Optimal enzyme-assisted extraction of Phenolics from leaves of Pongamia pinnata via response surface methodology and artificial neural networking

This research work seeks to evaluate the impact of selected enzyme complexes on the optimised release of phenolics from leaves of Pongamia pinnata. After preliminary solvent extraction, the P. pinnata leaf extract was subjected to enzymatic treatment, using enzyme cocktails such as kemzyme dry-plus,...

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Main Authors: Aziz, Tariq, Qadir, Rahman, Anwar, Farooq, Naz, Sumaira, Nazir, Nausheen, Nabi, Ghulam, Haiying, Cui, Lin, Lin, Alharbi, Metab, Alasmari, Abdullah F
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Published: Springer 2024
Online Access:http://psasir.upm.edu.my/id/eprint/106135/
https://link.springer.com/article/10.1007/s12010-024-04875-w
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Institution: Universiti Putra Malaysia
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spelling my.upm.eprints.1061352024-10-08T06:57:02Z http://psasir.upm.edu.my/id/eprint/106135/ Optimal enzyme-assisted extraction of Phenolics from leaves of Pongamia pinnata via response surface methodology and artificial neural networking Aziz, Tariq Qadir, Rahman Anwar, Farooq Naz, Sumaira Nazir, Nausheen Nabi, Ghulam Haiying, Cui Lin, Lin Alharbi, Metab Alasmari, Abdullah F This research work seeks to evaluate the impact of selected enzyme complexes on the optimised release of phenolics from leaves of Pongamia pinnata. After preliminary solvent extraction, the P. pinnata leaf extract was subjected to enzymatic treatment, using enzyme cocktails such as kemzyme dry-plus, natuzyme, and zympex-014. It was noticed that zympex-014 had a greater extract yield (28.0) than kemzyme dry-plus (17.0) and natuzyme (18.0). Based on the better outcomes, zympex-014-based extract values were subsequently applied to several RSM parameters. The selected model is suggested to be significant by the F value (12.50) and R2 value (0.9669). The applicability of the ANN model was shown by how closely the projected values from the ANN were to the experimental values. In terms of total phenolic contents (18.61 mg GAE/g), total flavonoid contents (12.56 mg CE/g), and DPPH test (IC50) (6.5 g/mL), antioxidant activities also shown significant findings. SEM analysis also revealed that the cell walls were damaged during enzymatic hydrolysis, as opposed to non-hydrolysed material. Using GC-MS, five potent phenolic compounds were identified in P. pinnata extract. According to the findings of this study, the recovery of phenolic bioactives and subsequent increase in the antioxidant capacity of P. pinnata leaf extract were both positively impacted by the optimisation approaches suggested, including the use of zympex-014. Springer 2024-02 Article PeerReviewed Aziz, Tariq and Qadir, Rahman and Anwar, Farooq and Naz, Sumaira and Nazir, Nausheen and Nabi, Ghulam and Haiying, Cui and Lin, Lin and Alharbi, Metab and Alasmari, Abdullah F (2024) Optimal enzyme-assisted extraction of Phenolics from leaves of Pongamia pinnata via response surface methodology and artificial neural networking. Applied Biochemistry and Biotechnology, 2024. ISSN 0273-2289; eISSN: 1559-0291 https://link.springer.com/article/10.1007/s12010-024-04875-w 10.1007/s12010-024-04875-w
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description This research work seeks to evaluate the impact of selected enzyme complexes on the optimised release of phenolics from leaves of Pongamia pinnata. After preliminary solvent extraction, the P. pinnata leaf extract was subjected to enzymatic treatment, using enzyme cocktails such as kemzyme dry-plus, natuzyme, and zympex-014. It was noticed that zympex-014 had a greater extract yield (28.0) than kemzyme dry-plus (17.0) and natuzyme (18.0). Based on the better outcomes, zympex-014-based extract values were subsequently applied to several RSM parameters. The selected model is suggested to be significant by the F value (12.50) and R2 value (0.9669). The applicability of the ANN model was shown by how closely the projected values from the ANN were to the experimental values. In terms of total phenolic contents (18.61 mg GAE/g), total flavonoid contents (12.56 mg CE/g), and DPPH test (IC50) (6.5 g/mL), antioxidant activities also shown significant findings. SEM analysis also revealed that the cell walls were damaged during enzymatic hydrolysis, as opposed to non-hydrolysed material. Using GC-MS, five potent phenolic compounds were identified in P. pinnata extract. According to the findings of this study, the recovery of phenolic bioactives and subsequent increase in the antioxidant capacity of P. pinnata leaf extract were both positively impacted by the optimisation approaches suggested, including the use of zympex-014.
format Article
author Aziz, Tariq
Qadir, Rahman
Anwar, Farooq
Naz, Sumaira
Nazir, Nausheen
Nabi, Ghulam
Haiying, Cui
Lin, Lin
Alharbi, Metab
Alasmari, Abdullah F
spellingShingle Aziz, Tariq
Qadir, Rahman
Anwar, Farooq
Naz, Sumaira
Nazir, Nausheen
Nabi, Ghulam
Haiying, Cui
Lin, Lin
Alharbi, Metab
Alasmari, Abdullah F
Optimal enzyme-assisted extraction of Phenolics from leaves of Pongamia pinnata via response surface methodology and artificial neural networking
author_facet Aziz, Tariq
Qadir, Rahman
Anwar, Farooq
Naz, Sumaira
Nazir, Nausheen
Nabi, Ghulam
Haiying, Cui
Lin, Lin
Alharbi, Metab
Alasmari, Abdullah F
author_sort Aziz, Tariq
title Optimal enzyme-assisted extraction of Phenolics from leaves of Pongamia pinnata via response surface methodology and artificial neural networking
title_short Optimal enzyme-assisted extraction of Phenolics from leaves of Pongamia pinnata via response surface methodology and artificial neural networking
title_full Optimal enzyme-assisted extraction of Phenolics from leaves of Pongamia pinnata via response surface methodology and artificial neural networking
title_fullStr Optimal enzyme-assisted extraction of Phenolics from leaves of Pongamia pinnata via response surface methodology and artificial neural networking
title_full_unstemmed Optimal enzyme-assisted extraction of Phenolics from leaves of Pongamia pinnata via response surface methodology and artificial neural networking
title_sort optimal enzyme-assisted extraction of phenolics from leaves of pongamia pinnata via response surface methodology and artificial neural networking
publisher Springer
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/106135/
https://link.springer.com/article/10.1007/s12010-024-04875-w
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