Literature DB >> 19115855

(1)H NMR-based metabolomic approach for understanding the fermentation behaviors of wine yeast strains.

Hong-Seok Son1, Geum-Sook Hwang, Ki Myong Kim, Eun-Young Kim, Frans van den Berg, Won-Mok Park, Cherl-Ho Lee, Young-Shick Hong.   

Abstract

(1)H NMR spectroscopy coupled with multivariate statistical analysis was used for the first time to investigate metabolic changes in musts during alcoholic fermentation and wines during aging. Three Saccharomyces cerevisiae yeast strains (RC-212, KIV-1116, and KUBY-501) were also evaluated for their impacts on the metabolic changes in must and wine. Pattern recognition (PR) methods, including PCA, PLS-DA, and OPLS-DA scores plots, showed clear differences for metabolites among musts or wines for each fermentation stage up to 6 months. Metabolites responsible for the differentiation were identified as valine, 2,3-butanediol (2,3-BD), pyruvate, succinate, proline, citrate, glycerol, malate, tartarate, glucose, N-methylnicotinic acid (NMNA), and polyphenol compounds. PCA scores plots showed continuous movements away from days 1 to 8 in all musts for all yeast strains, indicating continuous and active fermentation. During alcoholic fermentation, the highest levels of 2,3-BD, succinate, and glycerol were found in musts with the KIV-1116 strain, which showed the fastest fermentation or highest fermentative activity of the three strains, whereas the KUBY-501 strain showed the slowest fermentative activity. This study highlights the applicability of NMR-based metabolomics for monitoring wine fermentation and evaluating the fermentative characteristics of yeast strains.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19115855     DOI: 10.1021/ac802305c

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  9 in total

Review 1.  Wine and grape marc spirits metabolomics.

Authors:  Dimitra Diamantidou; Anastasia Zotou; Georgios Theodoridis
Journal:  Metabolomics       Date:  2018-12-11       Impact factor: 4.290

2.  Metabolic footprint analysis of metabolites that discriminate single and mixed yeast cultures at two key time-points during mixed culture alcoholic fermentations.

Authors:  Chuantao Peng; Tiago Viana; Mikael Agerlin Petersen; Flemming Hofmann Larsen; Nils Arneborg
Journal:  Metabolomics       Date:  2018-07-04       Impact factor: 4.290

3.  Unraveling the concentration-dependent metabolic response of Pseudomonas sp. HF-1 to nicotine stress by ¹H NMR-based metabolomics.

Authors:  Yangfang Ye; Xin Wang; Limin Zhang; Zhenmei Lu; Xiaojun Yan
Journal:  Ecotoxicology       Date:  2012-03-22       Impact factor: 2.823

4.  A metabolomic approach to the study of wine micro-oxygenation.

Authors:  Panagiotis Arapitsas; Matthias Scholz; Urska Vrhovsek; Stefano Di Blasi; Alessandra Biondi Bartolini; Domenico Masuero; Daniele Perenzoni; Adelio Rigo; Fulvio Mattivi
Journal:  PLoS One       Date:  2012-05-25       Impact factor: 3.240

5.  Translational Metabolomics: Current Challenges and Future Opportunities.

Authors:  Farhana R Pinu; Seyed Ali Goldansaz; Jacob Jaine
Journal:  Metabolites       Date:  2019-06-06

Review 6.  Yeast-Yeast Interactions: Mechanisms, Methodologies and Impact on Composition.

Authors:  Fanny Bordet; Alexis Joran; Géraldine Klein; Chloé Roullier-Gall; Hervé Alexandre
Journal:  Microorganisms       Date:  2020-04-20

Review 7.  NMR in the Service of Wine Differentiation.

Authors:  Marko Viskić; Luna Maslov Bandić; Ana-Marija Jagatić Korenika; Ana Jeromel
Journal:  Foods       Date:  2021-01-08

8.  (1)H NMR-based metabolite profiling of planktonic and biofilm cells in Acinetobacter baumannii 1656-2.

Authors:  Jinki Yeom; Ji-Hyun Shin; Ji-Young Yang; Jungmin Kim; Geum-Sook Hwang
Journal:  PLoS One       Date:  2013-03-06       Impact factor: 3.240

9.  MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics.

Authors:  Youzhong Liu; Kirill Smirnov; Marianna Lucio; Régis D Gougeon; Hervé Alexandre; Philippe Schmitt-Kopplin
Journal:  BMC Bioinformatics       Date:  2016-03-02       Impact factor: 3.169

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.