Abstract
The need for noninvasive, easy-to-use, and inexpensive methods for point-of-care diagnostics of a variety of ailments motivates researchers to develop methods for analyzing complex biological samples, in particular human breath, that could aid in screening and early diagnosis. There are hopes that electronic noses, that is, devices based on arrays of semiselective or nonselective chemical sensors, can fill this niche. Electronic olfaction uses data processing and machine learning to build classification models based on the responses of several sensors in the form of multivariate datasets in order to discriminate between disease and healthy control based on a unique fingerprint. However, the introduction of this technique in clinical settings is limited by methodological issues which can, to some extent, be remedied using artificial intelligence. In this chapter, we provide a brief introduction to the electronic nose technique and outline its applications in medical diagnostics. We also discuss the ways in which data processing and machine learning techniques can be used to facilitate the use of electronic olfaction in the detection of disease.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Persaud K, Dodd G. Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature [Internet]. 1982 Sep 23 [cited 2021 Feb 28];299(5881):352–5. https://doi.org/10.1038/299352a0
McEntegart CM, Penrose WR, Strathmann S, Stetter JR. Detection and discrimination of coliform bacteria with gas sensor arrays. Sensors Actuators B Chem [Internet]. 2000 Nov [cited 2021 Feb 28];70(1–3):170–6. http://www.sciencedirect.com/science/article/pii/S092540050000561X
Santonico M, Pennazza G, Grasso S, D’Amico A, Bizzarri M. Design and test of a biosensor-based multisensorial system: a proof of concept study. Sensors (Basel) [Internet]. 2013 Jan 4 [cited 2021 Feb 28];13(12):16625–40. http://www.mdpi.com/1424-8220/13/12/16625/htm
Shimizu FM, Braunger ML, Riul A, Oliveira ON. Electronic tongues. In: Smart sensors for environmental and medical applications. Wiley; 2020. p. 61–80.
Shepherd GM. Smell images and the flavour system in the human brain [Internet]. Vol. 444, Nature. Nature Publishing Group; [Internet] 2006 [cited 2021 Feb 28]. p. 316–21. https://www.nature.com/articles/nature05405
Röck F, Barsan N, Weimar U. Electronic nose: current status and future trends. Chem Rev [Internet]. 2008 [cited 2021 Feb 28];108(2):705–25. http://pubs3.acs.org/acs/journals/doilookup?in_doi=10.1021/cr068121q
Buck L, Axel R. A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell. 1991;65(1):175–87.
Marcinkowska R, Namieśnik J, Tobiszewski M. Green and equitable analytical chemistry. Vol. 19, Current opinion in green and sustainable chemistry. Elsevier B.V.; 2019. p. 19–23.
Majchrzak T, Wojnowski W, Dymerski T, Gębicki J, Namieśnik J. Electronic noses in classification and quality control of edible oils: a review. Food Chem. 2018;246:192–201.
Majchrzak T, Wojnowski W, Piotrowicz G, Gębicki J, Namieśnik J. Sample preparation and recent trends in volatolomics for diagnosing gastrointestinal diseases. TrAC – Trends Anal Chem. 2018;108:38–49.
Wojnowski W, Kalinowska K, Majchrzak T, Płotka-Wasylka J, Namieśnik J. Prediction of the biogenic amines index of poultry meat using an electronic nose. Sensors. 2019;19(7):1580.
Hotel O, Poli JP, Mer-Calfati C, Scorsone E, Saada S. A review of algorithms for SAW sensors e-nose based volatile compound identification. Sensors Actuators, B: Chem Elsevier B.V.; [Internet] Feb 1, 2018 cited [2021 Feb 28] p. 2472–82. https://linkinghub.elsevier.com/retrieve/pii/S0925400517317057
Wojnowski W, Dymerski T, Gębicki J, Namieśnik J. Electronic noses in medical diagnostics. Curr Med Chem. 2019;26(1):197–215.
Moons KGM, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice [Internet]. Vol. 338, BMJ British Medical Journal Publishing Group; 2009 [cited 2021 Feb 24]. p. 1487–90. https://www.bmj.com/content/338/bmj.b606
Leopold JH, Bos LDJ, Sterk PJ, Schultz MJ, Fens N, Horvath I, et al. Comparison of classification methods in breath analysis by electronic nose. J Breath Res. 2015;9(4):046002.
Marco S, Gutierrez-Galvez A. Signal and data processing for machine olfaction and chemical sensing: a review. IEEE Sensors J. 2012;12(11):3189–214.
Marco S. The need for external validation in machine olfaction: emphasis on health-related applications [Internet]. Vol. 406, Analytical and bioanalytical chemistry. Springer; 2014 [cited 2021 Feb 28]. p. 3941–56. https://link.springer.com/article/10.1007/s00216-014-7807-7
Marco S, Gutierrez-Galvez A. Signal and data processing for machine olfaction and chemical sensing: A review. IEEE Sensors J [Internet]. 2012 Nov [cited 2021 Feb 28];12(11):3189–214. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6183455
Scott SM, James D, Ali Z. Data analysis for electronic nose systems. Microchim Acta. 2006;156(3–4):183–207.
Wilson AD, Baietto M. Advances in electronic-nose technologies developed for biomedical applications. Sensors [Internet]. 2011 Jan 19 [cited 2021 Feb 28];11(12):1105–76. http://www.mdpi.com/1424-8220/11/1/1105/
Bernabei M, Pennazza G, Santonico M, Corsi C, Roscioni C, Paolesse R, et al. A preliminary study on the possibility to diagnose urinary tract cancers by an electronic nose. Sensors Actuators B Chem. 2008;131(1):1–4.
Bikov A, Lázár Z, Horvath I. Established methodological issues in electronic nose research: How far are we from using these instruments in clinical settings of breath analysis? J Breath Res [Internet]. 2015 Jun 9 [cited 2021 Feb 28];9(3):034001. https://doi.org/10.1088/1752-7155/9/3/034001
Palmer CK, Thomas MC, Von Wagner C, Raine R. Reasons for non-uptake and subsequent participation in the NHS Bowel cancer screening programme: a qualitative study. Br J Cancer [Internet]. 2014 [cited 2021 Feb 28];110(7):1705–11. https://www.nature.com/articles/bjc2014125.pdf
Tenero L, Sandri M, Piazza M, Paiola G, Zaffanello M, Piacentini G. Electronic nose: a pilot study to discriminate of children with uncontrolled asthma. J Breath Res. 2020;14:046003.
Kononov A, Korotetsky B, Jahatspanian I, Gubal A, Vasiliev A, Arsenjev A, et al. Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer. J Breath Res. 2020;14(1):016004.
van Velzen P, Brinkman P, Knobel HH, van den Berg JWK, Jonkers RE, Loijmans RJ, et al. Exhaled breath profiles before, during and after exacerbation of COPD: a prospective follow-up study. COPD J Chronic Obstr Pulm Dis. 2019;16:330.
Schnabel RM, Boumans MLL, Smolinska A, Stobberingh EE, Kaufmann R, Roekaerts PMHJ, et al. Electronic nose analysis of exhaled breath to diagnose ventilator-associated pneumonia. Respir Med. 2015;109(11):1454–9.
Shafiek H, Fiorentino F, Merino JL, López C, Oliver A, Segura J, et al. Using the electronic nose to identify airway infection during COPD exacerbations. Kostikas K, editor. PLoS One [Internet]. 2015 Sep 9 [cited 2021 Feb 11];10(9):e0135199. https://dx.plos.org/10.1371/journal.pone.0135199
Brinkman P, Wagener AH, Hekking PP, Bansal AT, Maitland-van der Zee AH, Wang Y, et al. Identification and prospective stability of electronic nose (eNose)–derived inflammatory phenotypes in patients with severe asthma. J Allergy Clin Immunol. 2019;143(5):1811–1820.e7.
Dragonieri S, Quaranta VN, Carratu P, Ranieri T, Resta O. Exhaled breath profiling by electronic nose enabled discrimination of allergic rhinitis and extrinsic asthma. Biomarkers. 2019;24(1):70–5.
De Vries R, Dagelet YWF, Spoor P, Snoey E, Jak PMC, Brinkman P, et al. Clinical and inflammatory phenotyping by breathomics in chronic airway diseases irrespective of the diagnostic label. Eur Respir J. 2018;51(1):1–10.
Breathomix. SpiroNose [Internet]. [cited 2021 Feb 24]. https://www.breathomix.com/spironose-2/
Kim H-Y. Analysis of variance (ANOVA) comparing means of more than two groups. Restor Dent Endod. 2014;39(1):74.
Bernabei M, Pennazza G, Santonico M, Corsi C, Roscioni C, Paolesse R, et al. A preliminary study on the possibility to diagnose urinary tract cancers by an electronic nose. Sensors Actuators, B Chem [Internet]. 2008 cited [2021 Feb 28];131(1):1–4. https://doi.org/10.1007/s12149-018-1247-y
Baldini C, Billeci L, Sansone F, Conte R, Domenici C, Tonacci A. Electronic nose as a novel method for diagnosing cancer: a systematic review. Biosensors [Internet]. 2020 Jul 25 [cited 2021 Feb 20];10(8):1–21. https://www.mdpi.com/2079-6374/10/8/84
Wilson AD. Electronic-nose applications in forensic science and for analysis of volatile biomarkers in the human breath. J Forensic Sci Criminol [Internet]. 2014 [cited 2021 Feb 28];1(1):1–21. https://www.srs.fs.usda.gov/pubs/ja/2014/ja_2014_wilson_001.pdf
Wilson AD. Application of electronic-nose technologies and VOC-biomarkers for the noninvasive early diagnosis of gastrointestinal diseases. Sensors (Switzerland). 2018;18(8):2613.
Wilson AD. Recent applications of electronic-nose technologies for the noninvasive early diagnosis of gastrointestinal diseases†. Proceedings. 2017;2(3):147.
Wilson AD. Applications of electronic-nose technologies for noninvasive early detection of plant, animal and human diseases. Chemosensors. 2018;6(4):1–36.
Fitzgerald JE, Bui ETH, Simon NM, Fenniri H. Artificial nose technology: status and prospects in diagnostics. Trends Biotechnol [Internet]. 2017 Jan [cited 2021 Feb 28];35(1):33–42. https://doi.org/10.1016/j.tibtech.2016.08.005
Anderson JC. Measuring breath acetone for monitoring fat loss: review [Internet]. Vol. 23, Obesity. Blackwell Publishing; 2015 [cited 2021 Feb 25]. p. 2327–34. https://doi.org/10.1002/oby.21242.
Guo D, Zhang D, Li N, Zhang L, Yang J. A novel breath analysis system based on electronic olfaction. IEEE Trans Biomed Eng. 2010;57(11):2753–63.
Dragonieri S, Quaranta VN, Carratu P, Ranieri T, Resta O. Exhaled breath profiling by electronic nose enabled discrimination of allergic rhinitis and extrinsic asthma. Biomarkers [Internet]. 2019 [cited 2021 Feb 28];24(1):70–5. https://doi.org/10.1080/1354750X.2018.1508307
Dragonieri S, Quaranta VN, Carratu P, Ranieri T, Marra L, D’Alba G, et al. An electronic nose may sniff out amyotrophic lateral sclerosis. Respir Physiol Neurobiol [Internet]. 2016 [cited 2021 Feb 28];232:22–5. https://doi.org/10.1016/j.resp.2016.06.005
De Heer K, Kok MGM, Fens N, Weersink EJM, Zwinderman AH, Van Der Schee MPC, et al. Detection of airway colonization by Aspergillus fumigatus by use of electronic nose technology in patients with cystic fibrosis (Journal of Clinical Microbiology (2016) 54:3 (569–575)). J Clin Microbiol. 2016;54(7):1926.
Ibrahim B, Basanta M, Cadden P, Singh D, Douce D, Woodcock A, et al. Non-invasive phenotyping using exhaled volatile organic compounds in asthma. Thorax. 2011;66(9):804–9.
Bannier MAGE, Van De Kant KDG, Jöbsis Q, Dompeling E. Feasibility and diagnostic accuracy of an electronic nose in children with asthma and cystic fibrosis. J Breath Res. 2019;13(3):036009.
Brinkman P, van de Pol M, Gerritsen M, Bos L, Dekker T, Smids B, et al. Exhaled breath profiles in the monitoring of loss of control and clinical recovery in asthma. Clin Exp Allergy. 2017;47:1159.
De Vries R, Dagelet YWF, Spoor P, Snoey E, Jak PMC, Brinkman P, et al. Clinical and inflammatory phenotyping by breathomics in chronic airway diseases irrespective of the diagnostic label. Eur Respir J [Internet]. 2018 [cited 2021 Feb 28];51(1):1–10. https://doi.org/10.1183/13993003.01817-2017
Saidi T, Zaim O, Moufid M, El Bari N, Ionescu R, Bouchikhi B. Exhaled breath analysis using electronic nose and gas chromatography–mass spectrometry for non-invasive diagnosis of chronic kidney disease, diabetes mellitus and healthy subjects. Sensors Actuators, B Chem [Internet]. 2018 [cited 2021 Feb 28];257:178–88. https://doi.org/10.1016/j.snb.2017.10.178
Westenbrink E, Arasaradnam RP, O’Connell N, Bailey C, Nwokolo C, Bardhan KD, et al. Development and application of a new electronic nose instrument for the detection of colorectal cancer. Biosens Bioelectron [Internet]. 2015 [cited 2021 Feb 28];67:733–8. https://doi.org/10.1016/j.bios.2014.10.044
Finamore P, Pedone C, Scarlata S, Di Paolo A, Grasso S, Santonico M, et al. Validation of exhaled volatile organic compounds analysis using electronic nose as index of COPD severity. Int J COPD. 2018;13:1441–8.
Wintjens AGWE, Hintzen KFH, Engelen SME, Lubbers T, Savelkoul PHM, Wesseling G, et al. Applying the electronic nose for pre-operative SARS-CoV-2 screening. Surg Endosc [Internet]. 2020 [cited 2021 Feb 28];(0123456789). https://doi.org/10.1007/s00464-020-08169-0
Shan B, Broza YY, Li W, Wang Y, Wu S, Liu Z, et al. Multiplexed nanomaterial-based sensor Array for detection of COVID-19 in exhaled breath. ACS Nano. 2020;14(9):12125–32.
Schuermans VNE, Li Z, Jongen ACHM, Wu Z, Shi J, Ji J, et al. Pilot study: detection of gastric cancer from exhaled air analyzed with an electronic nose in Chinese patients. Surg Innov. 2018;25(5):429–34.
van de Goor RMGE, Leunis N, van Hooren MRA, Francisca E, Masclee A, Kremer B, et al. Feasibility of electronic nose technology for discriminating between head and neck, bladder, and colon carcinomas. Eur Arch Oto-Rhino-Laryngol. 2017;274(2):1053–60.
van Hooren MRA, Leunis N, Brandsma DS, Dingemans AMC, Kremer B, Kross KW. Differentiating head and neck carcinoma from lung carcinoma with an electronic nose: a proof of concept study. Eur Arch Oto-Rhino-Laryngol. 2016;273(11):3897–903.
Van Geffen WH, Bruins M, Kerstjens HAM. Diagnosing viral and bacterial respiratory infections in acute COPD exacerbations by an electronic nose: a pilot study. J Breath Res. 2016;10(3):036001.
Plaza V, Crespo A, Giner J, Merino JL, Ramos-Barbón D, Mateus EF, et al. Inflammatory asthma phenotype discrimination using an electronic nose breath analyzer. J Investig Allergol Clin Immunol [Internet]. 2015 [cited 2021 Feb 28];25(6):431–7. http://europepmc.org/abstract/MED/26817140
Tiele A, Wicaksono A, Kansara J, Arasaradnam RP, Covington JA. Breath analysis using enose and ion mobility technology to diagnose inflammatory bowel disease – a pilot study. Biosensors. 2019;9(2):1–15.
Moor CC, Oppenheimer JC, Nakshbandi G, Aerts JGJV, Brinkman P, Maitland-Van Der Zee AH, et al. Exhaled breath analysis by use of eNose technology: a novel diagnostic tool for interstitial lung disease. Eur Respir J. 2021;57(1):2002042.
van de Goor RMGE, Hardy JCA, van Hooren MRA, Kremer B, Kross KW. Detecting recurrent head and neck cancer using electronic nose technology: a feasibility study. Head Neck. 2019;41(9):2983–90.
Huang CH, Zeng C, Wang YC, Peng HY, Lin CS, Chang CJ, et al. A study of diagnostic accuracy using a chemical sensor array and a machine learning technique to detect lung cancer. Sensors (Switzerland). 2018;18(9):2845.
Lu B, Fu L, Nie B, Peng Z, Liu H. A novel framework with high diagnostic sensitivity for lung cancer detection by electronic nose. Sensors (Switzerland). 2019;19(23):1–29.
Gasparri R, Santonico M, Valentini C, Sedda G, Borri A, Petrella F, et al. Volatile signature for the early diagnosis of lung cancer. J Breath Res [Internet]. 2016 Feb 9 [cited 2021 Feb 11];10(1):016007. https://iopscience.iop.org/article/10.1088/1752-7155/10/1/016007
Tirzīte M, Bukovskis M, Strazda G, Jurka N, Taivans I. Detection of lung cancer in exhaled breath with an electronic nose using support vector machine analysis. J Breath Res. 2017;11(3):036009.
van de Goor R, van Hooren M, Dingemans AM, Kremer B, Kross K. Training and validating a portable electronic nose for lung cancer screening. J Thorac Oncol [Internet]. 2018 [cited 2021 Feb 28];13(5):676–81. https://doi.org/10.1016/j.jtho.2018.01.024
McWilliams A, Beigi P, Srinidhi A, Lam S, MacAulay CE. Sex and smoking status effects on the early detection of early lung cancer in high-risk smokers using an electronic nose. IEEE Trans Biomed Eng. 2015;62(8):2044–54.
Lamote K, Brinkman P, Vandermeersch L, Vynck M, Sterk PJ, Van Langenhove H, et al. Breath analysis by gas chromatography-mass spectrometry and electronic nose to screen for pleural mesothelioma: a cross-sectional case-control study. Oncotarget. 2017;8(53):91593–602.
De Meij TGJ, Van Der Schee MPC, Berkhout DJC, Van De Velde ME, Jansen AE, Kramer BW, et al. Early detection of necrotizing enterocolitis by fecal volatile organic compounds analysis. J Pediatr [Internet]. 2015 [cited 2021 Feb 28];167(3):562–567.e1. https://doi.org/10.1016/j.jpeds.2015.05.044
Dragonieri S, Porcelli F, Longobardi F, Carratu P, Aliani M, Ventura VA, et al. An electronic nose in the discrimination of obese patients with and without obstructive sleep apnoea. J Breath Res [Internet]. 2015 Jun 1 [cited 2021 Feb 11];9(2):026005. https://iopscience.iop.org/article/10.1088/1752-7155/9/2/026005
Peters Y, Schrauwen RWM, Tan AC, Bogers SK, De Jong B, Siersema PD. Detection of Barrett’s oesophagus through exhaled breath using an electronic nose device. Gut. 2020;69(7):1169–72.
Dragonieri S, Quaranta VN, Carratu P, Ranieri T, Resta O. Exhaled breath profiling in patients with COPD and OSA overlap syndrome: a pilot study. J Breath Res [Internet]. 2016 Nov 3 [cited 2021 Feb 11];10(4):041001. https://iopscience.iop.org/article/10.1088/1752-7155/10/4/041001
Finberg JPM, Schwartz M, Jeries R, Badarny S, Nakhleh MK, Abu Daoud E, et al. Sensor array for detection of early stage Parkinson’s disease before medication. ACS Chem Neurosci [Internet]. 2018 [cited 2021 Feb 28];9(11). https://doi.org/10.1021/acschemneuro.8b00245
Cavaleiro Rufo J, Paciência I, Mendes FC, Farraia M, Rodolfo A, Silva D, et al. Exhaled breath condensate volatilome allows sensitive diagnosis of persistent asthma. Allergy Eur J Allergy Clin Immunol. 2019;74(3):527–34.
Yang H-Y, Peng H-Y, Chang C-J, Chen P-C. Diagnostic accuracy of breath tests for pneumoconiosis using an electronic nose. J Breath Res [Internet]. 2017 Nov 29 [cited 2021 Feb 28];12(1):016001. https://iopscience.iop.org/article/10.1088/1752-7163/aa857d
Suarez-Cuartin G, Giner J, Merino JL, Rodrigo-Troyano A, Feliu A, Perea L, et al. Identification of Pseudomonas aeruginosa and airway bacterial colonization by an electronic nose in bronchiectasis. Respir Med [Internet]. 2018 [cited 2021 Feb 28];136(December 2017):111–7. https://doi.org/10.1016/j.rmed.2018.02.008
Brekelmans M, Fens N, Brinkman P, Bos L, Sterk P, Gerlag D. Smelling the diagnosis: the electronic nose as diagnostic tool in inflammatory arthritis. A case-reference study. PLoS One [Internet]. 2016 [cited 2021 Feb 28];11. https://doi.org/10.1371/journal.pone.0151715
De Vries R, Muller M, Van Der Noort V, Theelen WSME, Schouten RD, Hummelink K, et al. Prediction of response to anti-PD-1 therapy in patients with non-small-cell lung cancer by electronic nose analysis of exhaled breath. Ann Oncol [Internet]. 2019 [cited 2021 Feb 28];30(10):1660–6. https://doi.org/10.1093/annonc/mdz279
Saidi T, Tahri K, El Bari N, Ionescu R, Bouchikhi B. Detection of seasonal allergic rhinitis from exhaled breath VOCs using an electronic nose based on an array of chemical sensors. 2015 IEEE Sensors – Proc. 2015. p. 1–4.
Berkhout DJC, Niemarkt HJ, Buijck M, Van Weissenbruch MM, Brinkman P, Benninga MA, et al. Detection of sepsis in preterm infants by fecal volatile organic compounds analysis: a proof of principle study. J Pediatr Gastroenterol Nutr. 2017;65(3):e47–52.
Coronel Teixeira R, Rodríguez M, Jiménez de Romero N, Bruins M, Gómez R, Yntema JB, et al. The potential of a portable, point-of-care electronic nose to diagnose tuberculosis. J Infect [Internet]. 2017 [cited 2021 Feb 28];75(5):441–7. https://doi.org/10.1016/j.jinf.2017.08.003
Zetola NM, Modongo C, Matsiri O, Tamuhla T, Mbongwe B, Matlhagela K, et al. Diagnosis of pulmonary tuberculosis and assessment of treatment response through analyses of volatile compound patterns in exhaled breath samples. J Infect [Internet]. 2017 [cited 2021 Feb 28];74(4):367–76. https://doi.org/10.1016/j.jinf.2016.12.006
Mohamed EI, Mohamed MA, Moustafa MH, Abdel-Mageed SM, Moro AM, Baess AI, et al. Qualitative analysis of biological tuberculosis samples by an electronic nose-based artificial neural network. Int J Tuberc Lung Dis. 2017;21(7):810–7.
Chen CY, Lin WC, Yang HY. Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research. Respir Res. 2020;21(1):1–12.
Liao YH, Wang ZC, Zhang FG, Abbod MF, Shih CH, Shieh JS. Machine learning methods applied to predict ventilator-associated pneumonia with Pseudomonas aeruginosa infection via sensor array of electronic nose in intensive care unit. Sensors (Switzerland). 2019;19(8):1866.
Schnabel RM, Boumans MLL, Smolinska A, Stobberingh EE, Kaufmann R, Roekaerts PMHJ, et al. Electronic nose analysis of exhaled breath to diagnose ventilator-associated pneumonia. Respir Med [Internet]. 2015 [cited 2021 Feb 28];109(11):1454–9. https://doi.org/10.1016/j.rmed.2015.09.014
He P, Pengfei J, Qiao S, Duan S. Self-taught learning based on sparse autoencoder for E-nose in wound infection detection. Sensors (Switzerland). 2017;17(10):2279.
Saviauk T, Kiiski JP, Nieminen MK, Tamminen NN, Roine AN, Kumpulainen PS, et al. Electronic nose in the detection of wound infection bacteria from bacterial cultures: a proof-of-principle study. Eur Surg Res. 2018;59:1–11.
Smith D, Španěl P. The challenge of breath analysis for clinical diagnosis and therapeutic monitoring. Analyst [Internet]. 2007 Apr 30 [cited 2021 Feb 28];132(5):390–6. http://xlink.rsc.org/?DOI=B700542N
Jha SK, Yadava RDS. Performance assessment of PCA, MF and SVD methods for denoising in chemical sensor array based electronic nose system. Sensors Transducers [Internet]. 2011 [cited 2021 Feb 24];129(6):57–68. http://www.sensorsportal.com
Wijaya DR, Sarno R, Zulaika E. Noise filtering framework for electronic nose signals: an application for beef quality monitoring. Comput Electron Agric [Internet]. 2019 Feb 1 [cited 2021 Feb 23];157:305–21. www.elsevier.com/locate/compag
Distante C, Leo M, Siciliano P, Persaud KC. On the study of feature extraction methods for an electronic nose. Sensors Actuators B Chem. 2002;87(2):274–88.
Wijaya DR, Sarno R, Zulaika E. DWTLSTM for electronic nose signal processing in beef quality monitoring. Sensors Actuators B Chem. 2021;326:128931.
Yatabe K, Oikawa Y. Convex optimization-based windowed Fourier filtering with multiple windows for wrapped-phase denoising. Appl Opt [Internet]. 2016 Jun 10 [cited 2021 Feb 27];55(17):4632. https://doi.org/10.1364/AO.55.004632
Arboleda C, Wang Z, Stampanoni M. Wavelet-based noise-model driven denoising algorithm for differential phase contrast mammography. Opt Express [Internet]. 2013 May 6 [cited 2021 Feb 27];21(9):10572. https://www.osapublishing.org/viewmedia.cfm?uri=oe-21-9-10572&seq=0&html=true
Agarwal S, Rani A, Singh V, Mittal AP. EEG signal enhancement using cascaded S-Golay filter. Biomed Signal Process Control. 2017;36:194–204.
Zhang W, Tian F, Song A, Hu Y. Research on an optical e-nose denoising method based on LSSVM. Optik (Stuttg). 2018;168:118–26.
Rehman A ur, Belhaouari SB, Ijaz M, Bermak A, Hamdi M. Multi-classifier tree with transient features for drift compensation in electronic nose. IEEE Sensors J [Internet]. 2020 [cited 2021 Feb 28]; http://www.ieee.org/publications_standards/publications/rights/index.html
Falco A, Loghin FC, Becherer M, Lugli P, Salmerón JF, Rivadeneyra A. Low-cost gas sensing: dynamic self-compensation of humidity in CNT-based devices. ACS Sensors [Internet]. 2019 [cited 2021 Feb 23];4(12):3141–6. https://pubs.acs.org/sharingguidelines
Ma Z, Luo G, Qin K, Wang N, Niu W. Online sensor drift compensation for E-nose systems using domain adaptation and extreme learning machine. Sensors (Switzerland) [Internet]. 2018 Mar 1 [cited 2021 Feb 24];18(3):742. http://www.mdpi.com/1424-8220/18/3/742
Liu Q, Li X, Ye M, Ge SS, Du X. Drift compensation for electronic nose by semi-supervised domain adaption. IEEE Sensors J. 2014;14(3):657–65.
Vergara A, Vembu S, Ayhan T, Ryan MA, Homer ML, Huerta R. Chemical gas sensor drift compensation using classifier ensembles. Sensors Actuators, B Chem [Internet]. 2012 [cited 2021 Feb 28];166–167:320–9. https://doi.org/10.1016/j.snb.2012.01.074
Zhang L, Liu Y, He Z, Liu J, Deng P, Zhou X. Anti-drift in E-nose: a subspace projection approach with drift reduction. Sensors Actuators B Chem. 2017;253:407–17.
Rehman AU, Bermak A. Drift-insensitive features for learning artificial olfaction in E-nose system. IEEE Sensors J. 2018;18(17):7173–82.
Yi Z. Discriminative dimensionality reduction for sensor drift compensation in electronic nose: a robust, low-rank, and sparse representation method. Expert Syst Appl. 2020;148:113238.
Liu T, Li D, Chen Y, Wu M, Yang T, Cao J. Online drift compensation by adaptive active learning on mixed kernel for electronic noses. Sensors Actuators B Chem. 2020;316:128065.
Liu T, Li D, Chen J. An active method of online drift-calibration-sample formation for an electronic nose. Meas J Int Meas Confed. 2021;171:108748.
Liu T, Li D, Chen J, Chen Y, Yang T, Cao J. Active learning on dynamic clustering for drift compensation in an electronic nose system. Sensors (Switzerland) [Internet]. 2019 Aug 19 [cited 2021 Feb 24];19(16):3601. https://www.mdpi.com/1424-8220/19/16/3601
Steinbach J, Goedicke-Fritz S, Tutdibi E, Stutz R, Kaiser E, Meyer S, et al. Bedside measurement of volatile organic compounds in the atmosphere of neonatal incubators using ion mobility spectrometry. Front Pediatr [Internet]. 2019 Jun 18 [cited 2021 Feb 28];7:4–8. https://www.frontiersin.org/article/10.3389/fped.2019.00248/full
Casas-Ferreira AM, Nogal-Sánchez M del, Pérez-Pavón JL, Moreno-Cordero B. Non-separative mass spectrometry methods for non-invasive medical diagnostics based on volatile organic compounds: a review. Anal Chim Acta [Internet]. 2019 Jan [cited 2021 Feb 28];1045:10–22. https://linkinghub.elsevier.com/retrieve/pii/S0003267018308560
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this entry
Cite this entry
Wojnowski, W., Kalinowska, K. (2022). Machine Learning and Electronic Noses for Medical Diagnostics. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-64573-1_329
Download citation
DOI: https://doi.org/10.1007/978-3-030-64573-1_329
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64572-4
Online ISBN: 978-3-030-64573-1
eBook Packages: MedicineReference Module Medicine