2015
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Li, J; Gutierrez-Osuna, R; Hodges, R D; Luckey, G; Crowell, J; Schiffman, S S; Nagle, H T Odor Assessment of Automobile Interior Components Using Ion Mobility Spectrometry Proceedings Article In: IEEE Sensors Conference, 2015. @inproceedings{li2015sensors,
title = {Odor Assessment of Automobile Interior Components Using Ion Mobility Spectrometry},
author = {J Li and R Gutierrez-Osuna and R D Hodges and G Luckey and J Crowell and S S Schiffman and H T Nagle},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/li2015sensors.pdf},
year = {2015},
date = {2015-11-01},
booktitle = {IEEE Sensors Conference},
keywords = {Chemical sensors, Infrared spectroscopy, Olfaction},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2014
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Li, J; Hodges, R D; Gutierrez-Osuna, R; Luckey, G; Crowell, J; Schiffman, S S; Nagle, H T Odor Assessment of Automobile Cabin Air by Machine Olfaction Proceedings Article In: Proc. IEEE Sensors Conference, 2014. @inproceedings{li2004sensorsconf,
title = {Odor Assessment of Automobile Cabin Air by Machine Olfaction},
author = {J Li and R D Hodges and R Gutierrez-Osuna and G Luckey and J Crowell and S S Schiffman and H T Nagle},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/li2004sensorsconf.pdf},
year = {2014},
date = {2014-11-02},
booktitle = {Proc. IEEE Sensors Conference},
keywords = {Chemical sensors, Electronic nose, Olfaction},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2008
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Yamanaka, T; Gutierrez-Osuna, R Extracting functional clusters of glomeruli in rat olfactory bulb by non-negative matrix factorization Technical Report 2008. @techreport{yamanaka08extracting,
title = {Extracting functional clusters of glomeruli in rat olfactory bulb by non-negative matrix factorization},
author = {T Yamanaka and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/yamanaka08extracting.pdf},
year = {2008},
date = {2008-11-17},
abstract = {Ensemble coding in the early olfactory pathway has been extensively investigated using imaging techniques. These studies have shown that glomeruli with similar affinity gather in close proximity in olfactory bulb, forming a module. In this work, we propose computational methods for analyzing this neural code. Specifically, we show how non-negative matrix factorization (NMF), a machine-learning method for extracting the intrinsic parts of objects, can be used to automatically extract glomerular modules from a database of bulbar activity patterns, as measured with 2-deoxyglucose. The modules extracted by NMF correspond to localized areas in olfactory bulb, in consistency with experimental results from imaging studies on glomerular activity. To validate the emerging representation, we analyzed the relationship between neural activity on these modules and perceptual descriptions of the odorants. We first used pattern-classification techniques to predict ten perceptual descriptors for 53 odorants from their activity on the modules. Our results indicate that NMF is able to extract modules that are intrinsic to the odor coding mechanism. Furthermore, we used mutual information to analyze the relationship between modules and olfactory perception. This analysis revealed the contribution of each module to the olfactory percepts.},
keywords = {Olfaction},
pubstate = {published},
tppubtype = {techreport}
}
Ensemble coding in the early olfactory pathway has been extensively investigated using imaging techniques. These studies have shown that glomeruli with similar affinity gather in close proximity in olfactory bulb, forming a module. In this work, we propose computational methods for analyzing this neural code. Specifically, we show how non-negative matrix factorization (NMF), a machine-learning method for extracting the intrinsic parts of objects, can be used to automatically extract glomerular modules from a database of bulbar activity patterns, as measured with 2-deoxyglucose. The modules extracted by NMF correspond to localized areas in olfactory bulb, in consistency with experimental results from imaging studies on glomerular activity. To validate the emerging representation, we analyzed the relationship between neural activity on these modules and perceptual descriptions of the odorants. We first used pattern-classification techniques to predict ten perceptual descriptors for 53 odorants from their activity on the modules. Our results indicate that NMF is able to extract modules that are intrinsic to the odor coding mechanism. Furthermore, we used mutual information to analyze the relationship between modules and olfactory perception. This analysis revealed the contribution of each module to the olfactory percepts. |
2004
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Gutierrez-Osuna, R Olfactory Interaction Book Chapter In: Encyclopedia of Human-Computer Interaction, pp. 507-511, Berkshire Publishing Group, 2004. @inbook{gutierrez04interaction,
title = {Olfactory Interaction},
author = {R Gutierrez-Osuna},
year = {2004},
date = {2004-01-01},
booktitle = {Encyclopedia of Human-Computer Interaction},
pages = {507-511},
publisher = {Berkshire Publishing Group},
keywords = {Olfaction},
pubstate = {published},
tppubtype = {inbook}
}
|
2001
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Gutierrez-Osuna, R; Schiffman, S S; Nagle, H T Correlation of sensory analysis with electronic nose data for swine odor remediation assessment Conference Proceedings of the 3rd European Congress on Odours, Metrology and Electronic Noses, 2001. @conference{gutierrez2001correlation,
title = {Correlation of sensory analysis with electronic nose data for swine odor remediation assessment},
author = {R Gutierrez-Osuna and S S Schiffman and H T Nagle},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2001correlation.pdf},
year = {2001},
date = {2001-01-01},
booktitle = {Proceedings of the 3rd European Congress on Odours, Metrology and Electronic Noses},
abstract = {This article presents an evaluation of the electronic nose technology as an alternative to sensory analysis for assessing the effectiveness of biofilters. An AromaScan® A32S electronic nose and a human panel at Duke University’s Taste and Smell Research Lab were used to measure typical volatile compounds from swine confinement buildings. Chemometrics techniques were employed to predict the olfactory scores of the human panel from the electronic nose data. The cross-sensitivity of the sensor array to the humidity of the samples is discussed. Our results indicate that the electronic nose generates responses that are correlated with sensory analysis ratings of swine malodors at different concentrations.},
keywords = {Electronic nose, Olfaction},
pubstate = {published},
tppubtype = {conference}
}
This article presents an evaluation of the electronic nose technology as an alternative to sensory analysis for assessing the effectiveness of biofilters. An AromaScan® A32S electronic nose and a human panel at Duke University’s Taste and Smell Research Lab were used to measure typical volatile compounds from swine confinement buildings. Chemometrics techniques were employed to predict the olfactory scores of the human panel from the electronic nose data. The cross-sensitivity of the sensor array to the humidity of the samples is discussed. Our results indicate that the electronic nose generates responses that are correlated with sensory analysis ratings of swine malodors at different concentrations. |
1997
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Classen, J J; Schiffman, S S; Nagle, H T; Gutierrez-Osuna, R Electronic nose evaluation of synthetic hog farm odor Conference Ammonia and odour emissions from animal production facilities, International Symposium, 1997. @conference{classen1997electronic,
title = {Electronic nose evaluation of synthetic hog farm odor},
author = {J J Classen and S S Schiffman and H T Nagle and R Gutierrez-Osuna},
year = {1997},
date = {1997-01-01},
booktitle = {Ammonia and odour emissions from animal production facilities, International Symposium},
keywords = {Electronic nose, Olfaction},
pubstate = {published},
tppubtype = {conference}
}
|