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Electronic nose

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An electronic nose was tuned to the perceptual axis of odorant pleasantness, i.e., an axis ranging from very pleasant (e.g., rose) to very unpleasant (e.g., skunk). This allowed the eNose to then smell novel odorants it never encountered before, yet still generate odor pleasantness estimates in high agreement with human assessments regardless of the subject's cultural background. This suggests an innate component of odorant pleasantness that is tightly linked to molecular structure[1]

An electronic nose is an electronic sensing device intended to detect odors or flavors. The expression "electronic sensing" refers to the capability of reproducing human senses using sensor arrays and pattern recognition systems.

Since 1982,[2] research has been conducted to develop technologies, commonly referred to as electronic noses, that could detect and recognize odors and flavors. The stages of the recognition process are similar to human olfaction and are performed for identification, comparison, quantification and other applications, including data storage and retrieval. Some such devices are used for industrial purposes.

Other techniques to analyze odors

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In all industries, odor assessment is usually performed by human sensory analysis, by chemosensors, or by gas chromatography. The latter technique gives information about volatile organic compounds but the correlation between analytical results and mean odor perception is not direct due to potential interactions between several odorous components.

In the Wasp Hound odor detector, the mechanical element is a video camera and the biological element is five parasitic wasps who have been conditioned to swarm in response to the presence of a specific chemical.[3]

History

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Scientist Alexander Graham Bell popularized the notion that it was difficult to measure a smell,[4] and in 1914 said the following:

Did you ever measure a smell? Can you tell whether one smell is just twice strong as another? Can you measure the difference between two kinds of smell and another? It is very obvious that we have very many different kinds of smells, all the way from the odour of violets and roses up to asafetida. But until you can measure their likeness and differences, you can have no science of odour. If you are ambitious to find a new science, measure a smell.

— Alexander Graham Bell, 1914[5]

In the decades since Bell made this observation, no such science of odor materialised, and it was not until the 1950s and beyond that any real progress was made.[4] A common problem for odor-detecting is that it does not involve measuring energy, but physical particles.[6]

Working principle

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The electronic nose was developed in order to mimic human olfaction that functions as a non-separative mechanism: i.e. an odor / flavor is perceived as a global fingerprint.[7] Essentially the instrument consists of head space sampling, a chemical sensor array, and pattern recognition modules, to generate signal patterns that are used for characterizing odors.[8]

Electronic noses include three major parts: a sample delivery system, a detection system, a computing system.[9]

The sample delivery system enables the generation of the headspace (volatile compounds) of a sample, which is the fraction analyzed. The system then injects this headspace into the detection system of the electronic nose. The sample delivery system is essential to guarantee constant operating conditions.[8]

The detection system, which consists of a sensor set, is the "reactive" part of the instrument. When in contact with volatile compounds, the sensors react, which means they experience a change of electrical properties.[8]

In most electronic noses, each sensor is sensitive to all volatile molecules but each in their specific way. However, in bio-electronic noses, receptor proteins which respond to specific odor molecules are used. Most electronic noses use chemical sensor arrays that react to volatile compounds on contact: the adsorption of volatile compounds on the sensor surface causes a physical change of the sensor.[10] A specific response is recorded by the electronic interface transforming the signal into a digital value. Recorded data are then computed based on statistical models.[11]

Bio-electronic noses use olfactory receptors – proteins cloned from biological organisms, e.g. humans, that bind to specific odor molecules. One group has developed a bio-electronic nose that mimics the signaling systems used by the human nose to perceive odors at a very high sensitivity: femtomolar concentrations.[12]

The more commonly used sensors for electronic noses include

  • metal–oxide–semiconductor (MOS) devices – metal–oxide–semiconductor sensors contain a metal oxide coating with an electrical resistance that changes in the presence of a target gas. The presence of the target gas can be inferred by measuring the change in the resistance of the metal oxide layer over time.[13]
  • conducting polymers – organic polymers that conduct electricity.[14]
  • polymer composites – similar in use to conducting polymers but formulated of non-conducting polymers with the addition of conducting material such as carbon black.
  • quartz crystal microbalance (QCM) – a way of measuring mass per unit area by measuring the change in frequency of a quartz crystal resonator. This can be stored in a database and used for future reference.
  • surface acoustic wave (SAW) – a class of microelectromechanical systems (MEMS) which rely on the modulation of surface acoustic waves to sense a physical phenomenon.[15]
  • Mass spectrometers can be miniaturised to form general purpose gas analysis device.[16]

Some devices combine multiple sensor types in a single device, for example polymer coated QCMs. The independent information leads to vastly more sensitive and efficient devices.[17] Studies of airflow around canine noses, and tests on lifesize models have indicated that a cyclic 'sniffing action' similar to that of a real dog is beneficial in terms of improved range and speed of response[18]

In recent years, other types of electronic noses have been developed that utilize mass spectrometry or ultra-fast gas chromatography as a detection system.[11]

The computing system works to combine the responses of all of the sensors, which represents the input for the data treatment. This part of the instrument performs global fingerprint analysis and provides results and representations that can be easily interpreted. Moreover, the electronic nose results can be correlated to those obtained from other techniques (sensory panel, GC, GC/MS). Many of the data interpretation systems are used for the analysis of results. These systems include artificial neural network (ANN),[19] fuzzy logic, chemometrics methods,[20] pattern recognition modules, etc.[21] Artificial intelligence, included artificial neural network (ANN), is a key technique for the environmental odour management.[22]

Performing an analysis

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As a first step, an electronic nose needs to be trained with qualified samples so as to build a database of reference. Then the instrument can recognize new samples by comparing a volatile compound's fingerprint to those contained in its database. Thus they can perform qualitative or quantitative analysis. This however may also cause a problem as many odors are made up of multiple different molecules, which may be wrongly interpreted by the device as it will register them as different compounds, resulting in incorrect or inaccurate results depending on the primary function of a nose.[23] The example of e-nose dataset is also available.[24] This dataset can be used as a reference for e-nose signal processing, notably for meat quality studies. The two main objectives of this dataset are multiclass beef classification and microbial population prediction by regression.

Applications

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Electronic nose developed in Analytical Chemistry Department (Chemical Faculty of Gdańsk University of Technology) allows for rapid classification of food or environmental samples

Electronic nose instruments are used by research and development laboratories, quality control laboratories and process & production departments for various purposes:

In quality control laboratories

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  • Conformity of raw materials, intermediate and final products
  • Batch to batch consistency
  • Detection of contamination, spoilage, adulteration[25][26]
  • Origin or vendor selection
  • Monitoring of storage conditions[27]
  • Meat quality monitoring.[28][29]

In process and production departments

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  • Managing raw material variability
  • Comparison with a reference product
  • Measurement and comparison of the effects of manufacturing process on products
  • Following-up cleaning in place process efficiency
  • Scale-up monitoring
  • Cleaning in place monitoring.

In product development phases

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  • Sensory profiling and comparison of various formulations or recipes
  • Benchmarking of competitive products
  • Evaluation of the impact of a change of process or ingredient on sensory features.

Possible and future applications in the fields of health and security

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  • The detection of dangerous and harmful bacteria, such as software that has been specifically developed to recognise the smell of the MRSA (Methicillin-resistant Staphylococcus aureus).[30] It is also able to recognise methicillinsusceptible S. aureus (MSSA) among many other substances. It has been theorised that if carefully placed in hospital ventilation systems, it could detect and therefore prevent contamination of other patients or equipment by many highly contagious pathogens.
  • The detection of lung cancer or other medical conditions by detecting the VOC's (volatile organic compounds) that indicate the medical condition.[31][32][33]
  • The detection of viral and bacterial infections in COPD Exacerbations.[34]
  • The quality control of food products as it could be conveniently placed in food packaging to clearly indicate when food has started to rot or used in the field to detect bacterial or insect contamination.[35]
  • Nasal implants could warn of the presence of natural gas, for those who had anosmia or a weak sense of smell.
  • The Brain Mapping Foundation used the electronic nose to detect brain cancer cells.[36][37][38][39][40][41]

Possible and future applications in the field of crime prevention and security

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  • The ability of the electronic nose to detect odorless smells makes it ideal for use in the police force, such as the ability to detect bomb odors despite other airborne odors capable of confusing police dogs.
  • It may also be used as a drug detection method in airports. Through careful placement of several or more electronic noses and effective computer systems, one could triangulate the location of drugs to within a few metres of their location in less than a few seconds.
  • Demonstration systems that detect the vapours given off by explosives exist, but are currently some way behind a well trained sniffer dog.

In environmental monitoring

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  • For identification of volatile organic compounds in air, water and soil samples.[42][43]
  • For environmental protection.[44][45]


Various application notes describe analysis in areas such as flavor and fragrance, food and beverage, packaging, pharmaceutical, cosmetic and perfumes, and chemical companies. More recently they can also address public concerns in terms of olfactive nuisance monitoring with networks of on-field devices.[46][47] Since emission rates on a site can be extremely variable for some sources, the electronic nose can provide a tool to track fluctuations and trends and assess the situation in real time.[48] It improves understanding of critical sources, leading to pro-active odor management. Real time modeling will present the current situation, allowing the operator to understand which periods and conditions are putting the facility at risk. Also, existing commercial systems[49] can be programmed to have active alerts based on set points (odor concentration modeled at receptors/alert points or odor concentration at a nose/source) to initiate appropriate actions.

See also

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References

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  1. ^ Haddad, Rafi; Medhanie, Abebe; Roth, Yehudah; Harel, David; Sobel, Noam (15 April 2010). "Predicting Odor Pleasantness with an Electronic Nose". PLOS Computational Biology. 6 (4): e1000740. Bibcode:2010PLSCB...6E0740H. doi:10.1371/journal.pcbi.1000740. PMC 2855315. PMID 20418961.
  2. ^ Persaud, Krishna; Dodd, George (1982). "Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose". Nature. 299 (5881): 352–5. Bibcode:1982Natur.299..352P. doi:10.1038/299352a0. PMID 7110356. S2CID 4350740.
  3. ^ "Wasp Hound". Science Central. Archived from the original on 16 July 2011. Retrieved 23 February 2011.
  4. ^ a b Graham Bell (September 2003). "Measuring Odours and Odorants" (PDF). ChemoSense. Archived from the original (PDF) on 2012-03-31. Retrieved 2011-08-22.
  5. ^ Wise, P. M.; Olsson, MJ; Cain, WS (2000). "Quantification of Odor Quality". Chemical Senses. 25 (4): 429–43. doi:10.1093/chemse/25.4.429. PMID 10944507.
  6. ^ Wagstaff, Jeremy (2016-06-23). "Nose job: smells are smart sensors' last frontier". Reuters. Retrieved 2020-12-13.
  7. ^ Mendez, Maria Luz Rodriguez (2016-02-19). Electronic Noses and Tongues in Food Science. Academic Press. ISBN 978-0-12-800402-9.
  8. ^ a b c Gardner, J.; Yinon, Jehuda (2004-08-17). Electronic Noses and Sensors for the Detection of Explosives. Springer Science & Business Media. ISBN 978-1-4020-2318-7.
  9. ^ Karami, H., Rasekh, M. & Mirzaee-Ghaleh, E. Qualitative analysis of edible oil oxidation using an olfactory machine. Food Measure 14, 2600–2610 (2020). https://doi.org/10.1007/s11694-020-00506-0
  10. ^ "Chemical Sensing". sensigent.com. 11 March 2018. Retrieved 26 July 2023.
  11. ^ a b "Sensory expert and Analytical Instruments". alpha-mos.com. Archived from the original on 2008-10-23.
  12. ^ Jin, Hye Jun; Lee, Sang Hun; Kim, Tae Hyun; Park, Juhun; Song, Hyun Seok; Park, Tai Hyun; Hong, Seunghun (2012). "Nanovesicle-based bioelectronic nose platform mimicking human olfactory signal transduction". Biosensors and Bioelectronics. 35 (1): 335–41. doi:10.1016/j.bios.2012.03.012. PMID 22475887.
  13. ^ Nazemi, Haleh; Joseph, Aashish; Park, Jaewoo; Emadi, Arezoo (2019). "Advanced Micro- and Nano-Gas Sensor Technology: A Review". Sensors. 19 (6): 1285. Bibcode:2019Senso..19.1285N. doi:10.3390/s19061285. PMC 6470538. PMID 30875734.
  14. ^ Summary of electronic nose technologies – Andrew Horsfield [verification needed]
  15. ^ Röck, Frank; Barsan, Nicolae; Weimar, Udo (2008). "Electronic Nose: Current Status and Future Trends". Chemical Reviews. 108 (2): 705–25. doi:10.1021/cr068121q. PMID 18205411.
  16. ^ "Status and Future Trends of the Miniaturization of Mass Spectrometry" (PDF).
  17. ^ Paul Wali, R.; Wilkinson, Paul R.; Eaimkhong, Sarayoot Paul; Hernando-Garcia, Jorge; Sánchez-Rojas, Jose Luis; Ababneh, Abdallah; Gimzewski, James K. (2010-06-03). "Fourier transform mechanical spectroscopy of micro-fabricated electromechanical resonators: A novel, information-rich pulse method for sensor applications" (PDF). Sensors and Actuators B: Chemical. Vol. 147, no. 2. pp. 508–516. doi:10.1016/j.snb.2010.03.086. ISSN 0925-4005. Archived from the original on 2012-07-14. Retrieved 2021-02-14.
  18. ^ Staymates, Matthew E.; MacCrehan, William A.; Staymates, Jessica L.; Kunz, Roderick R.; Mendum, Thomas; Ong, Ta-Hsuan; Geurtsen, Geoffrey; Gillen, Greg J.; Craven, Brent A. (1 December 2016). "Biomimetic Sniffing Improves the Detection Performance of a 3D Printed Nose of a Dog and a Commercial Trace Vapor Detector". Scientific Reports. 6 (1): 36876. Bibcode:2016NatSR...636876S. doi:10.1038/srep36876. PMC 5131614. PMID 27906156.
  19. ^ Skarysz, Angelika; Alkhalifah, Yaser; Darnley, Kareen; Eddleston, Michael; Hu, Yang; McLaren, Duncan B.; Nailon, William H.; Salman, Dahlia; Sykora, Martin; Thomas, C L Paul; Soltoggio, Andrea (2018). "Convolutional neural networks for automated targeted analysis of raw gas chromatography-mass spectrometry data". 2018 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. doi:10.1109/IJCNN.2018.8489539. ISBN 978-1-5090-6014-6. S2CID 52989098.
  20. ^ Rasekh, Mansour; Karami, Hamed (2021-03-23). "Application of electronic nose with chemometrics methods to the detection of juices fraud". Journal of Food Processing and Preservation. 45 (5). Wiley. doi:10.1111/jfpp.15432. ISSN 0145-8892. S2CID 233676947.
  21. ^ "What the nose knows". The Economist. 9 March 2006. Archived from the original on 31 May 2011.
  22. ^ Zarra, Tiziano; Galang, Mark Gino; Ballesteros, Florencio; Belgiorno, Vincenzo; Naddeo, Vincenzo (December 2019). "Environmental odour management by artificial neural network – A review". Environment International. 133 (Pt B): 105189. doi:10.1016/j.envint.2019.105189. PMID 31675561.
  23. ^ Summary of electronic nose technologies[verification needed]
  24. ^ Wijaya, D.R.; Sarno, Riyanarto; Zulaika, Enny (2018). "Electronic nose dataset for beef quality monitoring in uncontrolled ambient conditions". Data in Brief. 21: 2414–2420. Bibcode:2018DIB....21.2414W. doi:10.1016/j.dib.2018.11.091. PMC 6282642. PMID 30547068.
  25. ^ Karami, H, Rasekh, M, Mirzaee-Ghaleh, E. Application of the E-nose machine system to detect adulterations in mixed edible oils using chemometrics methods. J Food Process Preserv. 2020; 44:e14696. https://doi.org/10.1111/jfpp.14696
  26. ^ Rasekh, M, Karami, H. Application of electronic nose with chemometrics methods to the detection of juices fraud. J Food Process Preserv. 2021; 45:e15432. https://doi.org/10.1111/jfpp.15432
  27. ^ Karami, H., Rasekh, M., & Mirzaee- Ghaleh, E. (2020). Comparison of chemometrics and AOCS official methods for predicting the shelf life of edible oil. Chemometrics and Intelligent Laboratory Systems, 206, 104165. https://doi.org/10.1016/j.chemolab.2020.104165
  28. ^ Wijaya, D.R.; Sarno, Riyanarto; Zulaika, Enny (2017). "Development of mobile electronic nose for beef quality monitoring". Procedia Computer Science. 124: 728–735. doi:10.1016/j.procs.2017.12.211.
  29. ^ Karunathilaka, Sanjeewa R.; Ellsworth, Zachary; Yakes, Betsy Jean (September 2021). "Detection of decomposition in mahi-mahi, croaker, red snapper, and weakfish using an electronic-nose sensor and chemometric modeling" (PDF). Journal of Food Science. 86 (9). United States: Wiley-Blackwell: 4148–4158. doi:10.1111/1750-3841.15878. ISSN 1750-3841. PMID 34402528. S2CID 237149759.
  30. ^ Dutta, Ritaban; Dutta, Ritabrata (2006). "Intelligent Bayes Classifier (IBC) for ENT infection classification in hospital environment". BioMedical Engineering OnLine. 5: 65. doi:10.1186/1475-925X-5-65. PMC 1764885. PMID 17176476.
  31. ^ Dragonieri, Silvano; Van Der Schee, Marc P.; Massaro, Tommaso; Schiavulli, Nunzia; Brinkman, Paul; Pinca, Armando; Carratú, Pierluigi; Spanevello, Antonio; Resta, Onofrio (2012). "An electronic nose distinguishes exhaled breath of patients with Malignant Pleural Mesothelioma from controls". Lung Cancer. 75 (3): 326–31. doi:10.1016/j.lungcan.2011.08.009. hdl:11586/130383. PMID 21924516.
  32. ^ Timms, Chris; Thomas, Paul S; Yates, Deborah H (2012). "Detection of gastro-oesophageal reflux disease (GORD) in patients with obstructive lung disease using exhaled breath profiling". Journal of Breath Research. 6 (1): 016003. Bibcode:2012JBR.....6a6003T. doi:10.1088/1752-7155/6/1/016003. PMID 22233591. S2CID 5307745.
  33. ^ Bikov, Andras; Hernadi, Marton; Korosi, Beata Zita; Kunos, Laszlo; Zsamboki, Gabriella; Sutto, Zoltan; Tarnoki, Adam Domonkos; Tarnoki, David Laszlo; Losonczy, Gyorgy; Horvath, Ildiko (December 2014). "Expiratory flow rate, breath hold and anatomic dead space influence electronic nose ability to detect lung cancer". BMC Pulmonary Medicine. 14 (1): 202. doi:10.1186/1471-2466-14-202. PMC 4289562. PMID 25510554. S2CID 5908556.
  34. ^ van Geffen, Wouter H; Bruins, Marcel; Kerstjens, Huib A M (16 June 2016). "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. PMID 27310311.
  35. ^ Degenhardt, David C.; Greene, Jeremy K.; Khalilian, Ahmad (2012). "Temporal Dynamics and Electronic Nose Detection of Stink Bug-Induced Volatile Emissions from Cotton Bolls". Psyche: A Journal of Entomology. 2012: 1–9. doi:10.1155/2012/236762.
  36. ^ "NASA's Electronic Nose May Provide Neurosurgeons With A New Weapon Against Brain Cancer". sciencedaily.com. Archived from the original on 10 August 2017. Retrieved 30 April 2018.
  37. ^ Babak Kateb, M. A. Ryan, M. L. Homer, L. M. Lara, Yufang Yin, Kerin Higa, Mike Y.Chen; Sniffing Out Cancer Using the JPL Electronic Nose: A Novel Approach to Detection and Differentiation of Brain Cancer, NeuroImage 47(2009), T5-9
  38. ^ "NASA's e-nose to fight brain cancer: Study". NDTV.com. 4 May 2009. Archived from the original on 18 December 2011.
  39. ^ "NASA's ENose sniffs for cancer". theregister.co.uk. Archived from the original on 2017-08-10.
  40. ^ Ross Miller. "NASA's new e-nose can detect scent of cancerous brain cells". Engadget. AOL. Archived from the original on 2017-08-10.
  41. ^ Michael Cooney (30 April 2009). "NASA's electronic nose can sniff out cancer, space stench". Network World. Archived from the original on 3 July 2013.
  42. ^ Edward J. Staples (1 November 2006). "A Sensitive Electronic Nose". Environmental protection. Archived from the original on 2011-10-08. Retrieved 2011-08-22.
  43. ^ Arroyo, Patricia; Herrero, José Luis; Suárez, José Ignacio; Lozano, Jesús (2019-02-08). "Wireless Sensor Network Combined with Cloud Computing for Air Quality Monitoring". Sensors. 19 (3): 691. Bibcode:2019Senso..19..691A. doi:10.3390/s19030691. PMC 6387342. PMID 30744013.
  44. ^ Pogfay, Tawee; Watthanawisuth, Natthapol; Pimpao, W.; Wisitsoraat, A.; Mongpraneet, S.; Lomas, T.; Sangworasil, M.; Tuantranont, Adisorn (19–21 May 2010). Development of Wireless Electronic Nose for Environment Quality Classification. 2010 International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology. pp. 540–3.
  45. ^ Cangialosi, Federico; Bruno, Edoardo; De Santis, Gabriella (2 July 2021). "Application of Machine Learning for Fenceline Monitoring of Odor Classes and Concentrations at a Wastewater Treatment Plant" (PDF). Sensors. 21 (14). Basel, Switzerland: MDPI: 4716. Bibcode:2021Senso..21.4716C. doi:10.3390/s21144716. ISSN 1424-8220. PMC 8309642. PMID 34300455.
  46. ^ "Sensory expert and Analytical Instruments". alpha-mos.com. Archived from the original on 2009-05-18.
  47. ^ "Pima County Marks Years of Odor Management Innovation". Odotech. Archived from the original on 2010-09-18.
  48. ^ Odour impact assessment handbook. Naddeo, V.,, Belgiorno, V.,, Zarra, T. Chichester, West Sussex, United Kingdom. 2012-11-26. ISBN 9781118481288. OCLC 818466563.{{cite book}}: CS1 maint: location missing publisher (link) CS1 maint: others (link)
  49. ^ "Portable Benchtop Instruments". sensigent.com. 14 March 2018. Retrieved 17 July 2023.
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