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This study used physical-chemical analysis and computational models to track red wine aging. Artificial Neural Network and Random Forest models accurately predicted wine age, aiding wine authenticity certification.

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Area of Science:

  • Food Chemistry
  • Analytical Chemistry
  • Computational Science

Background:

  • Wine aging significantly impacts chemical composition and sensory properties.
  • Monitoring wine aging is crucial for quality control and authenticity verification.
  • D.O. Toro red wines were selected to investigate aging patterns.

Purpose of the Study:

  • To monitor the aging process of D.O. Toro red wines using physical-chemical analysis.
  • To develop computational models for distinguishing wines based on aging duration.
  • To create an effective tool for wine authenticity certification.

Main Methods:

  • Physical-chemical analysis was employed to assess wine composition over time.
  • Computational models including Artificial Neural Networks (ANNs), Support Vector Machine (SVM), and Random Forest (RF) were developed.
  • Models were trained and validated using wine samples aged for one, four, seven, and ten months.

Main Results:

  • Significant changes in chemical composition were observed correlating with wine aging.
  • Artificial Neural Network (ANN) and Random Forest (RF) models demonstrated high accuracy in predicting wine age.
  • The developed ANN and RF models achieved an average absolute percentage deviation below 1% for age prediction.

Conclusions:

  • Physical-chemical analysis combined with advanced computational models can effectively monitor wine aging.
  • ANN and RF models show strong potential as reliable tools for predicting wine age and ensuring authenticity.
  • This research provides a robust method for certifying the aging status of red wines.