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Related Experiment Video

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Fruit Volatile Analysis Using an Electronic Nose
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A Comprehensive Review on Sensor-Based Electronic Nose for Food Quality and Safety.

Teodora Sanislav1, George D Mois1, Sherali Zeadally2,3

  • 1Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
|July 30, 2025
PubMed
Summary

Electronic noses offer automated food quality and safety assessment, achieving over 90% accuracy in many studies. Challenges in sensor response and reliability remain, necessitating adaptive machine learning and standardized protocols for broader application.

Keywords:
artificial olfactionelectronic nosefood qualityfood safetypattern recognition

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Sensor Technology

Background:

  • Food quality and safety are critical for public health, consumer trust, and trade.
  • Automated assessment methods are increasingly important for streamlining food quality evaluation.
  • Electronic noses (e-noses) are emerging as key technologies in this domain.

Purpose of the Study:

  • To provide a comprehensive review of research on electronic noses for food quality and safety.
  • To analyze the current state, challenges, and future directions in e-nose development for food applications.
  • To identify solutions for enhancing the robustness and applicability of e-nose technology.

Main Methods:

  • Systematic review of research papers from three scientific databases over the past decade.
  • Analysis of e-nose technologies, sensor responses, pattern recognition algorithms, and application domains.
  • Evaluation of reported accuracies, limitations, and proposed solutions in the field.

Main Results:

  • Most studies utilize portable, low-cost e-noses with pattern recognition for specific food classes, achieving >90% accuracy.
  • Key challenges include sensor response diversity, odor differentiation, sensor drift compensation, and real-world reliability.
  • A fully developed e-nose mimicking human olfactory capabilities is not yet available.

Conclusions:

  • Adaptive machine learning models and standardized protocols are recommended to address current limitations.
  • Future e-nose development should focus on robustness, interpretability, and field-readiness for industrial applications.
  • A roadmap is proposed to transition e-nose technology from controlled studies to scalable industrial use.