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

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.

Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data

“…Weather information for contrasting seasons for the same vineyard has been previously reported [9,50]. From all nine seasons, the most contrasting vintage was 2011, presenting higher and anomalous rainfall with lower irrigation input, resulting in a water balance of 673.7 mm and lowest solar exposure between veraison and harvest of 15.6 MJ m −2 .…”

“…Recent studies and developments have made it possible to implement new and emerging technologies to make these analyses more affordable and user-friendly. Some of these are, for example, the development of robotic pourers coupled with computer vision, machine learning and gas release analysis of beers [65,78] and sparkling wines [75], low-cost electronic noses for aroma profile and faults detection [73], low-cost near-infrared spectroscopy devices and color sensors that can be attached to smartphones with applications in food and beverages [50,79], and sensory analysis of consumers using a newly developed computer application, which can be downloaded by users and deployed in Android-based devices to obtain normal sensory analysis (self-reported) plus biometrics for emotional response and physiological changes of participants, such as heart rate, blood pressure [80], and body temperature among others [59].…”

“…As described in the publication from Fuentes et al [50], integrative weather information for each vintage was obtained from the Bureau of Meteorology (BoM). The derived weather parameters based on temperature and rainfall data consisted of (i) degree days from September to harvest (DD-S-H), (ii) maximum January temperature (MJT), (iii) mean maximum temperature from veraison to harvest (MeanMaxTV-H), (iv) mean minimum temperature from veraison to harvest (MeanMinTV-H), and (v) water balance (WB).…”

“…ML and other AI tools have been previously applied for different beverages such as beer, sparkling water, cider, and wines to assess their quality and consumer preferences after the beverage has been produced [49]. For Pinot Noir, recent research has shown the applicability of ML modeling using weather and management practices as inputs to model the aroma profile of resulting wines in a vertical vintage assessment [50]. This research offers an integrative ML tool based on near-infrared spectroscopy (NIR) from wines from a vertical vintage (Model 1) and the effects of seasonal weather patterns and water management practices (Model 2) to assess sensory profiles of wines before the winemaking process.…”

A Digital Approach to Evaluate the Effect of Berry Cell Death on Pinot Noir Wines’ Quality Traits and Sensory Profiles Using Non-Destructive Near-Infrared Spectroscopy

“…The soils in this vineyard are composed of sand, silt and clay in % up to 100 cm depth of 25, 12 and 45, respectively, supporting potato and grape growing, with a bulk density of 1.35 g cm 3 , 15% available water content to 100 cm and depth of soil of 75 cm. More details of the boutique vineyard can be found in the study by Fuentes et al [25]. Two blocks were selected, namely B92 (elevation 520 m.a.s.l) and B96 (elevation 545 m.a.s.l.…”