Skip to main content

Introduction

  • Chapter
  • First Online:
Electronic Nose: Algorithmic Challenges

Abstract

This chapter provides an overview of E-nose research and technology. We first review the progress of E-noses in applications, systems, and algorithms during the past two decades. Then, we propose to address these key challenges in E-nose, which are sensor induced and sensor specific. This chapter is closed by a statement of the objective of the research, a brief summary of the work, and a general outline of the overall structure of this book.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. K. Persaud, G. Dodd, Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 299, 352–355 (1982)

    Article  Google Scholar 

  2. J.W. Gardner, P.N. Bartlett, A brief history of electronic noses. Sens. Actuators B: Chem. 18–19(1), 210–211 (1994)

    Article  Google Scholar 

  3. F. Rӧck, N. Barsan, U. Weimar, Electronic nose: current status and future trends. Chem. Rev. 108, 705–725 (2008)

    Article  Google Scholar 

  4. K. Brudzewski, S. Osowski, T. Markiewicz, Classification of milk by means of an electronic nose and SVM neural network. Sens. Actuators B: Chem. 98, 291–298 (2004)

    Article  Google Scholar 

  5. P.C. Lorenzen, H.G. Walte, B. Bosse, Development of a method for butter type differentiation by electronic nose technology. Sens. Actuators B: Chem. 181, 690–693 (2013)

    Article  Google Scholar 

  6. N. Bhattacharyya, R. Bandyopadhyay, M. Bhuyan, B. Tudu, D. Ghosh, A. Jana, Electronic nose for black tea classification and correlation of measurement with “Tea Taster” marks. IEEE Trans. Instrum. Measure. 57(7), 1313–1321 (2008)

    Article  Google Scholar 

  7. Q. Chen, J. Zhao, Z. Chen, H. Lin, D.A. Zhao, Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools. Sens. Actuators B: Chem. 159(1), 294–300 (2011)

    Article  Google Scholar 

  8. R. Dutta, E.L. Hines, J.W. Gardner, K.R. Kashwan, M. Bhuyan, Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligent approach. Sens. Actuators B: Chem. 94, 228–237 (2003)

    Article  Google Scholar 

  9. G. Hui, Y. Wu, D. Ye, W. Ding, Fuji apple storage time predictive method using electronic nose. Food Anal. Methods 6, 82–88 (2013)

    Article  Google Scholar 

  10. M.G. Varnamkhasti, S.S. Mohtasebi, M. Siadat, J. Lozano, H. Ahmadi, S.H. Razavi, A. Dicko, Aging fingerprint characterization of beer using electronic nose. Sens. Actuators B: Chem. 159(1), 51–59 (2011)

    Article  Google Scholar 

  11. M. Peris, L.E. Gilabert, A 21st century technique for food control: electronic noses. Anal. Chim. Acta 638, 1–15 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. E.A. Baldwin, J. Bai, A. Plotto, S. Dea, Electronic noses and tongues: applications for the food and pharmaceutical industries. Sensors 11, 4744–4766 (2011)

    Article  Google Scholar 

  14. A. D’Amico, C. Di Natale, R. Paolesse, A. Macagnano, E. Martinelli, G. Pennazza, M. Santonico, M. Bernabei, C. Roscioni, G. Galluccio, Olfactory systems for medical applications. Sens. Actuators B: Chem. 130, 458–465 (2008)

    Article  Google Scholar 

  15. K. Yan, D. Zhang, Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sens. Actuators B: Chem. 212, 353–363 (2015)

    Article  Google Scholar 

  16. C. Di Natale, A. Macagnano, E. Martinelli, R. Paolesse, G. D’Arcangelo, C. Roscioni, A.F. Agro, A. D’Amico, Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensosrs. Biosens. Bioelectron. 18, 1209–1218 (2003)

    Article  Google Scholar 

  17. A.K. Pavlou, N. Magan, C. McNulty, J.M. Jones, D. Sharp, J. Brown, A.P.F. Turner, Use of an electronic nose system for diagnoses of urinary tract infections. Biosens. Bioelectron. 17, 893–899 (2002)

    Article  Google Scholar 

  18. J. Getino, M.C. Horrillo, J. Gutiérrez, L. Arés, J.I. Robla, C. Garcia, I. Sayago, Analysis of VOCs with a tin oxide sensor array. Sens. Actuators B: Chem. 43, 200–205 (1997)

    Article  Google Scholar 

  19. E.J. Wolfrum, R.M. Meglen, D. Peterson, J. Sluiter, Metal oxide sensor arrays for the detection, differentiation, and quantification of volatile organic compounds at sub-part-per-million concentration levels. Sens. Actuators B: Chem. 115, 322–329 (2006)

    Article  Google Scholar 

  20. L. Zhang, F. Tian, C. Kadri, G. Pei, H. Li, L. Pan, Gases concentration estimation using heuristics and bio-inspired optimization models for experimental chemical electronic nose. Sens. Actuators B: Chem. 160(1), 760–770 (2011)

    Article  Google Scholar 

  21. L. Zhang, F. Tian, S. Liu, J. Guo, B. Hu, Q. Ye, L. Dang, X. Peng, C. Kadri, J. Feng, Chaos based neural network optimization for concentration estimation of indoor air contaminants by an electronic nose. Sens. Actuators, A 189, 161–167 (2013)

    Article  Google Scholar 

  22. L. Dentoni, L. Capelli, S. Sironi, R.D. Rosso, S. Zanetti, M.D. Torre, Development of an electronic nose for environmental odour monitoring. Sensors 12, 14363–14381 (2012)

    Article  Google Scholar 

  23. R.E. Baby, M. Cabezas, E.N.W. de Reca, Electronic nose: a useful tool for monitoring environmental contamination. Sens. Actuators B: Chem. 69, 214–218 (2000)

    Article  Google Scholar 

  24. A. Fort, N. Machetti, S. Rocchi, M.B.S. Santos, L. Tondi, N. Ulivieri, V. Vignoli, G. Sberveglieri, Tin oxide gas sensing: comparison among different measurement techniques for gas mixture classification. IEEE Trans. Instrum. Measure. 52(3), 921–926 (2003)

    Article  Google Scholar 

  25. J.W. Gardner, H.W. Shin, E.L. Hines, C.S. Dow, An electronic nose system for monitoring the quality of potable water. Sens. Actuators B: Chem. 69, 336–341 (2000)

    Article  Google Scholar 

  26. M. Cano, V. Borrego, J. Roales, J. Idígoras, T.L. Costa, P. Mendoza, J.M. Pedrosa, Rapid discrimination and counterfeit detection of perfumes by an electronic olfactory system. Sens. Actuators B: Chem. 156, 319–324 (2011)

    Article  Google Scholar 

  27. K. Brudzewski, S. Osowski, A. Golembiecka, Differential electronic nose and support vector machine for fast recognition of tobacco. Expert Syst. Appl. 39, 9886–9891 (2012)

    Article  Google Scholar 

  28. K. Brudzewski, S. Osowski, A. Dwulit, Recognition of coffee using differential electronic nose. IEEE Trans. Instrum. Measure. 61(6), 1803–1810 (2012)

    Article  Google Scholar 

  29. P. Ciosek, Z. Brzózka, W. Wróblewski, Classification of beverages using a reduced sensor array. Sens. Actuators B: Chem. 103, 76–83 (2004)

    Article  Google Scholar 

  30. K. Brudzewski, S. Osowski, W. Pawlowski, Metal oxide sensor arrays for detection of explosives at sub-parts-per million concentration levels by the differential electronic nose. Sens. Actuators B: Chem. 161, 528–533 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. L. Zhang, F. Tian, Performance study of multilayer perceptrons in a low-cost electronic nose. IEEE Trans. Instrum. Measure. 63(7), 1670–1679 (2014)

    Article  Google Scholar 

  33. L. Zhang, F. Tian, X. Peng, X. Yin, G. Li, L. Dang, Concentration estimation using metal oxide semi-conductor gas sensor array based e-noses. Sens. Rev. 34, 284–290 (2014)

    Article  Google Scholar 

  34. H.K. Hong, C.H. Kwon, S.R. Kim, D.H. Yun, K. Lee, Y.K. Sung, Portable electronic nose system with gas sensor array and artificial neural network. Sens. Actuators B: Chem. 66, 49–52 (2000)

    Article  Google Scholar 

  35. I.R. Lujan, J. Fonollosa, A. Vergara, M. Homer, R. Huerta, On the calibration of sensor arrays for pattern recognition using the minimal number of experiments. Chemometr. Intell. Lab. Syst. 130, 123–134 (2014)

    Article  Google Scholar 

  36. A.P. Lee, B.J. Reedy, Temperature modulation in semiconductor gas sensing. Sens. Actuators B: Chem. 60, 35–42 (1999)

    Article  Google Scholar 

  37. E. Llobet, R. Ionescu, S.A. Khalifa, J. Brezmes, X. Vilanova, X. Correig, N. Barsan, J.W. Gardner, Multicomponent gas mixture analysis using a single tin oxide sensor and dynamic pattern recognition. IEEE Sens. J. 1(3), 207–213 (2001)

    Article  Google Scholar 

  38. E. Martinelli, D. Polese, A. Catini, A. D’Amico, C. Di Natale, Self-adapted temperature modulation in metal-oxide semiconductor gas sensors. Sens. Actuators B: Chem. 161, 534–541 (2012)

    Article  Google Scholar 

  39. F. Hossein-Babaei, A. Amini, A breakthrough in gas diagnosis with a temperature-modulated generic metal oxide gas sensor. Sens. Actuators B: Chem. 166–167, 419–425 (2012)

    Article  Google Scholar 

  40. F. Hossein-Babaei, A. Amini, Recognition of complex odors with a single generic tin oxide gas sensor. Sens. Actuators B: Chem. 194, 156–163 (2014)

    Article  Google Scholar 

  41. X. Yin, L. Zhang, F. Tian, D. Zhang, Temperature modulated gas sensing e-nose system for low-cost and fast detection. IEEE Sens. J. (2015). https://doi.org/10.1109/JSEN.2015.2483901

    Article  Google Scholar 

  42. R. Gosangi, R. Gutierrez-Osuna, Active temperature programming for metal-oxide chemoresistors. IEEE Sens. J. 10(6), 1075–1082 (2010)

    Article  Google Scholar 

  43. R. Gosangi, R. Gutierrez-Osuna, Active temperature modulation of metal-oxide sensors for quantitative analysis of gas mixtures. Sens. Actuators B: Chem. 185, 201–210 (2013)

    Article  Google Scholar 

  44. F. Herrero-Carrón, D.J. Yáñez, F.D.B. Rodríguez, P. Varona, An active, inverse temperature modulation strategy for single sensor odorant classification. Sens. Actuators B: Chem. 206, 555–563 (2015)

    Article  Google Scholar 

  45. M. Imahashi, K. Hayashi, Odor clustering and discrimination using an odor separating system. Sens. Actuators B: Chem. 166–167, 685–694 (2012)

    Article  Google Scholar 

  46. S.K. Jha, K. Hayashi, A novel odor filtering and sensing system combined with regression analysis for chemical vapor quantification. Sens. Actuators B: Chem. 200, 269–287 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zhang, L., Tian, F., Zhang, D. (2018). Introduction. In: Electronic Nose: Algorithmic Challenges. Springer, Singapore. https://doi.org/10.1007/978-981-13-2167-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-2167-2_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2166-5

  • Online ISBN: 978-981-13-2167-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics