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. oil yield was predicted using network. performance the ANN response surface methodology models compared. yielded 7.567% yield, 185.676 mg/l phenol, 45.087 terpineol at 75.57 °C temperature, 5.77 h time, 10.68 g/mol n-hexane concentration, respectively. These data show that output is poor but has a significant phenol terpenoid content may be employed in pharmaceutical sectors. FT-IR analysis revealed high level unsaturated hydrocarbons esters, making appropriate for paint industry creating cosmetics.