Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network

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


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.


Artificial Neural Networks; Luffa cylindrica seed oil; alkyd resin; phytochemicals

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Copyright (c) 2023 Udemgba Chinonso Stanley, Amarachi Anyawu Solace, Amaefule Excel Obumneme, Odoemelam Patience Ogechi, Odo Godfrey Ifeanyi, Okam Chukwu Emmanuel, Nnaemeka Nwachuckwu Chukwudi, Sandra Ijeoma Izuchukwu, Iheanacho Eberechi Clement, Ogbonna Chiemezuo Chinedu

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