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Yield prediction of supercritical fluid extraction of Nigella sativa using neutral networks / Sarah Diana Isnin and Sitinoor Adeib Idris

A feed-forward multi-layer neural network with Levenberg-Marquardt training algorithm was developed to predict yield for supercritical carbon dioxide (SC-CO2) extraction of Nigella sativa essential oil. Yield of extraction depends on these variables: pressure, temperature, and extraction time hence...

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Main Authors: Isnin, Sarah Diana, Adeib Idris, Sitinoor
Format: Article
Language:English
Published: Penerbit UiTM (UiTM Press) 2025
Subjects:
Seeds. Seed technology
Online Access:https://ir.uitm.edu.my/id/eprint/109347/1/109347.pdf
https://ir.uitm.edu.my/id/eprint/109347/
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spelling my.uitm.ir.1093472025-01-18T04:09:11Z https://ir.uitm.edu.my/id/eprint/109347/ Yield prediction of supercritical fluid extraction of Nigella sativa using neutral networks / Sarah Diana Isnin and Sitinoor Adeib Idris srj Isnin, Sarah Diana Adeib Idris, Sitinoor Seeds. Seed technology A feed-forward multi-layer neural network with Levenberg-Marquardt training algorithm was developed to predict yield for supercritical carbon dioxide (SC-CO2) extraction of Nigella sativa essential oil. Yield of extraction depends on these variables: pressure, temperature, and extraction time hence were chosen as the input to the network. Different number of neurons in hidden layer were trained and tested using training and testing data sets. The validating data set was used to determine the network that having lowest mean-squared error (MSE) value and highest regression coefficient. The optimal ANN model, featuring four neurons in hidden layer, demonstrated high predictive accuracy with the lowest MSE of 0.42, 1.43 and 1.25 for training, validation and test model, respectively. The regression plots indicated high R-values of 0.99641, 0.99513, and 0.98874 for the training, validation, and testing sets, respectively, confirming the model's robustness in predicting experimental data. A very good fitting between the predicted data and experimental data was observed with R2 of 0.9891 indicates ANN shows good accuracy in predicting yield of Nigella sativa Penerbit UiTM (UiTM Press) 2025-01 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/109347/1/109347.pdf Yield prediction of supercritical fluid extraction of Nigella sativa using neutral networks / Sarah Diana Isnin and Sitinoor Adeib Idris. (2025) Scientific Research Journal <https://ir.uitm.edu.my/view/publication/Scientific_Research_Journal/>, 22: 7. pp. 107-119. ISSN 1675-7009
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Seeds. Seed technology
spellingShingle Seeds. Seed technology
Isnin, Sarah Diana
Adeib Idris, Sitinoor
Yield prediction of supercritical fluid extraction of Nigella sativa using neutral networks / Sarah Diana Isnin and Sitinoor Adeib Idris
description A feed-forward multi-layer neural network with Levenberg-Marquardt training algorithm was developed to predict yield for supercritical carbon dioxide (SC-CO2) extraction of Nigella sativa essential oil. Yield of extraction depends on these variables: pressure, temperature, and extraction time hence were chosen as the input to the network. Different number of neurons in hidden layer were trained and tested using training and testing data sets. The validating data set was used to determine the network that having lowest mean-squared error (MSE) value and highest regression coefficient. The optimal ANN model, featuring four neurons in hidden layer, demonstrated high predictive accuracy with the lowest MSE of 0.42, 1.43 and 1.25 for training, validation and test model, respectively. The regression plots indicated high R-values of 0.99641, 0.99513, and 0.98874 for the training, validation, and testing sets, respectively, confirming the model's robustness in predicting experimental data. A very good fitting between the predicted data and experimental data was observed with R2 of 0.9891 indicates ANN shows good accuracy in predicting yield of Nigella sativa
format Article
author Isnin, Sarah Diana
Adeib Idris, Sitinoor
author_facet Isnin, Sarah Diana
Adeib Idris, Sitinoor
author_sort Isnin, Sarah Diana
title Yield prediction of supercritical fluid extraction of Nigella sativa using neutral networks / Sarah Diana Isnin and Sitinoor Adeib Idris
title_short Yield prediction of supercritical fluid extraction of Nigella sativa using neutral networks / Sarah Diana Isnin and Sitinoor Adeib Idris
title_full Yield prediction of supercritical fluid extraction of Nigella sativa using neutral networks / Sarah Diana Isnin and Sitinoor Adeib Idris
title_fullStr Yield prediction of supercritical fluid extraction of Nigella sativa using neutral networks / Sarah Diana Isnin and Sitinoor Adeib Idris
title_full_unstemmed Yield prediction of supercritical fluid extraction of Nigella sativa using neutral networks / Sarah Diana Isnin and Sitinoor Adeib Idris
title_sort yield prediction of supercritical fluid extraction of nigella sativa using neutral networks / sarah diana isnin and sitinoor adeib idris
publisher Penerbit UiTM (UiTM Press)
publishDate 2025
url https://ir.uitm.edu.my/id/eprint/109347/1/109347.pdf
https://ir.uitm.edu.my/id/eprint/109347/
_version_ 1823097901833781248
score 13.250246

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