Differentiating Agarwood Oil Quality Using Artificial Neural Network

• Main Authors: Saiful Nizam, Tajuddin, Nurlaila, Ismail, Nor Azah, Mohd Ali, Mailina, Jamil, Mohd Hezri, Fazalul Rahiman, Mohd Nasir, Taib
• Format: Article
• Language: English
• Published: Universiti Kebangsaan Malaysia 2013
• Subjects: Q Science (General); QD Chemistry
• Online Access: http://umpir.ump.edu.my/id/eprint/6253/1/Differentiating_Agarwood_Oil_Quality_Using_Artificial_Neural_Network.pdf; http://umpir.ump.edu.my/id/eprint/6253/; http://www.ukm.my/mjas/v17_n3/Nor%20Azah.pdf
• Institution: Universiti Malaysia Pahang

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.