Abstract
We present the development of a prototype that can classify odours based on chemical sensor data. Data are produced as sensors’ conductivity varies according to the volatile substance that contacts their surface. We experimented with Conducting Polymer (CP) sensors and concluded the last validation phase with Metal Oxide (MO) sensors. The aim was to investigate the power of uncertainty modelling techniques like fuzzy logic, neural networks and machine learning on chemical sensors data, as it is difficult to model the electrochemical interactions that take place on the surface of the sensor. Three parallel classification modules are developed using fuzzy sets, linguistic description and neural networks. Each module considers and treats data in a different way in order to provide greater system robustness. Classification results can be either merged or considered separately. Validation was based on the problem of food packaging quality control, where the packaging material can chemically interact with other substances (like ink for labelling) and emit bad odours that degrade food quality.
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References-Bibliography
Baldwin, J. F. (1992). “The Management of Fuzzy and Probabilistic Uncertainties for Knowledge Based Systems” in Encyclopedia of AI, Ed. S. A. Shapiro, John Wiley. (2nd ed.) 528–537.
Baldwin, J. F., Gooch, R. M. and Martin, T. P. (1996). “Fuzzy Processing of Hydrophone Sounds.” Fuzzy Sets and Systems 77(1): 35–47.
Baldwin, J. F., Lawry, J. and Martin, T. P. (1997). “A mass assignment based ID3 algorithm for decision tree induction.” International Journal of Intelligent Systems 12(7): 523–552.
Baldwin, J. F. and Martin, T. P. (1995). “Refining Knowledge from Uncertain Relations — a Fuzzy Data Browser based on Fuzzy Object-Oriented Programming in Fril”, Proc. 4th IEEE International Conference on Fuzzy Systems, Yokohama, Japan, 27–34.
Baldwin, J. F. and Martin, T. P. (1996). “A Fuzzy Data Browser in Fril” in Fuzzy Logic in AI, Ed. J. F. Baldwin, John Wiley. 101–124.
Baldwin, J. F., Martin, T. P. and McCoy, S. A. (1998). “Incremental Learning in a Fril-based Odour Classifier”, Proc. EUFIT-98, Aachen, Germany, 1216–1220.
Baldwin, J. F., Martin, T. P. and Pilsworth, B. W. (1988). “FRIL Manual (Version 4.0)”. Fril Systems Ltd, Bristol Business Centre, Maggs House, Queens Road, Bristol, BS8 IQX, UK. 1–697.
Baldwin, J. F., Martin, T. P. and Pilsworth, B. W. (1995). “FRIL — Fuzzy and Evidential Reasoning in AI”, Research Studies Press (John Wiley).
Gardner J.W., 1991, “Detection of Vapours and Odours from a Multisensor Array Using Pattem Recognition — Part 1. Principal Component and Cluster Analysis”, Sensors and Actuators B, Vol. 4, pp. 109–115.
Gardner J.W., E.L. Hines, H.C. Tang, 1992, “Detection of Vapours and Odours from a Multisensor Array Using Pattem Recognition techniques — Part 2. Artificial Neural Network”, Sensors and Actuators B, Vol. 9, pp. 9–15.
Nagle, H. T., 1997, “Gas Sensor Arrays and the Electronic Nose”, IEEE-electronic nose workshop (Enose’97), Marriott Marquis Hotel, Atlanta, GA, Nov. 7.
Khodja, L., Foully L., Benoit E., Talou T., “Fuzzy techniques for coffee flavour Classification”. Proceedings of IPMU 96, 1996, pp. 709–714.
Nagle, H.T., Gutierrez-Osuna R., Schiffman S.S., 1998, “The How and Why of Electronic Noses”, IEEE Spectrum, September.
Schiffman, S. S., Kermani B. G., and Nagle H. T., 1996, ‘dUse of an Electronic Nose to Detect the Source of Environmental Pollutants”, Paper 497, Pittcon’ 96, Chicago, IL., Mar. 3–9.
Schiffman, S. S., Classen J. J., Kermani B. G., and Nagle H. T., 1996, “Application of an Electronic Nose to Differentiate Odors from Exhaust Fans and Lagoon”, Proc. International Conf on Air Polution from Agricultural Operations, Kansas City, MO, Feb., pp. 255–261.
Savitzky, A., Golay, J, 1964, “Smoothing and Differentation of Data by Simplified Least Squares Procedures”. Analytical Chemistry no 36, p. 1627.
Singh S., Hines E.L., Gardner J.W., 1996, “Fuzzy neural computing of coffee and tainted-water data from and electronic nose”. Sensors and Actuators B, Vol. 30, pp. 185–190.
Strackeljan J., D. Behr, T. Kocher, 1997, “Fuzzy-pattern recognition for automatic detection of different teeth substances”. Fuzzy Sets and Systems, No. 85, p. 275.
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Tselentis, G., Marcelloni, F., Martin, T.P., Sensi, L. (2002). Odour Classification based on Computational Intelligence Techniques. In: Zimmermann, HJ., Tselentis, G., van Someren, M., Dounias, G. (eds) Advances in Computational Intelligence and Learning. International Series in Intelligent Technologies, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0324-7_27
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DOI: https://doi.org/10.1007/978-94-010-0324-7_27
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