<abstract xmlns="http://www.w3.org/1999/xhtml">

This study uses artificial neural network (ANN) to predict the thermo-physical properties of deep-fat frying plantain chips (<italic>ipekere</italic>). 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.
</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.

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

Department of Food Science and Technology

Acta Universitatis Cibiniensis. Series E: Food Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

This study uses artificial neural network (ANN) to predict the thermo-physical properties of deep-fat frying plantain chips (ipekere). The frying was conducted...

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

Pubblicato online: 30 dic 2021

Pagine: 253 - 260

Ricevuto: 02 ott 2021

Accettato: 10 dic 2021

DOI: https://doi.org/10.2478/aucft-2021-0023

Parole chiave
frying temperature, frying time, modelling, physical properties, thermal properties, Artificial Neural Network ANN

© 2020 James Abiodun Adeyanju et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Pubblicato online: 30 dic 2021

Pagine: 253 - 260

Ricevuto: 02 ott 2021

Accettato: 10 dic 2021

DOI: https://doi.org/10.2478/aucft-2021-0023

Parole chiavefrying temperature, frying time, modelling, physical properties, thermal properties, Artificial Neural Network ANN

© 2020 James Abiodun Adeyanju et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
frying temperature, frying time, modelling, physical properties, thermal properties, Artificial Neural Network ANN