title: Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques.
abstract: Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.
authors.affiliation: Department of Analytical Chemistry, University of Seville, c/Profesor García González 1, E-41012 Seville, Spain.
authors.forename: Ana
authors.initials: A
authors.lastname: Palacios-Morillo
authors.forename: Angela
authors.initials: A
authors.lastname: Alcázar
authors.forename: Fernando
authors.initials: F
authors.lastname: de Pablos
authors.forename: José Marcos
authors.initials: JM
authors.lastname: Jurado
mesh_terms: D016002:Discriminant Analysis; D010363:Pattern Recognition, Automated / Q000191:economics / Q000379:methods*; D025341:Principal Component Analysis; D013056:Spectrophotometry, Ultraviolet / Q000191:economics / Q000379:methods*; D013662:Tea / Q000737:chemistry*; D013997:Time Factors
chemical_list: D013662:Tea