Terroir 2020 banner
IVES 9 IVES Conference Series 9 International Terroir Conferences 9 Terroir 2020 9 History and innovation of terroir 9 How geographical origin and vineyard management influence cv. Cabernet-Sauvignon in Chile – Machine learning based quality prediction

How geographical origin and vineyard management influence cv. Cabernet-Sauvignon in Chile – Machine learning based quality prediction

Abstract

Aims: The aims of this study were to i) characterize the impact of geographical origin and viticulture treatments on Chilean Cabernet-Sauvignon, and ii) develop machine learning models to predict its quality. 

Methods and Results: 100 vineyard plots representing the typical percentage distribution of geographical and viticulture impact factors on Chilean Cabernet-Sauvignon were monitored across two seasons, 2018 and 2019. Chemical analysis of grapes and wines included the quantification of phenolic compounds by liquid chromatography and UV-vis spectral measurements, aroma compounds by gas chromatography mass spectrometry (GC/MS), and maturity parameters. Spearman correlation and Principal component analysis (PCA) identified correlations of several non-volatile and volatile compounds with quality, mainly by means of their anthocyanins, flavonols, flavan‑3‑ols, total tannins and hydroxycinnamic acids. Furthermore by trans-2-hexenol, trans-3-hexenol, hexanal, 2-isobutyl-3-methoxypyrazine (IBMP), yeast assimilable nitrogen (YAN), total soluble solids and acidity. Experimental winemaking of 600 kg per plot followed a standardized procedure, and the wines were analyzed by an expert quality rating. A sensory quality profiling for the wines was performed through a Napping Ultra Flash Profile (UFP). It revealed the distinction of three different quality levels by mainly mouthfeel attributes, and fruity and green aromas. However, neither the observed correlations of chemical analysis and sensory quality ratings, nor origin or viticulture treatment could fully explain quality. Different clustering methods, namely k-means, k-medioids and spectral clustering were evaluated in order to find categories given by the chemical analysis data itself as unsupervised machine learning. Spectral clustering led to optimum results, and independently of sample origin and viticulture traits, quality ratings were characterized to be significantly different across the clusters allowing their interpretation as quality categories. 

Conclusions: 

Chilean Cabernet-Sauvignon quality is associated with chemical quality markers known for this variety in Australia and California, including phenolic compounds, C6 alcohols and aldehydes, IBMP, maturity parameters and YAN. However, evaluation of sensory quality is fairly subjective and viticulture treatments in practical application contain interdependency, therefore it is challenging to establish supervised models involving this data. The application of unsupervised spectral clustering is proposed as an objective quality classification approach, which can be trained using supervised models for predictive purposes.

Significance and Impact of the Study: There is a high industrial need for objective quality classification. For the first time chemical quality markers for Chilean Cabernet-Sauvignon were determined, and an unsupervised machine learning approach based on these markers could be proposed for objective quality classification.

DOI:

Publication date: March 19, 2021

Issue: Terroir 2020

Type: Video

Authors

Doreen Schober1*, Martin Legues1,2, Hugo Guidez3, Jose Carlos Caris Maldonado1, Sebastian Vargas1,  Alvaro Gonzalez Rojas1

1Center for Research and Innovation (CRI), Viña Concha y Toro, Ruta k-650 km 10, Pencahue, Región de Maule, Chile
2Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Macul, Región Metropolitana, Santiago, Chile
3Institut National Supérieur des Sciences Agronomiques, Agroalimentaires, Horticoles et du Paysage, Agrocampus Ouest Campus d´Angers, France

Contact the author

Keywords

Cabernet-Sauvignon, spectral clustering, quality, terroir, vineyard management

Tags

IVES Conference Series | Terroir 2020

Citation

Related articles…

Grape berry size is a key factor in determining New Zealand Pinot noir wine composition

Making high quality but affordable Pinot noir (PN) wine is challenging in most terroirs and New Zealand’s (NZ) situation is no exception. To increase the probability of making highly typical PN wines producers choose to grow grapes in cool climates on lower fertility soils while adopting labour intensive practices. Stringent yield targets and higher input costs necessarily mean that PN wine cost is high, and profitability lower, in line-priced varietal wine ranges. To understand the reasons why higher yielding vines are perceived to produce wines of lower quality we have undertaken an extensive study of PN in NZ. Since 2018, we established a network of twelve trial sites in three NZ regions to find individual vines that produced acceptable commercial yields (above 2.5kg per vine) and wines of composition comparable to “Icon” labels. Approximately 20% of 660 grape lots (N = 135) were selected from within a narrow juice Total Soluble Solids (TSS) range and made into single vine wines under controlled conditions. Principal Component Analysis of the vine, berry, juice and wine parameters from three vintages found grape berry mass to be most effective clustering variable. As berry mass category decreased there was a systematic increase in the probability of higher berry red colour and total phenolics with a parallel increase in wine phenolics, changed aroma fraction and decreased juice amino acids. The influence of berry size on wine composition would appear stronger than the individual effects of vintage, region, vineyard or vine yield. Our observations support the hypothesis that it is possible to produce PN wines that fall within an “Icon” benchmark composition range at yields above 2.5kg per vine provided that the Leaf Area:Fruit Weight ratio is above 12cm2 per g, mean berry mass is below 1.2g and juice TSS is above 22°Brix.

Anthocyanin profile is differentially affected by high temperature, elevated CO2 and water deficit in Tempranillo (Vitis vinifera L.) clones

Anthocyanin potential of grape berries is an important quality factor in wine production. Anthocyanin concentration and profile differ among varieties but it also depends on the environmental conditions, which are expected to be greatly modified by climate change in the future. These modifications may significantly modify the biochemical composition of berries at harvest, and thus wine typicity. Among the diverse approaches proposed to reduce the potential negative effects that climate change may have on grape quality, genetic diversity among clones can represent a source of potential candidates to select better adapted plant material for future climatic conditions. The effects of individual and combined factors associated to climate change (increase of temperature, rise of air CO2 concentration and water deficit) on the anthocyanin profile of different clones of Tempranillo that differ in the length of their reproductive cycle were studied. The aim was to highlight those clones more adapted to maintain specific Tempranillo typicity in the future. Fruit-bearing cuttings were grown in controlled conditions under two temperatures (ambient temperature versus ambient temperature + 4ºC), two CO2 levels (400 ppm versus 700 ppm) and two water regimes (well-watered versus water deficit), both in combination or independently, in order to simulate future climate change scenarios. Elevated temperature increased anthocyanin acylation, whereas elevated CO2 and water deficit favoured the accumulation of malvidin derivatives, as well as the acylation and tri-hydroxylation level of anthocyanins. Although the changes in anthocyanin profile observed followed a common pattern among clones, such impact of environmental conditions was especially noticeable in one of the most widely distributed Tempranillo clones, the accession RJ43.

Mechanisms involved in the heating of the environment by the aerodynamic action of a wind machine to protect a vineyard against spring frost

One of the main consequences of global warming is the rise of the mean temperature. Thus, the heat summation by the plants begins sooner in the early spring, and by cumulating growing degree-days, phenological development tends to happen earlier. However, spring frost is still a recurrent phenomenon causing serious damages to buds and therefore, threatening the harvests of the winegrowers. The wind machine is a solution to protect fruit crops against spring frost that is increasingly used. It is composed of a 10-m mast with a blowing fan at its peak. By tapping into the strength of the nocturnal thermal inversion, it sweeps the crop by propelling warm air above to the ground. Thus, stratification is momentarily suppressed. Furthermore, the continuous action of the machine, alone or in synergy, or the addition of a heater allow the bud to be bathed in a warmer environment. Also, the punctual action of the tower’s warm gust reaches the bud directly at each rotation period. All these actions allow the bud to continuously warm up, but with different intensities and over a different period. Although there is evidence of the effectiveness of the wind machines, the thermal transfers involved in those mechanisms raise questions about their true nature. Field measurements based on ultrasonic anemometers and fast responding thermocouples complemented by laboratory measurements on a reduced scale model allow to characterize both the airflow produced by the wind machine and the local temperature in its vicinity. Those experiments were realized in the vineyard of Quincy, in the framework of the SICTAG project. In the future paper, we will detail the aeraulic characterization of the wind machine and the thermal effects resulting from it and we will focus on how the wind machine warms up the local atmosphere and enables to reduce the freezing risk.

VineyardFACE: Investigation of a moderate (+20%) increase of ambient CO2 level on berry ripening dynamics and fruit composition

Climate change and rising atmospheric carbon dioxide concentration is a concern for agriculture, including viticulture. Studies on elevated carbon dioxide have already been on grapevines, mainly taking place in greenhouses using potted plants or using field grown vines under higher CO2 enrichment, i.e. >650 ppm. The VineyardFACE, located at Hochschule Geisenheim University, is an open field Free Air CO2 Enrichment (FACE) experimental set-up designed to study the effects of elevated carbon dioxide using field grown vines (Vitis vinifera L. cvs. Riesling and Cabernet Sauvignon). As the carbon dioxide fumigation started in 2014, the long term effects of elevated carbon dioxide treatment can be investigated on berry ripening parameters and fruit metabolic composition.
The present study aims to investigate the effect on fruit composition under a moderate increase (+20%; eCO2) of carbon dioxide concentration, as predicted for 2050 on both Riesling and Cabernet Sauvignon. Berry composition was determined for primary (sugars, organic acids, amino acids) and secondary metabolites (anthocyanins). Special focus was given on monitoring of berry diameter and ripening rates throughout three growing seasons. Compared to previous results of the early adaptative phase of the vines [1], our results show little effect of eCO2 treatment on primary metabolites composition in berries. However, total anthocyanins concentration in berry skin was lower for eCO2 treatment in 2020, although the ratio between anthocyanins derivatives did not differ.
[1] Wohlfahrt Y., Tittmann S., Schmidt D., Rauhut D., Honermeier B., Stoll M. (2020) The effect of elevated CO2 on berry development and bunch structure of Vitis vinifera L. cvs. Riesling and Cabernet Sauvignon. Applied Science Basel 10: 2486

Downscaling of remote sensing time series: thermal zone classification approach in Gironde region

In viticulture, the challenges of local climate modelling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed: