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


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. 


Publication date: June 22, 2020

Issue: GiESCO 2019

Type: Article


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

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

Contact the author


wine quality, machine learning, weather, aromas


GiESCO 2019 | IVES Conference Series


Related articles…

Significance of factors making Riesling an iconic grape variety

Riesling is the iconic grape variety of Germany and accounts for 23% of the German viticulture acreage, which comprises 45% of the worldwide Riesling plantings. Riesling wines offer a wide array of styles from crisp sparkling wines to highly concentrated and sweet Trockenbeerenauslese or Icewines. However, its thin berry skin makes Riesling more vulnerable to detrimental environmental threats than other white wine varieties.  

Malolactic fermentation in wine production

What influence do these bacteria have on wines? What new bacteria are being studied to carry out this fermentation? Find below articles about malolactic fermentation published in our 3 media (OENO One, IVES Technical Reviews and IVES Conference Series). OENO One...

Metabolomics screening of Vitis sp. interspecific hybrids to select natural ingredients with cosmetic purposes

Introducing natural ingredients using green chemistry practices is a major challenge in cosmetics industry to follow the market trend. Among the plants of cosmetic interest, vine products show a remarkable diversity of natural substances with high potential for the cosmetic and dermatological sectors. To date, research focuses on well-known compounds like E-resveratrol and E-ε-viniferin,

WAC 2022: Abstracts are available on IVES Conference Series

In order to disseminate the scientific results presented during the WAC 2022 , the organizers have decided to share the abstracts of the oral communications and posters with Open Access on IVES Conference Series. The fifth edition of the International Conference...

Winemaking techniques and wine tasting methods at the end of the Middle Ages

Les pratiques de vinification et de dégustation du vin sont souvent perçues, à travers un discours marketing très puissant, sous l’angle d’une tradition millénaire qui perdure depuis le Moyen Âge. En Bourgogne, il est courant de rattacher les racines de ces pratiques à l’activité des institutions ecclésiastiques qui possédaient d