Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.
References
[1]
Persoon, G.A. Growing ‘the wood of the Gods’: Agarwood production in Southeast Asia. In Smallholder Tree Growing for Rural 245 Development and Environmental Services; Snelder, D.J., Lasco, R.D., Eds.; Springer: Dordrecht, The Netherlands, 2008; pp. 245–262.
[2]
Barden, A.; Anak, N.A.; Mulliken, T.; Song, M. Hearth of the Matter: Agarwood Use and Trade and CITES Implementation for Aquilaria Malaccensis. Traffic Network Report 1998–2000;; TRAFFIC International: Cambridge, UK, 2000; pp. 1–51.
[3]
Keller, P.E. Mimicking biology: applications of cognitive systems to electronic noses. Proceedings of IEEE International Symposium on Intelligent Control/Intelligent Systems and Semiotics, Cambridge, MA, USA, September 1999.
[4]
Cyranose 320 User Manual; Smith Detection: Pasadena, CA, USA, 2004.
[5]
Gardner, J.W.; Bartlett, P.N. Electronic Noses: Principles and Applications; Oxford University Press: New York, NY, USA, 1999.
[6]
Luo, D.; Hosseini, H.G.; Stewart, J.R. Application of ANN with extracted parameters from an electronic nose in cigarette brand identification. Sens. Actuat. B: Chem？2004, 99, 253–257.
[7]
Scott, S.M.; James, D.; Ali, Z. Review: Data analysis for electronic nose systems. Microchim. Acta？2007, 156, 183–207.
[8]
Hines, E.L.; Iobet, E.L.; Gardner, J.W. Electronic noses: a review of signal processing techniques. Proceedings of IEEE Circuits Device System, Salte Lake City, UT, USA, December 1999; 146.
[9]
Jolliffe, I.T. Principal Component Analysis, 2nd ed. ed.; Springer-Verlag: New York, NY, USA, 2002.
[10]
Penza, M.; Cassano, G. Application of principal component analysis and artificial neural networks to recognize the individual VOCs of methanol/2-propanol in a binary mixture by SAW multi-sensor array. Sens. Actuat. B: Chem？2003, 89, 269–284.
[11]
Gardner, J.W.; Boilot, P.; Hines, E.L. Enhancing electronic nose performance by sensor selection using a new integer-based genetic algorithm approach. Sens. Actuat. B: Chem？2005, 106, 114–121.
[12]
Dutta, R.; Das, A.; Stocks, N.G.; Morgan, D. Stochastic resonance-based electronic nose: A novel way to classify bacteria. Sens. Actuat. B: Chem？2006, 115, 17–27.
