An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation Method

Yan, Luchun; Liu, Jiemin; Wang, Guihua; Wu, Chuandong

doi:10.3390/s140712256

Cited by 20 publications

(18 citation statements)

References 14 publications

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“…Odor samples of both individual aldehydes ( Table 1 ) and their binary mixtures were prepared using chemicals purchased from J&K Scientific (Beijing, China). Each odor sample was prepared through transferring a certain amount of standard gas to an odor-free plastic bag (Sinodour, Tianjin, China) and diluted with purified air [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, the OI of these unmixed components in the above mentioned models are still rated by sensory panels. In a previous study, the odor interaction of a binary odor mixture was investigated by referencing the partial molar volume measurement method [ 10 ]. After displacing the key parameters of partial molar volume calculating formulas with suitable perceptual measures, a partial differential equation (PDE) model was proposed.…”

Section: Introductionmentioning

confidence: 99%

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Research on Odor Interaction between Aldehyde Compounds via a Partial Differential Equation (PDE) Model

Yan¹,

Liu²,

Qu³

et al. 2015

Sensors

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In order to explore the odor interaction of binary odor mixtures, a series of odor intensity evaluation tests were performed using both individual components and binary mixtures of aldehydes. Based on the linear relation between the logarithm of odor activity value and odor intensity of individual substances, the relationship between concentrations of individual constituents and their joint odor intensity was investigated by employing a partial differential equation (PDE) model. The obtained results showed that the binary odor interaction was mainly influenced by the mixing ratio of two constituents, but not the concentration level of an odor sample. Besides, an extended PDE model was also proposed on the basis of the above experiments. Through a series of odor intensity matching tests for several different binary odor mixtures, the extended PDE model was proved effective at odor intensity prediction. Furthermore, odorants of the same chemical group and similar odor type exhibited similar characteristics in the binary odor interaction. The overall results suggested that the PDE model is a more interpretable way of demonstrating the odor interactions of binary odor mixtures.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Odor Interaction between Aldehyde Compounds via a Partial Differential Equation (PDE) Model

Yan¹,

Liu²,

Qu³

et al. 2015

Sensors

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“…Due to the age of some investigations, some of these values could be based on improper measurement techniques. The odour threshold concentration C OT is available for many chemical substances, but only a few investigations were performed to determine the coefficient of the Weber-Fechner law (van Ruth and O'Connor, 2001;Wu et al, 2015;Yan et al, 2014a). The parameters for 12 odorous substances which are used for the Korean air quality assurance program are summarised by Kim and Park (2008), based on the measurements by Nagata (2003).…”

Section: Measurements Of Single Substancesmentioning

confidence: 99%

Conversion of the chemical concentration of odorous mixtures into odour concentration and odour intensity: A comparison of methods

Liu

Zhao

et al. 2016

Atmospheric Environment

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Research Highlights Concentration of odorous substances as surrogate for odour concentration/intensity  Comparison of conversion methods with various degrees of complexity  Model evaluation by seven VOCs: 23 binary mixtures and 5 mixtures of 7 substances  Model input: odour threshold concentration and the slope of the Weber-Fechner law  No further calibration by olfactometric measurements necessary Wu et al.: Conversion of the chemical concentration of odorous mixtures into odour concentration and odour intensity 2/23Abstract Continuous odour measurements both of emissions as well as ambient concentrations are seldom realised, mainly because of their high costs. They are therefore often substituted by concentration measurements of odorous substances. Then a conversion of the chemical concentrations C (mg m -3 ) into odour concentrations C OD (ou E m -3 ) and odour intensities OI is necessary. Four methods to convert the concentrations of single substances to the odour concentrations and odour intensities of an odorous mixture are investigated: (1) direct use of measured concentrations, (2) the sum of the odour activity value SOAV, (3) the sum of the odour intensities SOI, and (4) the equivalent odour concentration EOC, as a new method. The methods are evaluated with olfactometric measurements of seven substances as well as their mixtures. The results indicate that the SOI and EOC conversion methods deliver reliable values. These methods use not only the odour threshold concentration but also the slope of the Weber-Fechner law to include the sensitivity of the odour perception of the individual substances. They fulfil the criteria of an objective conversion without the need of a further calibration by additional olfactometric measurements.

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“…For individual odorants, the Weber-Fechner law and Power Law model have been widely used for the conversion of chemical concentration and odor intensity [7]. The linear relationship between odor activity value (ratio of chemical concentration to its odor threshold) and odor intensity is also proposed for odor intensity prediction [8]. For odor mixtures, many models like the ERM model, Vector model, U model, Additivity model, and their extended versions are reported [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Visual Analysis of Odor Interaction Based on Support Vector Regression Method

Yan

Liu

2020

Sensors

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The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish odor intensity prediction models for binary esters, aldehydes, and aromatic hydrocarbon mixtures, respectively. The prediction accuracy to both training samples and test samples demonstrated the high prediction capacity of the support vector regression model. Then the optimized model was used to generate extra odor data by predicting the odor intensity of more simulated samples with various mixing ratios and concentration levels. Based on these olfactory measured and model predicted data, the odor interaction was analyzed in the form of contour maps. This intuitive method showed more details about the odor interaction pattern in the binary mixture. We found that that the antagonism effect was commonly observed in these binary mixtures and the interaction degree was more intense when the components' mixing ratio was close. Meanwhile, the odor intensity level of the odor mixture barely influenced the interaction degree. The machine learning algorithms were considered promising tools in odor researches.3 of 12 standard in odor intensity evaluation [26]. More details about the testing procedure and environmental requirements were described in these references. In this study, the collected dataset contains 31 samples of binary mixture EA+BA, 21 samples of EA+EB, 22 samples of BA+EB, 24 samples of PA+VA, 24 samples of PA+HEP, 24 samples of VA+HEP, 34 samples of B+T, 31 samples of B+E, and 24 samples of T+E.

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An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation Method

Cited by 20 publications

References 14 publications

Research on Odor Interaction between Aldehyde Compounds via a Partial Differential Equation (PDE) Model

Research on Odor Interaction between Aldehyde Compounds via a Partial Differential Equation (PDE) Model

Conversion of the chemical concentration of odorous mixtures into odour concentration and odour intensity: A comparison of methods

Visual Analysis of Odor Interaction Based on Support Vector Regression Method

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