Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine

Abstract This paper presents a laboratory study of multi-class classification problem for multiple indoor air contaminants which belongs to a completely linear-inseparable case. Six kinds of indoor air contaminations (formaldehyde, benzene, toluene, carbon monoxide, ammonia and nitrogen dioxide) were recognized as indicators of air quality in this project. The effectiveness of the proposed HSVM model has been rigorously evaluated on the experimental E-nose data sets. In addition, we have also compared it with existing five methods including Euclidean distance to centroids (EDC), simplified fuzzy ARTMAP network (SFAM), multilayer perceptron neural network (MLP) based on back-propagation learning rule, individual FLDA and single SVM. Experimental results have demonstrated that the HSVM model outperforms other classifiers in general. Also, HSVM classifier preliminarily shows its superiority in solution to discrimination in various electronic nose applications.

[1]  Patrycja Ciosek,et al.  The analysis of sensor array data with various pattern recognition techniques , 2006 .

[2]  Lei Zhang,et al.  On-line sensor calibration transfer among electronic nose instruments for monitoring volatile organic chemicals in indoor air quality , 2011 .

[3]  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.

[4]  J. Gardner,et al.  Application of artificial neural networks to an electronic olfactory system , 1990 .

[5]  B. Debska,et al.  Application of artificial neural network in food classification. , 2011, Analytica chimica acta.

[6]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[7]  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.

[8]  M. Peris,et al.  A 21st century technique for food control: electronic noses. , 2009, Analytica chimica acta.

[9]  H. Ulmer,et al.  MEMS Gas-Sensor Array for Monitoring the Perceived Car-Cabin Air Quality , 2006, IEEE Sensors Journal.

[10]  R. Brereton,et al.  Comparison of performance of five common classifiers represented as boundary methods: Euclidean Distance to Centroids, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Learning Vector Quantization and Support Vector Machines, as dependent on data structure , 2009 .

[11]  Annia García Pereira,et al.  Monitoring storage shelf life of tomato using electronic nose technique , 2008 .

[12]  Dean Zhao,et al.  Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools , 2011 .

[13]  Wan-Young Chung,et al.  An air quality sensor system with a momentum back propagation neural network , 2006 .

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

[15]  Tomasz Markiewicz,et al.  Classification of milk by means of an electronic nose and SVM neural network , 2004 .

[16]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[17]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[18]  S. Adeloju,et al.  Polypyrrole-based electronic noses for environmental and industrial analysis , 2005 .

[19]  Gang Wang,et al.  A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis , 2011, Expert Syst. Appl..

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

[21]  F. Xavier Rius,et al.  Multivariate standardization for correcting the ionic strength variation on potentiometric sensor arrays , 2000 .

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

[23]  Gilles Celeux,et al.  Variable selection in model-based discriminant analysis , 2011, J. Multivar. Anal..

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

[25]  Chu Kiong Loo,et al.  Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation , 2006, Neural Computing & Applications.

[26]  Giorgio Pennazza,et al.  A preliminary study on the possibility to diagnose urinary tract cancers by an electronic nose , 2008 .

[27]  J. Gardner,et al.  An electronic nose system for monitoring the quality of potable water , 2000 .

[28]  Jun Wang,et al.  Detection of age and insect damage incurred by wheat, with an electronic nose , 2007 .

[29]  J. Brezmes,et al.  Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thick-film tin oxide gas sensor array , 1997 .

[30]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[31]  Elizabeth A. Baldwin,et al.  Electronic Noses and Tongues: Applications for the Food and Pharmaceutical Industries , 2011, Sensors.

[32]  Daniel Cicerone,et al.  The use of an electronic nose to characterize emissions from a highly polluted river , 2008 .

[33]  Eduard Llobet,et al.  Fuzzy ARTMAP based electronic nose data analysis , 1999 .

[34]  Jin Luo,et al.  Pattern recognition for sensor array signals using Fuzzy ARTMAP , 2009 .

[35]  Desire L. Massart,et al.  Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data , 1996 .

[36]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[37]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[38]  Jun Wang,et al.  Evaluation of peach quality indices using an electronic nose by MLR, QPST and BP network , 2008 .

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