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dc.contributor.authorFuentes, S.
dc.contributor.authorTongson, E.
dc.contributor.authorTorrico, Damir
dc.contributor.authorGonzalez Viejo, C.
dc.date.accessioned2020-03-10T02:34:51Z
dc.date.available2019-12-30en
dc.date.issued2020-01
dc.date.submitted2019-12-27en
dc.identifier.issn2304-8158en
dc.identifier.urihttps://hdl.handle.net/10182/11565
dc.description.abstractWine aroma profiles are determinant for the specific style and quality characteristics of final wines. These are dependent on the seasonality, mainly weather conditions, such as solar exposure and temperatures and water management strategies from veraison to harvest. This paper presents machine learning modeling strategies using weather and water management information from a Pinot noir vineyard from 2008 to 2016 vintages as inputs and aroma profiles from wines from the same vintages assessed using gas chromatography and chemometric analyses of wines as targets. The results showed that artificial neural network (ANN) models rendered the high accuracy in the prediction of aroma profiles (Model 1; R = 0.99) and chemometric wine parameters (Model 2; R = 0.94) with no indication of overfitting. These models could offer powerful tools to winemakers to assess the aroma profiles of wines before winemaking, which could help adjust some techniques to maintain/increase the quality of wines or wine styles that are characteristic of specific vineyards or regions. These models can be modified for different cultivars and regions by including more data from vertical vintages to implement artificial intelligence in winemaking.en
dc.format.extent14en
dc.language.isoen
dc.publisherMDPI
dc.relationThe original publication is available from - MDPI - https://doi.org/10.3390/foods9010033en
dc.relation.urihttps://doi.org/10.3390/foods9010033en
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectwine qualityen
dc.subjectmachine learning modelingen
dc.subjectweatheren
dc.titleModeling Pinot noir aroma profiles based on weather and water management information using machine learning algorithms: A vertical vintage analysis using artificial intelligenceen
dc.typeJournal Article
lu.contributor.unitLincoln University
lu.contributor.unitFaculty of Agriculture and Life Sciences
lu.contributor.unitDepartment of Wine, Food and Molecular Biosciences
dc.identifier.doi10.3390/foods9010033en
dc.subject.anzsrc070604 Oenology and Viticultureen
dc.subject.anzsrc070105 Agricultural Systems Analysis and Modellingen
dc.subject.anzsrc090806 Wine Chemistry and Wine Sensory Scienceen
dc.relation.isPartOfFoodsen
pubs.issue1en
pubs.notesArticle number: 33en
pubs.organisational-group/LU
pubs.organisational-group/LU/Agriculture and Life Sciences
pubs.organisational-group/LU/Agriculture and Life Sciences/WFMB
pubs.publication-statusPublisheden
pubs.volume9en
dc.identifier.eissn2304-8158en
dc.rights.licenceAttributionen
dc.rights.licenceAttributionen
lu.identifier.orcid0000-0003-1482-2438


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