Predicting retention indices of PAHs in reversed-phase liquid chromatography: Quantitative structure retention relationship approach

Main Article Content

Nabil Bouarra
https://orcid.org/0000-0001-5438-8678
Nawel Nadji
Loubna Nouri
Amel Boudjemaa
Khaldoun Bachari
https://orcid.org/0000-0003-0624-8480
Djelloul Messadi
https://orcid.org/0000-0003-3257-9590

Abstract

In this work, the liquid chromatography retention time in monomeric and polymeric stationary phases of PAHs was investigated. Quantitative struc­ture retention relationship approach has been successfully performed. At first, 3224 molecular descriptors were calculated for the optimized PAHs structure using Dragon software. Afterwards, the modelled dataset was divided using the CADEX algorithm into two subsets for internal and external validation. The genetic algorithm-based on a multiple linear regression was used for feature selection of the most significant descriptors and the model development. The selected models with five descriptors: nCIR, GGI3, GGI4, JGT and DP14 were used for the monomeric column and nR10, EEig01x, L1m, H5v and HATS6v were introduced for the polymeric column. Robustness and predictive perform­ance of the suggested models were verified by both internal and external sta­tis­tical validation. The good quality of the statistical parameters indicates the sta­bility and predictive power of the suggested models. This study demonstrated the suitability of the established models in the prediction of liquid chromato­graphic retention indices of PAHs.

Article Details

How to Cite
[1]
N. Bouarra, N. Nadji, L. Nouri, A. Boudjemaa, K. Bachari, and D. Messadi, “Predicting retention indices of PAHs in reversed-phase liquid chromatography: Quantitative structure retention relationship approach”, J. Serb. Chem. Soc., vol. 86, no. 1, pp. 63-75, Jan. 2021.
Section
Theoretical Chemistry

References

M. Pogorzelec, K. Piekarska, Sci. Total Environ. 631 (2018)1431 (https://dx.doi.org/10.1016/j.scitotenv.2018.03.105)

H. I. Abdel-Shafy, M. S. M. Mansour, Egypt. J. Petrol. 25 (2016) 107 (https://dx.doi.org/10.1016/j.ejpe.2015.03.011)

N. E. Kaminski, B. L. Faubert Kaplan, M. P. Holsapple, Casarett and Doull’s Toxicology, the basic science of poisons, C. D. Klaassen (Ed.), Mc-Graw Hill, Inc., New York, 2008, p. 1280 (ISBN: 978-0071470513)

R. Put, Y. Vander Heyden, Anal. Chim. Acta 602(2007) 164 (https://dx.doi.org/10.1016/j.aca.2007.09.014)

K. D. Bartle,M. L Lee,S. A. Wise, Chem. Soc. Rev. 10 (1981) 113 (https://dx.doi.org/10.1039/CS9811000113).

EPA Test Method, Polynuclear Aromatic Hydrocarbons- Method 610, US Environmental Protection Agency, Environmental Monitoring and Support Laboratory, 1982 (https://www.epa.gov/sites/production/files/2015-10/documents/method_610_1984.pdf)

R. Kaliszan, Chem. Rev. 107 (2007) 3212 (https://dx.doi.org/10.1021/cr068412z)

N.Goudarzi, D. Shahsavani,F. Emadi-Gandaghi, M. Arab Chamjangali, J. Chromatogr., A 1333 (2014) 25 (https://dx.doi.org/10.1016/j.chroma.2014.01.048)

M. M. C. Ferreira, Chemosphere 44 (2001) 125 (https://dx.doi.org/10.1016/S0045-6535(00)00275-7)

F. A. L. Ribeiro, M. M. C. Ferreira, J. Mol. Struct.: Theochem. 663 (2003) 109 (https://dx.doi.org/10.1016/j.theochem.2003.08.107 )

T. Moon, M. W. Chi, S. J. Park, C. N. Yoon, J. Liq. Chromatogr. Rel. Technol. 26 (2003) 2987 (https://dx.doi.org/10.1081/JLC-120025413)

K. A. Lippa, L. C. Sander, S. A. Wise, Anal. Bioanal. Chem. 378 (2004) 365 (https://dx.doi.org/10.1007/s00216-003-2419-7)

L. C. Sander, S. A.Wise, J. Chromatogr. Libr. 57(1995) 337 (https://dx.doi.org/10.1016/S0301-4770(08)60622-3)

M. Popl, V. Dolansky,J.Mostecky, J. Chromatogr. 117 (1976) 117 (https://doi.org/10.1016/S0021-9673(00)81072-9)

National institute of standards and technology, https://webbook.nist.gov/chemistry/

A. D. Becke, J. Chem. Phys. 98 (1993) 5648 (https://dx.doi.org/10.1063/1.464913)

TaleteSrl. Dragon for windows (Software for Molecular Descriptor Calculation), version 5.5, Milano, 2007 (software available at: http://www.talete.mi.it)

R. W. Kennard, L. A. Stone, Technometrics 11 (1969) 137 (https://dx.doi.org/10.1080/00401706.1969.10490666)

P. Gramatica, N. Chirico, E. Papa, S. Cassani, S. Kovarich, QSARINS, Software for the Development and validation of QSAR MLR Models (available on request at http://www.qsar.it)

R. Todeschini, A. Maiocchi, V. Consonni, Chemometr. Intell. Lab. Sys. 46 (1999) 13 (https://dx.doi.org/10.1016/S0169-7439(98)00124-5)

P. Gramatica, QSAR Comb. Sci. 26 (2007) 694 (https://dx.doi.org/10.1002/qsar.200610151)

N. Chirico, P. Gramatica, J. Chem. Inf. Model. 51 (2011) 2320 (https://dx.doi.org/10.1021/ci200211n)

D. W. Osten. J. Chemometr. 2 (1998) 39 (https://dx.doi.org/10.1002/cem.1180020106)

N. Chirico, P. Gramatica, J. Chem. Inf. Model. 52 (2012) 2044 (https://dx.doi.org/10.1021/ci300084j)

R. Kiralj, M. M. C. Ferreira, J. Braz. Chem. Soc. 20 (2009) 770 (https://dx.doi.org/10.1590/S0103-50532009000400021)

P. Gramatica, Mol. Inf. 33 (2014) 311 (https://dx.doi.org/ 10.1002/minf.201400030)

G. Schüürmann, R. Ebert, J. Chen, B. Wang, R. Kühne. J. Chem. Inf. Model. 48 (2008) 2140 (https://doi.org/10.1021/ci800253u)

V. Consonni, D. Ballabio, R. Todeschini, J. Chem. Inf. Model. 49 (2009) 1669 (https://dx.doi.org/10.1021/ci900115y)

V. Consonni, D. Ballabio, R. Todeschini, J. Chemometr. 24 (2010) 194 (https://dx.doi.org/10.1002/cem.1290)

L. I. Lin, Biometrics 45 (1989) 255 (https://dx.doi.org/10.2307/2532051)

A. O. Aptula, N. G. Jeliazkova, T. W. Schultz, M. T. D. Cronin, QSAR Comb. Sci. 24 (2005) 385 (https://dx.doi.org/10.1002/qsar.200430909)

A. Tropsha, P. Gramatica, V. K. Gombar, QSAR Comb. Sci. 22 (2003) 69 (https://dx.doi.org/10.1002/qsar.200390007)

L. Eriksson, J. Jaworska, A. P. Worth, M. T. D. Cronin,R. M. McDowell, P. Gramatica. Environ. Health Perspect. 111 (2003)1361(https://dx.doi.org/10.1289/ehp.5758)

S. Kherouf, N. Bouarra,A. Bouakkadia, D. Messadi, J. Serb. Chem. Soc. 84 (2019) 575 (https://dx.doi.org/10.2298/JSC180820016K)

S. Chatterjee, A. Hadi, B. Price, Regression Analysis by Examples, Wiley-VCH, New York, 2000, p. 368 (ISBN-13: 978-0471319467)

V. Consonni, R. Todeschini, M. Pavan. J. Chem. Inf. Comput. Sci. 42 (2002) 682 (https://dx.doi.org/10.1021/ci015504a)

R. Todeschini, P. Gramatica, Quant. Struct.‐Act. Relat. 16 (1997) 113 (https://dx.doi.org/10.1002/qsar.19970160203)

R. Todeschini, V. Consonni, Molecular Descriptors for Chemoinformatics, Wiley-VCH, New York, 2009, p.1257 (ISBN-13: 978-3527318520)

J. Galvez, R. Garcia-Domenech, J. V. de Julian-Ortiz, R. Soler, J. Chem. Inf. Comput. Sci. 35 (1995) 272(https://dx.doi.org/10.1021/ci00024a017)

M. Randic, G. Krilov, Chem. Phys. Lett. 272 (1997) 115 (https://dx.doi.org/10.1016/S0009-2614(97)00447-8).

Most read articles by the same author(s)