A combined pattern separability and two-tiered classification approach for identification of binary mixtures of VOCs

Several classification techniques have been developed with varying degrees of success for automated identification of VOCs, however, the problem becomes considerably more challenging when more than one VOC is present. The reason is two-fold: first, the response of the sensors to certain VOCs may be too strong and mask the response of the sensors to other VOCs in the environment; and second the responses of the sensors to VOCs may not have enough separability information if the specificity of the sensors is not adequate. We propose the following procedures for these two issues in identification of binary mixtures of VOCs: a nonlinear cluster transformation technique or nonparametric discriminant analysis to increase pattern separability, followed by a two-tier classification to aid in identification of dominant and secondary VOCs separately. Results demonstrate the feasibility of the combined approach.

[1]  A. K. Srivastava,et al.  Detection of volatile organic compounds (VOCs) using SnO2 gas-sensor array and artificial neural network , 2003 .

[2]  J. Wade Davis,et al.  Statistical Pattern Recognition , 2003, Technometrics.

[3]  Tao Xiong,et al.  A combined SVM and LDA approach for classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[4]  E. Llobet,et al.  Multicomponent gas mixture analysis using a single tin oxide sensor and dynamic pattern recognition , 2001, IEEE Sensors Journal.

[5]  H. T. Nagle,et al.  Handbook of Machine Olfaction , 2002 .

[6]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[7]  H. Wohltjen.  Mechanism of Operation and Design Considerations for Surface Acoustic Wave Device Vapor Sensors. , 1984 .

[8]  Fabrizio Davide,et al.  A composed neural network for the recognition of gas mixtures , 1995 .

[9]  W. H. King.  Piezoelectric Sorption Detector. , 1964 .

[10]  Josep Samitier,et al.  New pattern recognition systems designed for electronic noses , 2000 .

[11]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[12]  H. V. Malmstadt,et al.  An Automatic Potentiometric Method for the Determination of the Absolute Activity of Glucose Oxidase. , 1964 .

[13]  Satish S. Udpa,et al.  Nonlinear cluster transformations for increasing pattern separability , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[14]  Michele Penza,et al.  Application of principal component analysis and artificial neural networks to recognize the individual VOCs of methanol/2-propanol in a binary mixture by SAW multi-sensor array , 2003 .

[15]  David G. Stork,et al.  Pattern Classification , 1973 .

[16]  H. Troy Nagle,et al.  Handbook of Machine Olfaction: Electronic Nose Technology , 2003 .

[17]  Vasant Honavar,et al.  Detection and identification of odorants using an electronic nose , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[18]  T. Moriizumi,et al.  Radial basis neural networks for identification of volatile organic compounds , 2000 .