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

Article Sidebar

PDF (2,150 kB) Supplementary Material (747 kB)
Published: Apr 28, 2019
DOI: https://doi.org/10.2298/JSC180817010D
Keywords:
essential oils retention indices QSRR multiple linear regression Thymus vulgaris (Lamiaceae).

Main Article Content

Youssouf Driouche
Environmental and Food Safety Laboratory, Badji Mokhtar-Annaba University, BP.12, 23000 Annaba
https://orcid.org/0000-0001-8024-407X
Djelloul Messadi
Environmental and Food Safety Laboratory, Badji Mokhtar-Annaba University, BP.12, 23000 Annaba

Abstract

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.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
[1]
Y. Driouche and D. Messadi, “Quantitative structure–retention relationship model for predicting retention indices of constituents of essential oils of Thymus vulgaris (Lamiaceae) (Short communication)”, J. Serb. Chem. Soc., vol. 84, no. 4, pp. 405–416, Apr. 2019.
Issue
Vol. 84 No. 4 (2019)
Section
Theoretical Chemistry

Creative Commons лиценца
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution license 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

Read more....

Crossref
Scopus
Google Scholar
Europe PMC

References

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).