Literature DB >> 11288303

Measurement of odor intensity by an electronic nose.

G Hudon1, C Guy, J Hermia.   

Abstract

The possibility of using electronic noses (ENs) to measure odor intensity was investigated in this study. Two commercially available ENs, an Aromascan A32S with conducting polymer sensors and an Alpha M.O.S. Fox 3000 with metal oxide sensors, as well as an experimental EN made of Taguchi-type tin oxide sensors, were used in the experiments. Odor intensity measurement by sensory analysis and EN sensor response were obtained for samples of odorous compounds (n-butanol, CH3COCH3, and C2H5SH) and for binary mixtures of odorous compounds (n-butanol and CH3COCH3). Linear regression analysis and artificial neural networks (ANN) were used to establish a relationship between odor intensity and EN sensor responses. The results, suggest that large differences in sensor response to samples of equivalent odor intensity exist and that sensitivity to odorous compounds varies according to the type of sensors. A linear relationship between odor intensity and averaged sensor response was found to be appropriate for the EN based on conducting polymer sensors with a correlation coefficient (r) of 0.94 between calculated and measured odor intensity. However, the linear regression approach was shown to be inadequate for both ENs, which included metal oxide-type sensors. Very strong correlation (r = 0.99) between measured odor intensity and calculated odor intensity using the ANN developed were obtained for both commercial ENs. A weaker correlation (r = 0.84) was found for the experimental instrument, suggesting an insufficient number of sensors and/or not enough diversity in sensor responses. The results demonstrated the ability of ENs to measure odor intensity associated with simple mixtures of odorous compounds and suggest that ANN are appropriate to model the relationship between odor intensity measurement and EN sensor response.

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Year:  2000        PMID: 11288303     DOI: 10.1080/10473289.2000.10464202

Source DB:  PubMed          Journal:  J Air Waste Manag Assoc        ISSN: 1096-2247            Impact factor:   2.235


  9 in total

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2.  A new intelligent electronic nose system for measuring and analysing livestock and poultry farm odours.

Authors:  Leilei Pan; Simon X Yang
Journal:  Environ Monit Assess       Date:  2007-03-24       Impact factor: 2.513

3.  Characterization of combinatorial patterns generated by multiple two-component sensors in E. coli that respond to many stimuli.

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Journal:  Biotechnol Bioeng       Date:  2010-12-01       Impact factor: 4.530

Review 4.  The Odor Characterizations and Reproductions in Machine Olfactions: A Review.

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Journal:  Sensors (Basel)       Date:  2018-07-18       Impact factor: 3.576

5.  Mixed Natural Gas Online Recognition Device Based on a Neural Network Algorithm Implemented by a FPGA.

Authors:  Tanghao Jia; Tianle Guo; Xuming Wang; Dan Zhao; Chang Wang; Zhicheng Zhang; Shaochong Lei; Weihua Liu; Hongzhong Liu; Xin Li
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6.  A Multi-Sensor System for Sea Water Iodide Monitoring and Seafood Quality Assurance: Proof-of-Concept Study.

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7.  Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks.

Authors:  Bartosz Szulczyński; Krzysztof Armiński; Jacek Namieśnik; Jacek Gębicki
Journal:  Sensors (Basel)       Date:  2018-02-08       Impact factor: 3.576

8.  Determination of Odour Interactions of Three-Component Gas Mixtures Using an Electronic Nose.

Authors:  Bartosz Szulczyński; Jacek Namieśnik; Jacek Gębicki
Journal:  Sensors (Basel)       Date:  2017-10-18       Impact factor: 3.576

9.  Monitoring of Cell Concentration during Saccharomyces cerevisiae Culture by a Color Sensor: Optimization of Feature Sensor Using ACO.

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Journal:  Sensors (Basel)       Date:  2019-04-30       Impact factor: 3.576

  9 in total

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