The grading of agarwood oil quality using k-Nearest Neighbor (k-NN)

Author

Ismail, Nur ; Rahiman, M.H.F. ; Taib, M.N. ; Ali, N.A.M. ; Jamil, M. ; Tajuddin, S.N.

Author_Institution

Fac. of Electr. Eng., UiTM Shah Alam, Shah Alam, Malaysia

fYear

2013

fDate

13-15 Dec. 2013

Firstpage

1

Lastpage

5

Abstract

This paper presents the application of k-Nearest Neighbor (k-NN) in grading the quality agarwood oil. Six agarwood oil samples obtained at Forest Research Institute Malaysia (FRIM) were extracted and their chemical compounds were examined by GC-MS. The work is followed by the grading system using the proposed k-NN. The study shows that there are 10 significant chemical compounds of agarwood oils. They are β-agarofuran, α-agarofuran, 10-epi-□-eudesmol, □-eudesmol, longifolol, oxo-agarospirol, hexadecanol and eudesmol. These compounds are used as inputs to the k-NN algorithm for grading them. The performance of the k-NN is measured and the highest accuracy obtained by k-NN which is above 83.3% shows that k-NN is a reliable classifier in grading the agarwood oil quality.

Keywords

chemical engineering computing; learning (artificial intelligence); oils; pattern classification; α-agarofuran; β-agarofuran; 10-epi-H-eudesmol; Forest Research Institute Malaysia; H-eudesmol; agarwood oil quality grading; eudesmol; hexadecanol; k- nearest neighbor; k-NN; longifolol; oxo-agarospirol; Accuracy; Chemical compounds; Conferences; Oils; Sensitivity; Testing; Training; agarwood oil; agarwood oil quality and chemical compounds; grading; k-Nearest Neighbor(k-NN);

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Process & Control (ICSPC), 2013 IEEE Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4799-2208-6

Type

conf

DOI

10.1109/SPC.2013.6735092

Filename

6735092