EXPERIMENTAL STUDY AND HIGH DIMENSIONAL QSAR MODELLING OF PHENYLPROPANOIDS OF ALPINIA GALANGA AS CORROSION INHIBITORS ON MILD STEEL

Authors

  • Sunday O. Ajeigbe Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Norazah Basar Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Zakariya Y. Algamal Department of Statistics and Informatics, College of Computer Science and Mathematics, University of Mosul, Iraq
  • Muhammad H. Lee Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Hasmerya Maarof Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Madzlan Aziz Advanced Membrane Technology Centre, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v79.9850

Keywords:

Corrosion inhibitor, potentiodynamic polarisation, phenylpropanoids, Alpinia galanga, high dimensional QSAR, penalised multiple linear regression (PMLR)

Abstract

Plant extracts as corrosion inhibitors have been extensively investigated and are found as an alternative to synthetic organic compounds. The corrosion inhibition of mild steel in 1 M HCl by 15 compounds comprising of five phenylpropanoids from Alpinia galanga and other related compounds was explored experimentally using potentiodynamic polarisation procedures. The inhibition efficiencies determined experimentally for the various inhibitors were used in the Quantitative Structure-Activity Relationship (QSAR) study with their molecular descriptors calculated using Dragon software. Penalised multiple linear regression (PMLR) was adopted as the method of variable selection using elastic net penalty. The elastic net results show low mean-squared error of the training set (MSEtrain) of 0.121 and test set (MSEtest) of 0.131. The model obtained can be applied to predict the corrosion inhibition efficiencies of related organic compounds. Results also reveal that the PMLR based on elastic net penalty is effective in dealing with high dimensional data.  

 

References

Singh, A., Ebenso, E. E., and Quraishi, M. A. 2012 Corrosion Inhibition of Carbon Steel in HCl Solution by Some Plant Extracts. International Journal of Corrosion. 2012: 1-20.

Li, X. and Deng, S. 2012. Inhibition Effect of Dendrocalamus brandisii Leaves Extract on Aluminum in HCl, H3PO4 Solutions. Corrosion Science. 65: 299-308.

Rani, B. E. A. and Basu, B. B. J. 2012 Green Inhibitors for Corrosion Protection of Metals and Alloys: An Overview. International Journal of Corrosion. 2012: 1-15.

Deng, S. and Li, X. 2012 Inhibition by Ginkgo Leaves Extract of the Corrosion of Steel in HCl and H2SO4 Solutions, Corrosion Science. 55: 407-415.

Ji, G., Shukla, S. K., Dwivedi, P., Sundaram, S., and Prakash, R. 2011. Inhibitive Effect ofArgemone mexicanaPlant Extract on Acid Corrosion of Mild Steel. Industrial & Engineering Chemistry Research. 50: 11954-11959.

Hooshmand Zaferani, S., Sharifi, M., Zaarei, D., and Shishesaz, M. R. 2013 Application of Eco-friendly Products as Corrosion Inhibitors for Metals in Acid Pickling Processes -A Review. Journal of Environmental Chemical Engineering. 1: 652-657.

Raja, P. B., Qureshi, A. K., Abdul Rahim, A., Osman, H., and Awang, K. 2013. Neolamarckia Cadamba Alkaloids as Eco-friendly Corrosion Inhibitors for Mild Steel in 1M HCl Media. Corrosion Science. 69: 292-301.

Wang, W.-X., Si, H., and Zhang, Z. 2012 Quantitative Structure–activity Relationship Study on Antitumour Activity of a Series of Flavonoids, Molecular Simulation. 38: 38-44.

Rasuleva, B. F., Abdullaev, N. D., Syrov, V. N., and Leszczynskia, J. 2005 A Quantitative Structure-Activity Relationship (QSAR) Study of the Antioxidant Activity of Flavonoids. Molecular Informatics. 24: 1056-1065.

Matsuda, H., Ando, S., Morikawa, T., Kataoka, S., and Yoshikawa, M. 2005 Structure–activity Relationships of 1′S-1′-acetoxychavicol Acetate for inhibitory Effect on NO Production in Lipopolysaccharide-activated Mouse Peritoneal Macrophages. Bioorganic and Medicinal Chemistry Letters. 15: 1949-1953.

Mousavi, M., Safarizadeh, H., and Khosravan, A. 2012 A New Cluster Model Based Descriptor for Structure-Inhibition Relationships: A Study of the Effects of Benzimidazole, Aniline and Their Derivatives on Iron Corrosion. Corrosion Science. 65: 249-258.

Szeptycka, B. 2005 Quantum-chemical Modeling (QCM) and Quantum Structure-activity relationship Modeling (QSARM) of acid Pickling Inhibition. Materials and Manufacturing Processes. 20: 9-21.

Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., et al. 2014 QSAR Modeling: Where Have You Been? Where are You Going To? Journal of Medicinal Chemistry. 57: 4977-5010

He, L. and Jurs, P. C. 2005 Assessing the Reliability of a QSAR Model's Predictions. Journal of Molecular Graphics and Modelling. 23: 503-523.

Bababdani, B. M. and Mousavi, M. 2013 Gravitational Search Algorithm: A New Feature Selection Method for QSAR Study of Anticancer Potency of Imidazo [4, 5-b] Pyridine Derivatives. Chemometrics and Intelligent Laboratory Systems. 122: 1-11.

Todeschini, R. and Consonni, V. 2009. Molecular Descriptors for Chemoinformatics. John Wiley & Sons.

DRAGON. Software for Molecular Descriptor Calculation, version 6.0, by Todeschini R, Consonni V, Mauri A, Pavan M. Milan, Italy: Talete srl. [Online]. Available: http://www.talete.mi.it/.

Filzmoser, P., Gschwandtner, M., and Todorov, V. 2012 Review of Sparse Methods in Regression and Classification with Application to Chemometrics. Journal of Chemometrics. 26: 42-51.

Algamal, Z. Y., Lee, M. H., Alâ€Fakih, A. M., and Aziz, M. 2015 Highâ€dimensional QSAR Prediction of Anticancer Potency of Imidazo [4, 5â€b] pyridine Derivatives Using Adjusted Adaptive LASSO. Journal of Chemometrics. 29: 547-556.

Algamal, Z. Y., Lee, M. H., and Alâ€Fakih, A. M. 2016 Highâ€dimensional Quantitative Structure–activity Relationship Modeling of Influenza Neuraminidase a/PR/8/34 (H1N1) Inhibitors based on a Twoâ€stage Adaptive Penalized Rank Regression. Journal of Chemometrics. 30: 50-57.

Al-Fakih, A. M., Aziz, M., Abdallah, H. H., Algamal, Z. Y., Lee, M. H., and Maarof, H. 2015 High Dimensional QSAR Study of Mild Steel Corrosion Inhibition in Acidic Medium by Furan Derivatives. International Jourmal of Electrochememical Science. 10: 3568-3583.

Arambewela, L. and Silva, R. 2006. Sri Lankan Medicinal Plant Monographs and Analysis: Alpinia galanga. ed Colombo: National Science Foundation.

Matsuda, H., Morikawa, T., Managi, H., and Yoshikawa, M. 2003. Antiallergic Principles from Alpinia galanga: Structural Requirements of Phenylpropanoids for Inhibition of Degranulation and Release of TNF-α and IL-4 in RBL-2H3 cells. Bioorganic and Medicinal Chemistry Letters. 13: 3197-3202.

Korkina, L., Kostyuk, V., De Luca, C., and Pastore, S. 2011. Plant Phenylpropanoids as Emerging Anti-inflammatory Agents. Mini Reviews in Medicinal Chemistry. 11: 823-835.

Bringas, J. E. 2004. Handbook of Comparative World Steel Standards. 3rd ed. USA: ASTM International.

Gunasekaran, G. and Chauhan, L. R. 2004 Eco Friendly Inhibitor for Corrosion Inhibition of Mild Steel in Phosphoric Acid Medium. Electrochimica Acta. 49: 4387-4395.

Amira, W. E., Rahim, A., Osman, H., Awang, K., and Raja, P. B. 2011. Corrosion Inhibition of Mild Steel in 1 M HCl Solution by Xylopia Ferruginea Leaves from Different Extract and Partitions. International Jourmal of Electrochememical Science. 6: 2998.

Frisch, M. J., Trucks, G., Schlegel, H., Scuseria, G., Robb, M., Cheeseman, J., et al. 2009. Gaussian 09, revision A. 1, Gaussian Inc., Wallingford, CT.

Hoerl, A. E. and Kennard, R. W. 1970. Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics. 12: 55-67.

Tibshirani, R. 1996 Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological). 58: 267-288.

Zou, H. and Hastie, T. 2005 Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 67: 301-320.

Elmsellem, H., Elyoussfi, A., Steli, H., Sebbar, N. K., Essassi, E. M., Dahmani, M., et al. 2016. The Theobromine (chocolate) as Green Inhibitor of Mild Steel Corrosion in hydrochloric Acid: Electrochemical And Theoretical Quantum Studies.Der Pharma Chemica. 8: 248-256.

Alâ€Fakih, A. M., Algamal, Z. Y., Lee, M. H., Abdallah, H. H., Maarof, H., and Aziz, M. 2016. Quantitative Structure–activity Relationship Model For Prediction Study Of Corrosion Inhibition Efficiency Using Twoâ€Stage Sparse Multiple Linear Regression. Journal of Chemometrics. 30: 361-368.

Algamal, Z., Lee, M., Al-Fakih, A., and Aziz, M. 2016. High-dimensional QSAR Modelling Using Penalized Linear Regression Model with L 1/2-norm. SAR and QSAR in Environmental Research. 27: 703-719.

Dariva, C. G. and Galio, A. F. 2014. Corrosion Inhibitors–Principles, Mechanisms and Applications. Developments in Corrosion Protection. ed: In Tech,

Soltani, N., Tavakkoli, N., Khayat Kashani, M., Mosavizadeh, A., Oguzie, E., and Jalali, M. 2013. Silybum Marianum Extract as a Natural Source Inhibitor for 304 Stainless Steel Corrosion in 1.0 M HCl. Journal of Industrial and Engineering Chemistry. 20: 3217-3227.

Matsuda, H., Pongpiriyadacha, Y., Morikawa, T., Ochi, M., and Yoshikawa, M. 2003. Gastroprotective Effects of Phenylpropanoids from the Rhizomes of Alpinia Galanga in Rats: Structural Requirements and Mode of Action. European Journal of Pharmacology. 471: 59-67.

Chaieb, E., Bouyanzer, A., Hammouti, B., and Benkaddour, M. 2005. Inhibition of the Corrosion of Steel in 1M Hcl by Eugenol Derivatives. Applied Surface Science. 246: 199-206.

Avdeev, Y. G., Kuznetsov, Y. I., and Buryak, A. K. 2013. Inhibition of Steel Corrosion by Unsaturated Aldehydes in Solutions of Mineral Acids. Corrosion Science. 69: 50-60.

Soltani, S., Haghaei, H., Shayanfar, A., Vallipour, J., Asadpour Zeynali, K., and Jouyban, A. 2013. QSBR Study of Bitter Taste of Peptides: Application of GA-PLS in Combination with MLR, SVM, and ANN Approaches. BioMed research International. http://dx.doi.org/10.1155/2013/501310.

Udhayakalaa, P., Rajendiranb, T. V., and Gunasekaranc, S. 2012. Theoretical Approach to the Corrosion Inhibition Efficiency of some Pyrimidine Derivatives using DFT Method. Journal of Computational Methods in Molecular Design. 2: 1-15.

Balaji, S., Karthikeyan, C., Moorthy, N. H. N., and Trivedi, P. 2004. QSAR Modelling of HIV-1 Reverse Transcriptase Inhibition by benzoxazinones using a Combination of P_VSA and Pharmacophore Feature Descriptors. Bioorganic and Medicinal Chemistry Letters. 14: 6089-6094.

Downloads

Published

2017-10-22

Issue

Section

Science and Engineering

How to Cite

EXPERIMENTAL STUDY AND HIGH DIMENSIONAL QSAR MODELLING OF PHENYLPROPANOIDS OF ALPINIA GALANGA AS CORROSION INHIBITORS ON MILD STEEL. (2017). Jurnal Teknologi, 79(7). https://doi.org/10.11113/jt.v79.9850