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Related Experiment Video

Updated: Jun 13, 2026

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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Portable and Digital MOX Sensor Electronic Nose with Thermal Modulation: Design, Stability Analysis, and Long-Term

Víctor González1, Juan Álvaro Fernández1, Patricia Arroyo1

  • 1Industrial Engineering School, University of Extremadura, 06006 Badajoz, Spain.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

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Discrimination of Spanish-Style Green Olives Inoculated with Undesirable Microbiota Using E-Nose, Chemometrics and Volatile Compound Profiles.

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MOX Sensors for Authenticity Assessment and Adulteration Detection in Extra Virgin Olive Oil (EVOO).

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A new portable electronic nose uses digital metal oxide (MOX) sensors with programmable temperature modulation for advanced odor analysis. This robust system achieved 100% accuracy in distinguishing oils, showing great potential for real-world applications.

Area of Science:

  • Analytical Chemistry
  • Sensor Technology
  • Materials Science

Background:

  • Traditional electronic noses often use fixed-temperature sensors, limiting their ability to capture complex odor profiles.
  • Metal oxide (MOX) gas sensors offer sensitivity but require optimization for stability and repeatability.

Purpose of the Study:

  • To develop and validate a portable electronic nose system utilizing digital MOX sensors with programmable temperature modulation.
  • To assess the long-term stability, repeatability, and pattern-recognition capabilities of the developed electronic nose.
  • To demonstrate the system's effectiveness in discriminating between different types of oils.

Main Methods:

  • Integration of four digital MOX sensors with programmable temperature modulation for generating temperature-dependent odor fingerprints.
Keywords:
chemometricsdigital MOX gas sensorsmachine learningtemperature-modulated electronic nose

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  • Assessment of sensor performance including stability, repeatability (using Root Mean Squared Error - RMSE), and pattern recognition.
  • Application of Principal Component Analysis (PCA) for signal processing and k-Nearest Neighbors (KNNs) and Multilayer Perceptrons (MLPs) for classification.
  • Proof-of-concept study involving the discrimination of Extra Virgin Olive Oil and pomace oil.
  • Main Results:

    • The electronic nose demonstrated stable responses over one month of measurements, indicating high repeatability.
    • Temperature-modulated signals, processed by PCA and classified using KNNs/MLPs, achieved 100% accuracy in oil discrimination after selecting the most repeatable sensor.
    • The system proved robust and capable of complex odor analysis, outperforming conventional fixed-temperature operation.

    Conclusions:

    • Temperature-modulated digital MOX sensors offer significant analytical potential and robustness for electronic nose applications.
    • A compact, highly reproducible electronic nose platform suitable for complex odor analysis in real-world scenarios has been successfully demonstrated.
    • The developed system shows promise for quality control and authentication in food and other industries.