An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation Method
A novel odor interaction model was proposed for binary mixtures of benzene and substituted benzenes by a partial differential equation (PDE) method. Based on the measurement method (tangent-intercept method) of partial molar volume, original parameters of corresponding formulas were reasonably displ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/7/12256 |
| _version_ | 1811307222126297088 |
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| author | Luchun Yan Jiemin Liu Guihua Wang Chuandong Wu |
| author_facet | Luchun Yan Jiemin Liu Guihua Wang Chuandong Wu |
| author_sort | Luchun Yan |
| collection | DOAJ |
| description | A novel odor interaction model was proposed for binary mixtures of benzene and substituted benzenes by a partial differential equation (PDE) method. Based on the measurement method (tangent-intercept method) of partial molar volume, original parameters of corresponding formulas were reasonably displaced by perceptual measures. By these substitutions, it was possible to relate a mixture’s odor intensity to the individual odorant’s relative odor activity value (OAV). Several binary mixtures of benzene and substituted benzenes were respectively tested to establish the PDE models. The obtained results showed that the PDE model provided an easily interpretable method relating individual components to their joint odor intensity. Besides, both predictive performance and feasibility of the PDE model were proved well through a series of odor intensity matching tests. If combining the PDE model with portable gas detectors or on-line monitoring systems, olfactory evaluation of odor intensity will be achieved by instruments instead of odor assessors. Many disadvantages (e.g., expense on a fixed number of odor assessors) also will be successfully avoided. Thus, the PDE model is predicted to be helpful to the monitoring and management of odor pollutions. |
| first_indexed | 2024-04-13T08:59:59Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:59:59Z |
| publishDate | 2014-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8d0669c233f6448bb22d67d48b7b14632022-12-22T02:53:09ZengMDPI AGSensors1424-82202014-07-01147122561227010.3390/s140712256s140712256An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation MethodLuchun Yan0Jiemin Liu1Guihua Wang2Chuandong Wu3School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, ChinaSchool of Chemistry and Biological Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, ChinaSchool of Chemistry and Biological Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, ChinaSchool of Chemistry and Biological Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, ChinaA novel odor interaction model was proposed for binary mixtures of benzene and substituted benzenes by a partial differential equation (PDE) method. Based on the measurement method (tangent-intercept method) of partial molar volume, original parameters of corresponding formulas were reasonably displaced by perceptual measures. By these substitutions, it was possible to relate a mixture’s odor intensity to the individual odorant’s relative odor activity value (OAV). Several binary mixtures of benzene and substituted benzenes were respectively tested to establish the PDE models. The obtained results showed that the PDE model provided an easily interpretable method relating individual components to their joint odor intensity. Besides, both predictive performance and feasibility of the PDE model were proved well through a series of odor intensity matching tests. If combining the PDE model with portable gas detectors or on-line monitoring systems, olfactory evaluation of odor intensity will be achieved by instruments instead of odor assessors. Many disadvantages (e.g., expense on a fixed number of odor assessors) also will be successfully avoided. Thus, the PDE model is predicted to be helpful to the monitoring and management of odor pollutions.http://www.mdpi.com/1424-8220/14/7/12256human sensingmonitoringodor intensityodor interactionarenesair pollution |
| spellingShingle | Luchun Yan Jiemin Liu Guihua Wang Chuandong Wu An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation Method Sensors human sensing monitoring odor intensity odor interaction arenes air pollution |
| title | An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation Method |
| title_full | An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation Method |
| title_fullStr | An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation Method |
| title_full_unstemmed | An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation Method |
| title_short | An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation Method |
| title_sort | odor interaction model of binary odorant mixtures by a partial differential equation method |
| topic | human sensing monitoring odor intensity odor interaction arenes air pollution |
| url | http://www.mdpi.com/1424-8220/14/7/12256 |
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