Document Type: Original Research Article

Authors

1 Department of Chemistry, Payame Noor University, P.O. BOX 19395-4697, Tehran, Iran

2 Department of Nanotechnology, Bharath University,BIHER Research Park, Chennai, Tamil Nadu 600073, India

3 Department of Chemical and Materials Engineering, College of Engineering, National College of Chemical Industry, Nancy, Polytechnic Institute of Lorraine, France Frankfurt Am Main Area, Germany.

Abstract

A quantitative structure–retention relation (QSRR) study was conducted on the retention times of 160 pesticides and 25 environmental organic pollutants in wine and grape. The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between selected molecular descriptors and retention time was achieved by linear (partial least square; PLS) and nonlinear (kernel PLS: KPLS and Levenberg-Marquardt artificial neural network; L-M ANN) methods. The QSRR models were validated by cross-validation as well as application of the models to predict the retention of external set compounds, which did not have contribution in model development steps. Linear and nonlinear methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R2 value (determination coefficient between observed and predicted values) for all compounds, which was superior to those of other statistical models. This is the first research on the QSRR of the compounds in wine and grape against the retention time using the GA-KPLS and L-M ANN.

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References

[1] B. Rankine, Making Good Wine: A Manual of Winemaking Practice for Australia and New Zealand, Sun Pan Macmillan, Australia, Sydney, 1995.

[2] M.J. Torija, N. Rozes, M. Poblet, J.M. Guillamon, A. Mas, Antonie van Leeuwenhoek., 2001, 79, 345-352.

[3] K. Hilscherova, M. Machala, K. Kannan, A.L. Blankenship, J.P. Giesy, Environ Sci Pollut Res., 2000, 7, 159-171.

[4] J.C. Fetzer, Polycycl. Aromat. Compd., 2000, 27, 143.

[5] J. Zrostlıkova, J. Hajslova, T. Cajka, J. Chromatogr. A., 2003, 1019, 173-182.

[6] M. Adahchour, J. Beens, R.J.J. Vreuls, A. Max Batenburg, U.A.Th. Brinkman, J. Chromatogr. A., 2004, 1054, 47-54.

[7] J.W. Wong, M.K. Hennessy, D.G. Hayward, A.J. Krynitsky, I. Cassias, F.J. Schenck, J. Agric. Food Chem., 2007, 55, 1117-1124.

[8] J.L.M. Vidal, F.J.A. Liebanas, M.J.G. Rodrıguez, A.G. Frenich, J.L.F. Moreno, Rapid Commun. Mass Spectrom., 2006, 20, 365-361.

[9] R. Put, Y. Vander Heyden, Anal. Chim. Acta., 2007, 602, 164-172.

[10] R. Kaliszan, Structure, Retention in Chromatography. A Chemometric Approach, Harwood Academic Publishers, Amsterdam, 1997.

[11] H. Noorizadeh, A. Farmany, Chromatographia., 2010, 72, 563-569.

[12] H. Noorizadeh, A. Farmany, Drug Test Anal, 2012., 4, 151-157.

[13] S. Dasgupta, K. Banerjee, S.H. Patil, M. Ghaste, K.N. Dhumal, P.G. Adsule, J. Chromatogr. A., 2010, 1217, 3881-3889.

[14] R. Todeschini, V. Consonni, A. Mauri, M. Pavan., DRAGON-Software for the calculation of molecular descriptors; Version 3.0 for Windows, 2003.

[15] S. Ahmad, M.M. Gromiha, J. Comput. Chem., 2003, 24, 1313-1320.

[16] S. Kara, A.S. Güven, M. Okandan, F. Dirgenali., Comput. Biol. Med., 2008, 36, 473-483

[17] P. Ghosh, M. Vracko, A.K. Chattopadhyay, M.C. Bagchi, Chem Biol Drug Des., 2008, 72, 155-162.

[18] R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, Wiley/VCH, Weinheim, 2000.

[19] P. Thanikaivelan, V. Subramanian, J.R. Rao, B.U. Nair, Chem. Phys. Lett., 2000, 323, 59-64.

[20] M.M. Heravi, H. Abdi Oskooie, Z. Latifi, H. Hamidi, Adv. J. Chem. A, 2018, 1, 7-11.

[21] A. Moghimi, M. Yari, J. Chem. Rev., 2019, 1, 1-18.