Prediction of the aroma quality and the threshold values of some pyrazines using artificial neural networks
- Author(s)
- Bettina Wailzer, Johanna Klocker, Gerhard Buchbauer, Gerhard Ecker, Karl Peter Wolschann
- Abstract
An artificial neural network is used to predict both the classification of aroma compounds and their flavor impression threshold values for a series of pyrazines. The classification set consists of 98 compounds (32 green, 43 bell-pepper, and 23 nutty smelling pyrazines), and the regression sets consist of 24 green and 37 bell-pepper odorous pyrazines. The best classification of the three aroma impressions (93.7%) is obtained by using a multilayer perceptron network architecture. To predict the threshold values of bell-pepper fragrance, a standard Pearson R correlation coefficient of 0.936 for the training set, 0.912 for the verification set, and 0.926 for the test set is received with two hidden layers consisting of two and one neurons. The network for the threshold prediction of the class of green-smelling pyrazines with one hidden layer containing three neurons turns out to be the best with a standard Pearson R correlation coefficient of 0.859 for the training, 0.918 for the verification, and 0.948 for the test set. These good correlations show that artificial neural networks are versatile tools for the classification of aroma compounds.
- Organisation(s)
- Department of Theoretical Chemistry
- External organisation(s)
- Universität Wien
- Journal
- Journal of Medicinal Chemistry
- Volume
- 44
- Pages
- 2805-2813
- No. of pages
- 9
- ISSN
- 0022-2623
- DOI
- https://doi.org/10.1021/jm001129m
- Publication date
- 2001
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 301209 Pharmacy, 104022 Theoretical chemistry, 104009 Food chemistry
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/prediction-of-the-aroma-quality-and-the-threshold-values-of-some-pyrazines-using-artificial-neural-networks(70392daf-e0d0-4fb5-89a9-826a9f79d9d8).html