Journal
IEEE SENSORS JOURNAL
Volume 2, Issue 3, Pages 189-202Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2002.800688
Keywords
Classification; clustering; dimensionality reduction; electronic nose; multicomponent analysis; pattern analysis; preprocessing; validation
Funding
- NSF/CAREER [9984426]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [9984426] Funding Source: National Science Foundation
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Pattern analysis constitutes a critical building block in the development of gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds, a technology that has been proposed as an artificial substitute of the human olfactory system. The successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. A considerable number of methods from statistical pattern recognition, neural networks, chemometrics, machine learning, and biological cybernetics has been used to process electronic nose data. The objective of this review paper is to provide a summary and guidelines for using the most widely used pattern analysis techniques, as well as to identify research directions that are at the frontier of sensor-based machine olfaction.
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