Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network
The research used an artificial neural network (ANN) to examine optimum extraction conditions and phytochemical contents of Luffa cylindrica seed oil. The oil yield was predicted using an artificial neural network. The performance of the ANN and response surface methodology models was compared. The...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Altezoro s.r.o. (Slovak Republic) and Publishing Center "Dialog" (Ukraine)
2023-01-01
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| Series: | Traektoriâ Nauki |
| Subjects: | |
| Online Access: | https://pathofscience.org/index.php/ps/article/view/2283 |
| _version_ | 1797867491534307328 |
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| author | Udemgba Chinonso Stanley Amarachi Anyawu Solace Amaefule Excel Obumneme Odoemelam Patience Ogechi Okam Chukwu Emmanuel Odo Godfrey Ifeanyi Nnaemeka Nwachuckwu Chukwudi Sandra Ijeoma Izuchukwu Iheanacho Eberechi Clement Ogbonna Chiemezuo Chinedu Sunday Okechukwu Moses Chukwuebuka Okwudifele |
| author_facet | Udemgba Chinonso Stanley Amarachi Anyawu Solace Amaefule Excel Obumneme Odoemelam Patience Ogechi Okam Chukwu Emmanuel Odo Godfrey Ifeanyi Nnaemeka Nwachuckwu Chukwudi Sandra Ijeoma Izuchukwu Iheanacho Eberechi Clement Ogbonna Chiemezuo Chinedu Sunday Okechukwu Moses Chukwuebuka Okwudifele |
| author_sort | Udemgba Chinonso Stanley |
| collection | DOAJ |
| description | The research used an artificial neural network (ANN) to examine optimum extraction conditions and phytochemical contents of Luffa cylindrica seed oil. The oil yield was predicted using an artificial neural network. The performance of the ANN and response surface methodology models was compared. The optimum extraction yielded 7.567% oil yield, 185.676 mg/l phenol, and 45.087 mg/l terpineol at 75.57 °C extraction temperature, 5.77 h extraction time, and 10.68 g/mol n-hexane concentration, respectively. These data show that the oil output is poor but has a significant phenol and terpenoid content that may be employed in pharmaceutical sectors. The FT-IR analysis of Luffa cylindrica seed oil revealed a high level of unsaturated hydrocarbons and esters, making the oil appropriate for using in the paint industry and creating cosmetics. |
| first_indexed | 2024-04-09T23:41:03Z |
| format | Article |
| id | doaj.art-ee844a4f74da442b81f7e702e4740e0c |
| institution | Directory Open Access Journal |
| issn | 2413-9009 |
| language | English |
| last_indexed | 2024-04-09T23:41:03Z |
| publishDate | 2023-01-01 |
| publisher | Altezoro s.r.o. (Slovak Republic) and Publishing Center "Dialog" (Ukraine) |
| record_format | Article |
| series | Traektoriâ Nauki |
| spelling | doaj.art-ee844a4f74da442b81f7e702e4740e0c2023-03-18T18:02:41ZengAltezoro s.r.o. (Slovak Republic) and Publishing Center "Dialog" (Ukraine)Traektoriâ Nauki2413-90092023-01-01911001101010.22178/pos.89-2882Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural NetworkUdemgba Chinonso Stanley0Amarachi Anyawu Solace1Amaefule Excel Obumneme2Odoemelam Patience Ogechi3Okam Chukwu Emmanuel4Odo Godfrey Ifeanyi5Nnaemeka Nwachuckwu Chukwudi6Sandra Ijeoma Izuchukwu7Iheanacho Eberechi Clement8Ogbonna Chiemezuo Chinedu9Sunday Okechukwu10Moses Chukwuebuka Okwudifele11Michael Okpara University of AgricultureUniversité de PoitiersMichael Okpara University of AgricultureMichael Okpara University of AgricultureMichael Okpara University of AgricultureMichael Okpara University of AgricultureMichael Okpara University of AgricultureMichael Okpara University of AgricultureMichael Okpara University of AgricultureMichael Okpara University of AgricultureMichael Okpara University of AgricultureMichael Okpara University of AgricultureThe research used an artificial neural network (ANN) to examine optimum extraction conditions and phytochemical contents of Luffa cylindrica seed oil. The oil yield was predicted using an artificial neural network. The performance of the ANN and response surface methodology models was compared. The optimum extraction yielded 7.567% oil yield, 185.676 mg/l phenol, and 45.087 mg/l terpineol at 75.57 °C extraction temperature, 5.77 h extraction time, and 10.68 g/mol n-hexane concentration, respectively. These data show that the oil output is poor but has a significant phenol and terpenoid content that may be employed in pharmaceutical sectors. The FT-IR analysis of Luffa cylindrica seed oil revealed a high level of unsaturated hydrocarbons and esters, making the oil appropriate for using in the paint industry and creating cosmetics.https://pathofscience.org/index.php/ps/article/view/2283artificial neural networksluffa cylindrica seed oilalkyd resinphytochemicals |
| spellingShingle | Udemgba Chinonso Stanley Amarachi Anyawu Solace Amaefule Excel Obumneme Odoemelam Patience Ogechi Okam Chukwu Emmanuel Odo Godfrey Ifeanyi Nnaemeka Nwachuckwu Chukwudi Sandra Ijeoma Izuchukwu Iheanacho Eberechi Clement Ogbonna Chiemezuo Chinedu Sunday Okechukwu Moses Chukwuebuka Okwudifele Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network Traektoriâ Nauki artificial neural networks luffa cylindrica seed oil alkyd resin phytochemicals |
| title | Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network |
| title_full | Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network |
| title_fullStr | Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network |
| title_full_unstemmed | Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network |
| title_short | Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network |
| title_sort | prediction of the phytochemical properties of luffa cylindrica seed oil using artificial neural network |
| topic | artificial neural networks luffa cylindrica seed oil alkyd resin phytochemicals |
| url | https://pathofscience.org/index.php/ps/article/view/2283 |
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