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…

Evaluation of climate change impacts at the Portuguese Dão terroir over the last decades: observed effects on bioclimatic indices and grapevine phenology

In the last decades the growers of the Portuguese Dão winegrowing region (center of Portugal) are experiencing changes in climate that are influencing either grape phenology berry health and ripening. Aiming to study the relationships between climate indices (CI), seasonal weather and grapevine phenology, in this work long-term climate and phenological data collected at the experimental vineyard of the Portuguese Dão research centre between 1958 and 2019 (61 years) for the red variety Touriga Nacional, was analyzed. The trends over time for the classical temperature-based indices (Growing Season Temperature – GST -, Growing Degree Days – GDD, Huglin Index – HI and Cool Night Index – CI) presented a significantly positive slope while the Dryness Index (DI) showed a negative trend over the last 61 years. Regarding grapevine phenology, an average advance of 4.5 days per decade in the harvest day was observed throughout the last 61 years. Consequently, the weather conditions during the ripening period have changed, showing an increasing trend over time in the average temperature (higher magnitude in the maximum than in the minimum temperature) and a decrease in the accumulated rainfall. A regression analysis showed that ~50% of harvest date variability over years was explained by the temperature-based indices variability. These observed effects of climate change on bioclimatic indices and corresponding anticipation of harvest date can still be considered advantageous for the Dão terroir as it allows to achieve an optimal berry ripening before the common equinox rains and, therefore, avoid the potential negative impacts of the rainfall on berry health and composition.

Effect of partial net shading on the temperature and radiation in the grapevine canopy, consequences on the grape quality of cv. Gros Manseng in PDO Pacherenc-du-vic-Bilh

As elsewhere, southwestern France vineyards face more recurrent summer heat waves these last years. Among the possibilities of adaptation to this climate changing parameter, the use of net shading is a technique that allow for limiting canopy exposure to radiations. In this trial, we tested net shading installed on one face of the canopy, on a north-south row-oriented plot of cv. Gros Manseng trained on VSP system in the PDO Pacherenc-du-Vic-Bilh. The purpose was to characterize the effects on the ambient canopy temperatures and radiations during the season and to observe the consequences on the composition of grapes and wines. Two sorts of net were used with two levels of obstruction (50% and 75%) of the photosynthesis active radiation (PAR). They have been installed on the west side of the canopy and compared to a netless control. Temperature and PAR sensors registered hourly data during the season. On specific summer day (hot and sunny) manual measurements took also place on bunches (temperature) and in different spots of the canopy (PAR). The results showed that, on clear days, the radiation is lowered by the shade nets respecting the supplier criteria. The effects on the ambient canopy temperature were inconstant on this plot when we observed the data from the global period of shading between fruit set and harvest. However, during hot days (>30°C), the temperature in the canopy was reduced during afternoon and the temperature of the bunch surface was reduced as well comparing to the control. A decrease of the maturity parameters of the berries, sugar and acidity, was also observed. Concerning the wine aromatic potential, no differences clearly appeared.

Inhibition of Oenococcus oeni during alcoholic fermentation by a selected Lactiplantibacillus plantarum strain

The use of selected cultures of the species Lactiplantibacillus plantarum in Oenology has grown in prominence in recent years. While initial applications of this species centred very much around malolactic fermentation (MLF), there is strong evidence to show that certain strains can be harnessed for their bio-protective effects. Unwanted spontaneous MLF during alcoholic fermentation (AF), driven by rogue Oenococcus oeni, is a winemaking deviation that is very difficult to manage when it occurs. This work set out to determine the efficacy of one particular strain of Lactiplantibacillus plantarum(Viniflora® NoVA™ Protect), against this problem in Cabernet Sauvignon must. The work was carried out at commercial scale and in a winery environment and compared the bio-protective culture with the more traditional approach of reducing must pH by the addition of tartaric acid. The combination of both was also investigated. The concentration of both Oenococcus oeni and Lactiplantibacillus plantarum was determined using qPCR. The adventitious Oenococcus oeni showed the most growth during AF in the control wine, whereas in the wines treated with Lactiplantibacillus plantarum a bacteriostatic effect against this species was observed. This effect was comparable to the wines treated with tartaric acid. This has particular commercial relevance for controlling the flora in musts with high pH, or when the addition of tartaric acid is either not permitted or is prohibitive for other reasons.

Spatiotemporal patterns of chemical attributes in Vitis vinifera L. cv. Cabernet Sauvignon vineyards in Central California

Spatial variability of vine productivity in winegrapes is important to characterise as both yield and quality are relevant for the production of different wine styles and products. The objectives were to understand how patterns of variability of Cabernet Sauvignon fruit composition changed over time and space, how these patterns could be characterised with indirect measurements, and how spatial patterns of the variation in fruit compositional attributes can aid in improving management. Prior to the 2017 vintage, 125 data vines were distributed across each of four vineyards in the Lodi American Viticultural Area (AVA) of California. Each data vine was sampled at commercial harvest in 2017, 2018, and 2019. Yield components and fruit composition were measured at harvest for each data vine, and maps of yield and fruit composition were produced for eight ‘objective measures of fruit quality’: total anthocyanins, polymeric tannins, quercetin glycosides, malic acid, yeast assimilable nitrogen, β-damascenone, C6 alcohols and aldehydes, and 3-isobutyl-2-methoxypyrazine. Patterns of variation in anthocyanins and phenolic compounds were found to be most stable over time. Given this relative stability, management decisions focused on fruit quality could be based on zonal descriptions of anthocyanins or phenolics to increase profitability in some vineyards. In each vineyard, dormant season pruning weights and soil cores were collected at each location, elevation and soil apparent electrical conductivity surveys were completed, and remotely sensed imagery was captured by fixed wing aircraft and two satellite platforms at major phenological stages. The data collected were used to develop relationships among biophysical data, soil, imagery, and fruit composition. The standardised and aggregated samples from four vineyards over three seasons were included in the estimation of ‘common variograms’ to assess how this technique could aid growers in producing geostatistically rigorous maps of fruit composition variability without cumbersome, single season sampling efforts.

Ecophysiological performance of Vitis rootstocks under water stress

The use of rootstocks tolerant to soil water deficit is an interesting strategy to cope with limited water availability. Currently, several nurseries are breeding new genotypes, but the physiological basis of its responses under water stress are largely unknown. To this end, an ecophysiological assessment of the conventional 110-Richter (110R) and SO4, and the new M1 and M4 rootstocks was carried out in potted ungrafted plants. During one season, these Vitis genotypes were grown under greenhouse conditions and subjected to two water regimes, well-watered and water deficit. Water potentials of plants under water deficit down to < -1.4 MPa, and net photosynthesis (AN) <5 μmol m-2 s-1 did not cause leaf oxidative stress damage compared to well-watered conditions in any of the genotypes. The antioxidant capacity was sufficient to neutralize the mild oxidative stress suffered. Under both treatments, gravimetric differences in daily water use were observed among genotypes, leading to differences in the biomass of root, shoot and leaf. Under well-watered conditions, SO4 and 110R were the most vigorous and M1 and M4 the least. However, under water stress, SO4 exhibited the greatest reduction in biomass while M4 showed the lowest. Remarkably, under these conditions, SO4 reached the least negative stem water potential (Ψstem), while M1 reduced stomatal conductance (gs) and AN the most. In addition, SO4 and M1 genotypes also showed the highest and lowest hydraulic conductance values, respectively. Our results suggest that there are differences in water use regulation among genotypes, not only attributed to differences in stomatal regulation or intrinsic water use efficiency at the leaf level. Therefore, because no differences in canopy-to-root ratio were achieved, it is hypothesized that xylem vessel anatomical differences may be driving the reported differences among rootstocks performance. Results demonstrate that each Vitis rootstock differs in its ecophysiological responses under water stress.