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Related Experiment Video

Updated: Jul 23, 2025

Fruit Volatile Analysis Using an Electronic Nose
11:02

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

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

Celso De-La-Cruz1, Jorge Trevejo-Pinedo2, Fabiola Bravo2

  • 1Department of Engineering, Pontifical Catholic University of Peru, Lima 15088, Peru.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary

Machine learning algorithms analyzed electronic nose data to differentiate Peruvian pisco varieties. Neural networks, trained with augmented data, provided the most accurate predictions for verifying beverage quality.

Keywords:
artificial neural networkbeverage qualityelectronic nosegas sensors arrayrandom forestsupport vector machine

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Computer Science

Background:

  • Pisco, Peru's flagship alcoholic beverage, is produced under strict quality standards.
  • Verifying pisco variety and quality is crucial for its Designation of Origin.
  • Electronic noses offer a method for analyzing volatile compounds in beverages.

Purpose of the Study:

  • To apply machine learning algorithms to electronic nose data for differentiating pisco varieties.
  • To enhance the performance of machine learning models through data augmentation.
  • To identify the most effective algorithm for pisco variety classification.

Main Methods:

  • Volatile compound data from pisco samples were collected using an electronic nose.
  • Machine learning algorithms including neural networks, multiclass support vector machines, and random forest were implemented.
  • A novel data augmentation procedure based on interpolation-extrapolation was developed and applied.

Main Results:

  • Data augmentation significantly improved the performance and reliability of all tested algorithms.
  • Neural networks demonstrated superior performance in differentiating pisco varieties compared to support vector machines and random forest.
  • The developed methods successfully aided in verifying pisco quality based on its Designation of Origin parameters.

Conclusions:

  • Machine learning, particularly neural networks, is effective for analyzing electronic nose data to differentiate pisco varieties.
  • Data augmentation is a valuable technique for improving the accuracy of volatile compound analysis in alcoholic beverages.
  • This approach supports the quality control and verification of pisco, ensuring compliance with its Designation of Origin.