Symbolic Regression via Genetic Programming Model for Prediction of Adsorption Efficiency of some Pesticides on MWCNT/PbO2 Nanocomposite

Document Type : Research Paper

Authors

Chemometrics Laboratory, Faculty of Chemistry, University of Mazandaran, Babolsar, Iran

Abstract

The present study quantitative structure-property relationship (QSPR) model developed for the adsorption efficiency (AE) of 70 pesticides in water sample on MWCNT/PbO2 solid phase extraction cartridge. Stepwise-multiple linear regression (SW-MLR) method employed for selection of descriptors. The selected descriptors are MATS7v, MATS6c, GATS3s, ATSC6i, C040, SpMin8_Bhi, E2v, JGI1 and Mor08u. Curiosity at the effective descriptors indicates electronic, topological and geometrical characteristics of studied pesticides are the most effective parameters on their AE on MWCNT/PbO2 nanocomposite adsorbent. Symbolic regression via genetic programming (SR-GP) utilized to offer the symbolic regression QSPR model. The accuracy and predictive power of the SR-GP model compared with traditional linear and nonlinear regression models contain multiple linear regression (MLR) and support vector regression (SVR). Inspection the fitness parameters confirmed the superiority of SR-GP model over MLR, and SVR models. In SR-GP model, the correlation coefficient (R) was 0.930 and 0.890, and the root mean square errors (RMSE) were 0.04 and 0.05 for the training and test sets, respectively. These results can be used to predict the AE for other pesticides by MWCNT/PbO2 adsorbent and designing a more efficient nano cartridge for SPE.

Keywords

References

[1]       J. Cooper, H. Dobson, Crop Protection 26 (2007) 1337.
[2]       D.L. Johnson, S.H. Ambrose, T.J. Bassett, M.L. Bowen, D.E. Crummey, J.S. Isaacson, D.N. Johnson, P. Lamb, M. Saul, A.E. Winter-Nelson, J. Environ. Qual. 26 (1997) 581.
[3]       J.A. Van Leeuwen, D. Waltner-Toews, T. Abernathy, B. Smit, M. Shoukri, Int. J. Epidemiol. 28 (1999) 836.
[4]       M. Younes, H. Galal-Gorchev, Food and Chem. Toxicol. 38 (2000) S87.
[5]       A. Balinova, J. Chromatogr. A 754 (1996) 125.
[6]       A. Junker-Buchheit, M. Witzenbacher, J. Chromatogr. A 737 (1996) 67.
[7]       B. Buszewski, M. Szultka, Crit. Rev. Anal. Chem. 42 (2012) 198.
[8]       J. Hernández‐Borges. Z. Aturki. A. Rocco, S. Fanali, J. Sep. Sci. 30 (2007) 1589.
[9]       A.V. Herrera-Herrera, M.Á. González-Curbelo, J. Hernández-Borges, M.Á. Rodríguez-Delgado, Anal. Chim. Acta 734 (2012) 1.
[10]    M. Valcárcel, S. Cárdenas, B.M. Simonet, Y. Moliner-Martínez, R. Lucena, TrAC, Trends Anal. Chem. 27 (2008) 34.
[11]    Z. Yan, M. Wu, B. Hu, M. Yao, L. Zhang, Q. Lu, J. Pang, J. Chromatogr. A 1542 (2018) 19.
[12]    X. Liu, X. Lu, Y. Huang, C. Liu, S. Zhao, Talanta 119 (2014) 341.
[13]    Z. Lin, W. Cheng, Y. Li, Z. Liu, X. Chen, C. Huang, Anal. Chim. Acta 720 (2012) 71.
[14]    J. Płotka-Wasylka, N. Szczepańska, M. de la Guardia, J. Namieśnik, TrAC, Trends Anal. Chem. 77 (2016) 23.
[15]    N. Baghban, E. Yilmaz, M. Soylak, J. Mol. Liq. 234 (2017) 260.
[16]    J.M. Jiménez-Soto, S. Cárdenas, M. Valcárcel, J. Chromatogr. A 1217 (2010) 3341.
[17]    M. Roldán-Pijuán, R. Lucena, S. Cárdenas, M. Valcárcel, Microchem. J. 115 (2014) 87.
[18]    A. Gholami, F. Bahrami, M. Faraji, Trends J. Sci. Res. 3 (2018) 82.
[19]    S. Abbasi, S.A. Haeri, J. Iran. Chem. Soc. (2018) 1.
[20]    Z.M.  Memon,  E.  Yilmaz,  M.  Soylak,   Talanta  174
 
 
(2017) 645.
[21]    Z. Han, K. Jiang, Z. Fan, J.D. Di Mavungu, M. Dong, W. Guo, K. Fan, K. Campbell, Z. Zhao, Y. Wu, Food Control 79 (2017) 177.
[22]    K. Jiang, P. Huang, L. Luan, K. Fan, W. Guo, Z. Zhao, Y. Wu, Z. Han, J. Chromatogr. A 1482 (2017) 1.
[23]    N. Rastkari, R. Ahmadkhaniha, J. Chromatogr. A 1286 (2013) 22.
[24]    M.R. Hadjmohammadi, M. Peyrovi, P. Biparva, J. Sep. Sci. 33 (2010) 1044.
[25]    J. Bibby. J. Kent, K. Mardia, Academic Press, London, 1979.
[26]    O. Deeb, M. Goodarzi, Mol. Phys. 108 (2010) 181.
[27]    T.N.G. Borhani, M. Saniedanesh, M. Bagheri, J.S. Lim, Water Res. 98 (2016) 344.
[28]    E.V. Ortiz, D.O. Bennardi, D.E. Bacelo, Environ. Sci. Pollution Res. 24 (2017) 27366.
[29]    Z. Dashtbozorgi, H. Golmohammadi, E. Konoz, Microchem. J. 106 (2013) 51.
[30]    H. Lei, S.A. Snyder, Water Res. 41 (2007) 4051.
[31]    S. Sudhakaran, J. Calvin, G.L. Amy, Chemosphere 87 (2012) 144.
[32]    M.L. Magnuson, T.F. Speth, Environ. Sci. Technol. 39 (2005) 7706.
[33]    C. Bellona, K. Budgell, D. Ball, K. Spangler, J. Drewes, S. Chellam, IDA J. Desalination and Water Reuse 3 (2011) 40.
[34]    J.R. Koza, MIT Press, Cambridge, MA, 1992.
[35]    D.Q. Do, R.C. Rowe, P. York, Int. J. Pharm. 351 (2008) 194.
[36]    M. Bagheri, M. Bagheri, A.H. Gandomi, A. Golbraikh, Thermochimca Acta 543 (2012) 96.
[37]    D.İ. Koç, M.L. Koç, Chemom. Intell. Lab. Syst. 144 (2015) 122.
[38]    M.H. Fatemi, Z.P. Yali, Eurasian. J. Anal. Chem. 12 (2017) 1001.
[39]    S. Ghasemi, H. Karami, H. Khanezar, J. Mater. Sci. 49 (2014) 1014.
 
 
 
 
 
 
 
[40]    HyperChem, HyperCube (version 2002). http://www.hyper.com
[41]    C.W. Yap, J. Comput. Chem. 32 (2011) 1466.
[42]    R. Todeschini, V. Consonni, Milano, Italy, 2003.
[43]    Y. Ren. Y. Zhang, X. Yao, Liq. Cryst. 45 (2018) 238.
[44]    P. Barmpalexis, K. Kachrimanis, A. Tsakonas, E. Georgarakis, Chemom. Intell. Lab. Syst. 107 (2011) 75.
[45]    K. Hennessy, M.G. Madden, J. Conroy, A.G. Ryder, Knowledge-Based Systems 18 (2005) 217.
[46]    B. Hrnjica, GPdotNET-Artificial Intelligence Tool, Cambridge, MA, 2013.
[47]    J.R. Koza, Statistics and Computing 4 (1994) 87.
[48]    R.C. Geary, The Incorporated Statistician 5 (1954) 115.
[49]    R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, John Wiley & Sons, USA, 2008.
[50]    A. Mauri, V. Consonni, M. Pavan, R. Todeschini, MATCH Commun. Math. Comput. Chem. 56 (2006) 237.
[51]    R. Todeschini, P. Gramatica, SAR and QSAR in Environ. Res. 7 (1997) 89.
[52]    F. Yan, W. He, Q. Jia, Q. Wang, S. Xia, P. Ma, Chem. Eng. Sci. 184 (2018) 134.
[53]    R. Koza, R. Poli, "Genetic programming." In Search Methodologies, Springer, Boston, MA, 2005.
[54]    W.B. Langdon, R. Poli, Foundations of Genetic Programming, Springer Science & Business Media, Germany, 2013.
[55]    R. Todeschini, (2010, August 12), Useful and Unuseful Summaries of Regression Models, Milano Chemometrics and QSAR Research Group, http://www.moleculardescriptors.eu./tutorials/tutorials.htm.
[56]    A.C.P. Atkinson, Transformations, and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis, Oxford University Press, New York, 1985.
[57]    A. Atkinson, Statis. Sci. 1 (1986) 397.
 
 
Analytical and Bioanalytical Chemistry Research
Volume 8, Issue 1
January 2021
Pages 65-77

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