Literature DB >> 11714294

Headspace solid phase microextraction (SPME) analysis of flavor compounds in wines. Effect of the matrix volatile composition in the relative response factors in a wine model.

S Rocha1, V Ramalheira, A Barros, I Delgadillo, M A Coimbra.   

Abstract

The application of headspace solid phase microextraction (SPME) for flavor analysis has been studied. Headspace SPME sampling was tested for nine common wine flavor compounds in 10% (v/v) aqueous ethanol: linalool, nerol, geraniol, 3-methyl-1-butanol, hexanol, 2-phenylethanol, ethyl hexanoate, ethyl octanoate, and ethyl decanoate. The chemical groups (monoterpenoids, aliphatic and aromatic alcohols, and esters) showed specific behavior in SPME analysis. SPME sampling parameters were optimized for these components. Relative response factors (RRFs), which establish the relationship between the concentration of the compound in the matrix liquid solution and the GC peak area, were estimated for all compounds. Log(10)(RRF) varied from 0 (3-methyl-1-butanol) to 3 (ethyl decanoate), according to their molecular weight. Quantification by SPME was shown to be highly dependent on the matrix composition; the compounds with higher RRF were the less affected. As a consequence, the data obtained with this methodology should be used taking into consideration these limitations, as shown in the analysis of four monovarietal Bairrada white wines (Arinto, Bical, Cerceal, and Maria Gomes).

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Year:  2001        PMID: 11714294     DOI: 10.1021/jf010566m

Source DB:  PubMed          Journal:  J Agric Food Chem        ISSN: 0021-8561            Impact factor:   5.279


  8 in total

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Authors:  Dong-Kyu Lee; TacGhee Yi; Kyung-Eun Park; Hyun-Joo Lee; Yun-Kyoung Cho; Seul Ji Lee; Jeongmi Lee; Jeong Hill Park; Mi-Young Lee; Sun U Song; Sung Won Kwon
Journal:  Sci Rep       Date:  2014-10-09       Impact factor: 4.379

2.  Second order kinetic modeling of headspace solid phase microextraction of flavors released from selected food model systems.

Authors:  Jiyuan Zhang; Mun-Wai Cheong; Bin Yu; Philip Curran; Weibiao Zhou
Journal:  Molecules       Date:  2014-09-04       Impact factor: 4.411

3.  Volatiles from the Mandibular Gland Reservoir Content of Colobopsis explodens Laciny and Zettel, 2018, Worker Ants (Hymenoptera: Formicidae).

Authors:  Michaela Hoenigsberger; Alexey G Kopchinskiy; Christoph Bueschl; Alexandra Parich; Alice Laciny; Herbert Zettel; Kamariah A Salim; Linda Bl Lim; Irina S Druzhinina; Rainer Schuhmacher
Journal:  Molecules       Date:  2019-09-24       Impact factor: 4.411

Review 4.  Aroma Clouds of Foods: A Step Forward to Unveil Food Aroma Complexity Using GC × GC.

Authors:  Sílvia M Rocha; Carina Pedrosa Costa; Cátia Martins
Journal:  Front Chem       Date:  2022-03-01       Impact factor: 5.221

5.  Study of Consumer Liking of Six Chinese Vinegar Products and the Correlation between These Likings and the Volatile Profile.

Authors:  Shan Liang; Ying Liu; Shao Yuan; Yixuan Liu; Baoqing Zhu; Min Zhang
Journal:  Foods       Date:  2022-07-26

6.  Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS.

Authors:  Bei Li; Miao Liu; Feng Lin; Cui Tai; Yanfei Xiong; Ling Ao; Yumin Liu; Zhixin Lin; Fei Tao; Ping Xu
Journal:  Molecules       Date:  2022-09-22       Impact factor: 4.927

7.  SPME Method Optimized by Box-Behnken Design for Impact Odorants in Reduced Alcohol Wines.

Authors:  Bithika Saha; Rocco Longo; Peter Torley; Anthony Saliba; Leigh Schmidtke
Journal:  Foods       Date:  2018-08-10

8.  Development and Evaluation of a HS-SPME GC-MS Method for Determining the Retention of Volatile Phenols by Cyclodextrin in Model Wine.

Authors:  Chao Dang; Kerry L Wilkinson; Vladimir Jiranek; Dennis K Taylor
Journal:  Molecules       Date:  2019-09-21       Impact factor: 4.411

  8 in total

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