Application of Machine Learning Algorithms to Classify Peruvian Pisco Varieties Using an Electronic Nose

Celso De-La-Cruz, Jorge Trevejo-Pinedo, Fabiola Bravo, Karina Visurraga, Joseph Peña-Echevarría, Angela Pinedo, Freddy Rojas, María R. Sun-Kou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin”. For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation–extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks.

Original languageEnglish
Article number5864
JournalSensors (Switzerland)
Volume23
Issue number13
DOIs
StatePublished - Jul 2023

Keywords

  • artificial neural network
  • beverage quality
  • electronic nose
  • gas sensors array
  • random forest
  • support vector machine

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