Open AccessEXPERIMENTAL STUDY AND HIGH DIMENSIONAL QSAR MODELLING OF PHENYLPROPANOIDS OF ALPINIA GALANGA AS CORROSION INHIBITORS ON MILD STEEL
Author(s) -
Sunday Osinkolu Ajeigbe,
Norazah Basar,
Zakariya Yahya Algamal,
Muhammad Hisyam Lee,
Hasmerya Maarof,
Madzlan Aziz
Publication year - 2017
Publication title -
jurnal teknologi/jurnal teknologi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.191
H-Index - 22
eISSN - 2180-3722
pISSN - 0127-9696
DOI - 10.11113/jt.v79.9850
Subject(s) - quantitative structure–activity relationship , corrosion , chemistry , molecular descriptor , test set , linear regression , computational chemistry , stereochemistry , mathematics , organic chemistry , statistics
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.