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New Electronic Tongue Sensor Array System for Accurate Liquor Beverage Classification.

Jersson X Leon-Medina1,2, Maribel Anaya3, Diego A Tibaduiza3

  • 1Department of Mechanical and Mechatronics Engineering, Universidad Nacional de Colombia-Sede Bogotá, Bogotá 111321, Colombia.

Sensors (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study presents an electronic tongue system for classifying liquor beverages using eight commercial sensors and machine learning. The developed system demonstrates high accuracy in analyzing and categorizing alcoholic drinks, offering advantages over traditional methods.

Keywords:
classificationdimensionality reductionelectronic tonguemachine learningmanifold learningsabajon

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Area of Science:

  • Electrochemistry
  • Sensors and Instrumentation
  • Artificial Intelligence

Background:

  • Advancements in sensor fabrication and processing equipment enable portable smart systems for real-time process monitoring.
  • Electronic tongues offer advantages over traditional methods for liquid classification, including reduced analysis time and online monitoring capabilities.
  • Improving data analysis strategies for electrochemical sensors is crucial for enhanced measurement accuracy.

Purpose of the Study:

  • To explore the application of an electronic tongue system for the classification of liquor beverages.
  • To develop and validate a machine-learning-based methodology for analyzing data from electrochemical sensors in alcoholic beverages.
  • To assess the system's accuracy and advantages in classifying regional Colombian alcoholic beverages.

Main Methods:

  • Utilized an electronic tongue system comprising eight commercial sensors and a data acquisition system.
  • Developed a machine-learning-based methodology for data analysis and beverage classification.
  • Applied the system to classify specific liquor beverages from Colombia.

Main Results:

  • The electronic tongue system achieved high accuracy in the analysis and classification of liquor beverages.
  • Demonstrated the system's effectiveness and advantages in real-world application.
  • The machine-learning approach successfully processed data from electrochemical sensors for classification.

Conclusions:

  • The developed electronic tongue system is effective for classifying liquor beverages.
  • The integration of machine learning enhances the analysis of electrochemical sensor data.
  • This technology offers a promising alternative for the quality control and authentication of alcoholic beverages.