Modelling wine astringency from its chemical composition using machine learning algorithms

  1. María Pilar Sáenz Navajas 1
  2. Sara Ferrero del Teso 2
  3. Miguel Romero 3
  4. Pascual Dario 3
  5. David Díaz 3
  6. Vicente Ferreira González 1
  7. Purificación Fernández Zurbano 2
  1. 1 Universidad de Zaragoza
    info

    Universidad de Zaragoza

    Zaragoza, España

    ROR https://ror.org/012a91z28

  2. 2 Instituto de Ciencias de la Vid y del Vino
    info

    Instituto de Ciencias de la Vid y del Vino

    Logroño, España

    ROR https://ror.org/01rm2sw78

  3. 3 SDG-Consulting Spain
Revista:
OENO ONE: Journal international des sciences de la vigne et du vin = International journal of vine and wine sciences

ISSN: 1151-0285

Ano de publicación: 2019

Volume: 53

Número: 3

Páxinas: 498-510

Tipo: Artigo

Outras publicacións en: OENO ONE: Journal international des sciences de la vigne et du vin = International journal of vine and wine sciences

Resumo

Aims: The present work aims to predict sensory astringency from wine chemical composition using machine learning algorithms. Material and results: Moristel grapes from different vineblocks and at different stages of ripening were collected. Eleven different wines were produced in 75 L tanks in triplicate, and further sensory factors were described by the rate-all-that-apply method with a trained panel of participants. The polyphenolic composition was characterised in wines by measuring the concentration and activity of tannins using UHPLC-UV/VIS, the mean degree of polymerisation (mDP. and the composition of tannins using thiolysis followed by UHPLC-MS. Conventional oenological parameters were analysed using FTIR and UV-Vis. Machine learning was applied to build models for predicting a wines astringency from its chemical composition. The best model was obtained using the support vector regressor (radial kernel) algorithm presenting a root-mean-square error (RMSE) value of 0.190. Conclusions: The main variables of the astringency model were the % of procyanidins constituting tannins and ethanol content, followed by other eight variables related to tannin structure and acidity. Significance of the study: These results increase the knowledge of chemical variables related to the perception of wine astringency and provide tools to control and optimise grape and wine production stages to modulate astringency and maximise quality and the consumer appeal of wines.