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.
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)...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/30495/ http://psasir.upm.edu.my/id/eprint/30495/1/Comparison%20of%20Partial%20Least%20Squares%20and%20Artificial%20Neural%20Network%20for%20the%20prediction%20of%20antioxidant%20activity%20in%20extract%20of%20pegaga.pdf |
| _version_ | 1848846693138694144 |
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| author | Maulidiani, Abas, Faridah Khatib, Alfi Shitan, Mahendran Shaari, Khozirah Lajis, Nordin |
| author_facet | Maulidiani, Abas, Faridah Khatib, Alfi Shitan, Mahendran Shaari, Khozirah Lajis, Nordin |
| author_sort | Maulidiani, |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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.
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| first_indexed | 2025-11-15T09:06:46Z |
| format | Article |
| id | upm-30495 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:06:46Z |
| publishDate | 2013 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-304952015-10-08T00:22:23Z http://psasir.upm.edu.my/id/eprint/30495/ 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. Maulidiani, Abas, Faridah Khatib, Alfi Shitan, Mahendran Shaari, Khozirah Lajis, Nordin 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. Elsevier 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30495/1/Comparison%20of%20Partial%20Least%20Squares%20and%20Artificial%20Neural%20Network%20for%20the%20prediction%20of%20antioxidant%20activity%20in%20extract%20of%20pegaga.pdf 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 10.1016/j.foodres.2013.08.029 |
| spellingShingle | Maulidiani, Abas, Faridah Khatib, Alfi Shitan, Mahendran Shaari, Khozirah Lajis, Nordin 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. |
| title | 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. |
| title_full | 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. |
| title_fullStr | 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. |
| title_full_unstemmed | 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. |
| title_short | 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. |
| title_sort | 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. |
| url | http://psasir.upm.edu.my/id/eprint/30495/ http://psasir.upm.edu.my/id/eprint/30495/ http://psasir.upm.edu.my/id/eprint/30495/1/Comparison%20of%20Partial%20Least%20Squares%20and%20Artificial%20Neural%20Network%20for%20the%20prediction%20of%20antioxidant%20activity%20in%20extract%20of%20pegaga.pdf |