Antioxidant activity prediction and classification of some teas using artificial neural networks.

Autor: Cimpoiu C; 'Babeş-Bolyai' University, Faculty of Chemistry and Chemical Engineering, 11 Arany Janos, 400082 Cluj-Napoca, Romania. Electronic address: ccimpoiu@chem.ubbcluj.ro., Cristea VM; 'Babeş-Bolyai' University, Faculty of Chemistry and Chemical Engineering, 11 Arany Janos, 400082 Cluj-Napoca, Romania., Hosu A; 'Babeş-Bolyai' University, Faculty of Chemistry and Chemical Engineering, 11 Arany Janos, 400082 Cluj-Napoca, Romania., Sandru M; 'Babeş-Bolyai' University, Faculty of Chemistry and Chemical Engineering, 11 Arany Janos, 400082 Cluj-Napoca, Romania., Seserman L; 'Babeş-Bolyai' University, Faculty of Chemistry and Chemical Engineering, 11 Arany Janos, 400082 Cluj-Napoca, Romania.
Jazyk: angličtina
Zdroj: Food chemistry [Food Chem] 2011 Aug 01; Vol. 127 (3), pp. 1323-8. Date of Electronic Publication: 2011 Jan 28.
DOI: 10.1016/j.foodchem.2011.01.091
Abstrakt: In order to characterise and to classify some teas a simple, rapid and economical method based on composition, antioxidant activity and artificial neural networks (ANNs) is proposed. For these purpose two types of ANN based applications have been developed: one for predicting the antioxidant activity and a second one for establishing the class of the teas. The complex relationship between the total antioxidant activity (AA) depending on the total flavonoids content (F), total catechins content (C) and total methyl-xanthines content (MX) of commercial teas was revealed by the first designed feed-forward ANN. Secondly, using a probabilistic ANN, successful tea classification in various classes (green tea, black tea and express black tea) was also performed.
(Copyright © 2011 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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