The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion

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

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Abstract

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.

Item Type:	Article (Journal)
Additional Information:	8629/66292
Uncontrolled Keywords:	Artificial Neural Network, Bioprocess Optimization, Citric Acid, Aspergillus niger, Solid State Fermentation, oil palm empty fruit bunches
Subjects:	T Technology > TP Chemical technology T Technology > TP Chemical technology > TP155 Chemical engineering T Technology > TP Chemical technology > TP248.13 Biotechnology
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Biotechnology Engineering
Depositing User:	Dr Ricca Rahman Nasaruddin
Date Deposited:	24 Apr 2019 15:31
Last Modified:	12 Jul 2019 10:30
URI:	http://irep.iium.edu.my/id/eprint/66292

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