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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/dspace/handle/123456789/11300

Title: Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors
Authors: Ammar, Zakaria
Ali Yeon, Md. Shakaff, Prof. Dr.
Abdul Hamid, Adom, Assoc. Prof. Dr.
Mohd Noor, Ahmad
Maz Jamilah, Masnan
Abdul Hallis, Abd Aziz
Nazifah, Ahmad Fikri
Abu Hassan, Abdullah
Latifah Munirah, Kamarudin
???metadata.dc.contributor.url???: aliyeon@unimap.edu.my
abdhamid@unimap.edu.my
mohdnoor@unimap.edu.my
mazjamilah@unimap.edu.my
abdulhallis@unimap.edu.my
naffe_five@yahoo.com
abuhassan@unimap.edu.my
munirahkamarudin@gmail.com
Keywords: Data fusion;Electronic nose;Electronic tongue;Linear Discriminant Analysis (LDA);Orthosiphon stamineus;Principal Component Analysis (PCA)
Issue Date: 28-Sep-2010
Publisher: MDPI Publishing
Citation: Sensors, vol. 10(10), 2010, pages 8782-8796
Abstract: An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.
Description: Link to publisher's homepage at http://www.mdpi.com/
URI: http://www.mdpi.com/1424-8220/10/10/8782/
http://hdl.handle.net/123456789/11300
ISSN: 1424-8220
Appears in Collections:Abu Hassan Abdullah, Dr.
School of Mechatronic Engineering (Articles)
Ali Yeon, Md Shakaff, Prof. Dr.
Abdul Hamid Adom, Prof. Dr.

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