title: Aroma quality differentiation of pyrazine derivatives using self-organizing molecular field analysis and artificial neural network.
abstract: The encoding of various aroma impressions and the distinction between different aroma qualities are unsolved problems, as differences between aroma impressions can be described only in a qualitative but not in a quantitative manner. As a consequence, classifications of various aroma qualities cannot easily be performed by standard QSAR methods. To find a proper way to encode aroma impressions for SAR studies, a total of 50 pyrazine-based aroma compounds showing the aroma quality of earthy, green-earthy, or green are analyzed. Special attention is thereby turned on the mixed aroma impression green-earthy. Classifications on the whole data set as well as on smaller subsets are calculated using self-organizing molecular field analysis (SOMFA) and artificial neural networks (ANNs). SOMFA classifies between two or three aroma impressions, leading to models satisfying in predictive power. ANN analysis using multilayer perceptron network architecture with one hidden layer and nominal output as well as genetic regression neural network) with two hidden layers and numerical output both lead to a rather good performance rate of 94%.
authors.affiliation: Institute of Theoretical Chemistry and Molecular Biology, University of Vienna, Waehringer Strasse 17, A-1090 Vienna, Austria.
authors.forename: Johanna
authors.initials: J
authors.lastname: Klocker
authors.forename: Bettina
authors.initials: B
authors.lastname: Wailzer
authors.forename: Gerhard
authors.initials: G
authors.lastname: Buchbauer
authors.forename: Peter
authors.initials: P
authors.lastname: Wolschann
mesh_terms: D016571:Neural Networks, Computer; D009812:Odorants; D011719:Pyrazines / Q000032:analysis* / Q000737:chemistry* / Q000145:classification; D012903:Smell; D013329:Structure-Activity Relationship
chemical_list: D011719:Pyrazines