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Vol. 16 (2024)

Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

https://doi.org/10.1007/s40820-024-01489-z
Haixia Mei
Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun 130022, People’s Republic of China
Jingyi Peng
Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun 130022, People’s Republic of China
Tao Wang
Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
Tingting Zhou
State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, People’s Republic of China
Hongran Zhao
State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, People’s Republic of China
Tong Zhang
State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, People’s Republic of China
Zhi Yang
National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China

Corresponding Author: Zhi Yang

zhiyang@sjtu.edu.cn

Nano-Micro Letters, Vol. 16 (2024), Article Number: 269

Abstract

As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.


Highlights:
1 The types, working principles, advantages and limitations of pattern recognition methods based on chemiresistive gas sensor array are reviewed and discussed comprehensively.
2 Outstanding and novel advancements in the application of machine learning methods for gas recognition in different important areas are compared, summarized and evaluated.
3 The current challenges and future prospects of machine learning methods in artificial olfactory systems are discussed and justified.

Keywords

Pattern recognition Sensor array Chemiresistive gas sensor Cross-sensitivity Artificial olfactory
Mei, Haixia, Jingyi Peng, Tao Wang, Tingting Zhou, Hongran Zhao, Tong Zhang, and Zhi Yang. 2024. “Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array”. Nano-Micro Letters 16 (August):269. https://doi.org/10.1007/s40820-024-01489-z.
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References
  1. C. Bushdid, M.O. Magnasco, L.B. Vosshall, A. Keller, Humans can discriminate more than 1 trillion olfactory stimuli. Science 343, 1370–1372 (2014). https://doi.org/10.1126/science.1249168
  2. J.P. McGann, Poor human olfaction is a 19th-century myth. Science 356, eaam7263 (2017). https://doi.org/10.1126/science.aam7263
  3. K. Izawa, H. Ulmer, A. Staerz, U. Weimar, N. Barsan, Application of SMOX-based sensors. Gas Sensors Based on Conducting Metal Oxides. Amsterdam: Elsevier, (2019)., pp. 217–257. https://doi.org/10.1016/b978-0-12-811224-3.00005-6
  4. A. Dey, Semiconductor metal oxide gas sensors: a review. Mater. Sci. Eng. B 229, 206–217 (2018). https://doi.org/10.1016/j.mseb.2017.12.036
  5. Y. Liang, Z. Wu, Y. Wei, Q. Ding, M. Zilberman et al., Self-healing, self-adhesive and stable organohydrogel-based stretchable oxygen sensor with high performance at room temperature. Nano-Micro Lett. 14, 52 (2022). https://doi.org/10.1007/s40820-021-00787-0
  6. H. Lim, H. Kwon, H. Kang, J.E. Jang, H.-J. Kwon, Laser-induced and MOF-derived metal oxide/carbon composite for synergistically improved ethanol sensing at room temperature. Nano-Micro Lett. 16, 113 (2024). https://doi.org/10.1007/s40820-024-01332-5
  7. Z. Yang, S. Lv, Y. Zhang, J. Wang, L. Jiang et al., Self-assembly 3D porous crumpled MXene spheres as efficient gas and pressure sensing material for transient all-MXene sensors. Nano-Micro Lett. 14, 56 (2022). https://doi.org/10.1007/s40820-022-00796-7
  8. X. Chen, T. Wang, J. Shi, W. Lv, Y. Han et al., A novel artificial neuron-like gas sensor constructed from CuS quantum dots/Bi2S3 nanosheets. Nano-Micro Lett. 14, 8 (2021). https://doi.org/10.1007/s40820-021-00740-1
  9. Y. Luo, J. Li, Q. Ding, H. Wang, C. Liu et al., Functionalized hydrogel-based wearable gas and humidity sensors. Nano-Micro Lett. 15, 136 (2023). https://doi.org/10.1007/s40820-023-01109-2
  10. M. Hilal, W. Yang, Y. Hwang, W. Xie, Tailoring MXene thickness and functionalization for enhanced room-temperature trace NO2 sensing. Nano-Micro Lett. 16, 84 (2024). https://doi.org/10.1007/s40820-023-01316-x
  11. K.H. Kim, C.S. Park, S.J. Park, J. Kim, S.E. Seo et al., In-situ food spoilage monitoring using a wireless chemical receptor-conjugated graphene electronic nose. Biosens. Bioelectron. 200, 113908 (2022). https://doi.org/10.1016/j.bios.2021.113908
  12. A. Khorramifar, M. Rasekh, H. Karami, J.A. Covington, S.M. Derakhshani et al., Application of MOS gas sensors coupled with chemometrics methods to predict the amount of sugar and carbohydrates in potatoes. Molecules 27, 3508 (2022). https://doi.org/10.3390/molecules27113508
  13. Y. Wang, X. Yan, S. Wang, S. Gao, K. Yang et al., Electronic nose application for detecting different odorants in source water: Possibility and scenario. Environ. Res. 227, 115677 (2023). https://doi.org/10.1016/j.envres.2023.115677
  14. X. Jia, P. Qiao, X. Wang, M. Yan, Y. Chen et al., Building feedback-regulation system through atomic design for highly active SO2 sensing. Nano-Micro Lett. 16, 136 (2024). https://doi.org/10.1007/s40820-024-01350-3
  15. I.G. van der Sar, C.C. Moor, J.C. Oppenheimer, M.L. Luijendijk, P.L.A. van Daele et al., Diagnostic performance of electronic nose technology in sarcoidosis. Chest 161, 738–747 (2022). https://doi.org/10.1016/j.chest.2021.10.025
  16. Y. Peters, R.W.M. Schrauwen, A.C. Tan, S.K. Bogers, B. de Jong et al., Detection of Barrett’s oesophagus through exhaled breath using an electronic nose device. Gut 69, 1169–1172 (2020). https://doi.org/10.1136/gutjnl-2019-320273
  17. F. Röck, N. Barsan, U. Weimar, Electronic nose: current status and future trends. Chem. Rev. 108, 705–725 (2008). https://doi.org/10.1021/cr068121q
  18. T. Yang, L. Gao, W. Wang, J. Kang, G. Zhao et al., Berlin green framework-based gas sensor for room-temperature and high-selectivity detection of ammonia. Nano-Micro Lett. 13, 63 (2021). https://doi.org/10.1007/s40820-020-00586-z
  19. S.Y. Chun, Y.G. Song, J.E. Kim, J.U. Kwon, K. Soh et al., An artificial olfactory system based on a chemi-memristive device. Adv. Mater. 35, e2302219 (2023). https://doi.org/10.1002/adma.202302219
  20. I. Cho, K. Lee, Y.C. Sim, J.S. Jeong, M. Cho et al., Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor. Light Sci. Appl. 12, 95 (2023). https://doi.org/10.1038/s41377-023-01120-7
  21. C. Wang, Z. Chen, C.L.J. Chan, Z. Wan, W. Ye et al., Biomimetic olfactory chips based on large-scale monolithically integrated nanotube sensor arrays. Nat. Electron. 7, 157–167 (2024). https://doi.org/10.1038/s41928-023-01107-7
  22. T. Saidi, O. Zaim, M. Moufid, N. El Bari, R. Ionescu et al., Exhaled breath analysis using electronic nose and gas chromatography–mass spectrometry for non-invasive diagnosis of chronic kidney disease, diabetes mellitus and healthy subjects. Sens. Actuat. B Chem. 257, 178–188 (2018). https://doi.org/10.1016/j.snb.2017.10.178
  23. M. Tohidi, M. Ghasemi-Varnamkhasti, V. Ghafarinia, M. Bonyadian, S.S. Mohtasebi, Development of a metal oxide semiconductor-based artificial nose as a fast, reliable and non-expensive analytical technique for aroma profiling of milk adulteration. Int. Dairy J. 77, 38–46 (2018). https://doi.org/10.1016/j.idairyj.2017.09.003
  24. M.R. Zarezadeh, M. Aboonajmi, M. Ghasemi-Varnamkhasti, The effect of data fusion on improving the accuracy of olive oil quality measurement. Food Chem. X 18, 100622 (2023). https://doi.org/10.1016/j.fochx.2023.100622
  25. S.-H. Sung, J.M. Suh, Y.J. Hwang, H.W. Jang, J.G. Park et al., Data-centric artificial olfactory system based on the eigengraph. Nat. Commun. 15, 1211 (2024). https://doi.org/10.1038/s41467-024-45430-9
  26. A.H. Jalal, F. Alam, S. Roychoudhury, Y. Umasankar, N. Pala et al., Prospects and challenges of volatile organic compound sensors in human healthcare. ACS Sens. 3, 1246–1263 (2018). https://doi.org/10.1021/acssensors.8b00400
  27. G. Verma, A. Gokarna, H. Kadiri, K. Nomenyo, G. Lerondel et al., Multiplexed gas sensor: fabrication strategies, recent progress, and challenges. ACS Sens. 8, 3320–3337 (2023). https://doi.org/10.1021/acssensors.3c01244
  28. Z.U. Abideen, W.U. Arifeen, Y.M.N.D.Y. Bandara, Emerging trends in metal oxide-based electronic noses for healthcare applications: a review. Nanoscale 16, 9259–9283 (2024). https://doi.org/10.1039/d4nr00073k
  29. C. Kim, K.K. Lee, M.S. Kang, D.M. Shin, J.W. Oh et al., Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review. Biomater. Res. 26, 40 (2022). https://doi.org/10.1186/s40824-022-00287-1
  30. H. Chen, D. Huo, J. Zhang, Gas recognition in E-nose system: a review. IEEE Trans. Biomed. Circuits Syst. 16, 169–184 (2022). https://doi.org/10.1109/TBCAS.2022.3166530
  31. A. Labidi, E. Gillet, R. Delamare, M. Maaref, K. Aguir, Ethanol and ozone sensing characteristics of WO3 based sensors activated by Au and Pd. Sens. Actuat. B Chem. 120, 338–345 (2006). https://doi.org/10.1016/j.snb.2006.02.015
  32. H.-J. Kim, J.-H. Lee, Highly sensitive and selective gas sensors using p-type oxide semiconductors: overview. Sens. Actuat. B Chem. 192, 607–627 (2014). https://doi.org/10.1016/j.snb.2013.11.005
  33. M.E. Franke, T.J. Koplin, U. Simon, Metal and metal oxide nanops in chemiresistors: does the nanoscale matter? Small 2, 36–50 (2006). https://doi.org/10.1002/smll.200500261
  34. H. Jin, J. Yu, D. Cui, S. Gao, H. Yang et al., Remote tracking gas molecular via the standalone-like nanosensor-based tele-monitoring system. Nano-Micro Lett. 13, 32 (2021). https://doi.org/10.1007/s40820-020-00551-w
  35. T. Wada, N. Murata, T. Suzuki, H. Uehara, H. Nitani et al., Improvement of a real gas-sensor for the origin of methane selectivity degradation by µ-XAFS investigation. Nano-Micro Lett. 7, 255–260 (2015). https://doi.org/10.1007/s40820-015-0035-7
  36. D. Wang, Z. Li, J. Zhou, H. Fang, X. He et al., Simultaneous detection and removal of formaldehyde at room temperature: Janus Au@ZnO@ZIF-8 nanops. Nano-Micro Lett. 10, 4 (2017). https://doi.org/10.1007/s40820-017-0158-0
  37. A.V. Agrawal, N. Kumar, M. Kumar, Strategy and future prospects to develop room-temperature-recoverable NO2 gas sensor based on two-dimensional molybdenum disulfide. Nano-Micro Lett. 13, 38 (2021). https://doi.org/10.1007/s40820-020-00558-3
  38. O. Gschwend, N.M. Abraham, S. Lagier, F. Begnaud, I. Rodriguez et al., Neuronal pattern separation in the olfactory bulb improves odor discrimination learning. Nat. Neurosci. 18, 1474–1482 (2015). https://doi.org/10.1038/nn.4089
  39. P.Y. Wang, Y. Sun, R. Axel, L.F. Abbott, G.R. Yang, Evolving the olfactory system with machine learning. Neuron 109, 3879-3892.e5 (2021). https://doi.org/10.1016/j.neuron.2021.09.010
  40. B.K. Lee, E.J. Mayhew, B. Sanchez-Lengeling, J.N. Wei, W.W. Qian et al., A principal odor map unifies diverse tasks in olfactory perception. Science 381, 999–1006 (2023). https://doi.org/10.1126/science.ade4401
  41. L. Lu, Z. Hu, X. Hu, D. Li, S. Tian, Electronic tongue and electronic nose for food quality and safety. Food Res. Int. 162, 112214 (2022). https://doi.org/10.1016/j.foodres.2022.112214
  42. P. Gupta, H. Gholami Derami, D. Mehta, H. Yilmaz, S. Chakrabartty et al., In situ grown gold nanoisland-based chemiresistive electronic nose for sniffing distinct odor fingerprints. ACS Appl. Mater. Interfaces 14, 3207–3217 (2022). https://doi.org/10.1021/acsami.1c22173
  43. A. Glielmo, B.E. Husic, A. Rodriguez, C. Clementi, F. Noé et al., Unsupervised learning methods for molecular simulation data. Chem. Rev. 121, 9722–9758 (2021). https://doi.org/10.1021/acs.chemrev.0c01195
  44. J.F. Hair, Multivariate data analysis: an overview. International Encyclopedia of Statistical Science. Berlin, Heidelberg: Springer Berlin Heidelberg, (2011). pp. 904–907. https://doi.org/10.1007/978-3-642-04898-2_395
  45. Y. Tang, K. Xu, B. Zhao, M. Zhang, C. Gong et al., A novel electronic nose for the detection and classification of pesticide residue on apples. RSC Adv. 11, 20874–20883 (2021). https://doi.org/10.1039/D1RA03069H
  46. N. Shauloff, A. Morag, K. Yaniv, S. Singh, R. Malishev et al., Sniffing bacteria with a carbon-dot artificial nose. Nano-Micro Lett. 13, 112 (2021). https://doi.org/10.1007/s40820-021-00610-w
  47. B. Junker, A. Kobald, C. Ewald, P. Janoschek, M. Schalk et al., Multivariate analysis of light-activated SMOX gas sensors. ACS Sens. 9, 1584–1591 (2024). https://doi.org/10.1021/acssensors.4c00078
  48. M. Jang, G. Bae, Y.M. Kwon, J.H. Cho, D.H. Lee et al., Artificial Q-grader: machine learning-enabled intelligent olfactory and gustatory sensing system. Adv. Sci. 11, 2308976 (2024). https://doi.org/10.1002/advs.202308976
  49. H. Zhao, Z. Lai, H. Leung, X. Zhang, Linear discriminant analysis. Information Fusion and Data Science. Cham: Springer International Publishing, (2020). pp. 71–85. https://doi.org/10.1007/978-3-030-40794-0_5
  50. B. Skiera, J. Reiner, S. Albers, Regression analysis. Handbook of Market Research. Cham: Springer International Publishing, (2021). pp. 299–327. https://doi.org/10.1007/978-3-319-57413-4_17
  51. J. Yin, Y. Zhao, Z. Peng, F. Ba, P. Peng et al., Rapid identification method for CH4/CO/CH4-CO gas mixtures based on electronic nose. Sensors 23, 2975 (2023). https://doi.org/10.3390/s23062975
  52. E. Aghdamifar, V.R. Sharabiani, E. Taghinezhad, M. Szymanek, A. Dziwulska-Hunek, E-nose as a non-destructive and fast method for identification and classification of coffee beans based on soft computing models. Sens. Actuat. B Chem. 393, 134229 (2023). https://doi.org/10.1016/j.snb.2023.134229
  53. T. Itoh, Y. Koyama, Y. Sakumura, T. Akamatsu, A. Tsuruta et al., Discrimination of volatile organic compounds using a sensor array via a rapid method based on linear discriminant analysis. Sens. Actuat. B Chem. 387, 133803 (2023). https://doi.org/10.1016/j.snb.2023.133803
  54. W.S. Noble, What is a support vector machine? Nat. Biotechnol. 24, 1565–1567 (2006). https://doi.org/10.1038/nbt1206-1565
  55. V.K. Chauhan, K. Dahiya, A. Sharma, Problem formulations and solvers in linear SVM: a review. Artif. Intell. Rev. 52, 803–855 (2019). https://doi.org/10.1007/s10462-018-9614-6
  56. X. Zhou, R. Stern, H. Müller, Case-based fracture image retrieval. Int. J. Comput. Assist. Radiol. Surg. 7, 401–411 (2012). https://doi.org/10.1007/s11548-011-0643-8
  57. V. Piccialli, M. Sciandrone, Nonlinear optimization and support vector machines. 4OR 16, 111–149 (2018). https://doi.org/10.1007/s10288-018-0378-2
  58. M. Rasekh, H. Karami, M. Kamruzzaman, V. Azizi, M. Gancarz, Impact of different drying approaches on VOCs and chemical composition of Mentha spicata L. essential oil: a combined analysis of GC/MS and E-nose with chemometrics methods. Ind. Crops Prod. 206, 117595 (2023). https://doi.org/10.1016/j.indcrop.2023.117595
  59. J. Chen, T. Luo, J. Yan, L. Zhang, A novel twin-center intuitionistic fuzzy large margin classifier with unified pinball loss for improving the performance of E-noses system. Expert Syst. Appl. 250, 123883 (2024). https://doi.org/10.1016/j.eswa.2024.123883
  60. C. Wiwie, J. Baumbach, R. Röttger, Comparing the performance of biomedical clustering methods. Nat. Meth. 12, 1033–1038 (2015). https://doi.org/10.1038/nmeth.3583
  61. A.M. Ikotun, A.E. Ezugwu, L. Abualigah, B. Abuhaija, H. Jia, K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data. Inf. Sci. 622, 178–210 (2023). https://doi.org/10.1016/j.ins.2022.11.139
  62. J.A. Hartigan, M.A. Wong, Algorithm AS 136: a K-means clustering algorithm. Appl. Stat. 28, 100 (1979). https://doi.org/10.2307/2346830
  63. N. Altman, M. Krzywinski, Clustering. Nat. Methods 14, 545–546 (2017). https://doi.org/10.1038/nmeth.4299
  64. Y. Meng, J. Liang, F. Cao, Y. He, A new distance with derivative information for functional k-means clustering algorithm. Inform. Sci. 463, 166–185 (2018). https://doi.org/10.1016/j.ins.2018.06.035
  65. S.-S. Yu, S.-W. Chu, C.-M. Wang, Y.-K. Chan, T.-C. Chang, Two improved k-means algorithms. Appl. Soft Comput. 68, 747–755 (2018). https://doi.org/10.1016/j.asoc.2017.08.032
  66. J. Zhu, Z. Jiang, G.D. Evangelidis, C. Zhang, S. Pang et al., Efficient registration of multi-view point sets by K-means clustering. Information Sci. 488, 205–218 (2019). https://doi.org/10.1016/j.ins.2019.03.024
  67. S. Licen, A. Di Gilio, J. Palmisani, S. Petraccone, G. de Gennaro et al., Pattern recognition and anomaly detection by self-organizing maps in a multi month E-nose survey at an industrial site. Sensors 20, 1887 (2020). https://doi.org/10.3390/s20071887
  68. S.N. Hidayat, T. Julian, A.B. Dharmawan, M. Puspita, L. Chandra et al., Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose. Artif. Intell. Med. 129, 102323 (2022). https://doi.org/10.1016/j.artmed.2022.102323
  69. L. Rokach, Decision forest: twenty years of research. Inform. Fusion 27, 111–125 (2016). https://doi.org/10.1016/j.inffus.2015.06.005
  70. S.L. Salzberg, C4.5: programs for machine learning by J. ross quinlan. morgan kaufmann publishers, inc., 1993. Mach. Learn. 16, 235–240 (1994). https://doi.org/10.1007/BF00993309
  71. P.S.P. Herrmann, M. Dos Santos Luccas, E.J. Ferreira, A. Torre Neto, Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime. Front. Plant Sci. 15, 1323296 (2024). https://doi.org/10.3389/fpls.2024.1323296
  72. L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324
  73. K. Fawagreh, M.M. Gaber, E. Elyan, Random forests: from early developments to recent advancements. Syst. Sci. Contr. Eng. 2, 602–609 (2014). https://doi.org/10.1080/21642583.2014.956265
  74. H. Kim, W. Seong, E. Rha, H. Lee, S.K. Kim et al., Machine learning linked evolutionary biosensor array for highly sensitive and specific molecular identification. Biosens. Bioelectron. 170, 112670 (2020). https://doi.org/10.1016/j.bios.2020.112670
  75. D. Du, J. Wang, B. Wang, L. Zhu, X. Hong, Ripeness prediction of postharvest kiwifruit using a MOS E-nose combined with chemometrics. Sensors 19, 419 (2019). https://doi.org/10.3390/s19020419
  76. Z. Saringat, A. Mustapha, R.D. Rohmat Saedudin, N.A. Samsudin, Comparative analysis of classification algorithms for chronic kidney disease diagnosis. Bull. Electr. Eng. Inform. 8, 1496–1501 (2019). https://doi.org/10.11591/eei.v8i4.1621
  77. X. Zeng, R. Cao, Y. Xi, X. Li, M. Yu et al., Food flavor analysis 4.0: a cross-domain application of machine learning. Trends Food Sci. Technol. 138, 116–125 (2023). https://doi.org/10.1016/j.tifs.2023.06.011
  78. H. Khalili, M. Rismani, M.A. Nematollahi, M.S. Masoudi, A. Asadollahi et al., Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Sci. Rep. 13, 960 (2023). https://doi.org/10.1038/s41598-023-28188-w
  79. N. Gerhardt, S. Schwolow, S. Rohn, P.R. Pérez-Cacho, H. Galán-Soldevilla et al., Quality assessment of olive oils based on temperature-ramped HS-GC-IMS and sensory evaluation: Comparison of different processing approaches by LDA, kNN, and SVM. Food Chem. 278, 720–728 (2019). https://doi.org/10.1016/j.foodchem.2018.11.095
  80. S. Sironi, L. Capelli, P. Céntola, R. Del Rosso, M. Il Grande, Continuous monitoring of odours from a composting plant using electronic noses. Waste Manag. 27, 389–397 (2007). https://doi.org/10.1016/j.wasman.2006.01.029
  81. S. Manocha, M.A. Girolami, An empirical analysis of the probabilistic K-nearest neighbour classifier. Pattern Recognit. Lett. 28, 1818–1824 (2007). https://doi.org/10.1016/j.patrec.2007.05.018
  82. S. Qiu, J. Wang, The prediction of food additives in the fruit juice based on electronic nose with chemometrics. Food Chem. 230, 208–214 (2017). https://doi.org/10.1016/j.foodchem.2017.03.011
  83. W. Dong, J. Zhao, R. Hu, Y. Dong, L. Tan, Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics. Food Chem. 229, 743–751 (2017). https://doi.org/10.1016/j.foodchem.2017.02.149
  84. T.P. Lillicrap, A. Santoro, L. Marris, C.J. Akerman, G. Hinton, Backpropagation and the brain. Nat. Rev. Neurosci. 21, 335–346 (2020). https://doi.org/10.1038/s41583-020-0277-3
  85. A. Derry, M. Krzywinski, N. Altman, Neural networks primer. Nat. Meth. 20, 165–167 (2023). https://doi.org/10.1038/s41592-022-01747-1
  86. J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015). https://doi.org/10.1016/j.neunet.2014.09.003
  87. A. Kalinichenko, L. Arseniyeva, Electronic nose combined with chemometric approaches to assess authenticity and adulteration of sausages by soy protein. Sens. Actuat. B Chem. 303, 127250 (2020). https://doi.org/10.1016/j.snb.2019.127250
  88. J. Wang, S. Viciano-Tudela, L. Parra, R. Lacuesta, J. Lloret, Evaluation of suitability of low-cost gas sensors for monitoring indoor and outdoor urban areas. IEEE Sens. J. 23, 20968–20975 (2023). https://doi.org/10.1109/JSEN.2023.3301651
  89. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagating errors. Nature 323, 533–536 (1986). https://doi.org/10.1038/323533a0
  90. S. Jiang, C. Ni, G. Chen, Y. Liu, A novel data fusion strategy based on multiple intelligent sensory technologies and its application in the quality evaluation of Jinhua dry-cured hams. Sens. Actuat. B Chem. 344, 130324 (2021). https://doi.org/10.1016/j.snb.2021.130324
  91. Y. Zhang, L. Li, Z. Ren, Y. Yu, Y. Li et al., Plant-scale biogas production prediction based on multiple hybrid machine learning technique. Bioresour. Technol. 363, 127899 (2022). https://doi.org/10.1016/j.biortech.2022.127899
  92. N. Zhang, S. Ding, J. Zhang, Multi layer ELM-RBF for multi-label learning. Appl. Soft Comput. 43, 535–545 (2016). https://doi.org/10.1016/j.asoc.2016.02.039
  93. G. Huang, G.-B. Huang, S. Song, K. You, Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015). https://doi.org/10.1016/j.neunet.2014.10.001
  94. H.G.J. Voss, S.L. Stevan, R.A. Ayub, Peach growth cycle monitoring using an electronic nose. Comput. Electron. Agric. 163, 104858 (2019). https://doi.org/10.1016/j.compag.2019.104858
  95. Q.-Y. Zhu, A.K. Qin, P.N. Suganthan, G.-B. Huang, Evolutionary extreme learning machine. Pattern Recognit. 38, 1759–1763 (2005). https://doi.org/10.1016/j.patcog.2005.03.028
  96. T. Wang, H. Ma, W. Jiang, H. Zhang, M. Zeng et al., Type discrimination and concentration prediction towards ethanol using a machine learning-enhanced gas sensor array with different morphology-tuning characteristics. Phys. Chem. Chem. Phys. 23, 23933–23944 (2021). https://doi.org/10.1039/d1cp02394b
  97. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
  98. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy et al., Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018). https://doi.org/10.1016/j.patcog.2017.10.013
  99. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017). https://doi.org/10.1145/3065386
  100. Y. Xiong, Y. Li, C. Wang, H. Shi, S. Wang et al., Non-destructive detection of chicken freshness based on electronic nose technology and transfer learning. Agriculture 13, 496 (2023). https://doi.org/10.3390/agriculture13020496
  101. G. Wei, G. Li, J. Zhao, A. He, Development of a LeNet-5 gas identification CNN structure for electronic noses. Sensors 19, 217 (2019). https://doi.org/10.3390/s19010217
  102. H. Hewamalage, C. Bergmeir, K. Bandara, Recurrent neural networks for time series forecasting: current status and future directions. Int. J. Forecast. 37, 388–427 (2021). https://doi.org/10.1016/j.ijforecast.2020.06.008
  103. S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9, 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
  104. S. Song, J. Chen, L. Ma, L. Zhang, S. He et al., Research on a working face gas concentration prediction model based on LASSO-RNN time series data. Heliyon 9, e14864 (2023). https://doi.org/10.1016/j.heliyon.2023.e14864
  105. S. Wakhid, R. Sarno, S.I. Sabilla, The effect of gas concentration on detection and classification of beef and pork mixtures using E-nose. Comput. Electron. Agric. 195, 106838 (2022). https://doi.org/10.1016/j.compag.2022.106838
  106. L. Liu, W. Li, Z. He, W. Chen, H. Liu et al., Detection of lung cancer with electronic nose using a novel ensemble learning framework. J. Breath Res. (2021). https://doi.org/10.1088/1752-7163/abe5c9
  107. J. Chu, W. Li, X. Yang, Y. Wu, D. Wang et al., Identification of gas mixtures via sensor array combining with neural networks. Sens. Actuat. B Chem. 329, 129090 (2021). https://doi.org/10.1016/j.snb.2020.129090
  108. T. Liu, W. Zhang, P. McLean, M. Ueland, S.L. Forbes et al., Electronic nose-based odor classification using genetic algorithms and fuzzy support vector machines. Int. J. Fuzzy Syst. 20, 1309–1320 (2018). https://doi.org/10.1007/s40815-018-0449-8
  109. H. Zhong, C. Miao, Z. Shen, Y. Feng, Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings. Neurocomputing 128, 285–295 (2014). https://doi.org/10.1016/j.neucom.2013.02.054
  110. T. Wang, Y. Wu, Y. Zhang, W. Lv, X. Chen et al., Portable electronic nose system with elastic architecture and fault tolerance based on edge computing, ensemble learning, and sensor swarm. Sens. Actuat. B Chem. 375, 132925 (2023). https://doi.org/10.1016/j.snb.2022.132925
  111. L. Xiong, M. He, C. Hu, Y. Hou, S. Han et al., Image presentation and effective classification of odor intensity levels using multi-channel electronic nose technology combined with GASF and CNN. Sens. Actuat. B Chem. 395, 134492 (2023). https://doi.org/10.1016/j.snb.2023.134492
  112. Y. Shi, B. Wang, C. Yin, Z. Li, Y. Yu, Performance improvement: a lightweight gas information classification method combined with an electronic nose system. Sens. Actuat. B Chem. 396, 134551 (2023). https://doi.org/10.1016/j.snb.2023.134551
  113. H. Sun, Z. Hua, C. Yin, F. Li, Y. Shi, Geographical traceability of soybean: an electronic nose coupled with an effective deep learning method. Food Chem. 440, 138207 (2024). https://doi.org/10.1016/j.foodchem.2023.138207
  114. F. Wu, R. Ma, Y. Li, F. Li, S. Duan et al., A novel electronic nose classification prediction method based on TETCN. Sens. Actuat. B Chem. 405, 135272 (2024). https://doi.org/10.1016/j.snb.2024.135272
  115. T. Zhang, R. Tan, W. Shen, D. Lv, J. Yin et al., Inkjet-printed ZnO-based MEMS sensor array combined with feature selection algorithm for VOCs gas analysis. Sens. Actuat. B Chem. 382, 133555 (2023). https://doi.org/10.1016/j.snb.2023.133555
  116. Y. Zhang, Q. Jiang, M. Xu, Y. Zhang, J. Liu et al., FTM-GCN: a novel technique for gas concentration predicting in space with sensor nodes. Sens. Actuat. B Chem. 399, 134830 (2024). https://doi.org/10.1016/j.snb.2023.134830
  117. X. Pan, J. Chen, X. Wen, J. Hao, W. Xu et al., A comprehensive gas recognition algorithm with label-free drift compensation based on domain adversarial network. Sens. Actuat. B Chem. 387, 133709 (2023). https://doi.org/10.1016/j.snb.2023.133709
  118. H. Se, K. Song, C. Sun, J. Jiang, H. Liu et al., Online drift compensation framework based on active learning for gas classification and concentration prediction. Sens. Actuat. B Chem. 398, 134716 (2024). https://doi.org/10.1016/j.snb.2023.134716
  119. R.J. Rath, S. Farajikhah, F. Oveissi, F. Dehghani, S. Naficy, Chemiresistive sensor arrays for gas/volatile organic compounds monitoring: a review. Adv. Eng. Mater. 25, 2200830 (2023). https://doi.org/10.1002/adem.202200830
  120. Z. Zheng, C. Zhang, Electronic noses based on metal oxide semiconductor sensors for detecting crop diseases and insect pests. Comput. Electron. Agric. 197, 106988 (2022). https://doi.org/10.1016/j.compag.2022.106988
  121. H.-Z. Chen, M. Zhang, Z. Guo, Discrimination of fresh-cut broccoli freshness by volatiles using electronic nose and gas chromatography-mass spectrometry. Postharvest Biol. Technol. 148, 168–175 (2019). https://doi.org/10.1016/j.postharvbio.2018.10.019
  122. X. Ren, Y. Wang, Y. Huang, M. Mustafa, D. Sun et al., A CNN-based E-nose using time series features for food freshness classification. IEEE Sens. J. 23, 6027–6038 (2023). https://doi.org/10.1109/JSEN.2023.3241842
  123. M.F. Rutolo, J.P. Clarkson, J.A. Covington, The use of an electronic nose to detect early signs of soft-rot infection in potatoes. Biosyst. Eng. 167, 137–143 (2018). https://doi.org/10.1016/j.biosystemseng.2018.01.001
  124. T. Wen, L. Zheng, S. Dong, Z. Gong, M. Sang et al., Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose. Postharvest Biol. Technol. 147, 156–165 (2019). https://doi.org/10.1016/j.postharvbio.2018.09.017
  125. A. Makarichian, R.A. Chayjan, E. Ahmadi, D. Zafari, Early detection and classification of fungal infection in garlic (A. sativum) using electronic nose. Comput. Electron. Agric. 192, 106575 (2022). https://doi.org/10.1016/j.compag.2021.106575
  126. C. Zhao, J. Ma, W. Jia, H. Wang, H. Tian et al., An apple fungal infection detection model based on BPNN optimized by sparrow search algorithm. Biosensors 12, 692 (2022). https://doi.org/10.3390/bios12090692
  127. J. Du, M. Zhang, X. Teng, Y. Wang, C. Lim Law et al., Evaluation of vegetable sauerkraut quality during storage based on convolution neural network. Food Res. Int. 164, 112420 (2023). https://doi.org/10.1016/j.foodres.2022.112420
  128. B. Mahata, S. Acharyya, S. Giri, T. Mahata, P. Banerji et al., Fruit freshness monitoring employing chemiresistive volatile organic compound sensor and machine learning. ACS Appl. Nano Mater. 6, 22829–22836 (2023). https://doi.org/10.1021/acsanm.3c04138
  129. Y. Mao, N. Li, B. Shi, L. Zhao, S. Cheng et al., Geographical origin determination of Red Huajiao in China using the electronic nose combined with ensemble recognition algorithm. J. Food Sci. 86, 4922–4931 (2021). https://doi.org/10.1111/1750-3841.15933
  130. H. Lin, H. Chen, C. Yin, Q. Zhang, Z. Li et al., Lightweight residual convolutional neural network for soybean classification combined with electronic nose. IEEE Sens. J. 22, 11463–11473 (2022). https://doi.org/10.1109/JSEN.2022.3174251
  131. J. Fu, R. Liu, Y. Chen, J. Xing, Discrimination of geographical indication of Chinese green teas using an electronic nose combined with quantum neural networks: a portable strategy. Sens. Actuat. B Chem. 375, 132946 (2023). https://doi.org/10.1016/j.snb.2022.132946
  132. N. Aghilinategh, M.J. Dalvand, A. Anvar, Detection of ripeness grades of berries using an electronic nose. Food Sci. Nutr. 8, 4919–4928 (2020). https://doi.org/10.1002/fsn3.1788
  133. A.R. Shalaby, Significance of biogenic amines to food safety and human health. Food Res. Int. 29, 675–690 (1996). https://doi.org/10.1016/S0963-9969(96)00066-X
  134. Z. Ma, P. Chen, W. Cheng, K. Yan, L. Pan et al., Highly sensitive, printable nanostructured conductive polymer wireless sensor for food spoilage detection. Nano Lett. 18, 4570–4575 (2018). https://doi.org/10.1021/acs.nanolett.8b01825
  135. R. Saeed, H. Feng, X. Wang, X. Zhang, Z. Fu, Fish quality evaluation by sensor and machine learning: a mechanistic review. Food Contr. 137, 108902 (2022). https://doi.org/10.1016/j.foodcont.2022.108902
  136. Q. Wang, L. Li, W. Ding, D. Zhang, J. Wang et al., Adulterant identification in mutton by electronic nose and gas chromatography-mass spectrometer. Food Contr. 98, 431–438 (2019). https://doi.org/10.1016/j.foodcont.2018.11.038
  137. S. Güney, A. Atasoy, Study of fish species discrimination via electronic nose. Comput. Electron. Agric. 119, 83–91 (2015). https://doi.org/10.1016/j.compag.2015.10.005
  138. M. Nurjuliana, Y.B. Che Man, D. Mat Hashim, A.K. Mohamed, Rapid identification of pork for halal authentication using the electronic nose and gas chromatography mass spectrometer with headspace analyzer. Meat Sci. 88, 638–644 (2011). https://doi.org/10.1016/j.meatsci.2011.02.022
  139. E. Mirzaee-Ghaleh, A. Taheri-Garavand, F. Ayari, J. Lozano, Identification of fresh-chilled and frozen-thawed chicken meat and estimation of their shelf life using an E-nose machine coupled fuzzy KNN. Food Anal. Meth. 13, 678–689 (2020). https://doi.org/10.1007/s12161-019-01682-6
  140. S.R. Karunathilaka, Z. Ellsworth, B.J. Yakes, Detection of decomposition in mahi-mahi, croaker, red snapper, and weakfish using an electronic-nose sensor and chemometric modeling. J. Food Sci. 86, 4148–4158 (2021). https://doi.org/10.1111/1750-3841.15878
  141. R.S. Andre, M.H.M. Facure, L.A. Mercante, D.S. Correa, Electronic nose based on hybrid free-standing nanofibrous mats for meat spoilage monitoring. Sens. Actuat. B Chem. 353, 131114 (2022). https://doi.org/10.1016/j.snb.2021.131114
  142. H. Li, Y. Wang, J. Zhang, X. Li, J. Wang et al., Prediction of the freshness of horse mackerel (Trachurus japonicus) using E-nose, E-tongue, and colorimeter based on biochemical indexes analyzed during frozen storage of whole fish. Food Chem. 402, 134325 (2023). https://doi.org/10.1016/j.foodchem.2022.134325
  143. S. Grassi, S. Benedetti, L. Magnani, A. Pianezzola, S. Buratti, Seafood freshness: e-nose data for classification purposes. Food Contr. 138, 108994 (2022). https://doi.org/10.1016/j.foodcont.2022.108994
  144. A.E.-D.A. Bekhit, B.W.B. Holman, S.G. Giteru, D.L. Hopkins, Total volatile basic nitrogen (TVB-N) and its role in meat spoilage: a review. Trends Food Sci. Technol. 109, 280–302 (2021). https://doi.org/10.1016/j.tifs.2021.01.006
  145. X. Tian, J. Wang, Z. Ma, M. Li, Z. Wei, Combination of an E-nose and an E-tongue for adulteration detection of minced mutton mixed with pork. J. Food Qual. 2019, 4342509 (2019). https://doi.org/10.1155/2019/4342509
  146. L.A. Putri, I. Rahman, M. Puspita, S.N. Hidayat, A.B. Dharmawan et al., Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication. NPJ Sci. Food 7, 31 (2023). https://doi.org/10.1038/s41538-023-00205-2
  147. K. Qian, Y. Bao, J. Zhu, J. Wang, Z. Wei, Development of a portable electronic nose based on a hybrid filter-wrapper method for identifying the Chinese dry-cured ham of different grades. J. Food Eng. 290, 110250 (2021). https://doi.org/10.1016/j.jfoodeng.2020.110250
  148. H. Reinhard, F. Sager, O. Zoller, Citrus juice classification by SPME-GC-MS and electronic nose measurements. LWT Food Sci. Technol. 41, 1906–1912 (2008). https://doi.org/10.1016/j.lwt.2007.11.012
  149. Q. Peng, R. Tian, F. Chen, B. Li, H. Gao, Discrimination of producing area of Chinese Tongshan Kaoliang spirit using electronic nose sensing characteristics combined with the chemometrics methods. Food Chem. 178, 301–305 (2015). https://doi.org/10.1016/j.foodchem.2015.01.023
  150. S. Roussel, V. Bellon-Maurel, J.-M. Roger, P. Grenier, Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry. J. Food Eng. 60, 407–419 (2003). https://doi.org/10.1016/S0260-8774(03)00064-5
  151. H. Yang, Y. Wang, J. Zhao, P. Li, L. Li et al., A machine learning method for juice human sensory hedonic prediction using electronic sensory features. Curr. Res. Food Sci. 7, 100576 (2023). https://doi.org/10.1016/j.crfs.2023.100576
  152. H.-B. Ren, B.-L. Feng, H.-Y. Wang, J.-J. Zhang, X.-S. Bai et al., An electronic sense-based machine learning model to predict formulas and processes for vegetable-fruit beverages. Comput. Electron. Agric. 210, 107883 (2023). https://doi.org/10.1016/j.compag.2023.107883
  153. S.L. Stevan, H.V. Siqueira, B.A. Menegotto, L.C. Schroeder, I.L. Pessenti et al., Discrimination analysis of wines made from four species of blueberry through their olfactory signatures using an E-nose. LWT 187, 115320 (2023). https://doi.org/10.1016/j.lwt.2023.115320
  154. J.C. Rodriguez, E.S. Gamboa, E. Albarracin, A.J. da Silva, L.L. de Andrade, T.A.E. Ferreira, Wine quality rapid detection using a compact electronic nose system: Application focused on spoilage thresholds by acetic acid. LWT 108, 377–384 (2019). https://doi.org/10.1016/j.lwt.2019.03.074
  155. J. Xu, L. Guo, T. Wang, M. Ma, B. Wang et al., Effect of inorganic and organic nitrogen supplementation on volatile components and aroma profile of cider. Food Res. Int. 161, 111765 (2022). https://doi.org/10.1016/j.foodres.2022.111765
  156. X. Jiang, P. Jia, R. Luo, B. Deng, S. Duan et al., A novel electronic nose learning technique based on active learning: EQBC-RBFNN. Sens. Actuat. B Chem. 249, 533–541 (2017). https://doi.org/10.1016/j.snb.2017.04.072
  157. S. Freddi, M.C. Rodriguez Gonzalez, A. Casotto, L. Sangaletti, S. De Feyter, Machine-learning-aided NO2 discrimination with an array of graphene chemiresistors covalently functionalized by diazonium chemistry. Chemistry 29, e202302154 (2023). https://doi.org/10.1002/chem.202302154
  158. P. Arroyo, F. Meléndez, J.I. Suárez, J.L. Herrero, S. Rodríguez et al., Electronic nose with digital gas sensors connected via bluetooth to a smartphone for air quality measurements. Sensors 20, 786 (2020). https://doi.org/10.3390/s20030786
  159. H. Yu, J. Wang, Y. Xu, Identification of adulterated milk using electronic nose. Sensor. Mater. 19, 275–285 (2007). https://doi.org/10.1007/978-0-387-71720-3_15
  160. M. Tohidi, M. Ghasemi-Varnamkhasti, V. Ghafarinia, S. Saeid Mohtasebi, M. Bonyadian, Identification of trace amounts of detergent powder in raw milk using a customized low-cost artificial olfactory system: a novel method. Measurement 124, 120–129 (2018). https://doi.org/10.1016/j.measurement.2018.04.006
  161. F. Ayari, E. Mirzaee- Ghaleh, H. Rabbani, K. Heidarbeigi, Using an E-nose machine for detection the adulteration of margarine in cow ghee. J. Food Process Eng 41, e12806 (2018). https://doi.org/10.1111/jfpe.12806
  162. Y. Yang, L. Wei, Application of E-nose technology combined with artificial neural network to predict total bacterial count in milk. J. Dairy Sci. 104, 10558–10565 (2021). https://doi.org/10.3168/jds.2020-19987
  163. H. Zeng, H. Han, Y. Huang, B. Wang, Rapid prediction of the aroma type of plain yogurts via electronic nose combined with machine learning approaches. Food Biosci. 56, 103269 (2023). https://doi.org/10.1016/j.fbio.2023.103269
  164. R. Wu, S. Xie, Spatial distribution of secondary organic aerosol formation potential in China derived from speciated anthropogenic volatile organic compound emissions. Environ. Sci. Technol. 52, 8146–8156 (2018). https://doi.org/10.1021/acs.est.8b01269
  165. A. Pozzer, S.C. Anenberg, S. Dey, A. Haines, J. Lelieveld et al., Mortality attributable to ambient air pollution: a review of global estimates. Geohealth 7, e2022GH000711 (2023). https://doi.org/10.1029/2022GH000711
  166. D. Fattorini, F. Regoli, Role of the chronic air pollution levels in the Covid-19 outbreak risk in Italy. Environ. Pollut. 264, 114732 (2020). https://doi.org/10.1016/j.envpol.2020.114732
  167. S. Gulas, M. Downton, K. D’Souza, K. Hayden, T.R. Walker, Declining Arctic Ocean oil and gas developments: opportunities to improve governance and environmental pollution control. Mar. Policy 75, 53–61 (2017). https://doi.org/10.1016/j.marpol.2016.10.014
  168. H.S. Hong, N.H. Phuong, N.T. Huong, N.H. Nam, N.T. Hue, Highly sensitive and low detection limit of resistive NO2 gas sensor based on a MoS2/graphene two-dimensional heterostructures. Appl. Surf. Sci. 492, 449–454 (2019). https://doi.org/10.1016/j.apsusc.2019.06.230
  169. R.G. Ewing, M.J. Waltman, D.A. Atkinson, J.W. Grate, P.J. Hotchkiss, The vapor pressures of explosives. Trac Trends Anal. Chem. 42, 35–48 (2013). https://doi.org/10.1016/j.trac.2012.09.010
  170. Y. Li, W. Zhou, B. Zu, X. Dou, Qualitative detection toward military and improvised explosive vapors by a facile TiO2 nanosheet-based chemiresistive sensor array. Front. Chem. 8, 29 (2020). https://doi.org/10.3389/fchem.2020.00029
  171. C.-S. Lee, H.-Y. Li, B.-Y. Kim, Y.-M. Jo, H.-G. Byun et al., Discriminative detection of indoor volatile organic compounds using a sensor array based on pure and Fe-doped In2O3 nanofibers. Sens. Actuat. B Chem. 285, 193–200 (2019). https://doi.org/10.1016/j.snb.2019.01.044
  172. L. Zhang, F. Tian, H. Nie, L. Dang, G. Li et al., Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine. Sens. Actuat. B Chem. 174, 114–125 (2012). https://doi.org/10.1016/j.snb.2012.07.021
  173. P. Liu, X. Guo, C. Liang, B. Du, Y. Tan et al., Rapid detection of trace nitro-explosives under UV irradiation by electronic nose with neural networks. ACS Appl. Mater. Interfaces 15, 36539–36549 (2023). https://doi.org/10.1021/acsami.3c06498
  174. R. López, M. Vega, L.M. Debán, R. Pardo, Detection of Triacetone Triperoxide in air combining SnO2 sensor e-nose enhanced with a kinetic model. Sens. Actuat. B Chem. 403, 135242 (2024). https://doi.org/10.1016/j.snb.2023.135242
  175. J. Chapman, V.K. Truong, A. Elbourne, S. Gangadoo, S. Cheeseman et al., Combining chemometrics and sensors: toward new applications in monitoring and environmental analysis. Chem. Rev. 120, 6048–6069 (2020). https://doi.org/10.1021/acs.chemrev.9b00616
  176. O. Attallah, Multitask deep learning-based pipeline for gas leakage detection via E-nose and thermal imaging multimodal fusion. Chemosensors 11, 364 (2023). https://doi.org/10.3390/chemosensors11070364
  177. K.R. Sinju, B. Bhangare, A. Pathak, S.J. Patil, N.S. Ramgir et al., ZnO nanowires based e-nose for the detection of H2S and NO2 toxic gases. Mater. Sci. Semicond. Process. 137, 106235 (2022). https://doi.org/10.1016/j.mssp.2021.106235
  178. O. Attallah, I. Morsi, An electronic nose for identifying multiple combustible/harmful gases and their concentration levels via artificial intelligence. Measurement 199, 111458 (2022). https://doi.org/10.1016/j.measurement.2022.111458
  179. A. Shahid, J.H. Choi, A.U.H.S. Rana, H.S. Kim, Least Squares neural network-based wireless E-nose system using an SnO2 sensor array. Sensors 18, 1446 (2018). https://doi.org/10.3390/s18051446
  180. H. Kang, S.-Y. Cho, J. Ryu, J. Choi, H. Ahn et al., Multiarray nanopattern electronic nose (E-nose) by high-resolution top-down nanolithography. Adv. Funct. Mater. 30, 2002486 (2020). https://doi.org/10.1002/adfm.202002486
  181. J. Zhang, Y. Xue, Q. Sun, T. Zhang, Y. Chen et al., A miniaturized electronic nose with artificial neural network for anti-interference detection of mixed indoor hazardous gases. Sens. Actuat. B Chem. 326, 128822 (2021). https://doi.org/10.1016/j.snb.2020.128822
  182. S. Ni, P. Jia, Y. Xu, L. Zeng, X. Li et al., Prediction of CO concentration in different conditions based on Gaussian-TCN. Sens. Actuat. B Chem. 376, 133010 (2023). https://doi.org/10.1016/j.snb.2022.133010
  183. G. Mao, Y. Zhang, Y. Xu, X. Li, M. Xu et al., An electronic nose for harmful gas early detection based on a hybrid deep learning method H-CRNN. Microchem. J. 195, 109464 (2023). https://doi.org/10.1016/j.microc.2023.109464
  184. R.H. Weiss, K. Kim, Metabolomics in the study of kidney diseases. Nat. Rev. Nephrol. 8, 22–33 (2012). https://doi.org/10.1038/nrneph.2011.152
  185. D.W. Dockery, C.A. Pope, X. Xu, J.D. Spengler, J.H. Ware et al., An association between air pollution and mortality in six U.S. cities. N. Engl. J. Med. 329, 1753–1759 (1993). https://doi.org/10.1056/nejm199312093292401
  186. H. Ma, T. Wang, B. Li, W. Cao, M. Zeng et al., A low-cost and efficient electronic nose system for quantification of multiple indoor air contaminants utilizing HC and PLSR. Sens. Actuat. B Chem. 350, 130768 (2022). https://doi.org/10.1016/j.snb.2021.130768
  187. P. Jia, F. Meng, H. Cao, S. Duan, X. Peng et al., Training technique of electronic nose using labeled and unlabeled samples based on multi-kernel LapSVM. Sens. Actuat. B Chem. 294, 98–105 (2019). https://doi.org/10.1016/j.snb.2019.05.034
  188. L. Wang, P. Jia, T. Huang, S. Duan, J. Yan et al., A novel optimization technique to improve gas recognition by electronic noses based on the enhanced krill herd algorithm. Sensors 16, 1275 (2016). https://doi.org/10.3390/s16081275
  189. H. Fan, V.H. Bennetts, E. Schaffernicht, A.J. Lilienthal, A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments. Sens. Actuat. B Chem. 259, 183–203 (2018). https://doi.org/10.1016/j.snb.2017.10.063
  190. M. Leidinger, T. Sauerwald, W. Reimringer, G. Ventura, A. Schütze, Selective detection of hazardous VOCs for indoor air quality applications using a virtual gas sensor array. J. Sens. Sens. Syst. 3, 253–263 (2014). https://doi.org/10.5194/jsss-3-253-2014
  191. J. He, L. Xu, P. Wang, Q. Wang, A high precise E-nose for daily indoor air quality monitoring in living environment. Integration 58, 286–294 (2017). https://doi.org/10.1016/j.vlsi.2016.12.010
  192. Y. Chen, Z. Zhu, S. Cheng, Industrial agglomeration and haze pollution: Evidence from China. Sci. Total. Environ. 845, 157392 (2022). https://doi.org/10.1016/j.scitotenv.2022.157392
  193. Y. Wang, Y. Wen, S. Zhang, G. Zheng, H. Zheng et al., Vehicular ammonia emissions significantly contribute to urban PM2.5 pollution in two Chinese megacities. Environ. Sci. Technol. 57, 2698–2705 (2023). https://doi.org/10.1021/acs.est.2c06198
  194. A. Rim-Rukeh, An assessment of the contribution of municipal solid waste dump sites fire to atmospheric pollution. Open J. Air Pollut. 3, 53–60 (2014). https://doi.org/10.4236/ojap.2014.33006
  195. Y. Su, J. Wang, B. Wang, T. Yang, B. Yang et al., Alveolus-inspired active membrane sensors for self-powered wearable chemical sensing and breath analysis. ACS Nano 14, 6067–6075 (2020). https://doi.org/10.1021/acsnano.0c01804
  196. D. Ma, J. Zhang, X. Li, C. He, Z. Lu et al., C3N monolayers as promising candidates for NO2 sensors. Sens. Actuat. B Chem. 266, 664–673 (2018). https://doi.org/10.1016/j.snb.2018.03.159
  197. G. Domènech-Gil, N.T. Duc, J.J. Wikner, J. Eriksson, S.N. Påledal et al., Electronic nose for improved environmental methane monitoring. Environ. Sci. Technol. 58, 352–361 (2024). https://doi.org/10.1021/acs.est.3c06945
  198. C. Zhang, M. Debliquy, A. Boudiba, H. Liao, C. Coddet, Sensing properties of atmospheric plasma-sprayed WO3 coating for sub-ppm NO2 detection. Sens. Actuat. B Chem. 144, 280–288 (2010). https://doi.org/10.1016/j.snb.2009.11.006
  199. I. Sayago, M. Aleixandre, J.P. Santos, Development of tin oxide-based nanosensors for electronic nose environmental applications. Biosensors 9, 21 (2019). https://doi.org/10.3390/bios9010021
  200. T.-M. Chen, W.G. Kuschner, J. Gokhale, S. Shofer, Outdoor air pollution: nitrogen dioxide, sulfur dioxide, and carbon monoxide health effects. Am. J. Med. Sci. 333, 249–256 (2007). https://doi.org/10.1097/MAJ.0b013e31803b900f
  201. S. Zhai, Z. Li, H. Zhang, L. Wang, S. Duan et al., A multilevel interleaved group attention-based convolutional network for gas detection via an electronic nose system. Eng. Appl. Artif. Intell. 133, 108038 (2024). https://doi.org/10.1016/j.engappai.2024.108038
  202. J. Burgués, S. Doñate, M.D. Esclapez, L. Saúco, S. Marco, Characterization of odour emissions in a wastewater treatment plant using a drone-based chemical sensor system. Sci. Total. Environ. 846, 157290 (2022). https://doi.org/10.1016/j.scitotenv.2022.157290
  203. J. Burgués, M.D. Esclapez, S. Doñate, S. Marco, RHINOS: a lightweight portable electronic nose for real-time odor quantification in wastewater treatment plants. iScience 24, 103371 (2021). https://doi.org/10.1016/j.isci.2021.103371
  204. M. Kang, I. Cho, J. Park, J. Jeong, K. Lee et al., High accuracy real-time multi-gas identification by a batch-uniform gas sensor array and deep learning algorithm. ACS Sens. 7, 430–440 (2022). https://doi.org/10.1021/acssensors.1c01204
  205. K. Lee, I. Cho, M. Kang, J. Jeong, M. Choi et al., Ultra-low-power E-nose system based on multi-micro-LED-integrated, nanostructured gas sensors and deep learning. ACS Nano 17, 539–551 (2023). https://doi.org/10.1021/acsnano.2c09314
  206. T. Wang, H. Zhang, Y. Wu, W. Jiang, X. Chen et al., Target discrimination, concentration prediction, and status judgment of electronic nose system based on large-scale measurement and multi-task deep learning. Sens. Actuat. B Chem. 351, 130915 (2022). https://doi.org/10.1016/j.snb.2021.130915
  207. A.H. Abdullah, M.A.A. Bakar, F.S.A. Saad, S. Sudin, Z.A. Ahmad et al., Development of cloud-based electronic nose for university laboratories air monitoring. IOP Conf. Ser. Mater. Sci. Eng. 932, 012082 (2020). https://doi.org/10.1088/1757-899x/932/1/012082
  208. H.Y. Chae, J. Cho, R. Purbia, C.S. Park, H. Kim et al., Environment-adaptable edge-computing gas-sensor device with analog-assisted continual learning scheme. IEEE Trans. Ind. Electron. 70, 10720–10729 (2023). https://doi.org/10.1109/TIE.2022.3220871
  209. J. Wang, J. Yang, D. Chen, L. Jin, Y. Li et al., Gas detection microsystem with MEMS gas sensor and integrated circuit. IEEE Sens. J. 18, 6765–6773 (2018). https://doi.org/10.1109/JSEN.2018.2829742
  210. Y.M. Kwon, B. Oh, R. Purbia, H.Y. Chae, G.H. Han et al., High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level. Sens. Actuat. B Chem. 375, 132939 (2023). https://doi.org/10.1016/j.snb.2022.132939
  211. F. Raspagliesi, G. Bogani, S. Benedetti, S. Grassi, S. Ferla et al., Detection of ovarian cancer through exhaled breath by electronic nose: a prospective study. Cancers 12, 2408 (2020). https://doi.org/10.3390/cancers12092408
  212. S. Sethi, R. Nanda, T. Chakraborty, Clinical application of volatile organic compound analysis for detecting infectious diseases. Clin. Microbiol. Rev. 26, 462–475 (2013). https://doi.org/10.1128/CMR.00020-13
  213. J. Cancer, R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics, 2020. CA. Clin. 70, 7–30 (2020). https://doi.org/10.3322/caac.21590
  214. A.T. Güntner, S. Abegg, K. Königstein, P.A. Gerber, A. Schmidt-Trucksäss et al., Breath sensors for health monitoring. ACS Sens. 4, 268–280 (2019). https://doi.org/10.1021/acssensors.8b00937
  215. A. Leiter, R.R. Veluswamy, J.P. Wisnivesky, The global burden of lung cancer: current status and future trends. Nat. Rev. Clin. Oncol. 20, 624–639 (2023). https://doi.org/10.1038/s41571-023-00798-3
  216. D. Shlomi, M. Abud, O. Liran, J. Bar, N. Gai-Mor et al., Detection of lung cancer and EGFR mutation by electronic nose system. J. Thorac. Oncol. 12, 1544–1551 (2017). https://doi.org/10.1016/j.jtho.2017.06.073
  217. A. Kononov, B. Korotetsky, I. Jahatspanian, A. Gubal, A. Vasiliev et al., Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer. J. Breath Res. 14, 016004 (2019). https://doi.org/10.1088/1752-7163/ab433d
  218. R. de Vries, N. Farzan, T. Fabius, F.H.C. De Jongh, P.M.C. Jak et al., Prospective detection of early lung cancer in patients with COPD in regular care by electronic nose analysis of exhaled breath. Chest 164, 1315–1324 (2023). https://doi.org/10.1016/j.chest.2023.04.050
  219. G. Zonta, G. Anania, B. Fabbri, A. Gaiardo, S. Gherardi et al., Detection of colorectal cancer biomarkers in the presence of interfering gases. Sens. Actuat. B Chem. 218, 289–295 (2015). https://doi.org/10.1016/j.snb.2015.04.080
  220. G. Zonta, G. Anania, C. Feo, A. Gaiardo, S. Gherardi et al., Use of gas sensors and FOBT for the early detection of colorectal cancer. Sens. Actuat. B Chem. 262, 884–891 (2018). https://doi.org/10.1016/j.snb.2018.01.225
  221. G. Zonta, C. Malagù, S. Gherardi, A. Giberti, A. Pezzoli et al., Clinical validation results of an innovative non-invasive device for colorectal cancer preventive screening through fecal exhalation analysis. Cancers 12, 1471 (2020). https://doi.org/10.3390/cancers12061471
  222. R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics. CA Cancer J. Clin. 68, 7–30 (2018). https://doi.org/10.3322/caac.21442
  223. F. Bray, J. Ferlay, I. Soerjomataram, R.L. Siegel, L.A. Torre et al., Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018). https://doi.org/10.3322/caac.21492
  224. C. Bax, G. Taverna, L. Eusebio, S. Sironi, F. Grizzi et al., Innovative diagnostic methods for early prostate cancer detection through urine analysis: a review. Cancers 10, 123 (2018). https://doi.org/10.3390/cancers10040123
  225. C. Bax, B.J. Lotesoriere, S. Sironi, L. Capelli, Review and comparison of cancer biomarker trends in urine as a basis for new diagnostic pathways. Cancers 11, 1244 (2019). https://doi.org/10.3390/cancers11091244
  226. L. Capelli, C. Bax, F. Grizzi, G. Taverna, Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis. Sci. Rep. 11, 20898 (2021). https://doi.org/10.1038/s41598-021-00033-y
  227. G. Taverna, F. Grizzi, L. Tidu, C. Bax, M. Zanoni et al., Accuracy of a new electronic nose for prostate cancer diagnosis in urine samples. Int. J. Urol. 29, 890–896 (2022). https://doi.org/10.1111/iju.14912
  228. C. Bax, S. Prudenza, G. Gaspari, L. Capelli, F. Grizzi et al., Drift compensation on electronic nose data for non-invasive diagnosis of prostate cancer by urine analysis. iScience 25, 103622 (2022). https://doi.org/10.1016/j.isci.2021.103622
  229. C.M. Durán Acevedo, J.K. Carrillo Gómez, C.A. Cuastumal Vasquez, J. Ramos, Prostate cancer detection in Colombian patients through E-senses devices in exhaled breath and urine samples. Chemosensors 12, 11 (2024). https://doi.org/10.3390/chemosensors12010011
  230. L. Lavra, A. Catini, A. Ulivieri, R. Capuano, L. Baghernajad Salehi et al., Investigation of VOCs associated with different characteristics of breast cancer cells. Sci. Rep. 5, 13246 (2015). https://doi.org/10.1038/srep13246
  231. J. Giró Benet, M. Seo, M. Khine, J. Gumà Padró, A. Pardo Martnez et al., Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine. Sci. Rep. 12, 14873 (2022). https://doi.org/10.1038/s41598-022-17795-8
  232. H.-Y. Yang, Y.-C. Wang, H.-Y. Peng, C.-H. Huang, Breath biopsy of breast cancer using sensor array signals and machine learning analysis. Sci. Rep. 11, 103 (2021). https://doi.org/10.1038/s41598-020-80570-0
  233. M.K. Nakhleh, H. Amal, R. Jeries, Y.Y. Broza, M. Aboud et al., Diagnosis and classification of 17 diseases from 1404 subjects via pattern analysis of exhaled molecules. ACS Nano 11, 112–125 (2017). https://doi.org/10.1021/acsnano.6b04930
  234. A. Kwiatkowski, S. Borys, K. Sikorska, K. Drozdowska, J.M. Smulko, Clinical studies of detecting COVID-19 from exhaled breath with electronic nose. Sci. Rep. 12, 15990 (2022). https://doi.org/10.1038/s41598-022-20534-8
  235. D.K. Nurputra, A. Kusumaatmaja, M.S. Hakim, S.N. Hidayat, T. Julian et al., Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition. NPJ Digit. Med. 5, 115 (2022). https://doi.org/10.1038/s41746-022-00661-2
  236. M.P. Bhandari, V. Veliks, I. Stonāns, M. Padilla, O. Šuba et al., Breath sensor technology for the use in mechanical lung ventilation equipment for monitoring critically ill patients. Diagnostics 12, 430 (2022). https://doi.org/10.3390/diagnostics12020430
  237. J. Li, A. Hannon, G. Yu, L.A. Idziak, A. Sahasrabhojanee et al., Electronic nose development and preliminary human breath testing for rapid, non-invasive COVID-19 detection. ACS Sens. 8, 2309–2318 (2023). https://doi.org/10.1021/acssensors.3c00367
  238. C.-Y. Chen, W.-C. Lin, H.-Y. Yang, Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research. Respir. Res. 21, 45 (2020). https://doi.org/10.1186/s12931-020-1285-6
  239. J.B. Soriano, P.J. Kendrick, K.R. Paulson, V. Gupta, E.M. Abrams et al., Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir. Med. 8, 585–596 (2020). https://doi.org/10.1016/S2213-2600(20)30105-3
  240. D. Pritchard, A. Adegunsoye, E. Lafond, J.V. Pugashetti, R. DiGeronimo et al., Diagnostic test interpretation and referral delay in patients with interstitial lung disease. Respir. Res. 20, 253 (2019). https://doi.org/10.1186/s12931-019-1228-2
  241. N. Hoyer, T.S. Prior, E. Bendstrup, T. Wilcke, S.B. Shaker, Risk factors for diagnostic delay in idiopathic pulmonary fibrosis. Respir. Res. 20, 103 (2019). https://doi.org/10.1186/s12931-019-1076-0
  242. I.G. van der Sar, M.S. Wijsenbeek, G.J. Braunstahl, J.O. Loekabino, A.C. Dingemans et al., Differentiating interstitial lung diseases from other respiratory diseases using electronic nose technology. Respir. Res. 24, 271 (2023). https://doi.org/10.1186/s12931-023-02575-3
  243. N. Wijbenga, R.A.S. Hoek, B.J. Mathot, L. Seghers, C.C. Moor et al., Diagnostic performance of electronic nose technology in chronic lung allograft dysfunction. J Heart Lung Transplant 42, 236–245 (2023). https://doi.org/10.1016/j.healun.2022.09.009
  244. N. Alkhouri, T. Singh, E. Alsabbagh, J. Guirguis, T. Chami et al., Isoprene in the exhaled breath is a novel biomarker for advanced fibrosis in patients with chronic liver disease: a pilot study. Clin. Transl. Gastroenterol. 6, e112 (2015). https://doi.org/10.1038/ctg.2015.40
  245. A. Tangerman, M.T. Meuwese-Arends, J.B. Jansen, Cause and composition of foetor hepaticus. Lancet 343, 483 (1994). https://doi.org/10.1016/s0140-6736(94)92729-4
  246. D.-D. Wu, D.-Y. Wang, H.-M. Li, J.-C. Guo, S.-F. Duan et al., Hydrogen sulfide as a novel regulatory factor in liver health and disease. Oxid. Med. Cell. Longev. 2019, 3831713 (2019). https://doi.org/10.1155/2019/3831713
  247. R.F. Del Río, M.E. O’Hara, A. Holt, P. Pemberton, T. Shah et al., Volatile biomarkers in breath associated with liver cirrhosis: comparisons of pre- and post-liver transplant breath samples. EBioMedicine 2, 1243–1250 (2015). https://doi.org/10.1016/j.ebiom.2015.07.027
  248. O. Zaim, A. Diouf, N. El Bari, N. Lagdali, I. Benelbarhdadi et al., Comparative analysis of volatile organic compounds of breath and urine for distinguishing patients with liver cirrhosis from healthy controls by using electronic nose and voltammetric electronic tongue. Anal. Chim. Acta 1184, 339028 (2021). https://doi.org/10.1016/j.aca.2021.339028
  249. C. Dalis, F.M. Mesfin, K. Manohar, J. Liu, W.C. Shelley et al., Volatile organic compound assessment as a screening tool for early detection of gastrointestinal diseases. Microorganisms 11, 1822 (2023). https://doi.org/10.3390/microorganisms11071822
  250. M. Buijck, D.J. Berkhout, E.F. de Groot, M.A. Benninga, M.P. van der Schee et al., Sniffing out paediatric gastrointestinal diseases: the potential of volatile organic compounds as biomarkers for disease. J. Pediatr. Gastroenterol. Nutr. 63, 585–591 (2016). https://doi.org/10.1097/MPG.0000000000001250
  251. S. Kurada, N. Alkhouri, C. Fiocchi, R. Dweik, F. Rieder, Review : breath analysis in inflammatory bowel diseases. Aliment. Pharmacol. Ther. 41, 329–341 (2015). https://doi.org/10.1111/apt.13050
  252. R.P. Arasaradnam, N. Ouaret, M.G. Thomas, N. Quraishi, E. Heatherington et al., A novel tool for noninvasive diagnosis and tracking of patients with inflammatory bowel disease. Inflamm. Bowel Dis. 19, 999–1003 (2013). https://doi.org/10.1097/mib.0b013e3182802b26
  253. N.D. McGuire, R.J. Ewen, B. de Lacy Costello, C.E. Garner, C.S. Probert et al., Towards point of care testing for C. difficile infection by volatile profiling, using the combination of a short multi-capillary gas chromatography column with metal oxide sensor detection. Meas. Sci. Technol. 25, 065108 (2014). https://doi.org/10.1088/0957-0233/25/6/065108
  254. B.A. Day, C.E. Wilmer, Computational design of MOF-based electronic noses for dilute gas species detection: application to kidney disease detection. ACS Sens. 6, 4425–4434 (2021). https://doi.org/10.1021/acssensors.1c01808
  255. K. Dhatariya, Blood ketones: measurement, interpretation, limitations, and utility in the management of diabetic ketoacidosis. Rev. Diabet. Stud. 13, 217–225 (2016). https://doi.org/10.1900/rds.2016.13.217
  256. P. Wang, Y. Tan, H. Xie, F. Shen, A novel method for diabetes diagnosis based on electronic nose 1 paper presented at Biosensors ’96, Bangkock, May 1996.1. Biosens. Bioelectron. 12, 1031–1036 (1997). https://doi.org/10.1016/S0956-5663(97)00059-6
  257. J.-Y. Jeon, J.-S. Choi, J.-B. Yu, H.-R. Lee, B.K. Jang et al., Sensor array optimization techniques for exhaled breath analysis to discriminate diabetics using an electronic nose. ETRI J. 40, 802–812 (2018). https://doi.org/10.4218/etrij.2017-0018
  258. S. Esfahani, A. Wicaksono, E. Mozdiak, R.P. Arasaradnam, J.A. Covington, Non-invasive diagnosis of diabetes by volatile organic compounds in urine using FAIMS and Fox4000 electronic nose. Biosensors 8, 121 (2018). https://doi.org/10.3390/bios8040121
  259. J.E. Lee, C.K. Lim, H. Song, S.-Y. Choi, D.-S. Lee, A highly smart MEMS acetone gas sensors in array for diet-monitoring applications. Micro Nano Syst. Lett. 9, 10 (2021). https://doi.org/10.1186/s40486-021-00136-1
  260. S.Y. Park, Y. Kim, T. Kim, T.H. Eom, S.Y. Kim et al., Chemoresistive materials for electronic nose: progress, perspectives, and challenges. InfoMat 1, 289–316 (2019). https://doi.org/10.1002/inf2.12029
  261. S.-Y. Jeong, J.-S. Kim, J.-H. Lee, Rational design of semiconductor-based chemiresistors and their libraries for next-generation artificial olfaction. Adv. Mater. 32, e2002075 (2020). https://doi.org/10.1002/adma.202002075
  262. S.-J. Choi, I.-D. Kim, Recent, developments in 2D nanomaterials for chemiresistive-type gas sensors. Electron. Mater. Lett. 14, 221–260 (2018). https://doi.org/10.1007/s13391-018-0044-z
  263. F. Zhu, J. Gao, J. Yang, N. Ye, Neighborhood linear discriminant analysis. Pattern Recognit. 123, 108422 (2022). https://doi.org/10.1016/j.patcog.2021.108422
  264. M. Greenacre, P.J.F. Groenen, T. Hastie, A.I. D’Enza, A. Markos et al., Principal component analysis. Nat. Rev. Meth. Primers 2, 100 (2022). https://doi.org/10.1038/s43586-022-00184-w
  265. S.A. Abdulrahman, W. Khalifa, M. Roushdy, A.-B.M. Salem, Comparative study for 8 computational intelligence algorithms for human identification. Comput. Sci. Rev. 36, 100237 (2020). https://doi.org/10.1016/j.cosrev.2020.100237
  266. R. Vidal, Y. Ma, S.S. Sastry, In Principal Component Analysis (Springer, New York, 2016), pp.25–62
  267. A. Bouguettaya, Q. Yu, X. Liu, X. Zhou, A. Song, Efficient agglomerative hierarchical clustering. Expert Syst. Appl. 42, 2785–2797 (2015). https://doi.org/10.1016/j.eswa.2014.09.054
  268. A. Sebastian, A. Pannone, S. Subbulakshmi Radhakrishnan, S. Das, Gaussian synapses for probabilistic neural networks. Nat. Commun. 10, 4199 (2019). https://doi.org/10.1038/s41467-019-12035-6
  269. V.K. Ojha, A. Abraham, V. Snášel, Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng. Appl. Artif. Intell. 60, 97–116 (2017). https://doi.org/10.1016/j.engappai.2017.01.013
  270. S. Ding, H. Zhao, Y. Zhang, X. Xu, R. Nie, Extreme learning machine: algorithm, theory and applications. Artif. Intell. Rev. 44, 103–115 (2015). https://doi.org/10.1007/s10462-013-9405-z
  271. G. Van Houdt, C. Mosquera, G. Nápoles, A review on the long short-term memory model. Artif. Intell. Rev. 53, 5929–5955 (2020). https://doi.org/10.1007/s10462-020-09838-1
  272. A.H. Gómez, J. Wang, G. Hu, A.G. Pereira, Monitoring storage shelf life of tomato using electronic nose technique. J. Food Eng. 85, 625–631 (2008). https://doi.org/10.1016/j.jfoodeng.2007.06.039
  273. Q. Liu, N. Zhao, D. Zhou, Y. Sun, K. Sun et al., Discrimination and growth tracking of fungi contamination in peaches using electronic nose. Food Chem. 262, 226–234 (2018). https://doi.org/10.1016/j.foodchem.2018.04.100
  274. M. Ghasemi-Varnamkhasti, A. Mohammad-Razdari, S.H. Yoosefian, Z. Izadi, G. Rabiei, Selection of an optimized metal oxide semiconductor sensor (MOS) array for freshness characterization of strawberry in polymer packages using response surface method (RSM). Postharvest Biol. Technol. 151, 53–60 (2019). https://doi.org/10.1016/j.postharvbio.2019.01.016
  275. S. Qiu, L. Gao, J. Wang, Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice. J. Food Eng. 144, 77–85 (2015). https://doi.org/10.1016/j.jfoodeng.2014.07.015
  276. M. Ghasemi-Varnamkhasti, Z.S. Amiri, M. Tohidi, M. Dowlati, S.S. Mohtasebi et al., Differentiation of cumin seeds using a metal-oxide based gas sensor array in tandem with chemometric tools. Talanta 176, 221–226 (2018). https://doi.org/10.1016/j.talanta.2017.08.024
  277. W. Jia, G. Liang, H. Tian, J. Sun, C. Wan, Electronic nose-based technique for rapid detection and recognition of moldy apples. Sensors 19, 1526 (2019). https://doi.org/10.3390/s19071526
  278. J. Wen, Y. Zhao, Q. Rong, Z. Yang, J. Yin et al., Rapid odor recognition based on reliefF algorithm using electronic nose and its application in fruit identification and classification. J. Food Meas. Charact. 16, 2422–2433 (2022). https://doi.org/10.1007/s11694-022-01351-z
  279. X. Hong, J. Wang, Detection of adulteration in cherry tomato juices based on electronic nose and tongue: comparison of different data fusion approaches. J. Food Eng. 126, 89–97 (2014). https://doi.org/10.1016/j.jfoodeng.2013.11.008
  280. E. Yavuzer, Determination of fish quality parameters with low cost electronic nose. Food Biosci. 41, 100948 (2021). https://doi.org/10.1016/j.fbio.2021.100948
  281. S. Grassi, S. Benedetti, M. Opizzio, E.D. Nardo, S. Buratti, Meat and fish freshness assessment by a portable and simplified electronic nose system (mastersense). Sensors 19, 3225 (2019). https://doi.org/10.3390/s19143225
  282. N.U. Hasan, N. Ejaz, W. Ejaz, H.S. Kim, Meat and fish freshness inspection system based on odor sensing. Sensors 12, 15542–15557 (2012). https://doi.org/10.3390/s121115542
  283. X. Hong, J. Wang, Z. Hai, Discrimination and prediction of multiple beef freshness indexes based on electronic nose. Sens. Actuat. B Chem. 161, 381–389 (2012). https://doi.org/10.1016/j.snb.2011.10.048
  284. X. Tian, J. Wang, S. Cui, Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors. J. Food Eng. 119, 744–749 (2013). https://doi.org/10.1016/j.jfoodeng.2013.07.004
  285. D.R. Wijaya, R. Sarno, E. Zulaika, DWTLSTM for electronic nose signal processing in beef quality monitoring. Sens. Actuat. B Chem. 326, 128931 (2021). https://doi.org/10.1016/j.snb.2020.128931
  286. M. Rasekh, H. Karami, E-nose coupled with an artificial neural network to detection of fraud in pure and industrial fruit juices. Inter. J. Food Properties 24, 592–602 (2021). https://doi.org/10.1080/10942912.2021.1908354
  287. F. Mu, Y. Gu, J. Zhang, L. Zhang, Milk source identification and milk quality estimation using an electronic nose and machine learning techniques. Sensors 20, 4238 (2020). https://doi.org/10.3390/s20154238
  288. H. Karami, M. Rasekh, E. Mirzaee-Ghaleh, Qualitative analysis of edible oil oxidation using an olfactory machine. J. Food Meas. Charact. 14, 2600–2610 (2020). https://doi.org/10.1007/s11694-020-00506-0
  289. H. Jiang, Y. He, Q. Chen, Qualitative identification of the edible oil storage period using a homemade portable electronic nose combined with multivariate analysis. J. Sci. Food Agric. 101, 3448–3456 (2021). https://doi.org/10.1002/jsfa.10975
  290. X. Dong, L. Gao, H. Zhang, J. Wang, K. Qiu et al., Comparison of sensory qualities in eggs from three breeds based on electronic sensory evaluations. Foods 10, 1984 (2021). https://doi.org/10.3390/foods10091984
  291. M. Rasekh, H. Karami, A.D. Wilson, M. Gancarz, Classification and identification of essential oils from herbs and fruits based on a MOS electronic-nose technology. Chemosensors 9, 142 (2021). https://doi.org/10.3390/chemosensors9060142
  292. R. Dutta, E.L. Hines, J.W. Gardner, K.R. Kash
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