Machine olfaction

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Machine olfaction is the automated simulation of the sense of smell. It is an emerging application of modern engineering where robots or other automated systems are needed to measure the existence of a particular chemical concentration in air. Such an apparatus is often called an electronic nose or e-nose. Machine olfaction is complicated by the fact that e-nose devices to date have had a limited number of elements, whereas each odor is produced by own unique set of (potentially numerous) odorant compounds. because[1] This technology is still in the early stages of development, but it promises many applications, such as:[2]

  • quality assessment of beverage products [3]
  • quality control in food processing (e.g. taints, bacterial spoilage)
  • detection and diagnosis in medicine
  • detection of drugs, explosives and other dangerous or illegal substances
  • military and law enforcement (e.g. chemical warfare agents)
  • disaster response (e.g. toxic industrial chemicals)
  • environmental monitoring (e.g. pollutants)

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 for 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.[4] As well, how to foretell or estimate the sensor response to aroma mixtures, is one of the problems in present research on machine olfaction devices.[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]

Detection[edit]

Main article: Electronic nose

There are three basic detection techniques using:

  • Conductive-polymer odour sensors (polypyrrole)
  • Tin-oxide gas sensors
  • Quartz-crystal micro-balance sensor

They generally comprise; an array of sensors of some type; the electronics to interrogate those sensors and produce the digital signals, and finally; the data processing and user interface software.

The entire system being a means of converting complex sensor responses into an output that is a qualitative profile of the odour, volatile or complex mixture of chemical volatiles that make up a smell.

Conventional electronic noses are not analytical instruments in the classical sense and very few claim to be able to quantify an odour. These instruments are first ‘trained’ with the target odour and then used to ‘recognise’ smells so that future samples can be identified as ‘good’ or ‘bad’ smells.

Research into alternative methods for pattern recognition, for chemical sensor arrays, propose solutions to discrepancies between artificial and biological olfaction related to dimensionality. This biologically inspired approach involves creating unique algorithms for information processing.[7]

Electronic noses have been demonstrated to discriminate between odours and volatiles from a wide range of sources. The list below shows just some of the typical applications for electronic nose technology – many are backed by research studies and published technical papers.

See also[edit]

References[edit]

  1. ^ http://journals1.scholarsportal.info.myaccess.library.utoronto.ca/tmp/3825622692240070571.pdf
  2. ^ Sensors Council. (2012). Special issue on Machine Olfaction. IEEE SENSORS JOURNAL, 11(12), 3486-3486 . Retrieved March 20, 2012, from the Scholars Portal Journal database.
  3. ^ http://journals1.scholarsportal.info.myaccess.library.utoronto.ca/tmp/3825622692240070571.pdf
  4. ^ Sensors Council. (2002). Pattern analysis for machine olfaction: a review . IEEE SENSORS JOURNAL, 2(3), 189-202 . Retrieved March 20, 2012, from the Scholars Portal database.
  5. ^ Phaisangittisagul, E., & Nagle, H. T. (2011). Predicting odor mixture's responses on machine olfaction sensors. Sensors & Actuators: B. Chemical, 155(2), 473-482
  6. ^ Vembu, S.;Vergara, A.;Muezzinoglu, M. K.;Huerta, R. (2012). On time series features and kernels for machine olfaction. Sensors &Actuators: B. Chemical,174, 535
  7. ^ Baranidharan Raman,"Sensor-based Machine Olfaction with Neuromorphic Models of the Olfactory System",University of Madras, India; M.S., Texas A&M University, December 2005

External links[edit]