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Communication Dans Un Congrès Année : 2006

Quantitative Structure - Odor Relationship: Using of Multidimensional Data Analysis and Neutral Network Approaches

Résumé

Structure - odor relationships (SOR) are key issues for the synthesis of new odorant molecules. But, this relation is hard to model, due to limited understanding of olfaction phenomena and the subjectivity of odor quantity and quality as stated in Rossitier's review (1996). Many molecular descriptors are used to correlate molecule's odor, but no universal rules emerge in this field. In this paper, we focus on the use of molecular descriptors as an alternative approach in the prediction of odors, by the mean of regression techniques. Principal Component Analysis (PCA) and Stepwise Collinearity Diagnosis (SCD) techniques are used to reduce the dimensionality of data, by the identification of significant molecular descriptors. Then, the chosen molecular descriptors are used with a neural networks algorithm to correlate the structure to molecular odor quality. The results are validated on balsamic flavor.

Domaines

Génie chimique
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Dates et versions

hal-04037229 , version 1 (20-03-2023)

Identifiants

  • HAL Id : hal-04037229 , version 1
  • OATAO : 1353

Citer

Mourad Korichi, Vincent Gerbaud, Pascal Floquet, A.-Hassan Meniai, Salif Nacef, et al.. Quantitative Structure - Odor Relationship: Using of Multidimensional Data Analysis and Neutral Network Approaches. 16th European Symposium on Computer Aided Process Engineering (ESCAPE 16) and 9th International Symposium on Process Systems Engineering, Jul 2006, Garmisch PartenKirschen, Germany. pp.895-900. ⟨hal-04037229⟩
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