Novas estratégias para classificação simultânea do tipo e origem geográfica de chás

• Ano de defesa: 2013
• Autor(a) principal: Diniz, Paulo Henrique Gonçalves Dias
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraí­ba; BR; Química; Programa de Pós-Graduação em Química; UFPB
• Palavras-chave em Português: Camellia sinensis; Classificação; Imagens digitais; Espectroscopia NIR; Composição química; SIMCA; PLS-DA; SPA-LDA; Classification; Digital images; NIR spectroscopy; Chemical composition; CIENCIAS EXATAS E DA TERRA::QUIMICA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/7145

Tea has an economic and cultural importance, not only for producers and consumers, but also for a scientific interest. The organoleptic quality of the Camellia sinensis infusion depends on the nature and amount of several secondary metabolites (such as polyphenols, caffeine, amino acids, etc.), which can be directly related to the geographical origin of the tea plants. These components are the basis of the economic value of teas and its beneficial effects on human health. Therefore, there is a growing consumer s interest in high quality teas with a distinct geographical identity. In last decades, the analytical methods employing modern instrumental techniques have become more sensitive, reliable and fast. However, these techniques have advantages and limitations for the application in the analyses of the tea quality and their geographic origins. Thus, a combination of different techniques could be more useful than relying on a single method. Following these principles, we propose three new strategies for simultaneous classification of teas according to both the type (green and black) and geographic origin (Argentina, Brazil and Sri Lanka). The proposed methodologies employ the use of (1) digital images, (2) NIR spectroscopy, and (3) chemical composition (moisture, ash, caffeine, total polyphenols, fluoride and fifteen metals (Na, Mg, Al, P, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd and Pb) in both tea leaves and infusions). A correct classification of all tea samples (100% of correct classification) was always obtained using the Linear Discriminant Analysis associated with the variable selection technique taken by the Successive Projections Algorithm. Soft Independent Modeling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were also used. The proposed strategies might be useful for the development of legislation for the quality control of teas in Brazil, which is still lacking