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

• Published in: Food chemistry Vol. 127; no. 3; pp. 1323 - 1328
• Main Authors: Cimpoiu, Claudia, Cristea, Vasile-Mircea, Hosu, Anamaria, Sandru, Mihaela, Seserman, Liana
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2011; Elsevier
• Subjects: Antioxidant activity; Antioxidants; Artificial neural networks; Biological and medical sciences; Black tea; Catechins; Classification; Coffee, tea and other stimulative beverage industries; Flavonoids; Food industries; Foods; Fundamental and applied biological sciences. Psychology; Learning theory; Methyl-xanthines; Neural networks; Tea; Methyl-xanthines; Tea; Catechins; Flavonoids; Antioxidant activity; Artificial neural networks; Classification; Prediction; Neural network; Antioxidant
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2011.01.091

► Characterisation and classification of some teas using artificial neural networks. ► Relationship between the total antioxidant activity and the flavonoids, catechins and methyl-xanthines content. ► Tea classification in various classes. 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.