ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail

Ismail, Nurlaila (2015) ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail. In: The Doctoral Research Abstracts. IPSis Biannual Publication, 7 (7). Institute of Graduate Studies, UiTM, Shah Alam.

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Abstract

This thesis presents a new ANN modelling in discriminating agarwood oil quality using selected significant chemical compounds of the oil. In order to accomplish the work, the analyses have been carried out in two categories. The first category is the abundances pattern of odor chemical compounds observation and investigation. The extraction of odor chemical compounds is done by solid phase micro-extraction (SPME). In this work two types of SPME fibers were used; divinylbenzenec a r b o x e n - p o l y d i m e t h y l s i l o x a n e ( D V B - C A R - P D M S ) and polydimethylsiloxane(PDMS) to analyze the odor compounds under three different sampling temperature conditions; 40˚C, 60˚C and 80˚C. A consistent abundances pattern of five significant odor chemical compounds as highlighted by Z-score were revealed. The compounds are 10-epi-ϒ-eudesmol, aromadendrane,β-agarofuran, α-agarofuran and ϒ-eudesmol. These odor chemical compounds are important as they contributed to the odor of high quality agarwood oils. Then the second category was performed by the extraction of the agarwood oil chemical compounds using gas chromatography-mass spectrometry (GC-MS). The identified compounds from SPME were used as marker compounds for agarwood oil quality discrimination using GC-MS data…

Item Type: Book Section
Creators:
CreatorsID Num.
Ismail, NurlailaUNSPECIFIED
Subjects: L Education > LB Theory and practice of education > Higher Education > Dissertations, Academic. Preparation of theses > Malaysia
Divisions: Institut Pengajian Siswazah (IPSis) : Institute of Graduate Studies (IGS)
Series Name: IPSis Biannual Publication
Volume: 7
Number: 7
Item ID: 19333
Uncontrolled Keywords: Abstract; Abstract of thesis; Newsletter; Research information; Doctoral graduates; IPSis; IGS; UiTM; ANN modelling
Last Modified: 12 Jun 2018 07:47
Depositing User: Staf Pendigitalan 5
URI: http://ir.uitm.edu.my/id/eprint/19333

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