Computer-assisted methods useful for the modeling of phenolic dyes wavelengths (λmax) using MLR and ANN methods

N. Bouarra, N. Nadji, L. Nouri, A. Boudjemaa, K. Bachari, D. Messadi


Abstract: In this work, a quantitative structure-property relationship (QSPR) was built by using multiple linear regression (MLR) and artificial neural networks (ANN) to  predict the wavelengths (λmax)of phenolic dyes. After many procedures to reduce the number of descriptors, a hybrid genetic algorithm and multiple linear regression (GA/MLR) method was used to select the descriptors that resulted in the best fitted models. The statistical parameters of the MLR model (R² = 89.01 %, Q²LOO = 85.39 %, s = 24.763) showed a good predictive capacity of λmax. The comparison between statistical parameters obtained by MLR and ANN models indicates the superiority of the ANN over that the MLR model, which illustrates that the ANN method is an excellent alternative for developing QSPR models for λmax than MLR method.

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Oze, A.; Akkaya, G.; Turabik M. The biosorption of acid red 337 and acid blue 324 on enteromorpha prolifera: The application of non linear regression analysis to dye biosorption. Journal Chemical Engineering 112 (2005) 181-190.

Jaikumar, V.; Kumar, K. S.; Prakash D. G. Biosorption of acid dyes using spent brewery grains : Characterization and modeling. Journal of Applied Science and Engineering 7 (2009) 115-125.

Gupta, V. K.; Kumar, R.; Nayak, A.; Saleh, T.A.; Barakat, M.A. Adsorptiv removal of dyes from aqueous solution onto carbon nanotubes: a review. Journal Colloid and Interface Science 193-194 (2013) 24-33.

Sapra, N. Treatment reuse of textile wastewater by overland flow. Journal Desalination 106 (1996) 179-182.

Sharma,B.k. Fuel and petroleum processing, 1st ed.; Krishna prakashan media(P) LTD, Meerut, India, 1998; p131.

Welham, A. Theory of dyeing (and the secret of life). Journal of the Society of Dyers and Colourists 116 (2000) 140-143.

Atabati, M.; Khandani, F. Ant colony optimization as a descriptor selection in QSPR modeling for prediction of λmax of azo dyes. Journal Chinese Chemical Letters 23 (2012) 1209-1212.

Ezeokonokwo, M.A.; Okoro, U.C. 3D-QSPR method of computational technique applied on red reactive dyesby using CoMFAstrategy, International Journal of Molecular Sciences 12 (2011) 8862-8877.

Robinson, T.; McMullan, G.; Marchant, R.; Nigam, P. Remediation of dyes in textile effluent: a critical review on current treatment technologies with a proposed alternative. Journal Bioresource Technology 77 (2001) 247-255.

Tunç, O.; Tanaci, H.; Aksu, Z. Potential use of cotton plant wastes for the removal of Remazol Black B reactive dye. Journal of Hazardous Materials 163 (2009) 187-198.

Auta, M.; Hameed, B.H. Preparation of waste tea activated carbon using potassium acetate as an activating agent for adsorption of acid blue 25 dye. Chemical Engineering Journal 171, (2011) 502-509.

Sen, T.K.; Afroze, S.; Ang H. Equilibrium, kinetics and mechanism of removal of methylene blue from aqueous solution by adsorption onto pine cone biomass of Pinus radiata. Journal Water, Air & Soil Pollution 218 (2011) 499-515.

Rangabhashiyam, S.; Anu, N.; Selvaraju, N. Sequestration of dye from textile industry wastewater using agricultural waste products as adsorbents. Journal of Environmental Chemical Engineering 1 (2013) 629-641.

Gupta, V. K.; Mittal, A.; Malviya, A.; Mittal, J. Adsorption of carmoisine A from wastewater using waste materials-Bottom ash and deoiled soya. Journal of Colloid and Interface Science 335 (2009) 24-33.

Suyambo, K.B.; Perumal, R.S. Equilibrium, Thermodynamic and kinetic studies on Adsorption of a basic dye by Citrullus Lanatus Rind. Journal Energy and Environment 3 (2012) 23-34.

Forgacs, E.; Cserhati, T.; Oros Gyula. Removal of synthetic dyes from wastewaters: a review. Journal Environment International 30 (2004) 953-971.

Gupta, V.K.; Suhas. Application of low-cost adsorbents for dye removal: a review. Journal of environmental management 90 (2009) 2313-2342.

Ghaly AE.; Ananthashankar R.; Alhattab M.; Ramakrishnan. Production, characterization and treatment of textile effluents: a critical review. Journal Chemical Engineering and Process Technology 5 (2014) 2-18.

Demirbas, A. Agricultural based activated carbons for the removal of dyes from aqueous solution:A review. Journal of Hazardous Materials 167 (2009) 1-9.

Hameed, B.H.; El-Khaiary, M.I. Removal of basic dye from aqueous medium using a novel agricultural waste material: Pumpkin seed hull. Journal of Hazardous Materials 155 (2008) 601-609.

Hao, J.; Kim, H.; Chiang, P. C. Decolorization of wastewater. Critical Reviews in Environmental Science and Technology 30 (2000) 449-505.

Yagub, M. T.; Sen, T. K.; Afroze, S.; Ang, H.M. Dye and its removal from aqueous solution by adsorption: A review. Journal of Advances in Colloid Interface science. 209 (2014) 172-184.

Xu, J.; Zheng, Z.; Chen, B.; Zhang, Q. A Linear QSPR model for prediction of maximum absorption wavelength of second-order NLO chromophores. QSAR & Combinator Science 25 (2006) 372-379.

Luan, F.; Xu, X.; Liu, H.; Cordeiro, M.N.D. Review of quantitative structure-activity/proprety relationship studies of dyes: recent advances and perspectives. Society of Dyes and Colourists, Coloration Technology 129 (2013) 173-186.

Xu, Y.; Chen, X.Y.; Li, Y.; Ge, F.; Zhu, R.L. Quantitative structure-property relationship (QSPR) study for the degradation of dye wastewater by Mo-Zn-Al-O catalyst. Journal of Molecular liquids 215 (2016) 461-466.

Pinheiro, L.M.V.; Ventura, M.C.M.M.; Li, Y.; Moita, M.L.C.J. Application of QSPR/MLR methodology to solvatochromic behavior of quinoline in binary solvent HBD/DMF mixtures. Journal of Molecular liquids 154 (2010) 102-110,.

Nikolova, N.; Jaworska, J. Approaches to measure chemical similarity: a Review. QSAR & Combinatorial Science 22 (2003) 1006-1026.

Venkatraman, V.; Alsberg, B.K.; Pinheiro, L.M.V. A quantitative structure-property relationship study of the photovoltaic performance of phenothiazine dyes. Journal of Dyes and Pigments 114 (2015) 69-77.

Hansch, C.; Hoekman, D.; Leo, A.; Weininger, D.; Selassie, C.D. Chem-Bioinformatics: Comparative QSPR at the interface between chemistry and biology. Chemical Reviews 102 (2002) 783-812.

Pasha, F.A.; Muddassar, M.; Chung, H.W.; Cho, S.J.; Cho H. Hologram and 3D-quantitative structure toxicity relationship studies of azo dyes. Journal of Molecular Modeling 14 (2008) 293-302.

Ding, G.H.; Li, X.; Zhang, F.; Chen, J.W.; Huang, L.P.; Qiao, X.L. Mechanism-based quantitative structure-activity relationships on toxicity of selected herbicides to Chlorella vulgaris and Raphidocelis subcapitata. Bulletin of Environmental Contamination and Toxicology 83 (2009) 520-524.

Gadaleta, D.; Mangiatordi, G. F.; Catto, M.; Carotti, A.; Nicolotti, O. Applicability Domain for QSAR Models: Where Theory Meets Reality. International Journal of Quantitative Structure-Property Relationships 1 (2016) 45-63.

Yao, X.J.; Panaye, A.; Doucet, J.P.; Zhang, R.S.; Chen, H.F.; Liu, M.C.; Hu, Z.D.; Fan, B.T. Comparative study of QSAR/QSPR correlations using support Vector Machines, Radial Basis function Neural Networks, and Multiple Linear Regression. Journal of Chemical Information and Computer Sciences. Comput. Sci 44 (2004) 1257-1266.

Katritzky, A.R.; Lobanov, V.S.; Karelson, M. QSPR: The correlation and quantitative prediction of chemical and physical properties from structure. Chemical Society Reviews 24 (1995) 279-287.

Taskinen, J.; Yliruusi, J. Prediction of physicochemical properties based on neural network modelling. Advanced drug delivery reviews 55 (2003) 1163-1183.

Livingstone, D.J.; Manallack, D.T. Neural Networks in 3D QSAR. QSAR & Combinatorial Science 22 (2003) 510-518.

Leardi, R. Genetic algorithms in chemometrics and chemistry: a review. Journal of Chemometrics 15 (2001) 559-569.

Katritzky, A.R.; Kuanar, M.; Slavov, S.; Hall, D. Quantitative correlation of physical and chemical properties with chemical structure: utility for prediction. Chemical Society Reviews 110 (2010) 5714-5789.

Banchero, M.; Manna, L. Comparative between multilinear and radical basis function Neural Network based QSPR models for the prediction of the critical temperature, critical pressure and acentric factor of organic compounds. Molecules 23 (2018) 1379-1391.

Rappoport, Z. The Chemistry of phenols.; John Wiley & Sons, Chichester, England ,2004; pp921-924.

ChemDraw Utra “Ultra-chemical structure drawing standard”. Version 7. 2002. Copyright CambridgeSoft Coperation.

HyperChem Pro. Molecular Modeling system.Version 8. 2008. Copyright Hypercube, Inc.

Todeschini, R.; Consonni, V.; Pavan, M.; DRAGON, Version 4.5, 2005, Copyright TALETE srl.

Liu, H.; Gramatica, P. QSPR study of selective ligands for the thyroid hormone receptor beta. Bioorganic & medicinal chemistry 15 (2007) 5251-5261.

Organization for Economic Cooperation and Development, Guidance Document on the Validation of Quantitative Structure Activity Relationships (QSPR) Models, ENV/JM/MONO, OECD Publishing, Paris, 2 (2007).

Todeschini, R.; Ballabio, D.; Consonni, V.; Mauri, A.; Pavan, M. MOBYDIGS Software for Multilinear Regression Analysis and variable Subset Selection by Genetic Algorithm. Release I.1 for Windows, Milano, 2009.

Leardi, R.; Boggia, R.; Terrible, M. Genetic algorithms as a strategy for feature selection. Journal of Chemometrics 6 (1992) 267-281.

Liu, P.; Long, W. Current mathematical methods used in QSPR/QSPR studies. Intnational Journal of Molecular Sciences 10 (2009) 1978-1998.

Todeschini, R.; Consonni, V.; Maiocchi, A. The K correlation index : theory development and its application in chemomerics. Chemometrics and Inteligent Laboratory Systems 46 (1999) 13-29.

Golbraikh, A.; Tropsha, A. Beware of q2!. Journal of molecular graphics and modeling 20 (2002) 269-276.

Li, J.; Gramatica, P. The importance of molecular structures, endpoints’ values, and predictivity parameters in QSPR research: QSPR analysis of a series of estrogen receptor binders. Molecular diversity 14 (2010) 687-696.

Tropsha, A.; Gramatica, P.; Gombar, V. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Combinatorial Science 22 (2003) 69–77.

Warren, S.M.; Walter, H. P. A logical calculus at the ideas immanent in Nervous Activity. Bulletin of mathematical Biophysics 5 (1943) 115-13.

Minsky, M.; Papert S. Perceptrons:An Introduction to Computational Geometry.; MIT Press: Cambridge, MAS, USA, 1969; pp.16-19.

J. Zupan, J. Gasteiger, Neural Networks in Chemistry and Drug Design, WileyVCH, Weinheim, 1999.

Svozil, D; Kvasnicka, V; Pospichal. Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems 39 (1997) 43–62.

Veerasamy, R.; Rajak; H.; Jain A.; Sivadasan, S.; P, Christapher.; Ram, V.; A, Kishore. Validation of QSAR models-strategies and importance. International Journal of Drug Design and Discovery 2 (2011) 511–519.

Atabati, M.; Zarei, K.; Mohsennia, M. Prediction of λmax of 1,4-naphthoquinone derivatives using ant colony optimization. Analytica Chimica Acta 663 (2010) 7-10.

Todeschini, R.; Cosonni, V. Molecular Descriptors for Chemoinformatics. Wiley-VCH, Weinheim, Germany, 2009; p. 1257.

Bordás, B.; Bélai, I.N.; Kőmíves, T.S.; Theoretical molecular descriptors relevant to the uptake of persistent organic pollutants from soil by Zucchini. A QSAR study. Journal of Agricultural and Food Chemistry 59 (2011) 2863–2869.


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