG50W-Xl2 cation-exchange resin (Bio-RadLaboratories,Richmond, CA). Silanized glass wool was placed at the top and bottom of the column to hold the resin in place. The ion chromatograph consisted of: a Varian (Varian Canada Inc., Georgetown, Ontario) Model 5000 liquid pump, a Rheodyne
0003-2700/81/0353-2187$01.25/00 1981 American Chemical Society
