Time Horizon Selection Using Feature Feedback for the Implementation of an E-Nose System

Several aspects of sensors should be considered for their practical application in reducing the cost and improving the performance of the system. Some factors, such as the power consumption, sampling period, processing time and memory size, are particularly important for efficient portable systems. In this paper, we propose a time horizon selection method for a portable sensor system. By using the feature feedback to investigate the relation between the input space and feature space, we find the distribution of discriminant information in a data sample and distinguish the time horizon with which we can improve the performance of the classification. The experimental results on different volatile organic compounds show that the proposed method provides for the good clustering of the different classes and increases classification rates from 95.3% to 96.9% and 98.2% for the selected time horizons, respectively.

[1]  Hua Yu,et al.  A direct LDA algorithm for high-dimensional data - with application to face recognition , 2001, Pattern Recognit..

[2]  Changsheng Xie,et al.  Identification and pattern recognition analysis of Chinese liquors by doped nano ZnO gas sensor array , 2005 .

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[4]  N. Bârsan,et al.  Electronic nose: current status and future trends. , 2008, Chemical reviews.

[5]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[6]  D. Yates,et al.  Breath Analysis of Lung Cancer Patients Using an Electronic Nose Detection System , 2010, IEEE Sensors Journal.

[7]  E. Martinelli,et al.  Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. , 2003, Biosensors & bioelectronics.

[8]  Seung-Chul Ha,et al.  A matched-profile method for simple and robust vapor recognition in electronic nose (E-nose) system , 2005 .

[9]  M. Martí,et al.  Electronic noses in the quality control of alcoholic beverages , 2005 .

[10]  Xudong Jiang,et al.  Eigenfeature Regularization and Extraction in Face Recognition , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Sunil K Jha,et al.  Denoising by Singular Value Decomposition and Its Application to Electronic Nose Data Processing , 2011, IEEE Sensors Journal.

[12]  M. Pardo,et al.  Classification of electronic nose data with support vector machines , 2005 .

[13]  Anne-Claude Romain,et al.  Using the classification model of an electronic nose to assign unknown malodours to environmental sources and to monitor them continuously , 2000 .

[14]  Mark Bücking,et al.  A novel electronic nose based on miniaturized SAW sensor arrays coupled with SPME enhanced headspace-analysis and its use for rapid determination of volatile organic compounds in food quality monitoring , 2006 .

[15]  Alphus D. Wilson,et al.  Applications and Advances in Electronic-Nose Technologies , 2009, Sensors.

[16]  Changsheng Xie,et al.  An alternate method of hierarchical classification for E-nose: Combined Fisher discriminant analysis and modified Sammon mapping , 2007 .

[17]  Calogero Pace,et al.  Least Square Regression Method for Estimating Gas Concentration in an Electronic Nose System , 2009, Sensors.

[18]  B. Tudu,et al.  Optimization of Sensor Array in Electronic Nose: A Rough Set-Based Approach , 2011, IEEE Sensors Journal.

[19]  Hakan Cevikalp,et al.  Discriminative common vectors for face recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Xiaodong Wang,et al.  Classification of data from electronic nose using relevance vector machines , 2009 .

[21]  Chan-Woo Moon,et al.  Pattern recognition using feature feedback: Application to face recognition , 2010 .

[22]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[23]  Gu-Min Jeong,et al.  LDA-Based Vapor Recognition Using Image-Formed Array Sensor Response for Portable Electronic Nose , 2009 .

[24]  Frank Stam,et al.  Packaging effects of a novel explosion-proof gas sensor , 2003 .

[25]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[26]  Su-Hyun Kim,et al.  Data Refinement and Channel Selection for a Portable E-Nose System by the Use of Feature Feedback , 2010, Sensors.