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

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.

[1]  A. Guadarrama,et al.  Discrimination of wine aroma using an array of conducting polymer sensors in conjunction with solid-phase micro-extraction (SPME) technique , 2001 .

[2]  Abdul Hamid Adom,et al.  Classification of Agarwood Oil Using an Electronic Nose , 2010, Sensors.

[3]  K. Héberger,et al.  Supervised pattern recognition in food analysis. , 2007, Journal of chromatography. A.

[4]  Ingemar Lundström,et al.  The combination of an electronic tongue and an electronic nose , 1999 .

[5]  V. Steinmetz,et al.  A Methodology for Sensor Fusion Design: Application to Fruit Quality Assessment , 1999 .

[6]  A. R. Othman,et al.  Chemometric Classification of Herb – Orthosiphon stamineus According to Its Geographical Origin Using Virtual Chemical Sensor Based Upon Fast GC , 2003 .

[7]  Bipan Tudu,et al.  Preemptive identification of optimum fermentation time for black tea using electronic nose , 2008 .

[8]  C. Di Natale,et al.  The features of the electronic tongue in comparison with the characteristics of the discrete ion-selective sensors , 1999 .

[9]  Evor L. Hines,et al.  Electronic nose based tea quality standardization , 2003, Neural Networks.

[10]  R. Gutierrez-Osuna,et al.  Fusion of three sensory modalities for the multimodal characterization of red wines , 2004, IEEE Sensors Journal.

[11]  Pascal Boilot,et al.  Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions , 2003 .

[12]  Felix Escher,et al.  Use of preconcentration techniques applied to a MS-based Electronic Nose , 2000 .

[13]  H. Nam,et al.  Multicomponent analysis of Korean green tea by means of disposable all-solid-state potentiometric electronic tongue microsystem , 2003 .

[14]  J W Gardner and P N Bartlett,et al.  Electronic Noses: Principles and Applications , 1999 .

[15]  Evor L. Hines,et al.  Enhancing electronic nose performance by sensor selection using a new integer-based genetic algorithm approach , 2005 .

[16]  K. Persaud,et al.  Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose , 1982, Nature.

[17]  K. Toko,et al.  Taste sensor. , 2005, Chemical senses.

[18]  Antonella Macagnano,et al.  Electronic nose and electronic tongue integration for improved classification of clinical and food samples , 2000 .

[19]  I. Lundström,et al.  A hybrid electronic tongue. , 2000 .

[20]  C. Natale,et al.  Electronic tongue: new analytical tool for liquid analysis on the basis of non-specific sensors and methods of pattern recognition , 2000 .

[21]  J. Delwiche.  The impact of perceptual interactions on perceived flavor , 2004 .