An Efficient and Simple Approach to Predict Kovat s Indexes of Polychlorinated Naphthalenes in Gas Chromatography

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1 Journal of the Chinese Chemical Society, 2003, 50, An Efficient and Simple Approach to Predict Kovat s Indexes of Polychlorinated Naphthalenes in Gas Chromatography Chunsheng Yin ab * ( ), Shushen Liu b ( ), Xiaodong Wang b ( ), Da Chen b ( ) and Liansheng Wang b ( ) a School of Environmental Science & Engineering, Shanghai Jiao Tong University, Shanghai , P. R. China b State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing , P. R. China Molecular structures of polychlorinated naphthalenes were numerically described with a simple but efficient encoding method. Correspondingly a set of structural parameters were obtained for these compounds and linearly correlated with their gas chromatography retention indexes. A quantitative structure-retention relationship Model (M1) was developed by using multiple linear regression (MLR) with correlation coefficient R = between the numeric structural codes and the gas chromatography retention indexes of 62 polychlorinated naphthalenes. If the leave-one-out cross-validation procedure was employed to construct QSPR model for all samples, the second model M2 with the correlation coefficient being R = was generated. The structural codes of polychlorinated naphthalenes were tested with MLR for estimation and prediction of the GC RI by models M1 and M2, and the results obtained were satisfactory. Keywords: Structural encoding; Kovat s retention indexes; Polychlorinated naphthalenes; Quantitative structure-retention relationships. INTRODUCTION Polychlorinated naphthalenes (PCN) are environmentally persistent organic pollutants (POPs). Industrial PCNs with 1-7 chlorine atoms have been produced and introduced into the environment since the 1920s. PCN homologues are found mainly in sediments, 1 water, 2 air 3,4 and biota. 5-7 They were analyzed by high-performance liquid chromatography (HPLC) 8 and gas chromatography (GC); 9 however, specific analysis for isomers requires purified standards of individual isomers. This provides opportunities to utilize quantitative structure-retention relationship (QSRR) to investigate the retention phenomena In this work, we have developed the structural coding method proposed by Cherqaoui et al. 15 and used an 8-bit digit to represent the structure of an arbitrary PCN. The numeric codes were used to correlate to Kovat s retention indexes (RI) of 62 PCN compounds. Herein the structure-property relations of the 62 PCNs have been built by using a multiple linear regression (MLR) through a crossvalidation procedure, thus the Kovat s indexes can be predicted effectively. The results obtained by MLR demonstrated that the quantitative estimation and accurate prediction may be implemented with simple structural encoding parameters. THEORETICAL SECTION In a chromatographic process, all the conditions may be kept constant or controlled, thus the molecular structure of the investigated organic compound is the only independent variable in the system. 16 It is possible for us to build a QSRR model to quantitatively predict the retention indexes of organic compounds. One of the structural representation methods for an organic compound is to assign a set of numerical codes to that molecular structure. Some numeric codes for organic compounds 15,16 have been reported in various literatures. Most of them have been derived from graph theory. Yin et al. 17 has expanded the numeric encoding method proposed by Cherqaoui 15 to express the molecular structures of hydrocarbons in the straight-run gasoline. A set of fifteen numbers was assigned to each hydrocarbon molecule. 150 Hydrocarbon compounds from the straight-run gasoline were numerically encoded to * Corresponding author. Fax: ; csyin@sjtu.edu.cn

2 876 J. Chin. Chem. Soc., Vol. 50, No. 4, 2003 Yin et al. be correlated with their gas-chromatographic retention indices. Using the encoded structural parameters, the gas-chromatographic retention indices of the hydrocarbons were successfully estimated and predicted. In the present study, the investigated compounds, 62 polychlorinated naphthalenes, have the same biphenyl structures with 3-7 chlorine substituents distributed in eight substituted sites. Each molecule was represented by a set of eight numbers. Herein two representative structure encoding illustrations of polychlorinated naphthalenes are shown in Fig. 1. We started by assigning a code to each substituted site of one polychlorinated naphthalene. If there is a chloro-substituent at the substituted site, the coding digit is 1; and if there is no chloro-substituent at this substituted site, the coding digit is 0. The numeric code was unique for each molecule. When the number of chlorine atoms in a molecule was less than 8, the rest of the numeric codes at the corresponding sites were filled out with zeros to ensure that the each encoded molecule in the whole encoded numeric data set has the same representational dimension, i.e., a 8-bit numeric code for an arbitrarily given molecule. A Multiple linear regression (MLR) method was used to develop a model for the relationship between the retention indexes of polychlorinated naphthalenes and the 8 structural codes (S). The model is expressed in the following form: 15 0 j 1 f( S) b bjsj (1) EXPERIMENTAL Relative retention times were determined for 62 PCN homologues in a Hitachi M-80B GC-MS using fused-silica capillary columns coated with DB-5 (J&W Scientific, Folsom, CA, USA) (30 m mm I.D., 0.25 mm film thickness). Chromatographic conditions were: Column temperature: programmed from 160 to 2808C at a rate of 48C/min Injector and detector temperatures: 280 and 1808C, respectively Carrier gas: Helium Kovat s retention indices were calculated based on relative retention to n-alkanes, viz., C 20 H 42 to be 2000 and C 30 H 62 to be For details regarding the determination of individual PCN homologues please refer to reference 1. RESULTS AND DISCUSSION Data set In this study, polychlorinated naphthalenes 14 are structurally encoded in terms of the above mentioned method. These numeric codes are listed in the Table 1. As shown in Table 1, a working data set is composed of all these 62 investigated compounds. Estimation and Prediction A multiple linear regression (MLR) was used to de- Fig. 1. Illustrations for structural encoding of two representative polychlorinated naphthalenes. (A set of 8-bit numeric codes is set to represent the structure of each substituted site in a polychlorinated naphthalene molecule. If there is a chloro-substituent at the substituted site, the coding digit is 1; and if there is no chloro-substituent at this substituted site, the coding digit is 0.)

3 Predict Kovat s Indexes of Polychlorinated Naphthalenes J. Chin. Chem. Soc., Vol. 50, No. 4, Table 1. Numeric Codes and GC RI of 62 Polychlorinated Naphthalenes Structural numeric coding No. Compounds RI exp RI M1 RI M2 S 1 S 2 S 3 S 4 S 5 S 6 S 7 S ,3,6-Trichloronaphthalene ,3,5-Trichloronaphthalene ,3,7-Trichloronaphthalene ,4,6-Trichloronaphthalene ,2,4-Trichloronaphthalene ,2,5-Trichloronaphthalene ,2,6-Trichloronaphthalene ,2,7-Trichloronaphthalene ,6,7-Trichloronaphthalene ,3,6-Trichloronaphthalene ,2,3-Trichloronaphthalene ,3,8-Trichloronaphthalene ,4,5-Trichloronaphthalene ,2,8-Trichloronaphthalene ,3,5,7-Tetrachloronaphthalene ,2,4,6-Tetrachloronaphthalene ,2,4,7-Tetrachloronaphthalene ,2,5,7-Tetrachloronaphthalene ,3,6,7-Tetrachloronaphthalene ,4,6,7-Tetrachloronaphthalene ,2,5,6-Tetrachloronaphthalene ,3,6,8-Tetrachloronaphthalene ,2,3,5-Tetrachloronaphthalene ,3,5,8-Tetrachloronaphthalene ,2,3,6-Tetrachloronaphthalene ,2,3,7-Tetrachloronaphthalene ,2,3,4-Tetrachloronaphthalene ,2,6,7-Tetrachloronaphthalene ,2,4,5-Tetrachloronaphthalene ,3,6,7-Tetrachloronaphthalene ,2,4,8-Tetrachloronaphthalene ,2,5,8-Tetrachloronaphthalene ,2,6,8-Tetrachloronaphthalene ,4,5,8-Tetrachloronaphthalene ,2,3,8-Tetrachloronaphthalene ,2,7,8-Tetrachloronaphthalene ,2,3,5,7-Pentachloronaphthalene ,2,4,6,7-Pentachloronaphthalene ,2,4,5,7-Pentachloronaphthalene ,2,4,6,8-Pentachloronaphthalene ,2,3,4,6-Pentachloronaphthalene ,2,3,5,6-Pentachloronaphthalene ,2,3,6,7-Pentachloronaphthalene ,2,4,5,6-Pentachloronaphthalene ,2,4,7,8-Pentachloronaphthalene ,2,3,5,8-Pentachloronaphthalene ,2,3,6,8-Pentachloronaphthalene ,2,4,5,8-Pentachloronaphthalene ,2,3,4,5-Pentachloronaphthalene

4 878 J. Chin. Chem. Soc., Vol. 50, No. 4, 2003 Yin et al ,2,3,7,8-Pentachloronaphthalene ,2,3,4,6,7-Hexachloronaphthalene ,2,3,5,6,7-Hexachloronaphthalene ,2,3,4,5,7-Hexachloronaphthalene ,2,3,5,6,8-Hexachloronaphthalene ,2,3,5,7,8-Hexachloronaphthalene ,2,4,5,6,8-Hexachloronaphthalene ,2,4,5,7,8-Hexachloronaphthalene ,2,3,4,5,6-Hexachloronaphthalene ,2,3,4,5,8-Hexachloronaphthalene ,2,3,6,7,8-Hexachloronaphthalene ,2,3,4,5,6,7-Heptachloronaphthalene ,2,3,4,5,6,8-Heptachloronaphthalene Note: RI exp is the observed GC retention index; RI M1 and RI M2 are the calculated RI with model M1 and M2, respectively. velop a linear model that links GC RI to the structural codes (S) of the examined compounds. This regression model was given by equation (1). By applying MLR, the QSRR model, noted as model M1, covering 8 variables for all compounds in the working data set is developed with rooted mean square error (RMSE) of , correlation coefficient of R= and explained variance of EV = 98.63%, F = M1 can be represented as follows as: M1 RI S S S S S S S S8 n =62 R = RMSE = EV = 98.63% F = (2) The observed GC retention index RI exp and calculated GC retention index, RI M1, from Model M1 are listed in Table 1. In the same way, the RI M1 data are plotted against RI exp, 2800 given in Fig. 2. From Table 1 and Fig. 2, it is shown that there is no observable deviation from the normal behavior. These results indicate that the model M1 has high internal stability. In order to further validate stability of Model M1, a new model, noted model M2, was obtained by using the leaveone-out cross validation method, in which one datum was selected from the data set to be the prediction set each time and all remaining 61 values were used as the calibration set, to be developed to predict all the GC retention indices. The new model, M2, was given as follows: M2 RI S S S S S S S S8 n =62 R = RMSE = (3) The estimated and predicted GC retention index RI M2 for all polychlorinated naphthalenes with Equation (3) are 2800 RI Cal N=62 R= RI Cal N=62 R CV = RI exp Fig. 2. Correlation plot of observed versus calculated RI by M RI exp Fig. 3. Correlation plot of observed versus calculated RI by M2.

5 Predict Kovat s Indexes of Polychlorinated Naphthalenes J. Chin. Chem. Soc., Vol. 50, No. 4, also listed in Table 1 (see column RI M2 ). The calculated values (RI M2 ) are plotted against the corresponding RI exp values, given in Fig. 3. These results demonstrate that the model M2 has not only high internal stability but also prediction ability for the retention index data. This study indicates that the structural codes of the hydrocarbons have a good prediction capability to the polychlorinated naphthalenes and that the structures of the compounds are well represented by the encoding method. CONCLUSION In this study, the molecular structure of each investigated PCN can be represented by a set of simple integer numeric codes. The results also demonstrate that it is possible to use a set of simple numeric codes to represent a molecular structure and to correlate the physicochemical properties of the investigated PCNs. ACKNOWLEDGEMENT We wish to thank the National Natural Science Fund (NNSFC) and the Research Fund for the Doctoral Program of Higher Education of P.R. China for the financial support. Received November 28, REFERENCES 1. Kannan, K.; Imagawa, T.; Blankenship, A.; Giesy, J. P. Environ. Sci. Technol. 1998, 32, Marti, I.; Ventura, F. J. Chromatogr. A 1997, 786, Dorr, G.; Hippelein, M.; Hutzinger, O. Chemosphere 1996, 33, Harner, T.; Bidleman, T. Atmos. Environ. 1997, 31, Falandysz, J.; Rappe, C. Environ. Sci. Technol. 1996, 30, Falandysz, J.; Strandberg, B.; Strandberg, L.; Bergqvist, P.; Rappe, C. Sci. Total Environ. 1997, 204, Lunden, A.; Noren, K. Arch. Environ. Contam. Toxicol. 1998, 34, Donkerbroek, J.; Gooijer, C.; Velthorst, N.; Frei, R. Int. J. Environ. Anal. Chem. 1983, 15, Weistrand, C.; Jakobsson, E.; Noren, K. J. Chromatogr. B 1995, 669, Payares, P.; Diaz, D.; Olivero, J.; Vivas, R.; Gomez, I. J. Chromatogr. A 1997, 771, Kang, J.; Cao, C.; Li, Z. J. Chromatogr. A 1998, 799, Woloszyn, T.; Jurs, P. Anal. Chem. 1992, 64, Katritzky, A.; Ignatchenko, E.; Barcock, R.; Lobanov, V.; Karelson, M. Anal. Chem. 1994, 66, Olivero, J.; Kannan, K. J. Chromatogr. A 1999, 849, Cherqaoui, D.; Villemin, D. J. Chem. Sci. Faraday Trans. 1994, 90, Kaliszan, R. Quantitative Structure-Chromatographic Retention Relationships; Wiley & Sons Inc: New York, Yin, C. S.; Liu, W.; Li, Z. L.; Pan, Z. X.; Lin, T.; Zhang, M. S. J. Sep. Sci. 2001, 24, 213.