Bayesian neural networks for aroma classification.
Bayesian Neural Networks (BNNs) are investigated to test their potential to distinguish between different aroma impressions. Special attention is thereby drawn on mixed aroma impressions, resulting from the flavor description of a single compound with more than one aroma quality. The structures of 133 pyrazine-derived aroma compounds as well as their aroma descriptions are selected for comparison. The information fed into the neural networks is based on molecular descriptors calculated from the geometrically optimized chemical structures. While in the case of the Probabilistic Neural Network (PNN) the networks' output consists of a categorical variable, the output for the General Regression Neural Network (GRNN) is defined in a numerical way. The best models attain comparable performance with a correct prediction of 90.8% of the cases for PNN and 89.9% for GRNN, respectively. Comparison of the BNN results to those obtained by Multiple Linear Regression (MLR) points out that the nonlinear methods work significantly better on the studied problem and that BNNs can be applied to multiple-category problems in structure-flavor relationships with good accuracy.
Klocker
Johanna
J
Institute of Theoretical Chemistry and Structural Biology, University of Vienna, Waehringer Strasse 17, A-1090 Vienna, Austria.
Wailzer
Bettina
B
Buchbauer
Gerhard
G
Wolschann
Peter
P
D001499:Bayes Theorem; D016014:Linear Models; D015394:Molecular Structure; D016571:Neural Networks, Computer; D009812:Odorants / Q000032:analysis*; D011719:Pyrazines / Q000737:chemistry* / Q000145:classification
D011719:Pyrazines