Skip to main content
SpringerLink
Log in

Rapid identification of rice samples using an electronic nose

  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

Four rice samples of long grain type were tested using an electronic nose (Cyranose-320). Samples of 5 g of each variety of rice were placed individually in vials and were analyzed with the electronic nose unit consisting of 32 polymer sensors. The Cyranose-320 was able to differentiate between varieties of rice. The chemical composition of the rice odors for differentiating rice samples needs to be investigated. The optimum parameter settings should be considered during the Cyranose-320 training process especially for multiple samples, which are helpful for obtaining an accurate training model to improve identification capability. Further, it is necessary to investigate the E-nose sensor selection for obtaining better classification accuracy. A reduced number of sensors could potentially shorten the data processing time, and could be used to establish an application procedure and reduce the cost for a specific electronic nose. Further research is needed for developing analytical procedures that adapt the Cyranose-320 as a tool for testing rice quality.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Monscoor M A, Proctorc A. Volatile component analysis of commercially milled head and broken rice. Journal of Food Science, 2004, 69, 632–636.

    Article  Google Scholar 

  2. Borjesson T, Eklöv T, Jonsson A, Sundgren H, Schnürer J. Electronic nose for odor classification of grains. Cereal Chem, 1996, 73, 457–461.

    Google Scholar 

  3. Saverio M, Simona B, Susanna B, Maria S C. The Application of Intelligent Sensor Array for Air Pollution Control in the Food Industry, Springer US, New York. 2006.

    Google Scholar 

  4. Srujan K D. Electrochemically Controlled Patterning for Biosensor Arrays, PhD Dissertation, University at Rovira I Virgili, Tarragona, Spain, 2006.

    Google Scholar 

  5. Haugen J-E, Kvaal K. Electronic nose and artificial neural network. Meat Science, 1998, 49, S273–S286.

    Article  Google Scholar 

  6. Stetter U, Aspelmeicr A, Baberschke K. The initial AC susceptibility of Gd/W(110) films in UHV: A new method to investigate magnetism in ultra thin films. Journal of Magnetism and Magnetic Materials, 1992, 117, 183–189.

    Article  Google Scholar 

  7. Rylander R. Dose-response relationships for traffic noise and annoyance. Archives of Environmental Health, 1986, 41, 7–10.

    Article  Google Scholar 

  8. Bush R, Portnoy J, Saxon A, Terr A, Wood R. The medical effects of mold exposure. Journal of Allergy and Clinical Immunology, 2006, 117, 326–333.

    Article  Google Scholar 

  9. Olsson J, Börjesson T, Lundstedt T, Schnürer J. Detection and quantification of ochratoxin A and deoxynivalenol in barley grains by GC-MS and electronic nose. International Journal of Food Microbiology, 2002, 72, 203–214.

    Article  Google Scholar 

  10. Ahlstrom U, Borjesson E. Segregation of motion structure from random visual noise. Perception, 1996, 25, 279–292.

    Article  Google Scholar 

  11. Patrycja C, Wojciech W. The analysis of sensor array data with various pattern recognition techniques. Sensors and Actuators B: Chemical, 2006, 114, 85–93.

    Article  Google Scholar 

  12. Schulbach K F, Rouseff R L, Sims C A. Relating descriptive sensory analysis to gas chromatography/olfactometry ratings of fresh strawberries using partial least squares regression. Journal of Food Science, 2004, 69, 273–277.

    Article  Google Scholar 

  13. Yoav S. Potential applications of artificial olfactory sensing for quality evaluation of fresh produce. Journal of Agricultural Engineering Research, 2000, 77, 239–258.

    Article  Google Scholar 

  14. Shi Z B, Yu T, Zhao Q, Li Y, Lan Y B. Comparison of algorithms for an electronic nose in identifying liquors. Journal of Bionic Engineering, 2008, 5, 253–257.

    Article  Google Scholar 

  15. Lan Y B, Zheng X Z, Wesbrook J K, Lopez J, Lacey R, Hoffmann W C. Identification of stink bugs using an electronic nose. Journal of Bionic Engineering, 2008, 5, S172–S180.

    Article  Google Scholar 

  16. Zhang Z, Tong J, Chen D H, Lan Y B. Electronic nose with an air sensor matrix for detecting beef freshness. Journal of Bionic Engineering, 2008, 5, 67–73.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Engineering, Northeast Agricultural University, Harbin, 150030, P. R. China

    Xian-zhe Zheng

  2. USDA-ARS, College Station, TX, 77845, USA

    Yu-bin Lan, John Westbrook & W. C. Hoffmann

  3. Department of Mathematics and Computer Science, Fort Valley State University, Fort Valley, GA, 31030, USA

    Jian-min Zhu

  4. Department of Biological and Agricultural Engineering, Texas A&M University, College Station, Texas, 77843-2117, USA

    R. E. Lacey

Authors
  1. Xian-zhe Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-bin Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian-min Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. John Westbrook
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. W. C. Hoffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. R. E. Lacey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu-bin Lan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zheng, Xz., Lan, Yb., Zhu, Jm. et al. Rapid identification of rice samples using an electronic nose. J Bionic Eng 6, 290–297 (2009). https://doi.org/10.1016/S1672-6529(08)60122-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1016/S1672-6529(08)60122-5

Keywords

Search

Navigation