Detection and identification of odorants using an electronic nose

Robi Polikar; Ruth Shinar; Vasant Honavar; Lalita Upda; Marc D. Porter

doi:10.1109/ICASSP.2001.940323

Detection and identification of odorants using an electronic nose

Robi Polikar, Ruth Shinar, Vasant Honavar, Lalita Upda, Marc D. Porter

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

Gas sensing systems for detection and identification of odorant molecules are of crucial importance in an increasing number of applications. Such applications include environmental monitoring, food quality assessment, airport security, and detection of hazardous gases. In this paper, we describe a gas sensing system for detecting and identifying volatile organic compounds (VOCs), and discuss the unique problems associated with the separability of signal patterns obtained by using such a system. We then present solutions for enhancing the separability of VOC patterns to enable classification. A new incremental learning algorithm that allows new odorants to be learned is also introduced.

Original language	English (US)
Pages (from-to)	3137-3140
Number of pages	4
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	5
DOIs	https://doi.org/10.1109/ICASSP.2001.940323
State	Published - 2001
Externally published	Yes

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/ICASSP.2001.940323

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language = "English (US)",

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AU - Shinar, Ruth

AU - Honavar, Vasant

AU - Upda, Lalita

AU - Porter, Marc D.

PY - 2001

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AB - Gas sensing systems for detection and identification of odorant molecules are of crucial importance in an increasing number of applications. Such applications include environmental monitoring, food quality assessment, airport security, and detection of hazardous gases. In this paper, we describe a gas sensing system for detecting and identifying volatile organic compounds (VOCs), and discuss the unique problems associated with the separability of signal patterns obtained by using such a system. We then present solutions for enhancing the separability of VOC patterns to enable classification. A new incremental learning algorithm that allows new odorants to be learned is also introduced.

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JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ER -

Detection and identification of odorants using an electronic nose

Abstract

All Science Journal Classification (ASJC) codes

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