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New Methods to Study Gustatory Coding
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Chocolate Classification by an Electronic Nose with Pressure Controlled Generated Stimulation.

Luis F Valdez1, Juan Manuel Gutiérrez2

  • 1Bioelectronics Section, Department of Electrical Engineering, CINVESTAV, Mexico City 07360, Mexico. fvaldez@cinvestav.mx.

Sensors (Basel, Switzerland)
|October 25, 2016
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Summary

A novel electronic nose (E-nose) using a Metal Oxide Gas Sensor (MOGS) array successfully identified chocolate types and features. This reliable system achieved high accuracy in classifying 26 chocolate samples.

Keywords:
E-nosesmetal oxide gas sensorsolfactometer

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Area of Science:

  • Sensory science and food analysis
  • Electronic instrumentation and sensor arrays
  • Machine learning for pattern recognition

Background:

  • Traditional methods for food quality assessment can be time-consuming and subjective.
  • Metal Oxide Gas Sensor (MOGS) arrays offer potential for rapid, objective analysis of volatile compounds.
  • Developing reliable stimulus generation is crucial for consistent electronic nose (E-nose) performance.

Purpose of the Study:

  • To construct and evaluate an E-nose system for automated chocolate analysis.
  • To identify specific chocolate types and recognize key features (type, ingredients, sweetener, expiration).
  • To validate the reliability of a pressure-controlled stimulus generation method for sensor studies.

Main Methods:

  • Fabrication of a homemade olfactometer for controlled gas stimulus generation.
  • Deployment of a Metal Oxide Gas Sensor (MOGS) array within the E-nose system.
  • Application of Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs) for data processing and classification.

Main Results:

  • The E-nose achieved 81.3% average classification rate for chocolate identification (0.99 accuracy, 0.86 precision, 0.84 sensitivity, 0.99 specificity).
  • Feature recognition (type, extra ingredient, sweetener, expiration) yielded an 85.36% classification rate (0.96 accuracy, 0.86 precision, 0.85 sensitivity, 0.96 specificity).
  • Preliminary aging analysis was conducted, and the stimulus generation method proved reliable.

Conclusions:

  • The developed E-nose, utilizing MOGS and advanced data analysis, demonstrates significant capability in objective chocolate assessment.
  • The system effectively distinguishes between chocolate samples and identifies critical quality-related features.
  • The pressure-controlled stimulus generation is validated as a reliable method for E-nose research and development.