Odor-Structure Relationship Studies of Tetralin and Indan Musks

Lavine, Barry K.; White, Collin G.; Mirjankar, Nikhil; Sundling, C. Matthew; Breneman, Curt M.

doi:10.1093/chemse/bjs058

Cited by 18 publications

(13 citation statements)

References 22 publications

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“…Although the specific PDE model of each odor mixture in this test was not individually explored, these odorants (e.g., BA, IBA, IVA and HEX) still fitted into the extended PDE model very well. It probably should be contributed to their same chemical group and similar molecular structure [ 24 ]. In a previous study, binary odor mixtures of benzene and substituted benzenes were also fitted into a same PDE model [ 10 ].…”

Section: Resultsmentioning

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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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: Resultsmentioning

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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“…Second, wavelet coefficients characteristic of the waxy condition of the samples were identified using a genetic algorithm for classification and feature selection [16][17][18][19][20][21]. The pattern recognition GA utilized both supervised and unsupervised learning to identify coefficients that optimized clustering of the spectra by class (i.e., the waxy condition of the wheat samples) in a plot of the two or three largest principal components of the data.…”

Section: Pattern Recognition Analysismentioning

confidence: 99%

Classification of the waxy condition of durum wheat by near infrared reflectance spectroscopy using wavelets and a genetic algorithm

Lavine

Mirjankar

Delwiche

2014

Microchemical Journal

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a b s t r a c t a r t i c l e i n f oNear infrared (NIR) reflectance spectroscopy has been applied to the problem of differentiating four genotypes of durum wheat: 'waxy', Wx A1 null null, wx-B1 null and wild type. The test data consisted of 95 NIR reflectance spectra of wheat samples obtained from a USDA-ARS wheat breeding program. A two-step procedure for pattern recognition analysis of NIR spectral data was employed. First, the wavelet packet transform [14,15] was applied to the NIR reflectance data using wavelet filters at different scales to extract and separate low-frequency signal components from high frequency noise components. By applying these filters, each reflectance spectrum was decomposed into wavelet coefficients that represented the sample's constituent frequencies. Second, wavelet coefficients characteristic of the waxy condition of the wheat samples were identified using a genetic algorithm for pattern recognition. The pattern recognition GA employed both supervised and unsupervised learning to identify wavelet coefficients that optimized clustering of the spectra by genotype in a plot of the two largest principal components of the data. By sampling key feature subsets, scoring their PC plots, and tracking those genotypes and samples that were difficult to classify, the pattern recognition GA was able to identify a set of wavelet coefficients whose PC plot showed clustering of the wheat samples on the basis of their 'waxy' condition. Object validation was also performed to assess the predictive ability of the proposed NIR method to identify the 'waxy' condition of the wheat. An overall classification success rate of 78% was achieved for the spectral data.

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“…There are major recent advances on neurobiology, biophysics, biochemical fields [4][5][6][7], though it is still unclear how humans recognize odor. At this scenario, quantitative structure-activity relationship models (QSAR models) are valuable for the proposal of new potential musky smelling molecules [8][9][10][11][12]. Such approach allows for the reduction in the number of costly and unnecessary chemical syntheses.…”

Section: Theories Regarding Odor Prediction and Recognitionmentioning

confidence: 99%

“…A mathematical formula had to be developed (7), indane (8), tetralin (9), and tonkene (10); nitromusks (NM): musk ketone (11), musk moskene (12), and musk ambrette (13); acyclic musks (ACM): romandolide (14), helvetolide (15), and cyclomusk (16); nonmusks: macrocyclic analogs (MCa) (17) and (18), acyclic analogs (Aa) (19) and (20), and nitro analogs (Na) (21), (22), and (23).…”

Section: Development Of a Frequency-weighted Average And Othermentioning

confidence: 99%

Quantum Calculation for Musk Molecules Infrared Spectra towards the Understanding of Odor

Maia

Magalhães

Lerner

et al. 2014

Advances in Chemistry

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It is not clear so far how humans can recognize odor. One of the theories regarding structure-odor relationship is vibrational theory, which claims that odors can be recognized by their modes of vibration. In this sense, this paper brings a novel comparison made between musky and nonmusky molecules, as to check the existence of correlation between their modes on the infrared spectra and odor. For this purpose, sixteen musky odorants were chosen, as well as seven other molecules that are structurally similar to them, but with no musk odor. All of them were submitted to solid theoretical methodology (using molecular mechanics/molecular dynamics and Neglect of Diatomic Differential Overlap Austin Model 1 methods to optimize geometries) as to achieve density functional theory spectra information, with both Gradient Corrected Functional Perdew-Wang generalizedgradient approximation (GGA/PW91) and hybrid Becke, three-parameter, Lee-Yang-Parr (B3LYP) functional. For a proper analysis over spectral data, a mathematical method was designed, generating weighted averages for theoretical frequencies and computing deviations from these averages. It was then devised that musky odorants satisfied demands of the vibrational theory, while nonmusk compounds belonging either to nitro group or to acyclic group failed to fulfill the same criteria.

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Odor-Structure Relationship Studies of Tetralin and Indan Musks

Cited by 18 publications

References 22 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

Classification of the waxy condition of durum wheat by near infrared reflectance spectroscopy using wavelets and a genetic algorithm

Quantum Calculation for Musk Molecules Infrared Spectra towards the Understanding of Odor

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