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,...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Published: |
Springer
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106135/ |
| _version_ | 1848864697937297408 |
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| author | Aziz, Tariq Qadir, Rahman Anwar, Farooq Naz, Sumaira Nazir, Nausheen Nabi, Ghulam Haiying, Cui Lin, Lin Alharbi, Metab Alasmari, Abdullah F |
| 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 |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T13:52:56Z |
| format | Article |
| id | upm-106135 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:52:56Z |
| publishDate | 2024 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/106135/ http://psasir.upm.edu.my/id/eprint/106135/ http://psasir.upm.edu.my/id/eprint/106135/ |