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Application of artificial neural network for yield prediction of lipase-catalyzed synthesis of dioctyl adipate


Citation

Abdul Rahman, Mohd Basyaruddin and Chaibakhsh, Naz and Basri, Mahiran and Salleh, Abu Bakar and Raja Abdul Rahman, Raja Noor Zaliha (2009) Application of artificial neural network for yield prediction of lipase-catalyzed synthesis of dioctyl adipate. Applied Biochemistry and Biotechnology, 158 (3). pp. 722-735. ISSN 0273-2289; ESSN: 1559-0291

Abstract / Synopsis

In this study, an artificial neural network (ANN) trained by backpropagation algorithm, Levenberg–Marquadart, was applied to predict the yield of enzymatic synthesis of dioctyl adipate. Immobilized Candida antarctica lipase B was used as a biocatalyst for the reaction. Temperature, time, amount of enzyme, and substrate molar ratio were the four input variables. After evaluating various ANN configurations, the best network was composed of seven hidden nodes using a hyperbolic tangent sigmoid transfer function. The correlation coefficient (R 2) and mean absolute error (MAE) values between the actual and predicted responses were determined as 0.9998 and 0.0966 for training set and 0.9241 and 1.9439 for validating dataset. A simulation test with a testing dataset showed that the MAE was low and R 2 was close to 1. These results imply the good generalization of the developed model and its capability to predict the reaction yield. Comparison of the performance of radial basis network with the developed models showed that radial basis function was more accurate but its performance was poor when tested with unseen data. In further part of the study, the feed forward back propagation model was used for prediction of the ester yield within the given range of the main parameters.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Institute of Bioscience
DOI Number: https://doi.org/10.1007/s12010-008-8465-z
Publisher: Humana Press
Keywords: Artificial neural network; Lipase; Dioctyl adipate; Neural network; Modeling; Prediction
Depositing User: Nida Hidayati Ghazali
Date Deposited: 24 Jul 2013 10:14
Last Modified: 27 Sep 2016 12:14
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1007/s12010-008-8465-z
URI: http://psasir.upm.edu.my/id/eprint/13222
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