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Optical Spectroscopic Sensing and Imaging

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: closed (25 August 2024) | Viewed by 2044

Special Issue Editors


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Guest Editor
School of Electrical and Electronic Engineering, Photonics Research Centre, Technological University Dublin, Grangegorman, D07 ADY7 Dublin, Ireland
Interests: optical sensing; whispering gallery mode effects in microfibre based resonators for chemical and bio-sensing; smart optical sensors for engineering applications; sensing of volatile organic compounds in environmental monitoring, medical diagnostics and industrial control; optics and applications of liquid crystals in photonics
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Guest Editor
Tyndall National Institute, University College Cork, Lee Maltings, Dyke Parade, Cork, Ireland
Interests: photonics integrated circuit packaging; fiber optic sensing application; fiber optical packaging; optical fiber sensor; fiber laser; fiber optics

Special Issue Information

Dear Colleagues,

Optical spectroscopic sensing and imaging have become popular tools in environmental detection, industrial process control, agricultural monitoring, and medical diagnostics, since they facilitate comprehensive, non-invasive, and non-destructive monitoring. Recent breakthroughs in light sources have resulted in higher detection sensitivity and more compact systems, moving optical spectroscopic sensing and imaging technologies into a new stage. The past decades have witnessed a booming development in optical sensors, which benefit from novel light sources and novel sensor structures.

This Special Issue of the journal Sensors entitled "Optical Spectroscopic Sensing and Imaging" will focus on all aspects of the research and development related to these areas. Original research papers that focus on the development of optical sources for sensing, as well as the design and experimental verification of new sensors and imaging systems. Both reviews and original research articles will be published. Reviews should provide an up-to-date, well-balanced overview of the current state of the art in a particular field and include main results from other groups. We look forward to and welcome your participation in this Special Issue.

Prof. Dr. Yuliya Semenova
Dr. Arun Kumar Mallik
Guest Editors

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Keywords

  • absorption spectroscopy
  • emission spectroscopy
  • photoluminescence
  • UV-Visible spectroscopy
  • infrared spectroscopy 
  • Raman spectroscopy
  • environmental monitoring
  • biomedical sensing
  • gas sensing
  • remote sensing
  • optical sensors
  • sensing mechanisms of optical fiber sensors
  • plastic optic fiber sensors
  • bio-medical sensors
  • physical sensors
  • imaging
  • spectral imaging
  • multispectral imaging
  • imaging spectroscopy
  • surface plasmon resonance

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Published Papers (1 paper)

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Research

22 pages, 3124 KiB  
Article
Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning
by Hao Jiang and Keith Kolaczyk
Sensors 2024, 24(21), 7082; https://doi.org/10.3390/s24217082 - 3 Nov 2024
Viewed by 1524
Abstract
To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter (PM) in specific particle size channels, providing essential information [...] Read more.
To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter (PM) in specific particle size channels, providing essential information for estimating lung deposition of vaping aerosols. For the sensor’s input, wavelength-specific optical attenuation signals are acquired for three separate wavelengths in the ultraviolet, red, and near-infrared range, and the inhalation pressure is collected from a pressure sensor. The sensor’s outputs are PM mass in three size bins, specified as 100–300 nm, 300–600 nm, and 600–1000 nm. Reference measurements of electronic cigarette aerosols, obtained using a custom vaping machine and a scanning mobility particle sizer, provided the ground truth for size-binned PM mass. A lightweight two-layer feedforward neural network was trained using datasets acquired from a wide range of puffing conditions. The performance of the neural network was tested using unseen data collected using new combinations of puffing conditions. The model-predicted values matched closely with the ground truth, and the accuracy reached 81–87% for PM mass in three size bins. Given the sensor’s straightforward optical configuration and the direct collection of signals from undiluted vaping aerosols, the achieved accuracy is notably significant and sufficiently reliable for point-of-interest sensing of vaping aerosols. To the best of our knowledge, this work represents the first instance where machine learning has been applied to directly characterize high-concentration undiluted electronic cigarette aerosols. Our sensor holds great promise in tracking electronic cigarette users’ puff topography with quantification of size-binned PM mass, to support long-term personalized health and wellness. Full article
(This article belongs to the Special Issue Optical Spectroscopic Sensing and Imaging)
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