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Machine learning techniques disclose the combined effect of fermentation conditions on yeast mixed-culture dynamics and wine quality

2022-01Artigo científico Acesso aberto
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dc.contributor.authorBarbosa, Catarina
dc.contributor.authorRamalhosa, Elsa
dc.contributor.authorVasconcelos, Isabel
dc.contributor.authorReis, Marco
dc.contributor.authorMendes-Ferreira, Ana
dc.date.accessioned2022-01-13T11:59:02Z
dc.date.available2022-01-13T11:59:02Z
dc.date.issued2022-01
dc.description.abstractThe 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/microorganisms10010107pt_PT
dc.identifier.eid85122221425
dc.identifier.issn2076-2607
dc.identifier.pmcPMC8781278
dc.identifier.pmid35056556
dc.identifier.urihttp://hdl.handle.net/10400.14/36454
dc.identifier.wos000757354300001
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAroma productionpt_PT
dc.subjectCentral composite designpt_PT
dc.subjectNitrogenpt_PT
dc.subjectNon-Saccharomyces yeastspt_PT
dc.subjectSugarpt_PT
dc.subjectSupervised and unsupervised machine learningpt_PT
dc.subjectTemperaturept_PT
dc.titleMachine learning techniques disclose the combined effect of fermentation conditions on yeast mixed-culture dynamics and wine qualitypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue1pt_PT
oaire.citation.titleMicroorganismspt_PT
oaire.citation.volume10pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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