Comparison of partial least squares and artificial neural network for the prediction of antioxidant activity in extract of pegaga (centella) varieties from H-1 nuclear magnetic resonance spectroscopy

., Maulidiani and Abas, Faridah and Khatib, Alfi and Shitan, Mahendran and Shaari, Khozirah and Lajis, Nordin H. (2013) Comparison of partial least squares and artificial neural network for the prediction of antioxidant activity in extract of pegaga (centella) varieties from H-1 nuclear magnetic resonance spectroscopy. Food Research International, 54. pp. 852-860. ISSN 0963-9969

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Abstract

Multivariate data analysis of 1H Nuclear Magnetic Resonance spectra was applied for the prediction of antioxidant activity in five different Pegaga (C. asiatica (var 1), C. asiatica (var 2), C. asiatica (var 3) H. bonariensis and H. sibthorpioides) varieties. Linear (Partial Least Square regression) and non linear (Artificial Neural Network) models have been developed and their performances were compared. The performances of the models were tested according to external validation of prediction set. The result showed that the Partial Least Square model provided better generalization than Artificial Neural Network. Despite those, bothmodels are considered reasonably acceptable. Regression coefficient and VIP values of the PLS model revealed that 3,5-O-dicaffeoyl-4-Omalonilquinic acid (irbic acid), 3,5-di-O-caffeoylquinic acid, 4,5-di-O-caffeoylquinic acid, 5-O-caffeoylquinic acid (chlorogenic acid), quercetin and kaempferol derivatives are the components responsible for the antioxidant activity. In addition, the spectroscopic pattern of the Pegaga varieties, as shown by the PLS score plots was consistent with the corresponding antioxidant activity. Prediction of the antioxidant activity from 1H NMR spectra using this approach is useful in assessing the quality of medicinal herb extracts.

Item Type:	Article (Journal)
Additional Information:	6915/32146
Uncontrolled Keywords:	Centella asiatica; Anti-oxidant activity; 1H Nuclear Magnetic Resonance; Partial Least Square regression; Artificial Neural Network
Subjects:	Q Science > QD Chemistry
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Pharmacy > Department of Pharmaceutical Chemistry
Depositing User:	Dr. Alfi Khatib
Date Deposited:	03 Oct 2013 10:03
Last Modified:	23 Feb 2016 16:17
URI:	http://irep.iium.edu.my/id/eprint/32146

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