• 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




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


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.  



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