Pattern classifier of chemical compounds in different qualities of agarwood oil parameter using scale conjugate gradient algorithm in MLP

Nurul Shakila, Ahmad Zubir and Mohamad Aqib Haqmi, Abas and Ismail, N. A. and Nor Azah, Mohd Ali and Mohd Hezri Fazalul, Rahiman and Ng, K. M. and Mohd Nasir, Taib and Saiful Nizam, Tajuddin (2017) Pattern classifier of chemical compounds in different qualities of agarwood oil parameter using scale conjugate gradient algorithm in MLP. In: 13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017 , 10 - 12 March 2017 , Batu Ferringhi Beach, Penang. pp. 18-22. (8064917). ISBN 9781509011841

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Abstract

This paper presents the modelling of agarwood oil (AO) significant compounds by different qualities using Scaled Conjugate Gradient (SCG) algorithm. This technique involved of data collection from Gas Chromatography-Mass Spectrometry (GC-MS) for compound extraction. The development of Multilayer perceptron (MLP) is used to discriminate the qualities of AO chemical compounds to the high and low quality. The input and output data was transferred to the MATLAB version R2013a for extended analysis. The input is the abundances of significant compounds (%) and the output is the oil quality either high or low. This involved of identification, selection and optimization of a MLP as classifiers to identify and classify the agarwood oil quality. The result showed that MLP as pattern classifier is successful classify agarwood oil quality using SCG algorithm with 100% accuracy. This finding is important in agarwood oil area especially in grading system.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Agarwood oil; Classification; Scale Conjugate Gradient (SCG); Multilayer perceptron (MLP)
Subjects: T Technology > TP Chemical technology
Faculty/Division: Faculty of Industrial Sciences And Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 19 Aug 2022 08:28
Last Modified: 19 Aug 2022 08:28
URI: http://umpir.ump.edu.my/id/eprint/28993
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