4.6 Article

The analysis of aroma/flavor compounds in green tea using ice concentration linked with extractive stirrer

Journal

JOURNAL OF CHROMATOGRAPHY A
Volume 1518, Issue -, Pages 8-14

Publisher

ELSEVIER
DOI: 10.1016/j.chroma.2017.08.049

Keywords

Green tea; Food flavor analysis; Aroma analysis; ICECLES; SBSE; GC-MS

Funding

  1. Qassim University (QU)
  2. State of South Dakota
  3. U.S. Joint Executive Office for Chem-Bio Defense, Joint Program Management Protection [W911SR-09-0059]

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Worldwide, green tea is one of the most popular beverages. It promotes blood circulation, liver function, and lowers the risk of cancer and cardiovascular diseases. This drink is characterized by the distinctive odors and flavors produced by its constituent compounds, with its value predicated on the amount and type of constituents extracted from the tea leaves during brewing. Ice concentration linked with extractive stirrer (ICECLES) is a novel sample preparation technique, especially applicable for the extraction of relatively polar compounds while retaining excellent extraction efficiencies for non-polar compounds. In this study, ICECLES was used to prepare green tea for analysis of aroma/flavor compounds by gas chromatography-mass spectrometry (GC MS). ICECLES performed very well, revealing 301 constituents as compared to 245 for SBSE (i.e., 56 more constituents were detected via ICECLES). Moreover, ICECLES produced stronger signal to noise ratios for all except 4 of 301 constituents, with a maximum signal enhancement of 19. Of the constituents which were only detectable using ICECLES, some very important aroma/flavor and/or medicinal compounds were easily identified, including furfural, furfural alcohol, maltol, eugenol, 2-methylpyrazine, phenethyl alcohol, 2,6-dimethoxyphenol, and alpha-terpineol. Overall, we confirmed that ICECLES sample preparation followed by GC MS consistently allowed more complete green tea aroma/flavor analysis, especially for relatively polar compounds, some of which are critical for flavor quality. (C) 2017 Elsevier B.V. All rights reserved.

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