Relation Structure moléculaire - Odeur Utilisation des Réseaux de Neurones pour l’estimation de l’Odeur Balsamique

Les molécules odorantes (parfums ou flaveurs) sont utilisées dans une grande variété de produits de consommation, pour inciter les consommateurs à associer les impressions favorables à un produit donné. La Relation Structure moléculaire-Odeur (SOR) est cruciale pour la synthèse de ces molécules mais est très difficile à établir due à la subjectivité de l’odeur. Ce travail présente une approche de prédiction de l'odeur des molécules basée sur les descripteurs moléculaires. Les techniques d’analyse en composantes principales (PCA) et de d’analyse de colinéarité permettent d’identifier les descripteurs les plus pertinents. un réseau de neurones supervisé5 à deux couches (cachée et sortie) est employé pour corréler la structure moléculaire à l’odeur. La base de données décrite précédemment est utilisée pour l’apprentissage. Un ensemble de paramètres est modifié jusqu’à la satisfaction de la meilleure régression. Les résultats obtenus sont encouragent, ainsi les descripteurs moléculaires convenables corrèlent efficacement l'odeur des molécules. C’est la première étape d’un modèle générique en développement pour corréler l'odeur avec les structures moléculaire

HAZard and OPerability Study Analysis as a Semi-Automatic Approach

Risk analysis is crucial in industrial conception. HAZOP is the top risk analysis method for the oil and gas sector. This paper presents a semi-automatic method to address HAZOP's limitations and produce automatic results. The method uses a knowledge base, initially filled with gas liquefaction data, and is enhanced with subsequent case studies. An inference engine processes this data to conduct a HAZOP study. Propagation rules identify potential deviation paths, enabling risk analysis and consequence prediction based on the knowledge base. This method uniquely illustrates deviation paths and introduces nodes along these paths for further study. The findings derive from dynamic knowledge of each system in the knowledge base and can be reviewed and amended by experts

Computer Aided Aroma Design. II. Quantitative structure-odour relationship

Computer Aided Aroma Design (CAAD) is likely to become a hot issue as the REACH EC document targets many aroma compounds to require substitution. The two crucial steps in CAMD are the generation of candidate molecules and the estimation of properties, which can be difficult when complex molecular structures like odours are sought and their odour quality are definitely subjective or their odour intensity are partly subjective as stated in Rossitier’s review (1996). The CAAD methodology and a novel molecular framework were presented in part I. Part II focuses on a classification methodology to characterize the odour quality of molecules based on Structure – Odour Relation (SOR). Using 2D and 3D molecular descriptors, Linear Discriminant Analysis (LDA) and Artificial Neural Network are compared in favour of LDA. The classification into balsamic / non balsamic quality was satisfactorily solved. The classification among five sub notes of the balsamic quality was less successful, partly due to the selection of the Aldrich’s Catalog as the reference classification. For the second case, it is shown that the sweet sub note considered in Aldrich’s Catalog is not a relevant sub note, confirming the alternative and popular classification of Jaubert et al., (1995), the field of odours

Computer Aided Aroma Design. I. Molecular knowledge framework

Computer Aided Aroma Design (CAAD) is likely to become a hot issue as the REACH EC document targets many aroma compounds to require substitution. The two crucial steps in CAMD are the generation of candidate molecules and the estimation of properties, which can be difficult when complex molecular structures like odours are sought and when their odour quality are definitely subjective whereas their odour intensity are partly subjective as stated in Rossitier’s review (1996). In part I, provided that classification rules like those presented in part II exist to assess the odour quality, the CAAD methodology presented proceeds with a multilevel approach matched by a versatile and novel molecular framework. It can distinguish the infinitesimal chemical structure differences, like in isomers, that are responsible for different odour quality and intensity. Besides, its chemical graph concepts are well suited for genetic algorithm sampling techniques used for an efficient screening of large molecules such as aroma. Finally, an input/output XML format based on the aggregation of CML and ThermoML enables to store the molecular classes but also any subjective or objective property values computed during the CAAD process

Approche multi-classes de représentation des molécules pour la conception des produits-procédés assistée par ordinateur

La Conception de Produits Assistée par Ordinateur (CPAO) est largement utilisée dans le domaine « Process System Engineering » (PSE), comme un outil puissant pour la recherche de nouveaux produits chimiques. Les étapes cruciales de la CPAO sont la génération des molécules et l'estimation des propriétés, particulièrement quand les structures moléculaires complexes comme les arômes sont recherchées. Dans cet article, nous présentons une approche multi-classes de représentation des molécules basée sur les graphes moléculaires et la connaissance chimique. Trois catégories de groupes fonctionnels sont proposées : groupes élémentaires, groupes de base et groupes composés. Ces derniers servent à générer quatre classes de représentation qui peuvent être utiles pour la prédiction des propriétés et dans le design des molécules (CAMD). Une structure d’informations entrée-sortie basée sur le langage XML est définie, pour favoriser l'interopérabilité entre les logiciel

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

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

Treatment Heterogeneous Photocatalysis; Factors Influencing the Photocatalytic Degradation by TiO2

AbstractIn order to optimize the purification of water and sewage water, a new technique of degradation «the heterogeneous photocatalysis» of the organic matter was underlined. As catalyst we chose a semiconductor which is dioxide TiO2 the titanium in the presence of a lamp UV as source of energy. One model substances present in many industrial effluents: the 4-iso propyl phenol was selected. The results of our experiments show that the adsorption of the pollutant (10-4mol/l) on TiO2 supported in absence of radiation UV is negligible. Compared to direct photolysis UV (365nm), the devolution of the pollutant is definitely faster in the presence of TiO2/UV for the same experimental conditions

Classification of the aroma quality of pyrazines derivatives using random forest tree technique

We present an alternative classification of the odour molecules based on 0D, 1D, 2D and 3D molecular descriptors by using the Random Forest Tree (RFT). 98 molecules of pyrazine derivatives are classified among three classes of aroma notes: Green, Nutty and Bell-Pepper. The classification model uses 180, 40, 45 and 50 trees in the forest respectively for the 0D, 1D, 2D and 3D descriptors. The use of descriptors 0D, 1D, 2D and 3D correctly classify 72.1%, 70.6%, 82.4% and 85.3% of the molecules during the learning phase. For the test phase, the classification rate is 80%, 86.7%, 93.3% and 90%. This show that RFT is able to develop the molecular Structure - Odour

Multiclass Molecular Knowledge Framework for Product and Process Design

Computer Aided Product Design (CAPD) is widely used in process system engineering as a powerful tool for searching novel chemicals. The crucial steps in CAPD are the generation of candidate molecules and the estimation of properties, especially when complex molecular structures like flavors are sought. In this paper, we present a multiclass molecular knowledge framework which is based on chemical graph theory and chemical knowledge. Three kinds of functional groups are defined: elementary, basic and composed groups. These serve to generate four classes of knowledge that can be useful for property estimation and molecular design. An Input/output structure basing on XML language is defined to favour the interoperability between softwares