Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-26T08:13:04.790Z Has data issue: false hasContentIssue false

Electronic Olfactory Systems Based on Metal Oxide Semiconductor Sensor Arrays

Published online by Cambridge University Press:  31 January 2011

Get access

Abstract

In this article, we present the Pico electronic nose, an artificial olfactory system based on thin-film semiconductor sensors, and two applications: food-quality control (coffee analysis) and environmental monitoring (odors at a landfill site). For both applications, the electronic nose data correlated with that of panels of trained judges. For the coffee, a global index (called the hedonic index) characterizing the sensorial appeal could be predicted with the electronic nose, and for the landfill site, the intensity of odors could be quantified. In this article, we stress the importance of stable and sensitive sensors, such as metal oxide thin films produced by sputtering, and of multivariate data analysis for extracting knowledge (e.g., gaining selectivity) from the data.

Type
Research Article
Copyright
Copyright © Materials Research Society 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1Gardner, J. and Bartlett, P., Electronic Noses (Oxford University Press, 1999).CrossRefGoogle Scholar
2Ulmer, H., Mitrovics, J., Noetzel, G., Weimar, U., and Gopel, W., Sens. Actuators, B 43 (1997) p. 24.CrossRefGoogle Scholar
3Bartlett, P.N., Elliot, T.M., and Barcher, J.W., Food Technol. 51 (1997) p. 44.Google Scholar
4Mellon, F., in Spectroscopic Techniques for Food Analysis, edited by Wilson, R. (Wiley-VCH, Weinheim, Germany, 1994).Google Scholar
5Pal, D., Sachdeva, S., and Singh, S., Food, J.Sci. Technol. 32 (1995) p. 357.Google Scholar
6Pardo, M., Sberveglieri, G., IEEE Trans. Instrum. Meas. 51 (6) (December 2002) p. 1334.CrossRefGoogle Scholar
7Falasconi, M., Pardo, M., Sberveglieri, G., Riccò, I., and Bresciani, A., “The Novel EOS835 Electronic Nose and Data Analysis for Evaluating Coffee Ripening,” IEEE Sens. J. (2004) in press.CrossRefGoogle Scholar
8Singh, S., Hines, E., and Gardner, J., Sens. Actuators, B 30 (1996) p. 185.CrossRefGoogle Scholar
9Pardo, M., Niederjaufner, G., Benussi, G., Comini, E., Faglia, G., Sberveglieri, G., Holmberg, M., and Lundstrom, I., Sens. Actuators, B 69 (2000).Google Scholar
10Stuetz, R.M., Fenner, R.A., Hall, S.J., Stratful, I., and Loke, D., Water Sci. Technol. 41 (6) (2000) p. 41.CrossRefGoogle Scholar
11Nicolas, J., Romain, A.-C., Monticelli, D., Maternova, J., and Ph. Andŕ, in Proc. 7th Int. Symp. on Olfaction and Electronic Noses, edited by Gardner, J. and Persaud, K. (Institute of Physics Publishing, Bristol, UK, 2000) p. 141.Google Scholar
12Sberveglieri, G., Groppelli, S., Nelli, P., and Perego, C., Sens. Actuators, B 15–16 (1993) p. 86.CrossRefGoogle Scholar
13Zampiceni, E., Bontempi, E., Sberveglieri, G., and Depero, L., Thin Solid Films 418 (1) (2002) p. 16CrossRefGoogle Scholar
14Duda, R.O., Hart, P.E., and Stork, D.G., Pattern Classification, 2nd ed. (John Wiley & Sons, New York, 2001).Google Scholar
15Webb, Andrew, Statistical Pattern Recognition, 2nd ed. (John Wiley & Sons, Chichester, UK, 2002).CrossRefGoogle Scholar
16Jain, A.K., Duin, R.P.W., and Mao, J., IEEE Trans. Pattern Analysis and Machine Intelligence 22 (1) (2000) p. 4.CrossRefGoogle Scholar
17Pardo, M., “Multivariate Data Analysis for Gas Sensor Arrays,” PhD thesis, Università di Brescia (March 2000).Google Scholar
18Gutierrez-Osuna, R. and Nagle, H.T., IEEE Trans. Systems, Man, and Cybernetics B 29 (5) (1999) p. 626.CrossRefGoogle Scholar
19Pardo, M. and Sberveglieri, G., IEEE Sens. J. 2 (3) (2002) p. 203.CrossRefGoogle Scholar
20Bishop, C.M., Neural Networks for Pattern Recognition (Oxford University Press, Oxford, 1995).CrossRefGoogle Scholar
21Sberveglieri, G., Faglia, G., Groppelli, S., Nelli, P., and Camanzi, A., Semicond. Sci. Technol. 5 (41) (1990) p. 1231.CrossRefGoogle Scholar
22Comini, E., Guidi, V., Frigeri, C., Ricco, I., and Sberveglieri, G., Sens. Actuators, B 84 (2002) p. 26.CrossRefGoogle Scholar
23Garzella, C., Bontempi, E., Depero, L.E., Vomiero, A., Mea, G. Della, and Sberveglieri, G., Sens. Actuators, B 93 (2003) p. 495.CrossRefGoogle Scholar
24Galatsis, K., Li, Y.X., Wlodarski, W., Comini, E., Sberveglieri, G., Cantalini, C., Santucci, S., and Passacantando, M., Sens. Actuators, B 83 (2002) p. 276.CrossRefGoogle Scholar
25Demuth, H. and Beale, M., Manual of the Neural Network Toolbox, Version 3 (MathWorks, Novi, MI, 1998).Google Scholar
26Pardo, M. and Sberveglieri, G., IEEE Sens. J. 4 (3) (June 2004) p. 355.CrossRefGoogle Scholar
27Pardo, M., Sberveglieri, G., Gardini, S., and Dalcanale, E., Sens. Actuators, B 69 (2000) p. 359.CrossRefGoogle Scholar
28Pardo, M., Sberveglieri, G., Masulli, F., and Valentini, G., Anal. Chim. Acta 446 (2001) p. 223.CrossRefGoogle Scholar
29Odello, L. and Odello, C., Espresso Italiano Tasting (Centro Studi e Formazione Assaggiatori, Brescia, Italy) 1998.Google Scholar
30Pardo, M. and Sberveglieri, G.. in Proc. 8th Int. Symp. on Olfaction and Electronic Noses, edited by Stetter, J. and Penrose, R. (The Electrochemical Society, Pennington, NJ, 2001) p. 15.Google Scholar
31Pardo, M., Niederjaufner, G., Comini, E., Faglia, G., and Sberveglieri, G., in Proc. 4th Italian Conf. on Sensors and Microsystems (World Scientific, Rome, 1999) p. 99.Google Scholar
32Falasconi, M., Gobbi, E., Pardo, M., Torre, M. della, Bresciani, A., and Sberveglieri, G., Sens. Actuators, B (2004) in press.Google Scholar