Repository logo
 
Publication

Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality

2022Journal article Open access
Simple item page
dc.contributor.authorBarbosa, Catarina
dc.contributor.authorRamalhosa, Elsa
dc.contributor.authorVasconcelos, Isabel
dc.contributor.authorReis, Marco
dc.contributor.authorMendes-Ferreira, Ana
dc.date.accessioned2022-04-19T15:54:55Z
dc.date.available2022-04-19T15:54:55Z
dc.date.issued2022
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.sponsorshipThis work was carried out under the project SMARTWINE—Smarter wine fermentations: Integrating OMICS tools for the development of novel mixed-starter cultures for tailor-made wine production, with reference PTDC/AGR-TEC/3315/2014—POCI-01-0145-FEDER-016834, funded by the Foundation for Science and Technology and co-financed by the European Regional Development Fund (ERDF) through COMPETE 2020—Competitiveness and Internationalization Operational Program (POCI) and the Lisbon Regional Operational Program. The authors also acknowledge the support provided through FCT/MCTES from Biosystems and Integrative Sciences Institute (BioISI; UIDB/04046/2020), Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB: UIDB/04033/2020), Chemical Process Engineering and Forest Products Research Centre (CIEPQPF: UID/EQU/00102/2019) and Mountain Research Centre (CIMO: UID/AGR/00690/2020).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBarbosa, Catarina; Ramalhosa, Elsa; Vasconcelos, Isabel; Reis, Marco; Mendes-Ferreira, Ana (2022). Machine learning techniques disclose the combined effect of fermentation conditions on yeast mixed-culture dynamics and wine quality. Microorganisms. ISSN 2076-2607. 10:1, p. 1-20pt_PT
dc.identifier.doi10.3390/microorganisms10010107pt_PT
dc.identifier.issn2076-2607
dc.identifier.urihttp://hdl.handle.net/10198/25391
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationUID/AGR/00690/2020pt_PT
dc.relationBiosystems and Integrative Sciences Institute
dc.relationCentre for the Research and Technology of Agro-Environmental and Biological Sciences
dc.relationResearch Center for Chemical Processes and Forest Products
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSupervised and unsupervised machine learningpt_PT
dc.subjectNon-Saccharomyces yeastspt_PT
dc.subjectNitrogenpt_PT
dc.subjectSugarpt_PT
dc.subjectTemperaturept_PT
dc.subjectAroma productionpt_PT
dc.subjectCentral composite designpt_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.awardTitleBiosystems and Integrative Sciences Institute
oaire.awardTitleCentre for the Research and Technology of Agro-Environmental and Biological Sciences
oaire.awardTitleResearch Center for Chemical Processes and Forest Products
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FAGR-TEC%2F3315%2F2014/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04046%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04033%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEQU%2F00102%2F2019/PT
oaire.citation.issue1pt_PT
oaire.citation.startPage107pt_PT
oaire.citation.titleMicroorganismspt_PT
oaire.citation.volume10pt_PT
oaire.fundingStream9471 - RIDTI
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameRamalhosa
person.givenNameElsa
person.identifier.ciencia-id1A1D-FC05-A05D
person.identifier.orcid0000-0003-2503-9705
person.identifier.scopus-author-id6602978189
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationd74be6c0-1703-4f77-bd81-e289ec981ee3
relation.isAuthorOfPublication.latestForDiscoveryd74be6c0-1703-4f77-bd81-e289ec981ee3
relation.isProjectOfPublicationc7058e1c-9655-4c2b-ac9b-460069cd9932
relation.isProjectOfPublication0ee48f8e-8986-4565-a589-0a1688fb9fd4
relation.isProjectOfPublicationac4fb709-719a-450b-8c96-17592d46f5e9
relation.isProjectOfPublication175fd9b7-7a3d-48d5-8f70-f18e36b68854
relation.isProjectOfPublication.latestForDiscovery0ee48f8e-8986-4565-a589-0a1688fb9fd4

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Barbosa-2022-Machine-learning-techniques-disclos.pdf
Size:
6.53 MB
Format:
Adobe Portable Document Format
Download

Collections

ESA - Artigos em Revistas Indexados à WoS/Scopus