Identification of chemical markers to detect abnormal wine fermentation using support vector machines

Alejandra Urtubia, Roberto León, Matías Vargas

Research output: Contribution to journalArticlepeer-review

Abstract

Support Vector Machine (SVM) was explored as a tool for the early detection of abnormal fermentations, which are common in the wine industry. A database of about 18,000 data from 38 fermentations and 45 variables was used. Two cases were studied: (I) measurements of five groups (fermentation control variables, organic acids, amino acids, saturated and unsaturated fatty acids); and (II) four variables (density, YAN, brix and acidity). In addition, different kernels, training/testing configurations, and cut-off time were evaluated. Main results indicated that 80% of wine fermentations were well predicted using information of amino acids. In addition, density and YAN were the best individual chemical markers for prediction, with over 90% of accuracy at first 48 h of the process. Therefore, SVM can be used as a decision support tool for wine fermentation monitoring. Using data from the first 72 h, it is possible classify abnormal fermentations with high precision.

Original languageEnglish
Article number107158
JournalComputers and Chemical Engineering
Volume145
DOIs
Publication statusPublished - Feb 2021

Keywords

  • Amino acids
  • Density
  • Machine learning
  • Prediction
  • Support vector machine
  • Wine fermentation

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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