Skip to main content
SpringerLink
Log in

Electronic-nose for detecting environmental pollutants: signal processing and analog front-end design

Analog Integrated Circuits and Signal Processing volume 70, pages 15–32 (2012)Cite this article

Abstract

Environmental monitoring relies on compact, portable sensor systems capable of detecting pollutants in real-time. An integrated chemical sensor array system is developed for detection and identification of environmental pollutants in diesel and gasoline exhaust fumes. The system consists of a low noise floor analog front-end (AFE) followed by a signal processing stage. In this paper, we present techniques to detect, digitize, denoise and classify a certain set of analytes. The proposed AFE reads out the output of eight conductometric sensors and eight amperometric electrochemical sensors and achieves 91 dB SNR at 23.4 mW quiescent power consumption for all channels. We demonstrate signal denoising using a discrete wavelet transform based technique. Appropriate features are extracted from sensor data, and pattern classification methods are used to identify the analytes. Several existing pattern classification algorithms are used for analyte detection and the comparative results are presented.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

References

  1. Reed, M. D., et al. (2008). Health effects of subchronic inhalation exposure to gasoline engine exhaust. Inhalation Toxicology, 20(13), 1125–1143.

    Article  Google Scholar 

  2. U.S. EPA. (2002). Health assessment document for diesel engine exhaust (Final 2002). U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington Office, Washington, DC, EPA/600/8-90/057F.

  3. Sydbom, A., et al. (2001). Health effects of diesel exhaust emissions. European Respiratory Journal, 17, 733–746.

    Article  Google Scholar 

  4. Bhatia, R., Lopipero, P., & Smith, A. H. (1998). Diesel exhaust exposure and lung cancer. Epidermology, 9(1), 84–91.

    Article  Google Scholar 

  5. Office of population genomics. Available online at http://www.genome.gov/.

  6. Scott, S. M., James, D., & Ali, Z. (2007). Data analysis for electronic nose systems. Microchimica Acta, 156(3–4), 183–207.

    Google Scholar 

  7. Gardner J. W., & Bartlett P. N. (1999). Electronic noses: Principles and applications. Oxford University Press, USA.

  8. Olafsdottir, G., Jonsdottir, R., Lauzon, H. L., Luten, J. B., & Kristbergsson, K. (2005). Characterization of volatile compounds in chilled cod (Gadus morhua) fillets by gas chromatography and detection of quality indicators by an electronic nose. Journal of Agricultural and Food Chemistry, 53(26), 10140–10147.

    Article  Google Scholar 

  9. Marina, S., et al. (2007). Use of a MS-electronic nose for prediction of early fungal spoilage of bakery products. International Journal of Food Microbiology, 114(1), 10–16.

    Article  Google Scholar 

  10. Di Natale, C., et al. (1999). Electronic nose analysis of urine samples containing blood. Physiological Measurement, 20, 377–384.

    Article  Google Scholar 

  11. Shykhon, M. E., Morgan, D. W., Dutta, R., Hines, E. L., & Gardner, J. W. (2004). Clinical evaluation of the electronic nose in the diagnosis of ear, nose and throat infection: a preliminary study. Journal of Laryngology and Otology, 118, 706–709.

    Google Scholar 

  12. Engin, G. (1997). Characterization of sewage and sewage odors by using an electronic nose. Master’s Thesis, Department of Civil Engineering, University of Hertfordshire, U.K.

  13. Rock, F., Barsan, N., & Weimar, U. (2008). Electronic nose: Current status and future trends. Chemical Reviews, 108(2), 705–725.

    Article  Google Scholar 

  14. Zhou, H., Homer, M. L., Shevade, A. V., & Ryan, M. A. (2006). Nonlinear least-squares based method for identifying and quantifying single and mixed contaminants in air with an electronic nose. IEEE Sensors Journal, 6, 1–18.

    Article  Google Scholar 

  15. Blaschke, M., et al. (2006). MEMS gas-sensor array for monitoring the perceived car-cabin air quality. IEEE Sensors Journal, 6(5), 1298–1308.

    Article  MathSciNet  Google Scholar 

  16. Nicolas, J., Romain, A. C., & Ledent, C. R. (2006). The electronic nose as a warning device of the odour emergence in a compost hall. Sensors and Actuators B, 116(1–2), 95–99.

    Article  Google Scholar 

  17. Mulchandani, A., Myung, N. V., Deshusses, M. A., Cocker, D., Wang, J., Bakkaloglu, B., et al. (2008). Nanosensor array for real-time monitoring of diesel and gasoline exhaust exposure. Epidemiology, 19(6), S62.

    Google Scholar 

  18. Konnanath, B., Kim, H., Spanias, A., Bakkaloglu, B., Wang, J., Mulchandani, A., & Myung, N. (2009). A real-time monitoring system for diesel and gasoline exhaust exposure. In Proceedings of the IEEE 16th International Conference on DSP, pp. 1–5, Jul 2009.

  19. Wang, J. (1985). Stripping analysis: Principles, instrumentation, and applications. VCH: Deerfield Beach.

    Google Scholar 

  20. Martin, S. M., Gebara, F. H., Strong, T. D., & Brown, R. B. (2004). A low-voltage chemical sensor interface for systems-on-chip: The fully-differential potentiostat. In Proceedings of the IEEE International Symposium on Circuits and Systems, vol. 4, pp. 892–895.

  21. Castello, R., & Gray, P. R. (1985). A high-performance micropower switched-capacitor filters. The IEEE Journal of Solid-State Circuits, SC-20(6), 1122–1132.

    Article  Google Scholar 

  22. Zhang, T., Mubeen, S., Myung, N., & Deshusses, M. A. (2008). Recent progress in carbon nanaotube-based gas sensor. Nanotechnology, IOP, pp. 1–14.

  23. Bakker, A., Thiele, K., & Huijsing, J. H. (2000). A CMOS nested-chopper instrumentation amplifier with 100-nV offset. The IEEE Journal of Solid-State Circuits, 35(12), 1877–1883.

    Article  Google Scholar 

  24. Wonseok Oh, Bakkaloglu, B., Wang, C., & Hoon, S. K. (2008). A CMOS low noise, chopper stabilized low-dropout regulator with current-mode feedback error amplifier. IEEE Transactions on Circuits and Systems, 55(10), Nov 2008.

  25. Wang, J. (1996). Electrochemical transduction. In R. F. Taylor & J. S. Schultz (Eds.), Handbook of chemical and biological sensors (pp. 123–137). Bristol, UK: IOP Publishing.

    Google Scholar 

  26. Donoho, D. L. (1995). De-noising by soft thresholding. IEEE Transactions on Information Theory, 41(3), 613–627.

    Article  MATH  MathSciNet  Google Scholar 

  27. Jagtiani, A. V., Sawant, R., Carletta, J., & Zhe, J. (2008). Wavelet transform-based methods for denoising of Coulter counter signals. Measurement Science and Technology, 19(6), 1–15.

    Article  Google Scholar 

  28. Deshusses, M. (2008). Dataset-S: Synthetic dataset, Department of Chemical and Environmental Engineering. University of California-Riverside.

  29. Duda, R. O. (2006). Pattern recognition for machine learning. Berlin: Springer.

    Google Scholar 

  30. Gardner, J. W., Shin, H. W., Hines, E. L., & Dow, C. S. (2000). An electronic nose system for monitoring the quality of potable water. Sensors and Actuators B, 69(3), 336–341.

    Article  Google Scholar 

  31. Barko, G., & Hlavay, J. (2000). Application of principal component analysis for the characterization of a piezoelectric sensors array. Analytica Chimica Acta, 367(1–3), 135–143.

    Google Scholar 

  32. Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  33. Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. New York: Wiley.

    MATH  Google Scholar 

  34. Discriminant analysis toolbox. Available online at http://www.mathworks.com/matlabcentral/fileexchange/authors/1714.

  35. Haykin, S. (1999). Neural networks: A comprehensive foundation. New Jersey: Prentice Hall.

    MATH  Google Scholar 

  36. Netlab neural network software. Available online at http://www.ncrg.aston.ac.uk/netlab/index.php.

  37. The Spider version 1.71 for MATLAB. Available online at http://www.kyb.mpg.de/bs/people/spider/main.html.

  38. Mulchandani, A. et al. (2009). Dataset-E1: Experimental dataset, Department of Chemical and Environmental Engineering, University of California-Riverside.

  39. Mulchandani, A. et al. (2009). Dataset-E2: Experimental dataset, Department of Chemical and Environmental Engineering, University of California-Riverside.

  40. Wang, J., et al. (2009). Dataset E3: Experimental dataset. University of California-San Diego: Laboratory for Nanobioelectronics.

    Google Scholar 

  41. Wang, J., et al. (2008). Dataset T1: Experimental dataset, Center for Bioelectronics and Biosensors, Arizona State University.

  42. Wang, J., et al. (2008). Dataset T2: Experimental dataset, Laboratory for Nanobioelectronics. San Diego: University of California.

    Google Scholar 

  43. Haddad, R., Carmel, L., & Harel, D. (2007). A feature extraction algorithm for multi-peak signals in electronic noses. Sensors and Actuators B, 120(2), 467–472.

    Article  Google Scholar 

  44. Bard, A. J., & Raulkner, L. R. (2001). Electrochemical methods: Fundamentals and applications. New York: Wiley.

    Google Scholar 

  45. Levine, P. M., Gong, P., Levicky, R., & Shepard, K. L. (2008). Active CMOS sensor array for electrochemical biomolecular detection. The IEEE Journal of Solid-State Circuits, 43(8), 1859–1871.

    Article  Google Scholar 

  46. Malcovati, P., Brigati, S., Francesconi, F., Maloberti, F., Cusinato, P., & Baschirotto, A. (2003). Behavioral modeling of switched-capacitor sigma-delta modulators. IEEE Transactions on Circuit and System, 50(3), 352–364.

    Article  Google Scholar 

  47. Schreier, R., & Temes, G. C. (2005). Understanding delta-sigma data converters. Hoboken, NJ: IEEE Press, Wiley-Interscience.

    Google Scholar 

  48. Martin, S. M., Gebara, F. H., Larivee, B. J., & Brown, R. B. (2004). A CMOS integrated microintrument for trace detection of heavy metals. In Proceedings of the IEEE International Symposium on Circuits and Systems, vol. 4, pp. 892–895.

Download references

Acknowledgment

This work was supported through the NIH Genes, Environment and Health Initiative through award 3U01ES016026-02S1.

Author information

Authors and Affiliations

  1. Arizona State University, Tempe, AZ, 85287, USA

    Hyuntae Kim, Bharatan Konnanath, Prasanna Sattigeri, Andreas Spanias & Bertan Bakkaloglu

  2. University of California-San Diego, San Diego, CA, 92093, USA

    Joseph Wang

  3. University of California-Riverside, Riverside, CA, 92501, USA

    Ashok Mulchandani & Nosang Myung

  4. Duke University, Durham, NC, 27708, USA

    Marc A. Deshusses

Authors
  1. Hyuntae Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bharatan Konnanath
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prasanna Sattigeri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joseph Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ashok Mulchandani
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nosang Myung
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marc A. Deshusses
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Andreas Spanias
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bertan Bakkaloglu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hyuntae Kim.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kim, H., Konnanath, B., Sattigeri, P. et al. Electronic-nose for detecting environmental pollutants: signal processing and analog front-end design. Analog Integr Circ Sig Process 70, 15–32 (2012). https://doi.org/10.1007/s10470-011-9638-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10470-011-9638-1

Keywords

search

Navigation