Abstract
Electronic noses, or e-noses, refer to systems powered by chemical gas sensors, signal processing, and machine learning algorithms for realizing artificial olfaction. They play a crucial role in various applications for decoding chemical environmental information. Despite decades of advances in gas-sensing technology and artificial intelligence, the reliability and stability of e-nose systems remain challenging, which is also one of the major obstacles that prevent e-noses from large-scale deployment. This paper presents a wide-ranging and structured review of the methods and algorithms developed in the e-nose literature over the past few decades. The review adopts a problem-oriented taxonomy aimed at clarifying the motivations and challenges of different methods and algorithms and their pros and cons. Moreover, several promising research directions in this field have been presented.
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References
117
[2]
Köster EP, Møller P, Mojet J. A “misfit” theory of spontaneous conscious odor perception (mitscop): Reflections on the role and function of odor memory in everyday life. Front Psychol. 2014;5:64. 10.3389/fpsyg.2014.00064
[3]
Vergara A, Llobet E. Sensor selection and chemo-sensory optimization: Toward an adaptable chemo-sensory system. Front Neuroeng. 2012;4:19. 10.3389/fneng.2011.00019
[5]
Cavarretta F, Marasco A, Hines ML, Shepherd GM, Migliore M. Glomerular and mitral-granule cell microcircuits coordinate temporal and spatial information processing in the olfactory bulb. Front Comput Neurosci. 2016;10:67. 10.3389/fncom.2016.00067
[8]
Covington JA, Marco S, Persaud KC, Schiffman SS, Nagle HT. Artificial olfaction in the 21st century. IEEE Sens J. 2021;21(11):12969–12990. 10.1109/jsen.2021.3076412
[9]
Hu W, Wan L, Jian Y, Ren C, Jin K, Su X, Bai X, Haick H, Yao M, Wu W. Electronic noses: From advanced materials to sensors aided with data processing. Adv Mater Technol. 2019;4(2):1800488. 10.1002/admt.201800488
[10]
Karakaya D, Ulucan O, Turkan M. Electronic nose and its applications: A survey. Int J Autom Comput. 2020;17(2):179–209. 10.1007/s11633-019-1212-9
[11]
Gutierrez-Osuna R. Pattern analysis for machine olfaction: A review. IEEE Sensors J. 2002;2(3):189–202. 10.1109/jsen.2002.800688
[13]
Hotel O, Poli J-P, Mer-Calfati C, Scorsone E, Saada S. A review of algorithms for saw sensors e-nose based volatile compound identification. Sens Actuators B Chem. 2018;255:2472–2482. 10.1016/j.snb.2017.09.040
[14]
Yan J, Guo X, Duan S, Jia P, Wang L, Peng C, Zhang S. Electronic nose feature extraction methods: A review. Sensors. 2015;15(11):27804–27831. 10.3390/s151127804
[15]
Chen H, Huo D, Zhang J. Gas recognition in e-nose system: A review. IEEE Trans Biomed Circuits Syst. 2022;16(2):169–184. 10.1109/tbcas.2022.3166530
[16]
Al-Dayyeni WS, Al-Yousif S, Taher MM, Al-Faouri AW, Tahir NM, Jaber MM, Ghabban F, Najm IA, Alfadli IM, Ameerbakhsh OZ, et al. A review on electronic nose: Coherent taxonomy, classification, motivations, challenges, recommendations and datasets. IEEE Access. 2021;9:88535–88551. 10.1109/access.2021.3090165
[17]
Pareek V, Chaudhury S, Singh S. Handling non-stationarity in e-nose design: A review. Sens Rev. 2022;42(1):39–61. 10.1108/sr-02-2021-0038
[18]
Zhang L, Tian F, Zhang D. Electronic nose: Algorithmic challenges.Singapore: Springer; 2019.
[19]
Chen X-x, Huang J. Odor source localization algorithms on mobile robots: A review and future outlook. Robot Auton Syst. 2019;112:123–136. 10.1016/j.robot.2018.11.014
[20]
Kowadlo G, Russell RA. Robot odor localization: A taxonomy and survey. Int J Robot Res. 2008;27(8):869–894. 10.1177/0278364908095118
[21]
Yan K, Zhang D. Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sens Actuators B Chem. 2015;212:353–363. 10.1016/j.snb.2015.02.025
[23]
Shakya P, Kennedy E, Rose C, Rosenstein JK. High-dimensional time series feature extraction for low-cost machine olfaction. IEEE Sens J. 2021;21(3):2495–2504.
[24]
Distante C, Leo M, Siciliano P, Persaud KC. On the study of feature extraction methods for an electronic nose. Sens Actuators B Chem. 2002;87(2):274–288. 10.1016/s0925-4005(02)00247-2
[25]
Liu T, Zhang W, Ye L, Ueland M, Forbes SL, Su SW. A novel multi-odour identification by electronic nose using non-parametric modelling-based feature extraction and time-series classification. Sens Actuators B Chem. 2019;298:126690. 10.1016/j.snb.2019.126690
[26]
Liu T, Zhang W, Li J, Ueland M, Forbes SL, Zheng WX, Su SW. A multiscale wavelet kernel regularization-based feature extraction method for electronic nose. IEEE Trans Syst Man Cybern Syst. 2022;52(11):7078–7089. 10.1109/tsmc.2022.3151761
[27]
He A Tang Z. A novel gas identification method based on gabor spectrogram using self-adapted temperature modulated gas sensors. Paper presented at: Proceedings of the 2019 International Conference on Sensing Diagnostics Prognostics and Control (SDPC); 2019 August 15–17; Beijing China. 10.1109/sdpc.2019.00135
[28]
Ionescu R, Hoel A, Granqvist C, Llobet E, Heszler P. Low-level detection of ethanol and H2S with temperature-modulated WO3 nanoparticle gas sensors. Sens Actuators B Chem. 2005;104(1):132–139. 10.1016/j.snb.2004.05.015
[29]
Vergara A, Martinelli E, Huerta R, D’Amico A, Di Natale C. Orthogonal decomposition of chemo-sensory cues. Sens Actuators B Chem. 2011;159(1):126–134. 10.1016/j.snb.2011.06.060
[30]
Yin Y, Yu H, Zhang H. A feature extraction method based on wavelet packet analysis for discrimination of Chinese vinegars using a gas sensors array. Sens Actuators B Chem. 2008;134(2):1005–1009. 10.1016/j.snb.2008.07.018
[32]
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. 2019;19(23):5333. 10.3390/s19235333
[33]
Längkvist M Loutfi A Unsupervised feature learning for electronic nose data applied to bacteria identification in blood. Paper presented at: Proceedings of the NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning; 2011 December 17; Sierra Nevada Spain.
[34]
Licen S, Barbieri G, Fabbris A, Briguglio SC, Pillon A, Stel F, Barbieri P. Odor control map: Self organizing map built from electronic nose signals and integrated by different instrumental and sensorial data to obtain an assessment tool for real environmental scenarios. Sens Actuators B Chem. 2018;263:476–485. 10.1016/j.snb.2018.02.144
[35]
He A, Wei G, Yu J, Tang Z, Lin Z, Wang P. A novel dictionary learning method for gas identification with a gas sensor array. IEEE Trans Ind Electron. 2017;64(12):9709–9715. 10.1109/tie.2017.2748034
[36]
Cheng Y, Wong K-Y, Hung K, Li W, Li Z, Zhang J. Deep nearest class mean model for incremental odor classification. IEEE Trans Instrum Meas. 2019;68(4):952–962. 10.1109/tim.2018.2863438
[37]
Gamboa JCR, da Silva AJ, Araujo ICS, Albarracin E ES, Duran A CM, Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines. Sens Actuators B Chem. 2021;327:128921. 10.1016/j.snb.2020.128921
[38]
Wang T, Zhang H, Wu Y, Jiang W, Chen X, Zeng M, Yang J, Su Y, Hu N, Yang Z. Target discrimination, concentration prediction, and status judgment of electronic nose system based on large-scale measurement and multi-task deep learning. Sens Actuators B Chem. 2022;351:130915. 10.1016/j.snb.2021.130915
[39]
Wang Q Xie T Wang S. Research on air pollution gases recognition method based on lstm recurrent neural network and gas sensors array. Paper presented at: Proceedings of the 2018 Chinese Automation Congress (CAC); 2018 November 30–December 2; Xi'an China. 10.1109/cac.2018.8623060
[41]
Yang Y, Liu H, Gu Y. A model transfer learning framework with back-propagation neural network for wine and Chinese liquor detection by electronic nose. IEEE Access. 2020;8:105278–105285. 10.1109/access.2020.2999591
[42]
Wijaya DR, Afianti F. Stability assessment of feature selection algorithms on homogeneous datasets: A study for sensor array optimization problem. IEEE Access. 2020;8:33944–33953. 10.1109/access.2020.2974982
[43]
Liu T, Zhang W, McLean P, Ueland M, Forbes SL, Su SW. Electronic nose-based odor classification using genetic algorithms and fuzzy support vector machines. Int J Fuzzy Syst. 2018;20(4):1309–1320. 10.1007/s40815-018-0449-8
[44]
Jia P, Tian F, He Q, Fan S, Liu J, Yang SX. Feature extraction of wound infection data for electronic nose based on a novel weighted KPCA. Sens Actuators B Chem. 2014;201:555–566. 10.1016/j.snb.2014.05.025
[45]
Yu H, Yin Y, Yuan Y, Shen X. A KECA identification method based on GA for e-nose data of six kinds of Chinese spirits. Sens Actuators B Chem. 2021;333:129518. 10.1016/j.snb.2021.129518
[46]
Zhang L, Liu Y, He Z, Liu J, Deng P, Zhou X. Anti-drift in e-nose: A subspace projection approach with drift reduction. Sens Actuators B Chem. 2017;253:407–417. 10.1016/j.snb.2017.06.156
[47]
Luo H, Jia P, Qiao S, Duan S. Enhancing electronic nose performance based on a novel QPSO-RBM technique. Sens Actuators B Chem. 2018;259:241–249. 10.1016/j.snb.2017.12.026
[48]
Wedge DC, Das A, Dost R, Kettle J, Madec M-B, Morrison JJ, Grell M, Kell DB, Richardson TH, Yeates S, et al. Real-time vapour sensing using an OFET-based electronic nose and genetic programming. Sens Actuators B Chem. 2009;143(1):365–372. 10.1016/j.snb.2009.09.030
[49]
Kumar R, Dwivedi R. Quaternion domain k-means clustering for improved real time classification of e-nose data. IEEE Sens J. 2016;16(1):177–184. 10.1109/jsen.2015.2475640
[50]
Liu T, Zhang W, Yuwono M, Zhang M, Ueland M, Forbes SL, Su SW. A data-driven meat freshness monitoring and evaluation method using rapid centroid estimation and hidden Markov models. Sens Actuators B Chem. 2020;311:127868. 10.1016/j.snb.2020.127868

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