Prediction of the aroma quality and the threshold values of some pyrazines using artificial neural networks.

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.

Threshold-based structure-activity relationships of pyrazines with bell-pepper flavor.

Quantitative structure activity relationships (QSAR) and comparative molecular field analysis (CoMFA) are applied in order to explain the aroma of 46 bell-pepper aroma compounds. Biological activities log(1/c) values are used, where c stands for the detection threshold value of the aroma compound in water. Results of conventional QSAR and CoMFA are both satisfactory in statistical significance and predictive ability. We construct a qualitative model using the graphic features of CoMFA together with the results of "classical" QSAR analysis, which is performed by multiple linear regression. Finally, the human olfactory detection threshold values of excluded pyrazines are successfully predicted. This makes CoMFA and QSAR two important tools for designing new aroma compounds and in elucidating the mechanism of odor-receptor interaction.