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Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality
The use of yeast starter cultures consisting of a blend of Saccharomyces cerevisiae and non-Saccharomyces yeasts has increased in recent years as a mean to address consumers’ demands for diversified wines. However, this strategy is currently limited by the lack of a comprehensive knowledge regarding...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8781278/ https://www.ncbi.nlm.nih.gov/pubmed/35056556 http://dx.doi.org/10.3390/microorganisms10010107 |
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author | Barbosa, Catarina Ramalhosa, Elsa Vasconcelos, Isabel Reis, Marco Mendes-Ferreira, Ana |
author_facet | Barbosa, Catarina Ramalhosa, Elsa Vasconcelos, Isabel Reis, Marco Mendes-Ferreira, Ana |
author_sort | Barbosa, Catarina |
collection | PubMed |
description | The use of yeast starter cultures consisting of a blend of Saccharomyces cerevisiae and non-Saccharomyces yeasts has increased in recent years as a mean to address consumers’ demands for diversified wines. However, this strategy is currently limited by the lack of a comprehensive knowledge regarding the factors that determine the balance between the yeast-yeast interactions and their responses triggered in complex environments. Our previous studies demonstrated that the strain Hanseniaspora guilliermondii UTAD222 has potential to be used as an adjunct of S. cerevisiae in the wine industry due to its positive impact on the fruity and floral character of wines. To rationalize the use of this yeast consortium, this study aims to understand the influence of production factors such as sugar and nitrogen levels, fermentation temperature, and the level of co-inoculation of H. guilliermondii UTAD222 in shaping fermentation and wine composition. For that purpose, a Central Composite experimental Design was applied to investigate the combined effects of the four factors on fermentation parameters and metabolites produced. The patterns of variation of the response variables were analyzed using machine learning methods, to describe their clustered behavior and model the evolution of each cluster depending on the experimental conditions. The innovative data analysis methodology adopted goes beyond the traditional univariate approach, being able to incorporate the modularity, heterogeneity, and hierarchy inherent to metabolic systems. In this line, this study provides preliminary data and insights, enabling the development of innovative strategies to increase the aromatic and fermentative potential of H. guilliermondii UTAD222 by modulating temperature and the availability of nitrogen and/or sugars in the medium. Furthermore, the strategy followed gathered knowledge to guide the rational development of mixed blends that can be used to obtain a particular wine style, as a function of fermentation conditions. |
format | Online Article Text |
id | pubmed-8781278 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87812782022-01-22 Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality Barbosa, Catarina Ramalhosa, Elsa Vasconcelos, Isabel Reis, Marco Mendes-Ferreira, Ana Microorganisms Article The use of yeast starter cultures consisting of a blend of Saccharomyces cerevisiae and non-Saccharomyces yeasts has increased in recent years as a mean to address consumers’ demands for diversified wines. However, this strategy is currently limited by the lack of a comprehensive knowledge regarding the factors that determine the balance between the yeast-yeast interactions and their responses triggered in complex environments. Our previous studies demonstrated that the strain Hanseniaspora guilliermondii UTAD222 has potential to be used as an adjunct of S. cerevisiae in the wine industry due to its positive impact on the fruity and floral character of wines. To rationalize the use of this yeast consortium, this study aims to understand the influence of production factors such as sugar and nitrogen levels, fermentation temperature, and the level of co-inoculation of H. guilliermondii UTAD222 in shaping fermentation and wine composition. For that purpose, a Central Composite experimental Design was applied to investigate the combined effects of the four factors on fermentation parameters and metabolites produced. The patterns of variation of the response variables were analyzed using machine learning methods, to describe their clustered behavior and model the evolution of each cluster depending on the experimental conditions. The innovative data analysis methodology adopted goes beyond the traditional univariate approach, being able to incorporate the modularity, heterogeneity, and hierarchy inherent to metabolic systems. In this line, this study provides preliminary data and insights, enabling the development of innovative strategies to increase the aromatic and fermentative potential of H. guilliermondii UTAD222 by modulating temperature and the availability of nitrogen and/or sugars in the medium. Furthermore, the strategy followed gathered knowledge to guide the rational development of mixed blends that can be used to obtain a particular wine style, as a function of fermentation conditions. MDPI 2022-01-05 /pmc/articles/PMC8781278/ /pubmed/35056556 http://dx.doi.org/10.3390/microorganisms10010107 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Barbosa, Catarina Ramalhosa, Elsa Vasconcelos, Isabel Reis, Marco Mendes-Ferreira, Ana Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality |
title | Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality |
title_full | Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality |
title_fullStr | Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality |
title_full_unstemmed | Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality |
title_short | Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality |
title_sort | machine learning techniques disclose the combined effect of fermentation conditions on yeast mixed-culture dynamics and wine quality |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8781278/ https://www.ncbi.nlm.nih.gov/pubmed/35056556 http://dx.doi.org/10.3390/microorganisms10010107 |
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