1. bookVolume 25 (2021): Edition 2 (December 2021)
Détails du magazine
License
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Magazine
eISSN
2344-150X
Première parution
30 Jul 2013
Périodicité
2 fois par an
Langues
Anglais
access type Accès libre

Prediction of the thermo-physical properties of deep-fat frying plantain chips (ipekere) using artificial neural network

Publié en ligne: 30 Dec 2021
Volume & Edition: Volume 25 (2021) - Edition 2 (December 2021)
Pages: 253 - 260
Reçu: 02 Oct 2021
Accepté: 10 Dec 2021
Détails du magazine
License
Format
Magazine
eISSN
2344-150X
Première parution
30 Jul 2013
Périodicité
2 fois par an
Langues
Anglais
Abstract

This study uses artificial neural network (ANN) to predict the thermo-physical properties of deep-fat frying plantain chips (ipekere). The frying was conducted with temperature and time ranged of 150 to 190 °C and 2 to 4 minutes using factorial design. The result revealed that specific heat was most influenced by temperature and time with the value 2.002 kJ/kg°C at 150 °C and 2.5 minutes. The density ranged from 0.997 – 1.005 kg/m3 while thermal diffusivity and conductivity were least affected with 0.192 x 10−6 m2/s and 0.332 W/m°C respectively at 190 °C and 4 minutes. The ANN architecture was developed using Levenberg–Marquardt (TRAINLM) and Feed-forward back propagation algorithm. The experimentation based on the ANN model produced a desirable prediction of the thermo-physical properties through the application of diverse amount of neutrons in the hidden layer. The predictive experimentation of the computational model with R2 ≥ 0.7901 and MSE ≤ 0.1125 does not only show the validity in anticipating the thermo-physical properties, it also indicates the capability of the model to identify a relevant association between frying time, frying temperatures and thermo-physical properties. Hence, to avoid a time consuming and expensive experimental tests, the developed model in this study is efficient in prediction of the thermo-physical properties of deep-fat frying plantain chips.

Keywords

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