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Intelligent Mobile Electronic Nose System Comprising a Hybrid Polymer-Functionalized Quartz Crystal Microbalance

Trisna Julian1,2, Shidiq Nur Hidayat1,2, Aditya Rianjanu3,4

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A new, low-cost mobile electronic nose (e-nose) system utilizes quartz crystal microbalance (QCM) sensors and machine learning for accurate volatile organic compound (VOC) detection. This versatile platform offers high classification accuracy for disease diagnosis and environmental monitoring.

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Sensor Technology

Background:

  • Volatile organic compounds (VOCs) are crucial biomarkers for disease diagnosis and environmental monitoring.
  • Existing detection methods can be expensive, complex, or lack portability.
  • Development of cost-effective and sensitive sensing platforms is essential.

Purpose of the Study:

  • To develop a low-cost, mobile electronic nose (e-nose) system for detecting and classifying volatile organic compounds (VOCs).
  • To functionalize quartz crystal microbalance (QCM) sensors with polymer-based thin films for enhanced sensitivity.
  • To employ machine learning algorithms for accurate analysis of complex gaseous mixtures.

Main Methods:

  • Fabrication of a QCM sensor array with polyacrylonitrile, poly(vinylidene fluoride), poly(vinyl pyrrolidone), and poly(vinyl acetate) films.
  • Implementation of a multichannel data acquisition (DAQ) system with a frequency resolution of 0.5 Hz.
  • Application of linear discriminant analysis (LDA) and support vector machine (SVM) algorithms for data classification.

Main Results:

  • The e-nose system demonstrated high reproducibility and consistency across four QCM sensors.
  • Sensors exhibited varying sensitivity to seven different volatile organic compounds (VOCs).
  • LDA and SVM models achieved classification accuracies of up to 98% and 99%, respectively.

Conclusions:

  • The developed low-cost mobile e-nose system provides a versatile platform for qualitative and quantitative analysis of VOCs.
  • High classification accuracies achieved by machine learning algorithms validate the system's potential for real-world applications.
  • This e-nose system is promising for early disease diagnosis and environmental quality monitoring.