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Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology

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

Deadline for manuscript submissions: 1 December 2024 | Viewed by 1571

Special Issue Editor


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Guest Editor
Faculty of Chemistry, Technion–Israel Institute of Technology, Haifa 32000, Israel
Interests: laser spectroscopy and sensors

Special Issue Information

Dear Colleagues,

Advanced spectroscopy plays a crucial role in the application of sensors across various industries, enabling precise and sensitive detection of substances based on their unique spectral signatures.

The application of spectroscopy to sensors offers several distinct advantages. One key benefit is the ability to provide a fast response in real-time analysis, crucial for applications where timely information is essential. Spectroscopic sensors enable on-line monitoring, allowing continuous data collection without the need for sample preparation or time-consuming laboratory analysis.

The advent of chip spectrometers has further revolutionized spectroscopy for sensors. These miniaturized, portable devices enhance accessibility and versatility, enabling on-site measurements in various environments. Their compact size and low power consumption make them suitable for integration into a wide range of sensing devices, facilitating deployment in diverse settings.

In the realm of environmental monitoring, spectroscopic sensors are employed to analyze air and water quality. Infrared spectroscopy, for instance, can detect pollutants by identifying specific absorption bands, offering a rapid and reliable method for environmental assessment.

In the field of healthcare, spectroscopy is utilized in sensors for medical diagnostics. Fluorescence spectroscopy, for example, aids in the detection of biomarkers associated with diseases. This non-invasive technique provides valuable information for early disease diagnosis and monitoring of treatment effectiveness.

In industrial settings, spectroscopic sensors are employed for quality control and process monitoring. Near-infrared spectroscopy is commonly used to analyze chemical composition and ensure the consistency of products in real-time.

In agriculture, spectroscopy-based sensors assist in soil analysis, allowing farmers to optimize nutrient levels and improve crop yield. Remote sensing techniques, such as hyperspectral imaging, provide valuable data for precision agriculture, enabling targeted interventions based on spectral information.

Laser spectroscopy further advances the capabilities of sensors by offering enhanced precision and sensitivity in material analysis. For example, laser-induced breakdown spectroscopy (LIBS) sensors utilize laser pulses to vaporize and analyze samples, allowing for the rapid detection of trace elements. Laser ringdown spectroscopy allows for extremely sensitive detection of compounds in the gas phase, with absolute quantification. Such techniques provide a powerful tool for on-site analysis, facilitating quick decision making.

This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of advanced spectroscopy-based sensors and spectral analysis technology.

Prof. Dr. Israel Schechter
Guest Editor

Manuscript Submission Information

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Keywords

  • sensors
  • spectroscopy
  • spectral analysis
  • laser spectroscopy
  • chip spectrometers
  • chemometry
  • monitoring

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Published Papers (1 paper)

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Research

14 pages, 920 KiB  
Article
Maximizing the Reliability and Precision of Measures of Prefrontal Cortical Oxygenation Using Frequency-Domain Near-Infrared Spectroscopy
by Elizabeth K. S. Fletcher, Joel S. Burma, Raelyn M. Javra, Kenzie B. Friesen, Carolyn A. Emery, Jeff F. Dunn and Jonathan D. Smirl
Sensors 2024, 24(8), 2630; https://doi.org/10.3390/s24082630 - 20 Apr 2024
Viewed by 1070
Abstract
Frequency-domain near-infrared spectroscopy (FD-NIRS) has been used for non-invasive assessment of cortical oxygenation since the late 1990s. However, there is limited research demonstrating clinical validity and general reproducibility. To address this limitation, recording duration for adequate validity and within- and between-day reproducibility of [...] Read more.
Frequency-domain near-infrared spectroscopy (FD-NIRS) has been used for non-invasive assessment of cortical oxygenation since the late 1990s. However, there is limited research demonstrating clinical validity and general reproducibility. To address this limitation, recording duration for adequate validity and within- and between-day reproducibility of prefrontal cortical oxygenation was evaluated. To assess validity, a reverse analysis of 10-min-long measurements (n = 52) at different recording durations (1–10-min) was quantified via coefficients of variation and Bland–Altman plots. To assess within- and between-day within-subject reproducibility, participants (n = 15) completed 2-min measurements twice a day (morning/afternoon) for five consecutive days. While 1-min recordings demonstrated sufficient validity for the assessment of oxygen saturation (StO2) and total hemoglobin concentration (THb), recordings ≥4 min revealed greater clinical utility for oxy- (HbO) and deoxyhemoglobin (HHb) concentration. Females had lower StO2, THb, HbO, and HHb values than males, but variability was approximately equal between sexes. Intraclass correlation coefficients ranged from 0.50–0.96. The minimal detectable change for StO2 was 1.15% (95% CI: 0.336–1.96%) and 3.12 µM for THb (95% CI: 0.915–5.33 µM) for females and 2.75% (95%CI: 0.807–4.70%) for StO2 and 5.51 µM (95%CI: 1.62–9.42 µM) for THb in males. Overall, FD-NIRS demonstrated good levels of between-day reliability. These findings support the application of FD-NIRS in field-based settings and indicate a recording duration of 1 min allows for valid measures; however, data recordings of ≥4 min are recommended when feasible. Full article
(This article belongs to the Special Issue Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology)
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