A novel sensor selection using pattern recognition in electronic nose

A novel sensor selection using pattern recognition technique in electronic nose (E-Nose) is proposed in this paper. This paper studies the portable E-Nose based on metal oxide semiconductor (MOS) gas sensors for detection of multiple kinds of indoor air contaminants. The characteristics of portability, low cost, multiple targets detection and high performance of E-Nose monitor are the main pursuit for home use. Formaldehyde, benzene, toluene, carbon monoxide, and ammonia are the primary targets of the proposed E-Nose which benefits from the characteristics of the broad spectrum, reproducibility, sensitivity and low-cost of MOS gas sensors. Therefore, a potential and full contribution analysis of the small sized sensor array, in detection of indoor air contaminants coupled with a kernel principal component analysis (KPCA) based linear discriminant analysis (LDA) pattern recognition technique, is presented in this paper. Some experimental findings on the roles of sensors in an E-Nose have also been concluded. The recognition results clearly demonstrate the contribution of each sensor to gas detection which helps the sensor selection in E-Nose design.

[1]  E. Massera,et al.  On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario , 2008 .

[2]  E. Llobet,et al.  Electronic Nose Based on Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Red Meat , 2008, Sensors.

[3]  Kiyoshi Sakai,et al.  A comparison of indoor air pollutants in Japan and Sweden: formaldehyde, nitrogen dioxide, and chlorinated volatile organic compounds. , 2004, Environmental research.

[4]  S C Lee,et al.  Indoor and outdoor air quality investigation at schools in Hong Kong. , 2000, Chemosphere.

[5]  Julian W. Gardner,et al.  Review of Conventional Electronic Noses and Their Possible Application to the Detection of Explosives , 2004 .

[6]  Eduard Llobet,et al.  A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine , 2011 .

[7]  S. Osowski,et al.  Metal oxide sensor arrays for detection of explosives at sub-parts-per million concentration levels by the differential electronic nose , 2012 .

[8]  Xin Yin,et al.  Chaotic time series prediction of E-nose sensor drift in embedded phase space , 2013 .

[9]  Giorgio Pennazza,et al.  An investigation on electronic nose diagnosis of lung cancer. , 2010, Lung cancer.

[10]  Lei Zhang,et al.  A new kernel discriminant analysis framework for electronic nose recognition. , 2014, Analytica chimica acta.

[11]  Fengchun Tian,et al.  A rapid discreteness correction scheme for reproducibility enhancement among a batch of MOS gas sensors , 2014 .

[12]  Khalifa Aguir,et al.  High performance of a gas identification system using sensor array and temperature modulation , 2007 .

[13]  Amalia Berna,et al.  Metal Oxide Sensors for Electronic Noses and Their Application to Food Analysis , 2010, Sensors.

[14]  Lei Zhang,et al.  Performance Study of Multilayer Perceptrons in a Low-Cost Electronic Nose , 2014, IEEE Transactions on Instrumentation and Measurement.

[15]  Arthur K. Kordon,et al.  Fault diagnosis based on Fisher discriminant analysis and support vector machines , 2004, Comput. Chem. Eng..

[16]  Eric Chanie,et al.  Shelf life determination by electronic nose: application to milk , 2005 .

[17]  Qi Ye,et al.  Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine , 2012 .

[18]  Sofian M. Kanan,et al.  Semiconducting Metal Oxide Based Sensors for Selective Gas Pollutant Detection , 2009, Sensors.

[19]  J. Yinon,et al.  Peer Reviewed: Detection of Explosives by Electronic Noses , 2003 .

[20]  Lei Zhang,et al.  A novel background interferences elimination method in electronic nose using pattern recognition , 2013 .

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

[22]  Juha Karhunen,et al.  Generalizations of principal component analysis, optimization problems, and neural networks , 1995, Neural Networks.

[23]  Xin Yin,et al.  A novel classifier ensemble for recognition of multiple indoor air contaminants by an electronic nose , 2014 .

[24]  Alphus D. Wilson,et al.  Advances in Electronic-Nose Technologies Developed for Biomedical Applications , 2011, Sensors.

[25]  Andrew P. Jones,et al.  Indoor air quality and health , 1999 .