Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 478830
Title Analytical Strategy Coupled with Response Surface Methodology To Maximize the Extraction of Antioxidants from Ternary Mixtures of Green, Yellow, and Red Teas (Camillia sinensis var. Sinensis)
Author(s) Granato, D.; Grevink, R.; Zielinski, R.; Nunes, D.S.; Ruth, S.M. van
Source Journal of Agricultural and Food Chemistry 62 (2014)42. - ISSN 0021-8561 - p. 10283 - 10296.
Department(s) RIKILT - BU Authenticity & Nutrients
Food Quality and Design
Publication type Refereed Article in a scientific journal
Publication year 2014
Keyword(s) ilex-paraguariensis - optimization - capacity - white - assay - polyphenols - catechins
Abstract This work aimed at using a simplex-centroid design to model the effects of green, yellow, and red tea mixtures (Camellia sinensis var. sinensis) on metal chelation activity, phenolic composition, antioxidant activity, and instrumental taste profile. The regression models that described the extraction of flavan-3-ols, o-diphenols, total phenolic compounds (TPC), free radical scavenging activity toward 2,2-diphenyl-1-picrylhydrazyl radical (DPPH), cupric ion reducing antioxidant activity (CUPRAC), and ferric reducing antioxidant power (FRAP) were significant, and data were fit satisfactorily (R2 > 80%). A mixture of green and red teas had a synergism in CUPRAC and TPC, whereas a mixture of yellow and red teas had a positive effect on CUPRAC and DPPH. An optimization was performed to maximize the antioxidant activity and flavan-3-ol content and to render a tea with mild bitterness, and results showed that a mixture of 14.81% green, 56.86% yellow, and 28.33% red teas would be the most suitable combination of factors.
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