ArticleSensors (Basel, Switzerland)2014
An odor interaction model of binary odorant mixtures by a partial differential equation method.
Article in Sensors (Basel, Switzerland), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Visual Analysis of Odor Interaction Based on Support Vector Regression Method.Sensors (Basel, Switzerland) · 2020Article
- Determination of Odor Intensity of Binary Gas Mixtures Using Perceptual Models and an Electronic Nose Combined with Fuzzy Logic.Sensors (Basel, Switzerland) · 2019Article
- Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks.Sensors (Basel, Switzerland) · 2018Article
- Pragmatic evaluation of odour emissions from a rendering plant in southern Brazil.Environmental science and pollution research international · 2016Article
- Use of a modified vector model for odor intensity prediction of odorant mixtures.Sensors (Basel, Switzerland) · 2015Article
- Research on odor interaction between aldehyde compounds via a partial differential equation (PDE) model.Sensors (Basel, Switzerland) · 2015Article
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4 authors.
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Abstract
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.
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