Abstract
Food safety issues across the global food supply chain have become paramount in promoting public health safety and commercial success of global food industries. As food regulations and consumer expectations continue to advance around the world, notwithstanding the latest technology, detection tools, regulations and consumer education on food safety and quality, there is still an upsurge of foodborne disease outbreaks across the globe. The development of the Electronic nose as a noninvasive technique suitable for detecting volatile compounds have been applied for food safety and quality analysis. Application of E-nose for pathogen detection has been successful and superior to conventional methods. E-nose offers a method that is noninvasive, fast and requires little or no sample preparation, thus making it ideal for use as an online monitoring tool. This manuscript presents an in-depth review of the application of electronic nose (E-nose) for food safety, with emphasis on classification and detection of foodborne pathogens. We summarise recent data and publications on foodborne pathogen detection (2006–2018) and by E-nose together with their methodologies and pattern recognition tools employed. E-nose instrumentation, sensing technologies and pattern recognition models are also summarised and future trends and challenges, as well as research perspectives, are discussed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdallah SA, Al-Shatti LA, Alhajraf AF, Al-Hammad N, Al-Awadi B (2013) The detection of foodborne bacteria on beef: the application of the electronic nose. SpringerPlus 2:687. https://doi.org/10.1186/2193-1801-2-687
Acevedo FJ, Maldonado S, Domínguez E, Narváez A, López F (2007) Probabilistic support vector machines for multi-class alcohol identification. Sens Actuators B Chem 122:227–235. https://doi.org/10.1016/j.snb.2006.05.033
Ampuero S, Zesiger T, Gustafsson V, Lundén A, Bosset J (2002) Determination of trimethylamine in milk using an MS based electronic nose. Eur Food Res Technol 214:163–167. https://doi.org/10.1007/s00217-001-0463-0
Avalos M, van Wezel GP, Raaijmakers JM, Garbeva P (2018) Healthy scents: microbial volatiles as new frontier in antibiotic research? Curr Opin Microbiol 45:84–91. https://doi.org/10.1016/j.mib.2018.02.011
Balasubramanian S, Panigrahi S, Logue CM, Doetkott C, Marchello M, Sherwood JS (2008) Independent component analysis-processed electronic nose data for predicting Salmonella typhimurium populations in contaminated beef. Food Control 19:236–246. https://doi.org/10.1016/j.foodcont.2007.03.007
Balasubramanian S, Amamcharla J, Panigrahi S, Logue CM, Marchello M, Sherwood JS (2012) Investigation of different gas sensor-based artificial olfactory systems for screening Salmonella typhimurium contamination in beef. Food Bioprocess Technol 5:1206–1219. https://doi.org/10.1007/s11947-010-0444-z
Balasubramanian S, Amamcharla J, Shin J-E (2016) Chapter 7-Possible application of electronic nose systems for meat safety: an overview. In: Rodríguez Méndez ML (ed) Electronic noses and tongues in food science. Academic Press, San Diego, pp 59–71. https://doi.org/10.1016/B978-0-12-800243-8.00007-X
Balbin JR, Sese JT, Babaan CVR, Poblete DMM, Panganiban RP, Poblete JG (2017) Detection and classification of bacteria in common street foods using electronic nose and support vector machine. In: 2017 7th IEEE international conference on control system, computing and engineering (ICCSCE), 24–26 Nov 2017, pp 247–252. https://doi.org/10.1109/iccsce.2017.8284413
Banerjee MB, Roy RB, Tudu B, Bandyopadhyay R, Bhattacharyya N (2019) Black tea classification employing feature fusion of E-nose and E-tongue responses. J Food Eng 244:55–63. https://doi.org/10.1016/j.jfoodeng.2018.09.022
Berna Z, Webb CC, Erickson MC (2013) Electronic nose and fast GC for detection of volatiles from Escherichia coli O157:H7 Escherichia coli and Salmonella in lettuce. In: International Society for Horticultural Science (ISHS), Leuven, Belgium, pp 1255–1261. https://doi.org/10.17660/ActaHortic.2013.1012.169
Cao LJ, Chua KS, Chong WK, Lee HP, Gu QM (2003) A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55:321–336. https://doi.org/10.1016/S0925-2312(03)00433-8
Carmel L, Levy S, Lancet D, Harel D (2003) A feature extraction method for chemical sensors in electronic noses. Sens Actuators B Chem 93:67–76. https://doi.org/10.1016/S0925-4005(03)00247-8
Chen S, Wang Y, Choi S (2013) Applications and technology of electronic nose for clinical diagnosis. Open J Appl Biosensor 02(02):12. https://doi.org/10.4236/ojab.2013.22005
Concina I, Falasconi M, Gobbi E, Bianchi F, Musci M, Mattarozzi M, Pardo M, Mangia A, Careri M, Sberveglieri G (2009) Early detection of microbial contamination in processed tomatoes by electronic nose. Food Control 20:873–880. https://doi.org/10.1016/j.foodcont.2008.11.006
Dębska B, Guzowska-Świder B (2011) Application of artificial neural network in food classification. Anal Chim Acta 705:283–291. https://doi.org/10.1016/j.aca.2011.06.033
Ding N-y, Lan Y-b, Zheng X-z (2010) Rapid detection of E. coli on goat meat by electronic nose. Adv Nat Sci. https://doi.org/10.3968/931
Distante C, Ancona N, Siciliano P (2003) Support vector machines for olfactory signals recognition. Sens Actuators B Chem 88:30–39. https://doi.org/10.1016/S0925-4005(02)00306-4
El Barbri N, Llobet E, El Bari N, Correig X, Bouchikhi B (2008) Application of a portable electronic nose system to assess the freshness of Moroccan sardines. Mater Sci Eng C 28:666–670. https://doi.org/10.1016/j.msec.2007.10.056
Elgaali H, Hamilton-Kemp TR, Newman MC, Collins RW, Yu K, Archbold DD (2002) Comparison of long-chain alcohols and other volatile compounds emitted from food-borne and related Gram positive and Gram negative bacteria. J Basic Microbiol 42:373–380. https://doi.org/10.1002/1521-4028(200212)42:6%3c373:AID-JOBM373%3e3.0.CO;2-4
Ezhilan M, Nesakumar N, Jayanth Babu K, Srinandan CS, Rayappan JBB (2018) An electronic nose for royal delicious apple quality assessment—a tri-layer approach. Food Res Int 109:44–51. https://doi.org/10.1016/j.foodres.2018.04.009
Franz CMAP, den Besten HMW, Böhnlein C, Gareis M, Zwietering MH, Fusco V (2018) Microbial food safety in the 21st century: emerging challenges and foodborne pathogenic bacteria. Trends Food Sci Technol 81:155–158. https://doi.org/10.1016/j.tifs.2018.09.019
Gardner JW, Bartlett PN (1994) A brief history of electronic noses. Sens Actuators B Chem 18:210–211. https://doi.org/10.1016/0925-4005(94)87085-3
Gardner JW, Craven M, Dow C, Hines EL (1998) The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perceptron network. Meas Sci Technol 9:120–127. https://doi.org/10.1088/0957-0233/9/1/016
Ghasemi-Varnamkhasti M, Apetrei C, Lozano J, Anyogu A (2018) Potential use of electronic noses, electronic tongues and biosensors as multisensor systems for spoilage examination in foods. Trends Food Sci Technol 80:71–92. https://doi.org/10.1016/j.tifs.2018.07.018
Giungato P, Di Gilio A, Palmisani J, Marzocca A, Mazzone A, Brattoli M, Giua R, de Gennaro G (2018) Synergistic approaches for odor active compounds monitoring and identification: state of the art, integration, limits and potentialities of analytical and sensorial techniques. TrAC Trends Anal Chem 107:116–129. https://doi.org/10.1016/j.trac.2018.07.019
Gobbi E, Falasconi M, Zambotti G, Sberveglieri V, Pulvirenti A, Sberveglieri G (2015) Rapid diagnosis of Enterobacteriaceae in vegetable soups by a metal oxide sensor based electronic nose. Sens Actuators B Chem 207:1104–1113. https://doi.org/10.1016/j.snb.2014.10.051
Green GC, Chan ADC, Dan H, Lin M (2011) Using a metal oxide sensor (MOS)-based electronic nose for discrimination of bacteria based on individual colonies in suspension. Sens Actuators B Chem 152:21–28. https://doi.org/10.1016/j.snb.2010.09.062
Green GC, Chan ADC, Lin M (2014) Robust identification of bacteria based on repeated odor measurements from individual bacteria colonies. Sens Actuators B Chem 190:16–24. https://doi.org/10.1016/j.snb.2013.08.001
Guan B, Zhao J, Lin H, Zou X (2014) Characterization of volatile organic compounds of vinegars with novel electronic nose system combined with multivariate analysis. Food Anal Methods 7:1073–1082. https://doi.org/10.1007/s12161-013-9715-4
Gutierrez-Osuna R, Nagle HT (1999) A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors. IEEE Trans Syst Man Cybern B (Cybern) 29:626–632. https://doi.org/10.1109/3477.790446
Havelaar AH, Kirk MD, Torgerson PR, Gibb HJ, Hald T, Lake RJ, Praet N, Bellinger DC, de Silva NR, Gargouri N, Speybroeck N, Cawthorne A, Mathers C, Stein C, Angulo FJ, Devleesschauwer B, on behalf of World Health Organization Foodborne Disease Burden Epidemiology Reference G (2015) World Health Organization global estimates and regional comparisons of the burden of foodborne disease in 2010. PLOS Med 12:e1001923. https://doi.org/10.1371/journal.pmed.1001923
Hierlemann A, Gutierrez-Osuna R (2008) Higher-order chemical sensing. Chem Rev 108:563–613. https://doi.org/10.1021/cr068116m
Huang X-J, Choi Y-K, Yun K-S, Yoon E (2006) Oscillating behaviour of hazardous gas on tin oxide gas sensor: Fourier and wavelet transform analysis. Sens Actuators B Chem 115:357–364. https://doi.org/10.1016/j.snb.2005.09.022
Hyvärinen A, Karhunen J, Oja E (2001) Noisy ICA. In: Haykin S (ed) Independent component analysis. Wiley, Hoboken. https://doi.org/10.1002/0471221317.ch15
Jha Sunil Kumar, Yadava RDS (2011) Power scaling of chemiresistive sensor array data for odor classification. J Pattern Recogn Res JPRR. https://doi.org/10.13176/11.247
Jha SK, Yadava RDS, Hayashi K, Patel N (2019) Recognition and sensing of organic compounds using analytical methods, chemical sensors, and pattern recognition approaches. Chemometr Intell Lab Syst 185:18–31. https://doi.org/10.1016/j.chemolab.2018.12.008
Jia W, Liang G, Wang Y, Wang J (2018) Electronic noses as a powerful tool for assessing meat quality: a mini review. Food Anal Methods 11:2916–2924. https://doi.org/10.1007/s12161-018-1283-1
Jiang H, Chen Q (2014) Development of electronic nose and near infrared spectroscopy analysis techniques to monitor the critical time in SSF process of feed protein. Sensors. https://doi.org/10.3390/s141019441
Jiang X, Jia P, Luo R, Deng B, Duan S, Yan J (2017) A novel electronic nose learning technique based on active learning: EQBC-RBFNN. Sens Actuators B Chem 249:533–541. https://doi.org/10.1016/j.snb.2017.04.072
Jolliffe I (2014) Principal component analysis, Statistics reference online, Wiley StatsRef. Wiley, Hoboken. https://doi.org/10.1002/9781118445112.stat06472
Kai M, Haustein M, Molina F, Petri A, Scholz B, Piechulla B (2009) Bacterial volatiles and their action potential. Appl Microbiol Biotechnol 81:1001–1012. https://doi.org/10.1007/s00253-008-1760-3
Kaur R, Kumar R, Gulati A, Ghanshyam C, Kapur P, Bhondekar AP (2012) Enhancing electronic nose performance: a novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze). Sens Actuators B Chem 166–167:309–319. https://doi.org/10.1016/j.snb.2012.02.067
Kizil Ü, Genç L, Genç TT, Rahman S, Khaitsa ML (2015) E-nose identification of Salmonella enterica in poultry manure. Br Poult Sci 56:149–156. https://doi.org/10.1080/00071668.2015.1014467
Laref R, Losson E, Sava A, Siadat M (2018) Support vector machine regression for calibration transfer between electronic noses dedicated to air pollution monitoring. Sensors 18:3716
Li M, Wang H, Sun L, Zhao G, Huang X (2016) Application of electronic nose for measuring total volatile basic nitrogen and total viable counts in packaged pork during refrigerated storage. J Food Sci 81:M906–M912. https://doi.org/10.1111/1750-3841.13238
Lippolis V, Cervellieri S, Damascelli A, Pascale M, Di Gioia A, Longobardi F, De Girolamo A (2018) Rapid prediction of deoxynivalenol contamination in wheat bran by MOS-based electronic nose and characterization of the relevant pattern of volatile compounds. J Sci Food Agric 98:4955–4962. https://doi.org/10.1002/jsfa.9028
Liu Q, Zhao N, Zhou D, Sun Y, Sun K, Pan L, Tu K (2018) Discrimination and growth tracking of fungi contamination in peaches using electronic nose. Food Chem 262:226–234. https://doi.org/10.1016/j.foodchem.2018.04.100
Liu Q, Sun K, Zhao N, Yang J, Zhang Y, Ma C, Pan L, Tu K (2019) Information fusion of hyperspectral imaging and electronic nose for evaluation of fungal contamination in strawberries during decay. Postharvest Biol Technol 153:152–160. https://doi.org/10.1016/j.postharvbio.2019.03.017
Luo D, Hosseini HG, Stewart JR (2004) Application of ANN with extracted parameters from an electronic nose in cigarette brand identification. Sens Actuators B Chem 99:253–257. https://doi.org/10.1016/j.snb.2003.11.022
Luo H, Jia P, Qiao S, Duan S (2018) Enhancing electronic nose performance based on a novel QPSO-RBM technique. Sens Actuators B Chem 259:241–249. https://doi.org/10.1016/j.snb.2017.12.026
Majchrzak T, Wojnowski W, Dymerski T, Gębicki J, Namieśnik J (2018) Electronic noses in classification and quality control of edible oils: a review. Food Chem 246:192–201. https://doi.org/10.1016/j.foodchem.2017.11.013
Martin AL, Satjaritanun P, Shimpalee S, Devivo BA, Weidner J, Greenway S, Henson JM, Turick CE (2018) In-situ electrochemical analysis of microbial activity. AMB Express 8:162. https://doi.org/10.1186/s13568-018-0692-2
Niu Y, Sun F, Xu Y, Cong Z, Wang E (2014) Applications of electrochemical techniques in mineral analysis. Talanta 127:211–218. https://doi.org/10.1016/j.talanta.2014.03.072
Nygren BL, Schilling KA, Blanton EM, Silk BJ, Cole DJ, Mintz ED (2013) Foodborne outbreaks of shigellosis in the USA, 1998–2008. Epidemiol Infect 141:233–241. https://doi.org/10.1017/S0950268812000222
Pallottino F, Costa C, Antonucci F, Strano MC, Calandra M, Solaini S, Menesatti P (2012) Electronic nose application for determination of Penicillium digitatum in Valencia oranges. J Sci Food Agric 92:2008–2012. https://doi.org/10.1002/jsfa.5586
Pardo M, Sberveglieri G (2005) Classification of electronic nose data with support vector machines. Sens Actuators B Chem 107:730–737. https://doi.org/10.1016/j.snb.2004.12.005
Pattarapon P, Zhang M, Bhandari B, Gao Z (2018) Effect of vacuum storage on the freshness of grass carp (Ctenopharyngodon idella) fillet based on normal and electronic sensory measurement. J Food Process Preserv 42:e13418. https://doi.org/10.1111/jfpp.13418
Peris M, Escuder-Gilabert L (2009) A 21st century technique for food control: electronic noses. Anal Chim Acta 638:1–15. https://doi.org/10.1016/j.aca.2009.02.009
Piechulla B, Degenhardt J (2014) The emerging importance of microbial volatile organic compounds. Plant Cell Environ 37:811–812. https://doi.org/10.1111/pce.12254
Preti G, Thaler E, Hanson CW, Troy M, Eades J, Gelperin A (2009) Volatile compounds characteristic of sinus-related bacteria and infected sinus mucus: analysis by solid-phase microextraction and gas chromatography–mass spectrometry. J Chromatogr B 877:2011–2018. https://doi.org/10.1016/j.jchromb.2009.05.028
Rayappan JBB, Kulandaisamy AJ, Ezhilan M, Srinivasan P, Mani GK (2017) Developments in electronic noses for quality and safety control. Adv Food Diagn. https://doi.org/10.1002/9781119105916.ch3
Robin Michael Statham T, John G (2012) Microbial volatile compounds in health and disease conditions. J Breath Res 6:024001
Romoli R, Papaleo MC, De Pascale D, Tutino ML, Michaud L, LoGiudice A, Fani R, Bartolucci G (2014) GC–MS volatolomic approach to study the antimicrobial activity of the antarctic bacterium Pseudoalteromonas sp. TB41. Metabolomics 10:42–51. https://doi.org/10.1007/s11306-013-0549-2
Sanaeifar A, ZakiDizaji H, Jafari A, Mdl Guardia (2017) Early detection of contamination and defect in foodstuffs by electronic nose: a review. TrAC Trends Anal Chem 97:257–271. https://doi.org/10.1016/j.trac.2017.09.014
Sberveglieri V, Núñez Carmona E, Pulvirenti A (2015) Detection of microorganism in water and different food matrix by electronic nose. In: Mason A, Mukhopadhyay SC, Jayasundera KP (eds) Sensing technology: current status and future trends III. Springer, Cham, pp 243–258. https://doi.org/10.1007/978-3-319-10948-0_12
Scott SM, James D, Ali Z (2006) Data analysis for electronic nose systems. Microchim Acta 156:183–207. https://doi.org/10.1007/s00604-006-0623-9
Selim KA, El Ghwas DE, Selim RM, Abdelwahab Hassan MI (2017) Microbial volatile in defense. In: Choudhary DK, Sharma AK, Agarwal P, Varma A, Tuteja N (eds) Volatiles and food security: role of volatiles in agro-ecosystems. Springer, Singapore, pp 135–170. https://doi.org/10.1007/978-981-10-5553-9_8
Senecal AG, Magnone J, Yeomans W, Powers EM (2002) Rapid detection of pathogenic bacteria by volatile organic compound (VOC) analysis. In: Environmental and industrial sensing, 2002. SPIE, p 11
Siripatrawan U (2008a) Rapid differentiation between E. coli and Salmonella typhimurium using metal oxide sensors integrated with pattern recognition. Sens Actuators B Chem 133:414–419. https://doi.org/10.1016/j.snb.2008.02.046
Siripatrawan U (2008b) Self-organizing algorithm for classification of packaged fresh vegetable potentially contaminated with foodborne pathogens. Sens Actuators B Chem 128:435–441. https://doi.org/10.1016/j.snb.2007.06.030
Siripatrawan U, Harte BR (2007) Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable. Anal Chim Acta 581:63–70. https://doi.org/10.1016/j.aca.2006.08.007
Siripatrawan U, Harte BR (2015) Data visualization of Salmonella typhimurium contamination in packaged fresh alfalfa sprouts using a Kohonen network. Talanta 136:128–135. https://doi.org/10.1016/j.talanta.2014.11.070
Siripatrawan U, Linz JE, Harte BR (2006) Detection of Escherichia coli in packaged alfalfa sprouts with an electronic nose and an artificial neural network. J Food Prot 69:1844–1850. https://doi.org/10.4315/0362-028X-69.8.1844
Siswantoro J, Hilman MY, Widiasri M (2017) Computer vision system for egg volume prediction using backpropagation neural network. IOP Conf Ser Mater Sci Eng 273:012002
Sizochenko N, Syzochenko M, Fjodorova N, Rasulev B, Leszczynski J (2019) Evaluating genotoxicity of metal oxide nanoparticles: application of advanced supervised and unsupervised machine learning techniques. Ecotoxicol Environ Saf 185:109733. https://doi.org/10.1016/j.ecoenv.2019.109733
Tait E, Perry JD, Stanforth SP, Dean JR (2014) Use of volatile compounds as a diagnostic tool for the detection of pathogenic bacteria. TrAC Trends Anal Chem 53:117–125. https://doi.org/10.1016/j.trac.2013.08.011
Tian X, Wang J, Shen R, Ma Z, Li M (2018) Discrimination of pork/chicken adulteration in minced mutton by electronic taste system. Int J Food Sci Technol. https://doi.org/10.1111/ijfs.13977
van den Berg RA, Hoefsloot HCJ, Westerhuis JA, Smilde AK, van der Werf MJ (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genom 7:142–142. https://doi.org/10.1186/1471-2164-7-142
Vapnik VN (2000) Methods of pattern recognition. In: Vapnik VN (ed) The nature of statistical learning theory. Springer, New York, pp 123–180. https://doi.org/10.1007/978-1-4757-3264-1_6
Wang D, Wang X, Liu T, Liu Y (2012) Prediction of total viable counts on chilled pork using an electronic nose combined with support vector machine. Meat Sci 90:373–377. https://doi.org/10.1016/j.meatsci.2011.07.025
Warren BR, Rouseff RL, Schneider KR, Parish ME (2007) Identification of volatile sulfur compounds produced by Shigella sonnei using gas chromatography–olfactometry. Food Control 18:179–182. https://doi.org/10.1016/j.foodcont.2005.09.017
Wasilewski T, Gębicki J, Kamysz W (2017) Bioelectronic nose: current status and perspectives. Biosens Bioelectron 87:480–494. https://doi.org/10.1016/j.bios.2016.08.080
Watson J, Ihokura K, Coles GSV (1993) The tin dioxide gas sensor. Meas Sci Technol 4:711–719. https://doi.org/10.1088/0957-0233/4/7/001
Wijaya D, Sarno R, Fathra Daiva A (2017) Electronic Nose for Classifying Beef and Pork using Naïve Bayes. https://doi.org/10.1109/ISSIMM.2017.8124272
Wijaya DR, Sarno R, Zulaika E (2019) Noise filtering framework for electronic nose signals: an application for beef quality monitoring. Comput Electron Agric 157:305–321. https://doi.org/10.1016/j.compag.2019.01.001
Wilson AD, Baietto M (2009) Applications and advances in electronic-nose technologies. Sensors (Basel, Switzerland) 9:5099–5148. https://doi.org/10.3390/s90705099
Xu M, Wang J, Gu S (2019) Rapid identification of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy. J Food Eng 241:10–17. https://doi.org/10.1016/j.jfoodeng.2018.07.020
Xue C, Lin Y, Zhao Y, Xitao Z (2012) The identification of Listeria monocytogenes based on the electronic nose. In: 2012 International conference on computer science and information processing (CSIP), 24–26 Aug 2012. pp 467–472. https://doi.org/10.1109/csip.2012.6308893
Yan J, Guo X, Duan S, Jia P, Wang L, Peng C, Zhang S (2015) Electronic nose feature extraction methods: a review. Sensors (Basel, Switzerland) 15:27804–27831. https://doi.org/10.3390/s151127804
Yongxin Y, Zhao Y (2012) Electronic nose integrated with chemometrics for rapid identification of foodborne pathogen. IntechOpen. https://doi.org/10.5772/32099
Yu Y-x, Sun X-h, Liu Y, Pan Y-j, Zhao Y (2014) Odor fingerprinting of Listeria monocytogenes recognized by SPME–GC–MS and E-nose. Can J Microbiol 61:367–372. https://doi.org/10.1139/cjm-2014-0652
Zhang WN, Qin GJ, Hu NQ (2014) Parallel factor analysis for gas sensor array signals. Appl Mech Mater 494–495:955–959. https://doi.org/10.4028/www.scientific.net/AMM.494-495.955
Zohora SE, Khan AM, Hundewale N (2013) Chemical sensors employed in electronic noses: a review. In: Meghanathan N, Nagamalai D, Chaki N (eds) Advances in computing and information technology. Springer, Berlin, pp 177–184
Acknowledgements
This work was sponsored by the National Natural Science Foundation of China (No. 31671932).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bonah, E., Huang, X., Aheto, J.H. et al. Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review. J Food Sci Technol 57, 1977–1990 (2020). https://doi.org/10.1007/s13197-019-04143-4
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13197-019-04143-4