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 An Electronic Nose Estimates Odor Pleasantness.png
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

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.

Contents

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

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

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

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

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

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 provide 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

Electronic nose developed in Analytical Chemistry Department (Chemical Faculty of Gdansk University of Technology) allows for rapid classification of food or environmental samples Enose prototype Analytical Dept Chemical Faculty GUT Gdansk.jpg
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

In process and production departments

In product development phases

Possible and future applications in the fields of health and security

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

In environmental monitoring


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

Related Research Articles

A biosensor is an analytical device, used for the detection of a chemical substance, that combines a biological component with a physicochemical detector. The sensitive biological element, e.g. tissue, microorganisms, organelles, cell receptors, enzymes, antibodies, nucleic acids, etc., is a biologically derived material or biomimetic component that interacts with, binds with, or recognizes the analyte under study. The biologically sensitive elements can also be created by biological engineering. The transducer or the detector element, which transforms one signal into another one, works in a physicochemical way: optical, piezoelectric, electrochemical, electrochemiluminescence etc., resulting from the interaction of the analyte with the biological element, to easily measure and quantify. The biosensor reader device connects with the associated electronics or signal processors that are primarily responsible for the display of the results in a user-friendly way. This sometimes accounts for the most expensive part of the sensor device, however it is possible to generate a user friendly display that includes transducer and sensitive element. The readers are usually custom-designed and manufactured to suit the different working principles of biosensors.

Nanosensors are nanoscale devices that measure physical quantities and convert these to signals that can be detected and analyzed. There are several ways proposed today to make nanosensors; these include top-down lithography, bottom-up assembly, and molecular self-assembly. There are different types of nanosensors in the market and in development for various applications, most notably in defense, environmental, and healthcare industries. These sensors share the same basic workflow: a selective binding of an analyte, signal generation from the interaction of the nanosensor with the bio-element, and processing of the signal into useful metrics.

<span class="mw-page-title-main">Aroma compound</span> Chemical compound that has a smell or odor

An aroma compound, also known as an odorant, aroma, fragrance or flavoring, is a chemical compound that has a smell or odor. For an individual chemical or class of chemical compounds to impart a smell or fragrance, it must be sufficiently volatile for transmission via the air to the olfactory system in the upper part of the nose. As examples, various fragrant fruits have diverse aroma compounds, particularly strawberries which are commercially cultivated to have appealing aromas, and contain several hundred aroma compounds.

<span class="mw-page-title-main">Geosmin</span> Chemical compound responsible for the characteristic odour of earth

Geosmin ( jee-OZ-min) is an irregular sesquiterpenoid, produced from the universal sesquiterpene precursor farnesyl pyrophosphate (also known as farnesyl diphosphate), in a two-step Mg2+-dependent reaction. Geosmin, along with the irregular monoterpene 2-methylisoborneol, together account for the majority of biologically-caused taste and odor outbreaks in drinking water worldwide. Geosmin has a distinct earthy or musty odor, which most people can easily smell. The geosmin odor detection threshold in humans is very low, ranging from 0.006 to 0.01 micrograms per liter in water. Geosmin is also responsible for the earthy taste of beetroots and a contributor to the strong scent (petrichor) that occurs in the air when rain falls after a spell of dry weather or when soil is disturbed.

The odor detection threshold is the lowest concentration of a certain odor compound that is perceivable by the human sense of smell. The threshold of a chemical compound is determined in part by its shape, polarity, partial charges, and molecular mass. The olfactory mechanisms responsible for a compound's different detection threshold is not well understood. As such, odor thresholds cannot be accurately predicted. Rather, they must be measured through extensive tests using human subjects in laboratory settings.

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 odorant compounds. The technology, though still in the early stages of development, promises many applications, such as: quality control in food processing, detection and diagnosis in medicine, detection of drugs, explosives and other dangerous or illegal substances, disaster response, and environmental monitoring.

Olfactory fatigue, also known as odor fatigue, olfactory adaptation, and noseblindness, is the temporary, normal inability to distinguish a particular odor after a prolonged exposure to that airborne compound. For example, when entering a restaurant initially the odor of food is often perceived as being very strong, but after time the awareness of the odor normally fades to the point where the smell is not perceptible or is much weaker. After leaving the area of high odor, the sensitivity is restored with time. Anosmia is the permanent loss of the sense of smell, and is different from olfactory fatigue.

A gas detector is a device that detects the presence of gases in an area, often as part of a safety system. A gas detector can sound an alarm to operators in the area where the leak is occurring, giving them the opportunity to leave. This type of device is important because there are many gases that can be harmful to organic life, such as humans or animals.

<span class="mw-page-title-main">Odor</span> Volatile chemical compounds perceived by the sense of smell

An odor or odour is caused by one or more volatilized chemical compounds that are generally found in low concentrations that humans and many animals can perceive via their sense of smell. An odor is also called a "smell" or a "scent", which can refer to either an unpleasant or a pleasant odor.

<span class="mw-page-title-main">Sense of smell</span> Sense that detects smells

The sense of smell, or olfaction, is the special sense through which smells are perceived. The sense of smell has many functions, including detecting desirable foods, hazards, and pheromones, and plays a role in taste.

<span class="mw-page-title-main">Olfactometer</span> Instrument used to detect and measure odor dilution

An olfactometer is an instrument used to detect and measure odor dilution. Olfactometers are used in conjunction with human subjects in laboratory settings, most often in market research, to quantify and qualify human olfaction. Olfactometers are used to gauge the odor detection threshold of substances. To measure intensity, olfactometers introduce an odorous gas as a baseline against which other odors are compared.

Breath gas analysis is a method for gaining information on the clinical state of an individual by monitoring volatile organic compounds (VOCs) present in the exhaled breath. Exhaled breath is naturally produced by the human body through expiration and therefore can be collected in non-invasively and in an unlimited way. VOCs in exhaled breath can represent biomarkers for certain pathologies. Breath gas concentration can then be related to blood concentrations via mathematical modeling as for example in blood alcohol testing. There are various techniques that can be employed to collect and analyze exhaled breath. Research on exhaled breath started many years ago, there is currently limited clinical application of it for disease diagnosis. However, this might change in the near future as currently large implementation studies are starting globally.

Robotic sensing is a subarea of robotics science intended to provide sensing capabilities to robots. Robotic sensing provides robots with the ability to sense their environments and is typically used as feedback to enable robots to adjust their behavior based on sensed input. Robot sensing includes the ability to see, touch, hear and move and associated algorithms to process and make use of environmental feedback and sensory data. Robot sensing is important in applications such as vehicular automation, robotic prosthetics, and for industrial, medical, entertainment and educational robots.

<span class="mw-page-title-main">Sniffing (behavior)</span> Nasal inhalation to sample odors

Sniffing is a perceptually-relevant behavior, defined as the active sampling of odors through the nasal cavity for the purpose of information acquisition. This behavior, displayed by all terrestrial vertebrates, is typically identified based upon changes in respiratory frequency and/or amplitude, and is often studied in the context of odor guided behaviors and olfactory perceptual tasks. Sniffing is quantified by measuring intra-nasal pressure or flow or air or, while less accurate, through a strain gauge on the chest to measure total respiratory volume. Strategies for sniffing behavior vary depending upon the animal, with small animals displaying sniffing frequencies ranging from 4 to 12 Hz but larger animals (humans) sniffing at much lower frequencies, usually less than 2 Hz. Subserving sniffing behaviors, evidence for an "olfactomotor" circuit in the brain exists, wherein perception or expectation of an odor can trigger brain respiratory center to allow for the modulation of sniffing frequency and amplitude and thus acquisition of odor information. Sniffing is analogous to other stimulus sampling behaviors, including visual saccades, active touch, and whisker movements in small animals. Atypical sniffing has been reported in cases of neurological disorders, especially those disorders characterized by impaired motor function and olfactory perception.

Optical air sensors center around the detection of some form of light created by a chemical process, in order to identify or measure amounts of individual molecules. Portable sensors are specifically sensors that are easy to transport and use in the field.

<span class="mw-page-title-main">Insect olfaction</span> Function of chemical receptors

Insect olfaction refers to the function of chemical receptors that enable insects to detect and identify volatile compounds for foraging, predator avoidance, finding mating partners and locating oviposition habitats. Thus, it is the most important sensation for insects. Most important insect behaviors must be timed perfectly which is dependent on what they smell and when they smell it. For example, olfaction is essential for locating host plants and hunting prey in many species of insects, such as the moth Deilephila elpenor and the wasp Polybia sericea, respectively.

The volatilome contains all of the volatile metabolites as well as other volatile organic and inorganic compounds that originate from an organism, super-organism, or ecosystem. The atmosphere of a living planet could be regarded as its volatilome. While all volatile metabolites in the volatilome can be thought of as a subset of the metabolome, the volatilome also contains exogenously derived compounds that do not derive from metabolic processes, therefore the volatilome can be regarded as a distinct entity from the metabolome. The volatilome is a component of the 'aura' of molecules and microbes that surrounds all organisms.

A chemical sensor array is a sensor architecture with multiple sensor components that create a pattern for analyte detection from the additive responses of individual sensor components. There exist several types of chemical sensor arrays including electronic, optical, acoustic wave, and potentiometric devices. These chemical sensor arrays can employ multiple sensor types that are cross-reactive or tuned to sense specific analytes.

Gas chromatography-olfactometry (GC-O) is a technique that integrates the separation of volatile compounds using a gas chromatograph with the detection of odour using an olfactometer. It was first invented and applied in 1964 by Fuller and co-workers. While GC separates volatile compounds from an extract, human olfaction detects the odour activity of each eluting compound. In this olfactometric detection, a human assessor may qualitatively determine whether a compound has odour activity or describe the odour perceived, or quantitatively evaluate the intensity of the odour or the duration of the odour activity. The olfactometric detection of compounds allows the assessment of the relationship between a quantified substance and the human perception of its odour, without instrumental detection limits present in other kinds of detectors. Compound identification still requires use of other detectors, such as mass spectrometry, with analytical standards.

Smell as evidence of disease has been long used, dating back to Hippocrates around 400 years BCE. It is still employed with a focus on volatile organic compounds (VOCs) found in body odor. VOCs are carbon-based molecular groups having a low molecular weight, secreted during cells’ metabolic processes. Their profiles may be altered by diseases such as cancer, metabolic disorders, genetic disorders, infections, and among others. Abnormal changes in VOC composition can be identified through equipment such as gas chromatography-mass spectrometry(GC-MS), electronic nose (e-noses), and trained non-human olfaction.

References

  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. 1 2 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. 1 2 3 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. 1 2 "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. Wiley. 45 (5). 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. United States: Wiley-Blackwell. 86 (9): 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. Basel, Switzerland: MDPI. 21 (14): 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.