GiESCO 2019 banner
IVES 9 IVES Conference Series 9 GiESCO 9 GiESCO 2019 9 Aroma and quality assessment for vertical vintages using machine learning modelling based on weather and management information

Aroma and quality assessment for vertical vintages using machine learning modelling based on weather and management information

Abstract

Context and purpose of the study ‐ Wine quality traits are usually given by parameters such as aroma profile, total acidity, alcohol content, colour and phenolic content, among others. These are highly dependent on the weather conditions during the growing season and management strategies. Therefore, it is important to develop predictive models using machine learning (ML) algorithms to assess and predict wine quality traits before the winemaking process.

Material and methods ‐ Samples in duplicates of Pinot Noir wines from vertical vintages (2008 to 2013) of the same winery located in Macedon Ranges, Victoria, Australia were used to assess different chemical analytics such as i) aromas using gas chromatography – mass spectrometry, ii) color density, iii) color hue, iv) degree of red pigmentation, v) total red pigments, vi) total phenolics, vii) pH, viii) total acidity (TA), and ix) alcohol content. Data from weather conditions from the specific vintages were obtained both from the bureau of meteorology (BoM) and the Australian Wine Availability Project (AWAP) climate databases. Such data consisted of: i) solar exposure from veraison to harvest (V‐H), ii) solar exposure from September to harvest (S‐H), iii) maximum January solar exposure, iv) degree days from S‐H, v) maximum January evaporation, vi) mean maximum temperature from veraison to harvest, vii) mean minimum temperature from V‐H, viii) water balance from S‐H, ix) solar exposure from V‐H, x) degree hour accumulation with base 25 – 30 °C, xi) degree hour accumulation with base 25 °C, xii) degree hour accumulation with base 30 °C, xiii) degree hour accumulation with base 35 °C, and xiv) total cumulative degree days accumulation with base 10 °C. All data were used to develop two machine learning (ML) regression models using Matlab® R2018b. The best models obtained were using artificial neural networks (ANN) with the Levenberg‐Marquardt algorithm with 5 neurons for Model 1 and 9 neurons for Model 2. Model 1 was developed using the 14 parameters from the weather data as inputs to predict 21 aromas found in the wines from the six different vinatges. Model 2 was developed using the same 14 parameters from weather data and the eight chemical parameters as targets and outputs.

Results ‐ Both models obtained presented very high accuracy to predict wine quality trait parameters. Model 1 had an overall correlation coefficient R = 0.99 with a high performance based on the mean squared error (MSE = 0.01), while Model 2 had an overall correlation coefficient R = 0.98 with a high performance (MSE = 0.03). These models would aid in the prediction of wine quality traits before its production, which would give anticipated information to winemakers about the product they would obtain to make early decisions on wine style variations.

DOI:

Publication date: June 22, 2020

Issue: GiESCO 2019

Type: Article

Authors

Sigfredo FUENTES, Claudia GONZALEZ VIEJO, Xiaoyi WANG, Damir D. TORRICO

School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, VIC 3010, Australia

Contact the author

Keywords

wine quality, machine learning, weather, aromas

Tags

GiESCO 2019 | IVES Conference Series

Citation

Related articles…

Water deficit differentially impacts the performances and the accumulation of grape metabolites of new varieties tolerant to fungi

The use of resistant varieties is a long-term but promising solution to reduce chemical input in viticulture. Several important breeding programs in Europe and abroad are now releasing a range of new hybrids performing well regarding fungi susceptibility and producing good quality wines. Unfortunately, insufficient attention is paid by the breeders to the adaptation of these varieties to climatic changes, notably to the increased climatic demand and water deficit (WD). Thus, prior to the adoption of such varieties by the wine industry in Mediterranean regions, there is a need to consider their suitability to WD. This study aimed to characterize the different drought-strategies adopted by 6 new resistant varieties selected by INRAE in comparison to Syrah. To allow the assessment of long-term impacts of WD, field-grown vines were exposed to contrasted WD from 2018 to 2021 under a semi-arid Mediterranean climate. A gradient of WD was applied in the field and controlled through plant measurements at the single plant level. Grape development was non-destructively monitored to determine the arrest of berry phloem unloading. The impacts of WD on berry composition, including water, primary metabolites (sugars, organic acids), secondary metabolites (anthocyanins, thiols precursors) and main cations contents, were assessed at this specific stage. Results showed different varietal responses during the year and inter-annual acclimation in terms of plant water use efficiency, biomass accumulation, as well as yield components and berry composition. WD differentially reduced the accumulation of primary metabolites at plant and berry levels, but it little changed their concentrations in the fruits at the ripe stage. Moreover, WD differentially impacted the accumulation of secondary metabolites and major cations between the varieties. In the talk, we’ll present the main results regarding the WD impacts on fruit metabolites and enlarge the reflection about the practical assessment of the grapevine acclimation to WD.

Effect of one-year cover crop and arbuscular mycorrhiza inocululation in the microbial soil community of a vineyard

The microbial composition of the soil is an important factor to consider in viticulture, since its influence on the “terroir” and on the organoleptic properties of the wine have been demonstrated. Different agronomic techniques have the potential to modify the composition and functionality of the soil microbial community. Maintaining green covers is known to increase soil microbial diversity. The direct application of inoculum of beneficial microorganisms to the soil has also been used to increase their abundance. However, the environmental conditions of each site seem to have a determining weight in the result of these practices. In this study, we compared the effect on the microbial community of a cover crop with legumes in autumn and the inoculation of grapevines with commercial inoculum bases on Rhizophagus irregularis and Funeliformis mosseae in the previous spring. The study has been carried out in a vineyard in Binissalem, Mallorca, Spain. After applying the treatments, we will analyze the soil microbial communities using the data obtained from Illumina amplification of soil DNA from the 16S and ITS regions to analyze bacteria and fungi community, respectively. In addition, we will record the physicochemical characteristics of the soil at each sampling point. The result showed that agronomic management, in the short term, has less influence than soil characteristics on the composition of the soil microbiome. With these results, we can conclude that in a vineyard, agricultural techniques should focus on improving the characteristics of the soil to improve the biodiversity of the soil microbiota.

An analytical framework to site-specifically study climate influence on grapevine involving the functional and Bayesian exploration of farm data time series synchronized using an eGDD thermal index

Climate influence on grapevine physiology is prevalent and this influence is only expected to increase with climate change. Although governed by a general determinism, climate influence on grapevine physiology may present variations according to the terroir. In addition, these site-specific differences are likely to be enhanced when climate influence is studied using farm data. Indeed, farm data integrate additional sources of variation such as a varying representativity of the conditions actually experienced in the field. Nevertheless, there is a real challenge in valuing farm data to enable grape growers to understand their own terroir and consequently adapt their practices to the local conditions. In such a context, this article proposes a framework to site-specifically study climate influence on grapevine physiology using farm data. It focuses on improving the analysis of time series of weather data. The analytical framework includes the synchronization of time series using site-specific thermal indices computed with an original method called Extended Growing Degree Days (eGDD). Synchronized time series are then analyzed using a Bayesian functional Linear regression with Sparse Steps functions (BLiSS) in order to detect site-specific periods of strong climate influence on yield development. The article focuses on temperature and rain influence on grape yield development as a case study. It uses data from three commercial vineyards respectively situated in the Bordeaux region (France), California (USA) and Israel. For all vineyards, common periods of climate influence on yield development were found. They corresponded to already known periods, for example around veraison of the year before harvest. However, the periods differed in their precise timing (e.g. before, around or after veraison), duration and correlation direction with yield. Other periods were found for only one or two vineyards and/or were not referred to in literature, for example during the winter before harvest.

Effect of multi-level and multi-scale spectral data source on vineyard state assessment

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

Late season canopy management practices to reduce sugar loading and improve color profile of Cabernet-Sauvignon grapes and wines in the high irradiance and hot conditions of California Central Valley

Global warming is accelerating grape ripening, leading to unbalanced wines from fruit with high sugar content but poor aroma and colour development. Reducing the size of the photosynthetic apparatus after veraison has been shown to delay technological ripeness in cool climates, but methods have not been tested in areas with high irradiance and temperature where fruit exposure could have disastrous effects on berry composition. In this Cabernet-Sauvignon trial, we compared the application of an antitranspirant (pinolene), to severe canopy topping and above bunch zone leaf removal, all performed at mid-ripening, with an untouched control. We monitored the vines weekly by measuring stem water potential, gas exchange, fruit zone light exposure. We sampled berries to measure berry weight, total soluble solids, pH, titratable acidity, and the anthocyanin profile. At harvest, we assessed yield components, measured carbon isotope discrimination, rated sunburn on clusters, and produced experimental wines. We submitted harvest samples to metabolomic profiling through PFP-Q Exactive MS/MS and wines to sensory analysis. Application of the antitranspirant significantly reduced stomatal conductance and assimilation rate but did not affect the stem water potential. Inversely, leaf removal and topping increased water potential but did not affect leaf gas exchange. The late topping was the only treatment able to decrease sugar content (up to 2Bx), increase titratable acidity and pH, and improve anthocyanin content because of lower degradation of di-hydroxylated forms. Late leaf removal above the bunch zone increased lightning conditions in the canopy and produced the most significant damage on fruits. Yield components were not affected. This work suggests that late-season canopy management can effectively control ripening speeds and improve grapes and wines. Still, the effect on grape exposure in a critical time must be well balanced to avoid problems with the appropriate technique.