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Design and Construction of Electronic Nose for Multi-purpose Applications by Sensor Array Arrangement Using IBGSA

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Abstract

Technological progresses in the gas sensor fields provide the possibility of designing and construction of Electronic nose (E-nose) based on the Biological nose. E-nose uses specific hardware and software units; Sensor array is one of the critical units in the E-nose and its types of sensors are determined based on the application. So far, many achievements have been reported for using the E-nose in different fields of application. In this work, an E-nose for handling multi-purpose applications is proposed, and the employed hardware and pattern recognition techniques are depicted. To achieve higher recognition rate and lower power consumption, the improved binary gravitational search algorithm (IBGSA) and the K-nearest neighbor (KNN) classifier are used for automatic selecting the best combination of the sensors. The designed E-nose is tested by classifying the odors in different case studies, including moldy bread recognition in food and beverage field, herbs recognition in the medical field, and petroleum products recognition in the industrial field. Experimental results confirm the efficiency of the proposed method for E-nose realization.

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References

  1. Patel, H.K.: The Electronic Nose: Artificial Olfaction Technology. Springer [2014] 978-81-322-1548-6 (2014)

  2. Gardner, J.W., Yinon, J., Division, N.S.A.: Electronic Noses & Sensors for the Detection of Explosives. Oxford University Press; 1 edition 978-01-985-5955-9 (1999)

  3. Pearce, T.C., Schiffman, S.S., Nagle, H.T., Gardner, J.W.: Handbook of Machine Olfaction: Electronic Nose Technology. Wiley 978-3-527-60563-7 (2006)

  4. Kurup, P.U.: An electronic nose for detecting hazardous chemicals and explosives. In: IEEE Conference on Technologies for Homeland Security, pp. 144–149. IEEE (2008)

  5. Griffin, M.: Electronic Noses: Multi-Sensor Arrays. Davidson College (2003)

  6. Casalinuovo, I.A., Di Pierro, D., Coletta, M., Di Francesco, P.: Application of electronic noses for disease diagnosis and food spoilage detection. Sensors 6(11), 1428–1439 (2006)

    Article  Google Scholar 

  7. Konvalina, G., Haick, H.: Sensors for breath testing: from nanomaterials to comprehensive disease detection. Acc. Chem. Res. 47(1), 66–76 (2013)

    Article  Google Scholar 

  8. Tisch, U., Haick, H.: Arrays of chemisensitive monolayer-capped metallic nanoparticles for diagnostic breath testing. Rev. Chem. Eng. 26(5-6), 171–179 (2010)

    Article  Google Scholar 

  9. Buszewski, B., Kêsy, M., Ligor, T., Amann, A.: Human exhaled air analytics: biomarkers of diseases. Biomed. Chromatogr. 21(6), 553–566 (2007)

    Article  Google Scholar 

  10. Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.: Dimensionality reduction using genetic algorithms. IEEE Trans. Evol. Comput. 4(2), 164–171 (2000)

    Article  Google Scholar 

  11. Oreski, S., Oreski, G.: Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Syst. Appl. 41(4), 2052–2064 (2014)

    Article  Google Scholar 

  12. Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)

    Article  Google Scholar 

  13. Fu, J., Huang, C., Xing, J., Zheng, J.: Pattern classification using an olfactory model with PCA feature selection in electronic noses: Study and application. Sensors 12(3), 2818–2830 (2012)

    Article  Google Scholar 

  14. Shao, X., Li, H., Wang, N., Zhang, Q.: Comparison of different classification methods for analyzing electronic nose data to characterize sesame oils and blends. Sensors 15(10), 26726–26742 (2015)

    Article  Google Scholar 

  15. Estakhroueiyeh, H.R., Rashedi, E.: Detecting moldy bread using an e-nose and the KNN classifier. In: 2015 5th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 251–255. IEEE (2015)

  16. Papadopoulou, O.S., Panagou, E.Z., Mohareb, F.R., Nychas, G.-J.E.: Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis. Food Res. Int. 50(1), 241–249 (2013)

    Article  Google Scholar 

  17. Omatu, S., Yano, M.: E-nose system by using neural networks. Neurocomputing 172, 394–398 (2016)

    Article  Google Scholar 

  18. Samadi, S.: Interface design techniques for electronic nose sensors: a survey. In: CENTRIC 2013: the Sixth International Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies and Services, IARIA, Citeseer (2013)

  19. Guüntner, A.T., Koren, V., Chikkadi, K., Righettoni, M., Pratsinis, S.E.: E-nose sensing of low-ppb formaldehyde in gas mixtures at high relative humidity for breath screening of lung cancer. ACS Sensors 1.5, 528–535 (2016)

    Article  Google Scholar 

  20. Capelli, L., Sironi, S., Del Rosso, R.: Electronic noses for environmental monitoring applications. Sensors 14(11), 19979–20007 (2014)

    Article  Google Scholar 

  21. Wilson, A.D., Baietto, M.: Applications and advances in electronic-nose technologies. Sensors 9(7), 5099–5148 (2009)

    Article  Google Scholar 

  22. Chilo, J., Pelegri-Sebastia, J., Cupane, M., Sogorb, T.: E-nose application to food industry production. IEEE Instrum. Meas. Mag. 19(1), 27–33 (2016)

    Article  Google Scholar 

  23. Blatt, R., Bonarini, A., Matteucci, M.: Pattern classification techniques for lung cancer diagnosis by an electronic nose. In: Computational Intelligence in Healthcare 4, pp. 397–423. Springer (2010)

  24. Kiani, S., Minaei, S., Ghasemi-Varnamkhasti, M.: Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: a review. Journal of Applied Research on Medicinal and Aromatic Plants 7(4), 143–214 (2016)

    Google Scholar 

  25. Li, D., Lei, T., Zhang, S., Shao, X., Xie, C.: A novel headspace integrated e-nose and its application in discrimination of Chinese medical herbs. Sens. Actuators B 221, 556–563 (2015)

    Article  Google Scholar 

  26. Rashedi, E., Nezamabadi-pour, H.: Feature subset selection using improved binary gravitational search algorithm. J. Intell. Fuzzy Syst. 26(3), 1211–1221 (2014)

    Google Scholar 

  27. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravi- tational search algorithm. Inform. Sci. 179(13), 2232–2248 (2009)

    Article  Google Scholar 

  28. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Nat. Comput. 9(3), 727–745 (2010)

    Article  MathSciNet  Google Scholar 

  29. Saeidi-Khabisi, F., Rashedi, E.: Fuzzy gravitational search algorithm. In: Proceedings 2nd International Econference on Computer and Knowledge Engineering, pp. 156–160 (2012)

  30. Shams, M., Rashedi, E., Hakimi, A.: Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier. Appl. Math. Comput. 258(22), 436–453 (2015)

    MathSciNet  MATH  Google Scholar 

  31. Rashedi, E., Nezamabadi-pour, H.: Improving the precision of CBIR systems by feature selection using binary gravitational search algorithm. In: 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 039–042. IEEE (2012)

  32. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: A simultaneous feature adaptation and feature selection method for content-based image retrieval systems. Knowl.-Based Syst. 39, 85–94 (2013)

    Article  Google Scholar 

  33. Soh, A.C., Chow, K., Yusuf, U.M., Ishak, A., Hassan, M., Khamis, S.: Development of neural network-based electronic nose for herbs recognition. Int. J. Smart Sens. Intell. Syst. 7(2), 584–609 (2014)

    Google Scholar 

  34. Scorsone, E., Pisanelli, A.M., Persaud, K.C.: Development of an electronic nose for fire detection. Sens. Actuators B 116(1), 55–61 (2006)

    Article  Google Scholar 

  35. De Vito, S., Massera, E., Miglietta, M., Di Palma, P., Fattoruso, G., Brune, K., et al.: Detection and quantification of composite surface contaminants with an e-nose for fast and reliable pre-bond quality assessment of aircraft components. Sens. Actuators B 222, 1264–1273 (2016)

    Article  Google Scholar 

  36. Tian, F., Kadri, C., Zhang, L., Feng, J., Juan, L., Na, P.: A novel cost-effective portable electronic nose for indoor-/in-car air quality monitoring. In: 2012 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring (CDCIEM), pp. 4–8. IEEE (2012)

  37. Özmen, A., Doğan, E.: Design of a portable E-nose instrument for gas classifications. IEEE Trans. Instrum. Meas. 58(10), 3609–3618 (2009)

    Article  Google Scholar 

  38. Zhu, X., Liu, D., Chen, Q., Lin, L., Jiang, S., Zhou, H., et al.: A paper-supported graphene–ionic liquid array for e-nose application. Chem. Commun. 52(14), 2859–2871 (2016)

    Article  Google Scholar 

  39. Loutfi, A.: Odour recognition using electronic noses in robotic and intelligent systems. Universitetsbiblioteket. Örebro Studies in Technology [2006]. 91-7668-472-5 (2006)

  40. Graf, J.: Monitoring the air quality in a closed chamber using an electronic nose. In: 27th International Conference on Environmental Systems. SAE 1997 Transactions - Journal of Aerospace - V106-1 (1997)

  41. Iskandarani, M., Shilbayeh, N.: Design and analysis of a smart multi purpose electronic nose system. J. Comput. Sci. 1(1), 63–71 (2005)

    Article  Google Scholar 

  42. Reimann, P., Schütze, A.: Sensor arrays, virtual multisensors, data fusion and gas sensor data evaluation. In: Gas Sensing Fundamentals, pp. 67–107. Springer (2013)

  43. Peters, C., Moser, T., Kuehnlein, T., Diehl, L., Guenschel, H.: Method for Manuafacturing a Solid Electrolyte Sensor Element for Detecting at At Least One Property of a Measuring Gas in a Measuring Gas Chamber, Containing Two Porous Ceramic Layers. (G01n27/407 Ed.) Robert Bosch GmbH, US (2016)

    Google Scholar 

  44. Gonzalez-Jimenez, J., Monroy, J.G., Blanco, J.L.: The multi-chamber electronic nose—an improved olfaction sensor for mobile robotics. Sensors 11(6), 6145–6164 (2011)

    Article  Google Scholar 

  45. De Vito, S., Palma, P.D., Ambrosino, C., Massera, E., Burrasca, G., Miglietta, M.L., et al.: Wireless sensor networks for distributed chemical sensing: addressing power consumption limits with on-board intelligence. IEEE Sensors J. 11(4), 947–955 (2011)

    Article  Google Scholar 

  46. Chiu, S.-W., Wang, J.-H., Lin, G.-T., Chang, C.-L., Chen, H., Tang, K.-T.: Towards a fully integrated electronic nose SoC. In: 2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 166–169. IEEE (2012)

  47. Chiu, S.-W., Tang, K.-T.: Towards a chemiresistive sensor-integrated electronic nose: a review. Sensors 13(10), 14214–14247 (2013)

    Article  Google Scholar 

  48. Adiguzel, Y., Kulah, H.: Breath sensors for lung cancer diagnosis. Biosens. Bioelectron. 65, 121–138 (2015)

    Article  Google Scholar 

  49. Capelli, L., Sironi, S., Del Rosso, R.: Odor sampling: techniques and strategies for the estimation of odor emission rates from different source types. Sensors 13(1), 938–955 (2013)

    Article  Google Scholar 

  50. Van Ruth, S.M.: Methods for gas chromatography-olfactometry: a review. Biomol. Eng. 17(4), 121–128 (2001)

    Article  Google Scholar 

  51. Nakamoto, T., Iguchi, A., Moriizumi, T.: Vapor supply method in odor sensing system and analysis of transient sensor responses. Sens. Actuators B 71(3), 155–160 (2000)

    Article  Google Scholar 

  52. Nakamoto, T., Fukuda, T., Moriizumi, T.: Gas identification system using plural sensors with characteristics of plasticity. Sens. Actuators B 3(1), 1–6 (1991)

    Article  Google Scholar 

  53. Wang, P., Liu, Q., Zhang, W., Cai, H., Xu, Y.: Design of biomimetic electronic nose and electronic tongue. Sens. Mater 19(5), 309–323 (2007)

    Google Scholar 

  54. Nakamura, K., Nakamoto, T., Moriizumi, T.: Prediction of quartz crystal microbalance gas sensor responses using a computational chemistry method. Sens. Actuators B 61(1), 6–11 (1999)

    Article  Google Scholar 

  55. Matsuura, K., Ariga, K., Endo, K., Aoyama, Y., Okahata, Y.: Dynamic analyses on induced-fit gaseous guest binding to organic crystals with a quartz-crystal microbalance. Chem. A Eur. J. 6(10), 1750–1756 (2000)

    Article  Google Scholar 

  56. Ionescu, R., Broza, Y., Shaltieli, H., Sadeh, D., Zilberman, Y., Feng, X., et al.: Detection of multiple sclerosis from exhaled breath using bilayers of polycyclic aromatic hydrocarbons and single-wall carbon nanotubes. ACS Chem. Neurosci. 2(12), 687–693 (2011)

    Article  Google Scholar 

  57. Cometto-Muñiz, J.E., Cain, W.S., Hiraishi, T., Abraham, M.H., Gola, J.M.: Comparison of two stimulus-delivery systems for measurement of nasal pungency thresholds. Chem. Senses 25(3), 285–291 (2000)

    Article  Google Scholar 

  58. Penza, M., Suriano, D., Cassano, G., Pfister, V., Amodio, M., Trizio, L., et al.: A case-study of microsensors for landfill air-pollution monitoring applications. Urban Climate 14, 351–369 (2015)

    Article  Google Scholar 

  59. China-total MQ series gas sensors datasheet, http://www.china-total.com/Product/meter/gas-sensor/Gas-sensor.htm

  60. Li, J., Lu, Y., Ye, Q., Delzeit, L., Meyyappan, M.: A gas sensor array using carbon nanotubes and microfabrication technology. Electrochem. Solid-State Lett. 8(11), H100–H102 (2005)

    Article  Google Scholar 

  61. Guerin, H., Le Poche, H., Pohle, R., Buitrago, E., Badía, M.F.-B., Dijon, J., et al.: Carbon nanotube gas sensor array for multiplex analyte discrimination. Sens. Actuators B 207, 833–842 (2015)

    Article  Google Scholar 

  62. Kurada, S., Alkhouri, N., Fiocchi, C., Dweik, R., Rieder, F.: Review article: breath analysis in inflammatory bowel diseases. Aliment. Pharmacol. Ther. 41(4), 329–341 (2015)

    Article  Google Scholar 

  63. Chen, X., Wong, C.K., Yuan, C.A., Zhang, G.: Nanowire-based gas sensors. Sens. Actuators B 177, 178–195 (2013)

    Article  Google Scholar 

  64. Cao, A., Sudhölter, E.J., de Smet, L.C.: Silicon nanowire-based devices for gas-phase sensing. Sensors 14(1), 245–271 (2013)

    Article  Google Scholar 

  65. Field, C.R., In, H.J., Begue, N.J., Pehrsson, P.E.: Vapor detection performance of vertically aligned, ordered arrays of silicon nanowires with a porous electrode. Anal. Chem. 83(12), 4724–4728 (2011)

    Article  Google Scholar 

  66. Field, C., In, H., Rose-Pehrsson, S., Pehrsson, P.: Trace vapor detection with vertical silicon nanowire arrays. Chemistry 83, 4724–4728 (2011)

    Google Scholar 

  67. Barth, S., Hernandez-Ramirez, F., Holmes, J.D., Romano-Rodriguez, A.: Synthesis and applications of one-dimensional semiconductors. Prog. Mater. Sci. 55(6), 563–627 (2010)

    Article  Google Scholar 

  68. Korotcenkov, G.: Handbook of Gas Sensor Materials. Springer (2013)

  69. Deisingh, A.K., Stone, D.C., Thompson, M.: Applications of electronic noses and tongues in food analysis. Int. J. Food Sci. Technol. 39(6), 587–604 (2004)

    Article  Google Scholar 

  70. O’Connell, M.J.: Carbon Nanotubes: Properties and Applications. CRC Press (2006)

  71. Koickal, T.J., Hamilton, A., Pearce, T.C., Tan, S.L., Covington, J.A., Gardner, J.W.: Analog VLSI design of an adaptive neuromorphic chip for olfactory systems. In: IEEE International Symposium on Circuits and Systems, pp. 4–4550. IEEE (2006)

  72. Yang, C., Mason, A., Xi, J., Zhong, P.: Configurable hardware-effcient interface circuit for multi-sensor microsystems. In: 5th IEEE Conference on Sensors, 2006, pp. 41–44. IEEE (2006)

  73. Tang, K.-T.: Neuromorphic VLSI circuits for an electronic nose chip. California Institute of Technology, http://resolver.caltech.edu/caltechTHESIS:10122010-085150166 (2001)

  74. Sudalina, D., Nalini, J.: FPGA implementation of a bio inspired olfactory system for odor identification and classification using ANN algorithm. International Journal of Emerging Trends in Electrical and Electronics 3(2), 68–72 (2013)

    Google Scholar 

  75. Kim, Y.S., Yang, Y.S., Ha, S.-C., Pyo, H.-B., Choi, C.A.: Miniaturized electronic nose system based on personal digital assistant. ETRI J. 27(5), 585–594 (2005)

    Article  Google Scholar 

  76. Thati, A., Biswas, A., Chowdhury, S.R., Sau, T.K.: Breath acetone-based non-invasive detection of blood glucose levels. Int. J. Smart Sens. Intell. Syst. 8(2), 1244–1260 (2015)

    Google Scholar 

  77. Siyang, S., Wongchoosuk, C., Kerdcharoen, T.: Diabetes diagnosis by direct measurement from urine odor using electronic nose. In: Biomedical Engineering International Conference (BMEiCON), pp. 1–4. IEEE (2012)

  78. Yan, J., Guo, X., Duan, S., Jia, P., Wang, L., Peng, C., et al.: Electronic nose feature extraction methods: a review. Sensors 15(11), 27804–27831 (2015)

    Article  Google Scholar 

  79. Kim, K.-H., Jahan, S.A., Kabir, E.: A review of breath analysis for diagnosis of human health. TrAC Trends Anal. Chem. 33, 1–8 (2012)

    Article  Google Scholar 

  80. Roine, A., Veskimäe, E., Tuokko, A., Kumpulainen, P., Koskimäki, J., Keinänen, T.A., et al.: Detection of prostate cancer by an electronic nose: a proof of principle study. J. Urol. 192(1), 230–235 (2014)

    Article  Google Scholar 

  81. Peng, G., Hakim, M., Broza, Y.Y., Billan, S., Abdah-Bortnyak, R., Kuten, A., et al.: Detection of lung, breast, colorectal and prostate cancers from exhaled breath using a single array of nanosensors. Br. J. Cancer 103(4), 542–551 (2010)

    Article  Google Scholar 

  82. Amal, H., Shi, D.Y., Ionescu, R., Zhang, W., Hua, Q. L., Pan, Y.Y., et al.: Assessment of ovarian cancer conditions from exhaled breath. Int. J. Cancer 136(6), E614–E622 (2015)

    Article  Google Scholar 

  83. Yu, H., Xu, L., Cao, M., Chen, X., Wang, P., Jiao, J., et al.: Detection volatile organic compounds in breath as markers of lung cancer using a novel electronic nose. In: Proceedings of IEEE Sensors, vol. 2, pp. 1333–1337. IEEE (2003)

  84. Hakim, M., Billan, S., Tisch, U., Peng, G., Dvrokind, I., Marom, O., et al.: Diagnosis of head-and-neck cancer from exhaled breath. Br. J. Cancer 104(10), 1649–1655 (2011)

    Article  Google Scholar 

  85. Beqa, L., Fan, Z., Singh, A.K., Senapati, D., Ray, P.C.: Gold nano-popcorn attached SWCNT hybrid nanomaterial for targeted diagnosis and photothermal therapy of human breast cancer cells. ACS Appl. Mater. Interfaces 3(9), 3316–3324 (2011)

    Article  Google Scholar 

  86. Xu, Z., Broza, Y.Y., Ionsecu, R., Tisch, U., Ding, L., Liu, H., et al.: A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions. Br. J. Cancer 108(4), 941–950 (2013)

    Article  Google Scholar 

  87. Nakhleh, M.K., Amal, H., Awad, H., Abu-Saleh, N., Jeries, R., Haick, H., et al.: Sensor arrays based on nanoparticles for early detection of kidney injury by breath samples. Nanomed.: Nanotechnol., Biol. Med. 10(8), 1767–1776 (2014)

    Article  Google Scholar 

  88. Chen, S., Wang, Y., Choi, S.: Applications and Technology of Electronic Nose for Clinical Diagnosis 2(2), 39–50 (2013)

  89. Simi, S., Ramesh, M.V.: Real-time monitoring of explosives using wireless sensor networks. In: Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India, p 44. ACM (2010)

  90. Gardner, J., Craven, M., Dow, C., Hines, E.: The prediction of bac- teria type and culture growth phase by an electronic nose with a multi- layer perceptron network. Meas. Sci. Technol. 9(1), 120 (1998)

    Article  Google Scholar 

  91. Schiffman, S., Wyrick, D., Gutierrez-Osuna, R., Nagle, H.: Effectiveness of an electronic nose for monitoring bacterial and fungal growth. In: Proceedings of ISOEN 2000, Brighton, 173-180 (2000)

  92. Dutta, R., Dutta, R.: “Maximum probability rule” based classification of MRSA infections in hospital environment: using electronic nose. Sens. Actuators B 120(1), 156–165 (2006)

    Article  Google Scholar 

  93. Joseph, P., Bakirtzis, D., Vieille, A.: An “electronic nose” as a potential device for fire detection of forest product fire loads in enclosures. Wood Mater. Sci. Eng. 10(1), 130–144 (2015)

    Article  Google Scholar 

  94. Berna, A.: Metal oxide sensors for electronic noses and their application to food analysis. Sensors 10(4), 3882–3910 (2010)

    Article  Google Scholar 

  95. Olafsdottir, G., Chanie, E., Westad, F., Jonsdottir, R., Thalmann, C.R., Bazzo, S., et al.: Prediction of microbial and sensory quality of cold smoked Atlantic salmon (Salmo salar) by electronic nose. J. Food Sci. 70(9), S563–S574 (2005)

    Article  Google Scholar 

  96. Winquist, F., Hornsten, E., Sundgren, H., Lundstrom, I.: Performance of an electronic nose for quality estimation of ground meat. Meas. Sci. Technol. 4(12), 1493 (1993)

    Article  Google Scholar 

  97. Yu, H.C., Wang, J., Xu, Y.: Identification of adulterated milk using electronic nose. Sensors and Materials 19(5), 275–285 (2007)

    Google Scholar 

  98. Labreche, S., Bazzo, S., Cade, S., Chanie, E.: Shelf life determination by electronic nose: application to milk. Sens. Actuators B 106(1), 199–206 (2005)

    Article  Google Scholar 

  99. Dutta, R., Hines, E.L., Gardner, J.W., Udrea, D.D., Boilot, P.: Non-destructive egg freshness determination: an electronic nose based approach. Meas. Sci. Technol. 14(2), 190 (2003)

    Article  Google Scholar 

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Estakhroyeh, H.R., Rashedi, E. & Mehran, M. Design and Construction of Electronic Nose for Multi-purpose Applications by Sensor Array Arrangement Using IBGSA. J Intell Robot Syst 92, 205–221 (2018). https://doi.org/10.1007/s10846-017-0759-3

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