Classification of Mixed Odors Using A Layered Neural Network
DOI:
https://doi.org/10.47839/ijc.16.1.870Keywords:
odor feature vector, layered neural networks, separation of mixed gasses, odor classification.Abstract
Odor classification has been forcused due to one of five senses of human being. If we could establish the odor classification technology, we would expect various new technology since human being requires five sences to acheive higher quality information processing and sophistcated decision making. For example, we could expect the odor classification and odor synthesis, which enable us to achieve odor communication technology. Furthermore, the odor classification would be applicable to keep the society safe by detecting the dangerous odors and to make our life more satisfactory by using additional odor information. In this paper we develop an electronic nose using a neural network. The neural network is a multi-layered neural network based on the gradient method. After classifying the various odors, we consider the classification in case that mixed odors are measured. To improve the classification accuracy we adopt a genetic algorithm to find a reduction factor to separate two mixed odors.References
L. Buck and R. Axel, “A novel multigene family may encode odorant receptors: a molecular basis for odor recognition”, Cell, 65, 175–187, 1991.
L. Buck, “Information coding in the vertebtrate olfactory system”, Annu. Rev. Neurosci., 19, 517–544, 1996.
J. Todrank, C. J. Wysocki, and G. K. Beauchamp, “The effects of adaptation on the perception of similar and dissimilar odors”, Chem. Senses, 16, 467–482, 1991.
W. S. Cain and E. H. Polak, Olfactory adaptation as an aspect of odor similarity, Chem. Senses, 17, 481–491, 1992.
W. S. Cain and B. C. Potts, Switch and bait: probing the discriminative basis of odor identification via recognition memory, Chem. Senses, 21, 35–44, 1996.
K.J. Rossiter, “Structure–odor relationships”, Chem. Rev. , 96, 3201–3240, 1996.
P. Callegari, J. Rouault, and P. Laffort, “Olfactory quality from descriptor profiles to similarities”, Chem. Senses, 22, 1–8, 1997.
M. L. Laska and D. Freyer, “Olfactory discrimination ability for aliphatic esters in squirrel monkeys and humans”, Chem. Senses, 22, 457–465, 1997.
Laska M. and P. Tuebner, “Olfactory discrimination ability of human subjects for ten pairs of enantiomers”, Chem. Senses, 24, 161–170, 1999.
P. M. Wise and W. S. Cain, “Latency and accuracy of discriminations of odor quality between binary mixtures and their components”, Chem. Senses, 25, 247–265, 2000.
D. A. Stevens and R. J. O’Connell, “Semantic-free scaling of odor quality”, Physiol. Behav. , 60, 211–215, 1996.
F. Wang, A. Nemes, M. Mendelsohn, and R. Axel, “Odorant receptors govern the formation of a precise topographic map”, Cell, 93, 47–60, 1998.
M. Yokoi, K. Mori, and N. Shigetada, “Refinement of odor molecule tuning by dendrodendritic synaptic inhibition in the olfactory bulb”, Proc. Natl Acad. Sci. USA , 92, 3371–3375, 1995.
N. Baric, M. Bucking, and M. Rapp, “A novel electronic nose based on minimized saw sensor arrays coupled with some enhanced headspace analysis and its use for rapid determination of volatile organic compounds in food quality monitoring,” Sensors and Actuator B, vol. 114, 482–488, 2006.
A. Norman, F. Stam, A. Morrissey, M. Hirschfelder, and D. Enderlein, “Packaging effects of a novel explosion-proof gas sensor”, Sensors and Actuator B, vol. 114, 287–290, 2003.
J. Ruther, T.Meiners, and J. M. Steidlem, “Rich in phenomena-lacking in terms. A classification of kairomones”, Chemoecology, vol. 12, 161–167, 2002.
M. Ghasemi-Varnamkhasti, S. Saeid and M. M. Siadat,, “Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements”, Journal of Food Engineering, , vol. 100, 377-387, 2010.
S. Marco; Agustín Gutierrez-Galvez, “Signal and data processing for machine olfaction and chemical sensing: A review”, IEEE Sensors Journal, vol. 12, 3189 – 3214.
R. Young, W. Buttner, B. Linnel, and R. Ramesham, “Electronic nose for space program applications”, Sensors and Actuator B, vol. 93, 7–16, 2003.
J. A. Milke, Application of neural networks for discriminating fire detectors, 1995, international Conference on Automatic Fire Detection, AUBE’95, 10th, Duisburg, Germany.
T. Fujinaka, M. Yoshioka, S. Omatu, and T. Kosaka, “Intelligent electronic nose systems for fire detection systems based on neural networks”, International Journal of Advances in Intelligent Systems, vol. 2, 268–277, 2009.
S. Omatu and M. Yano, “Mixed odors classification by neural networks”, The 8th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 24-26 September 2015, Warsaw, Poland, 24-26.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
