Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence.

Sigfredo Fuentes,Eden Tongson,Claudia Gonzalez Viejo,Damir D Torrico

doi:10.3390/foods9010033

Sigfredo Fuentes, Eden Tongson + Show 2 more

Open Access

https://doi.org/10.3390/foods9010033

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Abstract

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.

Highlights

Wine quality traits are difficult to assess in a rapid and objective way in vineyards, especially before winemaking
The extremes can be considered for low-quality wines produced in the 2010–2011 vintage due to heavy rains before harvest, which negatively affects the quality traits in berries and wine [47,48]; this low-quality assessment was obtained from anecdotal information from points received in those particular years and the sensory analysis conducted by the vineyard studied
Artificial intelligence techniques can be implemented in the wine industry from readily available weather and management practices data to assess quality traits in final wines

Summary

Introduction

Wine quality traits are difficult to assess in a rapid and objective way in vineyards, especially before winemaking. Quality assessments that are performed in the wine industry are related to the acidity and sugar content in berries (Brix or Baume) to assess maturity [1,2]. This assessment only gives information about the amount of alcohol and acidity in the final wine through fermentation. Higher temperatures are compressing phenological stages, resulting in earlier harvest during hotter months around the globe [5,6,7,8] This phenomenon produces a double global warming effect in

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