Prediction of the Aroma Quality and the Threshold Values of Some Pyrazines Using Artificial Neural Networks

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.

Artificial neural networks: a novel tool for detecting GMO

“…Vegetable oil stability was successfully predicted by ANN when partial oil composition is known (Przybylski and Zambiazi 2005). The application of ANN with nominal output allowed the discrimination between different classes of aroma impressions of pyrazine-derived flavor compounds (Wailzer et al 2001). Metal concentrations in wines were used to differentiate sweet wines using ANN (Frías et al 2002).…”

Microwave-Assisted Synthesis of Bioactive Six-Membered Heterocycles and Their Fused Analogues

“…This method can be used in parallel and also in combinatorial chemistry for the preparation of different libraries of highly functionalized piperazines, since the synthesis are completed within short times. Pyrazine is a six membered hetero-aromatic ring usually found in the structure of numerous natural products and synthetic compounds, some of which are used in the food industry for their flavor properties [222][223][224]. Moreover, they are versatile synthetic intermediates [225], and many functionalized pyrazines possess pharmacological activities, such as antiviral [226,227] (242, 243), ATR kinase inhibitor [228] (244), antitumor [229], vascular endothelial growth factor inhibitory activity [230], or as epithelial sodium channel blockers [231] (Figure 15).…”

“…Moreover, they are versatile synthetic intermediates [225], and many functionalized pyrazines possess pharmacological activities, such as antiviral [226,227] (242, 243), ATR kinase inhibitor [228] (244), antitumor [229], vascular endothelial growth factor inhibitory activity [230], or as epithelial sodium channel blockers [231] (Figure 15). Pyrazine is a six membered hetero-aromatic ring usually found in the structure of numerous natural products and synthetic compounds, some of which are used in the food industry for their flavor properties [222][223][224]. Moreover, they are versatile synthetic intermediates [225], and many functionalized pyrazines possess pharmacological activities, such as antiviral [226,227] (242, 243), ATR kinase inhibitor [228] (244), antitumor [229], vascular endothelial growth factor inhibitory activity [230], or as epithelial sodium channel blockers [231] (Figure 15).…”

Classification of the fragrance properties of chemical compounds based on support vector machine and linear discriminant analysis

“…The best classi¼cation of the three fragrance impressions (93.7%) was obtained using a multi-layered perceptron network architecture. 15 All the previous studies attempted to produce accurate and predictive models using different descriptors or statistical methods. In the present study, we calculated a large number of descriptors using CODESSA software and presented a modelling approach based on support vector machine (SVM) to classify some organic compounds on the basis of their fragrance properties.…”