Improved Classification of Orthosiphon stamineus by Data Fusion of Electronic Nose and Tongue Sensors

• Main Authors: Latifah Munirah Kamarudin, Abdul Hallis Abdul Aziz, Abu Hassan Abdullah, Nazifah Ahmad Fikri, Maz Jamilah Masnan, MohdNoor Ahmad, Abdul Hamid Adom, Ali Yeon Md. Shakaff, Ammar Zakaria
• Format: Article
• Language: English
• Published: MDPI AG 2010-09-01
• Series: Sensors
• Subjects: electronic nose; electronic tongue; data fusion; PCA; LDA; Orthosiphon stamineus
• Online Access: http://www.mdpi.com/1424-8220/10/10/8782/

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.