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Differentiating Agarwood Oil Quality Using Artificial Neural Network

Saiful Nizam, Tajuddin and Nurlaila, Ismail and Nor Azah, Mohd Ali and Mailina, Jamil and Mohd Hezri, Fazalul Rahiman and Mohd Nasir, Taib (2013) Differentiating Agarwood Oil Quality Using Artificial Neural Network. Malaysian Journal of Analytical Sciences, 17 (3). pp. 490-498. ISSN 1394-2506

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Abstract

Agarwood oil is well known as expensive oil extracted from the resinous of fragrant heartwood. The oil is getting high demand in the market especially from the Middle East countries, China and Japan because of its unique odor. As part of an on-going research in grading the agarwood oil quality, the application of Artificial Neural Network (ANN) is proposed in this study to analyze agarwood oil quality using its chemical profiles. The work involves of selected agarwood oil from low and high quality,the extraction of chemical compounds using GC-MS and Z-score to identify of the significant compounds as input to the network. The ANN programming algorithm was developed and computed automatically via Matlab software version R2010a. Back-propagation training algorithm and sigmoid transfer function were used to optimize the parameters in the training network. The result obtained showed the capability of ANN in analyzing the agarwood oil quality hence beneficial for the further application such as grading and classification for agarwood oil.

Item Type: Article
Uncontrolled Keywords: Agarwood oil; Chemical compounds; Quality; Gas chromatography-mass spectrometry (GC-MS); Artificial Neural Network (ANN)
Subjects: Q Science > Q Science (General)
Q Science > QD Chemistry
Faculty/Division: Faculty of Industrial Sciences And Technology
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 07 Aug 2014 01:37
Last Modified: 01 May 2018 23:38
URI: http://umpir.ump.edu.my/id/eprint/6253
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