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AI-driven 5G IoT e-nose for whiskey classification.

Jaume Segura-Garcia1,2, Rafael Fayos-Jordan2, Mohammad Alselek2

  • 1Computer Science Dpt, Universitat de València, Avda de la Universitat, s/n, Burjassot, 46100 Valencia Spain.

Applied Intelligence (Dordrecht, Netherlands)
|April 28, 2025
PubMed
Summary
This summary is machine-generated.

An AI-driven electronic nose architecture accurately classifies whiskey and acetone, distinguishing between different whiskey types with 99% accuracy. This technology aids in predicting final product quality in whiskey distilleries.

Keywords:
5G IoTMLOdor discriminationPCAe-nose

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Area of Science:

  • Artificial Intelligence
  • Chemical Sensing
  • Machine Learning

Background:

  • Quality control in whiskey production is crucial for final product assessment.
  • Distillery processes generate byproducts like acetone that require monitoring.
  • Electronic noses offer a non-destructive method for chemical compound analysis.

Purpose of the Study:

  • To design, implement, and validate an AI-driven electronic nose for classifying whiskey and acetone.
  • To differentiate between whiskey and acetone, and discriminate between three types of whiskey.
  • To enhance quality control in whiskey production through accurate odor classification.

Main Methods:

  • Utilized an electronic nose based on arrays of single-walled carbon nanotubes.
  • Investigated various strategies for classifying odor data.
  • Employed a random forest algorithm for data analysis and classification.

Main Results:

  • Achieved 99% accuracy in classifying whiskey and acetone, with inference times under 1.8 seconds.
  • Demonstrated high accuracy (around 96%) in distinguishing between different whiskey types.
  • Successfully validated the AI-driven electronic nose architecture for its intended applications.

Conclusions:

  • The developed AI-driven electronic nose architecture is highly effective for classifying whiskey and acetone.
  • The system provides a reliable tool for quality prediction in whiskey production.
  • The random forest approach offers a robust and efficient method for odor data classification.