Cargando…
Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence
Wine 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 lear...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7023421/ https://www.ncbi.nlm.nih.gov/pubmed/31905992 http://dx.doi.org/10.3390/foods9010033 |
_version_ | 1783498245956173824 |
---|---|
author | Fuentes, Sigfredo Tongson, Eden Torrico, Damir D. Gonzalez Viejo, Claudia |
author_facet | Fuentes, Sigfredo Tongson, Eden Torrico, Damir D. Gonzalez Viejo, Claudia |
author_sort | Fuentes, Sigfredo |
collection | PubMed |
description | Wine 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. |
format | Online Article Text |
id | pubmed-7023421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70234212020-03-12 Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence Fuentes, Sigfredo Tongson, Eden Torrico, Damir D. Gonzalez Viejo, Claudia Foods Article Wine 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. MDPI 2019-12-30 /pmc/articles/PMC7023421/ /pubmed/31905992 http://dx.doi.org/10.3390/foods9010033 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fuentes, Sigfredo Tongson, Eden Torrico, Damir D. Gonzalez Viejo, Claudia Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence |
title | Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence |
title_full | Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence |
title_fullStr | Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence |
title_full_unstemmed | Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence |
title_short | Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence |
title_sort | modeling pinot noir aroma profiles based on weather and water management information using machine learning algorithms: a vertical vintage analysis using artificial intelligence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7023421/ https://www.ncbi.nlm.nih.gov/pubmed/31905992 http://dx.doi.org/10.3390/foods9010033 |
work_keys_str_mv | AT fuentessigfredo modelingpinotnoiraromaprofilesbasedonweatherandwatermanagementinformationusingmachinelearningalgorithmsaverticalvintageanalysisusingartificialintelligence AT tongsoneden modelingpinotnoiraromaprofilesbasedonweatherandwatermanagementinformationusingmachinelearningalgorithmsaverticalvintageanalysisusingartificialintelligence AT torricodamird modelingpinotnoiraromaprofilesbasedonweatherandwatermanagementinformationusingmachinelearningalgorithmsaverticalvintageanalysisusingartificialintelligence AT gonzalezviejoclaudia modelingpinotnoiraromaprofilesbasedonweatherandwatermanagementinformationusingmachinelearningalgorithmsaverticalvintageanalysisusingartificialintelligence |