UPM Institutional Repository

Comparison of Partial Least Squares and Artificial Neural Network for the prediction of antioxidant activity in extract of pegaga (centella) varieties from 1H Nuclear Magnetic Resonance spectroscopy.


Citation

Maulidiani, and Abas, Faridah and Khatib, Alfi and Shitan, Mahendran and Shaari, Khozirah and Lajis, Nordin (2013) Comparison of Partial Least Squares and Artificial Neural Network for the prediction of antioxidant activity in extract of pegaga (centella) varieties from 1H Nuclear Magnetic Resonance spectroscopy. Food Research International, 54 (1). pp. 852-860. ISSN 0963-9969; ESSN: 1873-7145

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, both models are considered reasonably acceptable. Regression coefficient and VIP values of the PLS model revealed that 3,5-O-dicaffeoyl-4-O-malonilquinic 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.


Download File

[img]
Preview
PDF (Abstract)
Comparison of Partial Least Squares and Artificial Neural Network for the prediction of antioxidant activity in extract of pegaga.pdf

Download (84kB) | Preview

Additional Metadata

Item Type: Article
Divisions: Faculty of Food Science and Technology
DOI Number: https://doi.org/10.1016/j.foodres.2013.08.029
Publisher: Elsevier
Keywords: Centella asiatica; Anti-oxidant activity; 1H Nuclear Magnetic Resonance; Partial Least Square regression; Artificial Neural Network.
Depositing User: Khairil Ridzuan Khahirullah
Date Deposited: 11 Nov 2014 04:39
Last Modified: 08 Oct 2015 00:22
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.foodres.2013.08.029
URI: http://psasir.upm.edu.my/id/eprint/30495
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item