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Machine olfaction is the automated simulation of the sense of smell. An emerging application in modern engineering, it involves the use of robots or other automated systems to analyze air-borne chemicals. Such an apparatus is often called an electronic nose or e-nose. The development of machine olfaction is complicated by the fact that e-nose devices to date have responded to a limited number of chemicals, whereas odors are produced by unique sets of (potentially numerous) odorant compounds. The technology, though still in the early stages of development, promises many applications, such as:[1] quality control in food processing, detection and diagnosis in medicine,[2] detection of drugs, explosives and other dangerous or illegal substances,[3] disaster response, and environmental monitoring.
One type of proposed machine olfaction technology is via gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds. However, a critical element in the development of these instruments is pattern analysis, and 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.[4] Another challenge in current research on machine olfaction is the need to predict or estimate the sensor response to aroma mixtures.[5] Some pattern recognition problems in machine olfaction such as odor classification and odor localization can be solved by using time series kernel methods.[6]
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Sensors Councilwas invoked but never defined (see the help page). - ^ Geffen, Wouter H. van; Bruins, Marcel; Kerstjens, Huib A. M. (2016-01-01). "Diagnosing viral and bacterial respiratory infections in acute COPD exacerbations by an electronic nose: a pilot study". Journal of Breath Research. 10 (3): 036001. Bibcode:2016JBR....10c6001V. doi:10.1088/1752-7155/10/3/036001. ISSN 1752-7163. PMID 27310311.
- ^ Stassen, I.; Bueken, B.; Reinsch, H.; Oudenhoven, J. F. M.; Wouters, D.; Hajek, J.; Van Speybroeck, V.; Stock, N.; Vereecken, P. M.; Van Schaijk, R.; De Vos, D.; Ameloot, R. (2016). "Towards metal–organic framework based field effect chemical sensors: UiO-66-NH2 for nerve agent detection". Chem. Sci. 7 (9): 5827–5832. doi:10.1039/C6SC00987E. hdl:1854/LU-8157872. PMC 6024240. PMID 30034722.
- ^ Gutierrez-Osuna, R. (2002). "Pattern analysis for machine olfaction: A review". IEEE Sensors Journal. 2 (3): 189–202. Bibcode:2002ISenJ...2..189G. doi:10.1109/jsen.2002.800688.
- ^ Phaisangittisagul, Ekachai; Nagle, H. Troy (2011). "Predicting odor mixture's responses on machine olfaction sensors". Sensors and Actuators B: Chemical. 155 (2): 473–482. Bibcode:2011SeAcB.155..473P. doi:10.1016/j.snb.2010.12.049.
- ^ Vembu, Shankar; Vergara, Alexander; Muezzinoglu, Mehmet K.; Huerta, Ramón (2012). "On time series features and kernels for machine olfaction". Sensors and Actuators B: Chemical. 174: 535–546. Bibcode:2012SeAcB.174..535V. doi:10.1016/j.snb.2012.06.070.