Publication:
Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors
Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors
Date
2010
Authors
Ammar Zakaria
Ali Yeon Md Shakaff
Abdul Hamid Adom
Mohd Noor Ahmad
Masnan, Maz Jamilah
Abdul Hallis Abdul Aziz
Nazifah Ahmad Fikri
Abu Hassan Abdullah
Latifah Munirah Kamarudin
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Volume Title
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Research Projects
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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
Keywords
Electronic nose,
Electronic tongue,
Data fusion,
PCA,
LDA,
Orthosiphon stamineus