Artificial Neural Network Predictive Modelling of luffa cylindrica Seed Oil Antioxidant Yield

Year 2021, Volume: 8 Issue: 4, 494 - 504, 30.12.2021

Kenechi Nwosu-obieogu

https://doi.org/10.54287/gujsa.972137

Cited By: 4

Abstract

This study applied artificial neural network (ANN) in evaluating the models for terpineol and polyphenol yield from luffa cylindrica seed oil. The experiment was carried out at a temperature (60-80oC), time (4-6 hours), and solvent/seed ratio (8-12 ml/g) with response as antioxidant yield. FTIR (Fourier Transform Infra-red Spectroscopy) revealed the presence of terpineol and polyphenol at peaks of 1461.1cm-1 and 3008.0cm-1 respectively. The ANN prediction indices are thus; terpineol (R2= 9.9999E-1, MSE=2.25766E-9) and polyphenol (R2=9.9999E-1, MSE=4.42588E-10). This study reveals that the ANN technique can successfully predict antioxidants from luffa cylindrica seed oil.

Keywords

ANN, Terpineol, Polyphenol, Antioxidant

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