The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion
This work aims at optimizing the media constituents for citric acid production from oil palm empty fruit bunches (EFB) as renewable resource using artifiial neural networks (ANN) approach. The bioconversion process was done through solid state bioconversion using Aspergillus niger. ANN model was b...
Saved in:
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Faculty of Food Science & Technology, Universiti Putra Malaysia (UPM)
2012
|
Subjects: | |
Online Access: | http://irep.iium.edu.my/66292/1/66292_The%20potential%20of%20artificial%20neural%20network%20%28ANN%29.pdf http://irep.iium.edu.my/66292/ http://ifrj.upm.edu.my/19%20(02)%202012/(16)IFRJ-2012%20Saedi.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.iium.irep.66292 |
---|---|
record_format |
dspace |
spelling |
my.iium.irep.662922019-07-12T02:30:32Z http://irep.iium.edu.my/66292/ The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion Nasaruddin, Ricca Rahman Jami, Mohammed Saedi Alam, Md. Zahangir TP Chemical technology TP155 Chemical engineering TP248.13 Biotechnology This work aims at optimizing the media constituents for citric acid production from oil palm empty fruit bunches (EFB) as renewable resource using artifiial neural networks (ANN) approach. The bioconversion process was done through solid state bioconversion using Aspergillus niger. ANN model was built using MATLAB software. A dataset consists of 20 runs from our previous work was used to develop ANN. The predictive and generalization ability of ANN and the results of RSM were compared. The determination coeffiients (R2-value) for ANN and RSM models were 0.997 and 0.985, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. Validation process was done and the maximum citric acid production (147.74 g/kg-EFB) was achieved using the optimal solution from ANN which consists of 6.1% sucrose, 9.2% mineral solution and 15.0% inoculum. Faculty of Food Science & Technology, Universiti Putra Malaysia (UPM) 2012 Article PeerReviewed application/pdf en http://irep.iium.edu.my/66292/1/66292_The%20potential%20of%20artificial%20neural%20network%20%28ANN%29.pdf Nasaruddin, Ricca Rahman and Jami, Mohammed Saedi and Alam, Md. Zahangir (2012) The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion. International Food Research Journal, 19 (2). pp. 491-497. ISSN 2231 7546 http://ifrj.upm.edu.my/19%20(02)%202012/(16)IFRJ-2012%20Saedi.pdf |
institution |
Universiti Islam Antarabangsa Malaysia |
building |
IIUM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
International Islamic University Malaysia |
content_source |
IIUM Repository (IREP) |
url_provider |
http://irep.iium.edu.my/ |
language |
English |
topic |
TP Chemical technology TP155 Chemical engineering TP248.13 Biotechnology |
spellingShingle |
TP Chemical technology TP155 Chemical engineering TP248.13 Biotechnology Nasaruddin, Ricca Rahman Jami, Mohammed Saedi Alam, Md. Zahangir The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion |
description |
This work aims at optimizing the media constituents for citric acid production from oil palm empty
fruit bunches (EFB) as renewable resource using artifiial neural networks (ANN) approach. The bioconversion
process was done through solid state bioconversion using Aspergillus niger. ANN model was built using
MATLAB software. A dataset consists of 20 runs from our previous work was used to develop ANN. The
predictive and generalization ability of ANN and the results of RSM were compared. The determination
coeffiients (R2-value) for ANN and RSM models were 0.997 and 0.985, respectively, indicating the superiority
of ANN in capturing the non-linear behavior of the system. Validation process was done and the maximum citric
acid production (147.74 g/kg-EFB) was achieved using the optimal solution from ANN which consists of 6.1%
sucrose, 9.2% mineral solution and 15.0% inoculum. |
format |
Article |
author |
Nasaruddin, Ricca Rahman Jami, Mohammed Saedi Alam, Md. Zahangir |
author_facet |
Nasaruddin, Ricca Rahman Jami, Mohammed Saedi Alam, Md. Zahangir |
author_sort |
Nasaruddin, Ricca Rahman |
title |
The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion |
title_short |
The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion |
title_full |
The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion |
title_fullStr |
The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion |
title_full_unstemmed |
The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion |
title_sort |
potential of artificial neural network (ann) in optimizing media constituents of citric acid production by solid state bioconversion |
publisher |
Faculty of Food Science & Technology, Universiti Putra Malaysia (UPM) |
publishDate |
2012 |
url |
http://irep.iium.edu.my/66292/1/66292_The%20potential%20of%20artificial%20neural%20network%20%28ANN%29.pdf http://irep.iium.edu.my/66292/ http://ifrj.upm.edu.my/19%20(02)%202012/(16)IFRJ-2012%20Saedi.pdf |
_version_ |
1643619744783269888 |
score |
13.18916 |