Quantitative structure–retention relationship model for predicting retention indices of constituents of essential oils of Thymus vulgaris (Lamiaceae) (Short communication)

Youssouf Driouche, Djelloul Messadi


In this paper, a quantitative structure–retention relationship (QSRR) model was developed for predicting the retention indices (log RI) of 36 con­stituents of essential oils. First, the chemical structure of each compound was sketched using HyperChem software. Then, molecular descriptors covering dif­fer­ent information of molecular structures were calculated by Dragon software. The results illustrated that linear techniques, such as multiple linear regression (MLR), combined with a successful variable selection procedure are capable of generating an efficient QSRR model for predicting the retention indices of different compounds. This model, with high statistical significance (R2 = 0.9781, Q2LOO = 0.9691, Q2ext = 0.9546, Q2L(5)O = 0.9667, F = 245.27), could be used adequately for the prediction and description of the retention indices of other essential oil compounds. The reliability of the proposed model was further illus­trated using various evaluation techniques: leave-5-out cross-validation, boot­strap, randomization test and validation through the test set.


essential oils; retention indices; QSRR; multiple linear regression; Thymus vulgaris (Lamiaceae).


S. Burt, Int. J. Food Microbiol. 94 (2004) 223 (https://doi.org/10.1016/j.ijfoodmicro.2004.03.022)

S.-T. Chang, P.-F. Chen, S.-C. Chang, J. Ethnopharmacol. 77 (2001) 123 (https://doi.org/10.1016/S0378-8741(01)00273-2)

D. Kalemba, A. Kunicka, Curr. Med. Chem. 10 (2003) 813 (https://doi.org/10.2174/0929867033457719)

C. L. Wilson, J. M. Solar, A. El Ghaouth, M. E. Wisniewski, Plant Dis. 81 (1997) 204 (https://doi.org/10.1094/PDIS.1997.81.2.204)

M. Burits, F. Bucar, Phytother. Res. 14 (2000) 323 (https://doi.org/10.1002/1099-1573(200008)14:5<323::AID-PTR621>3.0.CO;2-Q)

P. H. Warnke, E. Sherry, P. A. J. Russo, Y. Acil, J. Wiltfang, S. Sivananthan, M. Sprengel, J. C. Roldàn, S. Schubert, J. P. Bredee, I. N. G. Springer, Phytomedicine 13 (2006) 463 (https://doi.org/10.1016/j.phymed.2005.09.012)

L.-T. Qin, S.-S. Liu, F. Chen, Q.-F. Xiao, Q.-S. Wu, Chemosphere 90 (2013) 300 (https://doi.org/10.1016/j.chemosphere.2012.07.010)

M. Rahimi, H. Farahbakhsh, N. Salehi, M. Nekoei, Int. J. Adv. Appl. Sci. 1 (2012) 91 (https://www.iaescore.com/journals/index.php/IJAAS/article/view/775)

S. Riahi, E. Pourbasheer, M. R. Ganjali, P. Norouzi, J. Hazard. Mater. 166 (2009) 853 (https://doi.org/10.1016/j.jhazmat.2008.11.097)

L. Liao, D. Qing, J. Li, G. Lei, J. Mol. Struct. 975 (2010) 389 (https://doi.org/10.1016/j.molstruc.2010.05.017)

H. Noorizadeh, A. Farmany, Chromatographia 72 (2010) 563 (https://doi.org/10.1365/s10337-010-1660-4)

H. Noorizadeh, A. Farmanya, A. Khosravi, J. Chin. Chem. Soc. 57 (2010) 982 (https://doi.org/10.1002/jccs.201000137)

H. Noorizadeh, A. Farmany, M. Noorizadeh, Quim. Nova 34 (2011) 242 (http://dx.doi.org/10.1590/S0100-40422011000200014)

P. A. Azar, M. Nekoei, R. Siavash, M. R. Ganjali, K. Zare, J. Serb. Chem. Soc. 76 (2011) 891 (https://doi.org/10.2298/JSC100219076A)

R. F. Teofilo, J. P. A. Martins, M. M. C. Ferreira. J. Chemom. 23 (2009) 32 (https://doi.org/10.1002/cem.1192)

OECD, Guidance Document on the Validation of (Quantitative) Structure–Activity Relationship [(Q)SAR] Models, Paris, 2007 (https://doi.org/10.1787/9789264085442-en)

M. B. P. Zanousi, M. Nekoei, M. Mohammadhosseini. J. Essent. Oil-Bear. Plants 20 (2017) 672 (https://doi.org/10.1080/0972060X.2017.1329669)

F. Conforti, F. Menichini, C. Formisano, D. Rigano, F. Senatore, N. A. Arnold, F. Piozzi, Food. Chem. 116 (2009) 898 (https://doi.org/10.1016/j.foodchem.2009.03.044)

A. M. Al-Fakih, Z. Y. Algamal, M. H. Lee, M. Aziz, SAR QSAR Environ. Res. 28 (2017) 691 (http://dx.doi.org/10.1080/1062936X.2017.1375010)

Y. Marrero-Ponce, S. J. Barigye, M. E. Jorge-Rodriguez, T. Tran-Thi-Thu, Chem. Pap. 72 (2018) 57 (https://doi.org/10.1007/s11696-017-0257-x)

A. Nezhadali, M. Nabavi, M. Rajabian, M. Akbarpour, P. Pourali, F. Amini, Beni-Seuf Univ. J. Appl. Sci. 3 (2014) 87 (https://doi.org/10.1016/j.bjbas.2014.05.001)

HyperChemTM. Release 6.02 for Windows, Molecular Modeling system, 2000 (http://www.hyper.com/)

TALETE srl, Dragon (Software for Molecular Descriptors Calculation) version 6.0, 2011 (http://www.talete.mi.it/)

E. Benfenati, J. R. Chrétien, G. Gini, N. Piclin, M. Pintore, A. Roncaglioni, Validation of the models, in Quantitative Structure–Activity Relationships (QSAR) for Pesticide Regulatory Purposes, Elsevier, Amsterdam, 2007, pp. 185–199 (https://doi.org/10.1016/B978-044452710-3/50008-2)

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

R. Todeschini, D. Ballabio, V. Consonni, A. Mauri, M. Paven, MobyDigs, version 1.1, Copyright TALETE srl, 2009 (http://www.talete.mi.it/)

J. Xu, H. Zhang, L. Wang, G. Liang, L. Wang, X. Shen, W. Xu, Spectrochim. Acta, Part A 76 (2010) 239 (https://doi.org/10.1016/j.saa.2010.03.027)

L. Eriksson, J. Jaworska, A. P. Worth, M. T. D. Cronin, R. M. McDowell, P. Gramatica, Environ. Health Perspect. 111 (2003) 1361 (https://www.ncbi.nlm.nih.gov/pubmed/12896860)

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

H. Kubinyi, F. A. Hamprecht, T. Mietzner, J. Med. Chem. 41 (1998) 2553 (https://doi.org/10.1021/jm970732a)

A. Golbraikh, A. Tropsha, J. Mol. Graphics Modell. 20 (2002) 269 (https://doi.org/10.1016/S1093-3263(01)00123-1)

B. Efron. The Jackknife, the Bootstrap and Other Resampling Plans, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1982 (http://dx.doi.org/doi:10.1137/1.9781611970319)

M. Shen, C. Béguin, A. Golbraikh, J.P. Stables, H. Kohn, A. Tropsha, J. Med. Chem. 47 (2004) 2356 (https://pubs.acs.org/doi/abs/10.1021/jm030584q)

R. Kaliszan, Quantitative structure–chromatographic retention relationships, Wiley, New York, 1987 (https://www.osti.gov/biblio/6478095).

DOI: https://doi.org/10.2298/JSC180817010D

Copyright (c) 2019 J. Serb. Chem. Soc.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

IMPACT FACTOR 0.797 (139 of 171 journals)
5 Year Impact Factor 0.923 (134 of 171 journals)