Applying Convolution-Based Processing Methods To A Dual-Channel, Large Array Artificial Olfactory Mucosa

Taylor, James E.; Harun, Fauzan Khairi Che; Covington, James A.; Gardner, Julian W.; Pardo, Matteo; Sberveglieri, Giorgio

doi:10.1063/1.3156502

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Some of these innovative achievements include the artificial olfactory microsystem (e-mucosa). This is a new type of electronic nose that mimics nasal chromatograph effect and have wealthier information content and so will enable a higher level of discrimination than present electronic nose systems (Che Harun et al, 2009aHarun et al, , 2009bTaylor et al, 2009).…”

Section: Future Trend and Perspectivementioning

confidence: 98%

Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements

Ghasemi-Varnamkhasti

Mohtasebi

Siadat

2010

Journal of Food Engineering

131

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Section: Future Trend and Perspectivementioning

confidence: 98%

Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements

Ghasemi-Varnamkhasti

Mohtasebi

Siadat

2010

Journal of Food Engineering

131

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“…However, no device can ever guarantee performance, at least in part, comparable with biological systems as long as a number of overlapping receptors is used to generate the odors' patterns [ 12 ]. To this end, recently in [ 13 ] the fabrication of a device containing three sensor arrays of 300 elements using 24 different sensitive materials was reported. Recently, a large-scale chemical sensor array (65, 536 sensors) was introduced in [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach for Preprocessing Data from a Large-Scale Chemical Sensor Array

Leo

Distante

Bernabei

et al. 2014

Sensors

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In this paper, an artificial olfactory system (Electronic Nose) that mimics the biological olfactory system is introduced. The device consists of a Large-Scale Chemical Sensor Array (16, 384 sensors, made of 24 different kinds of conducting polymer materials) that supplies data to software modules, which perform advanced data processing. In particular, the paper concentrates on the software components consisting, at first, of a crucial step that normalizes the heterogeneous sensor data and reduces their inherent noise. Cleaned data are then supplied as input to a data reduction procedure that extracts the most informative and discriminant directions in order to get an efficient representation in a lower dimensional space where it is possible to more easily find a robust mapping between the observed outputs and the characteristics of the odors in input to the device. Experimental qualitative proofs of the validity of the procedure are given by analyzing data acquired for two different pure analytes and their binary mixtures. Moreover, a classification task is performed in order to explore the possibility of automatically recognizing pure compounds and to predict binary mixture concentrations.

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Predicting Smell Perception from Molecular Descriptors Using Machine Learning Approach

Tyagi

Vishwakarma

Tiwary

et al. 2020

2020 International Conference Engineering and Telecommunication (En&T)

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Applying Convolution-Based Processing Methods To A Dual-Channel, Large Array Artificial Olfactory Mucosa

Cited by 4 publications

References 9 publications

Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements

Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements

An Efficient Approach for Preprocessing Data from a Large-Scale Chemical Sensor Array

Predicting Smell Perception from Molecular Descriptors Using Machine Learning Approach

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