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Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology
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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: 25 August 2026 | Viewed by 14091

Special Issue Editor

Prof. Dr. Israel Schechter

E-Mail Website
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (7 papers)

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Research

Jump to: Review

15 pages, 523 KB  
Article
Artificial Neural Networks for Discrimination of Automotive Clear Coats by Vehicle Manufacturer
by Barry K. Lavine, Collin G. White and Douglas R. Heisterkamp
Sensors 2026, 26(7), 2260; https://doi.org/10.3390/s26072260 - 6 Apr 2026
Viewed by 596
Abstract
Modern automotive paints have a thin undercoat and color coat layer protected by a thick clear coat layer. All too often, only the clear coat layer of the automotive paint is recovered at the crime scene of a vehicle-related fatality. Searches for motor [...] Read more.
Modern automotive paints have a thin undercoat and color coat layer protected by a thick clear coat layer. All too often, only the clear coat layer of the automotive paint is recovered at the crime scene of a vehicle-related fatality. Searches for motor vehicle paint databases of clear coats using commercial software typically generate large hitlists that are difficult for a forensic paint examiner to work through unless additional information is provided for the search. To address this problem, deep learning has been applied to the infrared spectra of automotive clear coats to identify patterns in their spectra indicative of the motor vehicle manufacturer. An in-house automotive paint library of 2796 clear coat infrared spectra from six automotive manufacturers and 100 assembly plants was partitioned into training, validation, and prediction sets. Each spectrum has 1880 measurements over the spectral range of 4000 cm−1 to 376 cm−1. Several multilayer perceptron neural network models, each with three hidden layers, were developed that achieved high classification success rates for the training, validation, and prediction sets. The addition of convolutional layers to the deep learning neural network models did not improve the performance of these models. Full article
(This article belongs to the Special Issue Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology)
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15 pages, 3332 KB  
Article
YOLOv11-XRBS: Enhanced Identification of Small and Low-Detail Explosives in X-Ray Backscatter Images
by Baolu Yang, Zhe Yang, Xin Wang, Baozhong Mu, Jie Xu and Hong Li
Sensors 2025, 25(19), 6130; https://doi.org/10.3390/s25196130 - 3 Oct 2025
Viewed by 1028
Abstract
Identifying concealed explosives in X-ray backscatter (XRBS) imagery remains a critical challenge, primarily due to low image contrasts, cluttered backgrounds, small object sizes, and limited structural details. To address these limitations, we propose YOLOv11-XRBS, an enhanced detection framework tailored to the characteristics of [...] Read more.
Identifying concealed explosives in X-ray backscatter (XRBS) imagery remains a critical challenge, primarily due to low image contrasts, cluttered backgrounds, small object sizes, and limited structural details. To address these limitations, we propose YOLOv11-XRBS, an enhanced detection framework tailored to the characteristics of XRBS images. A dedicated dataset (SBCXray) comprising over 10,000 annotated images of simulated explosive scenarios under varied concealment conditions was constructed to support training and evaluation. The proposed framework introduces three targeted improvements: (1) adaptive architectural refinement to enhance multi-scale feature representation and suppress background interference, (2) a Size-Aware Focal Loss (SaFL) strategy to improve the detection of small and weak-feature objects, and (3) a recomposed loss function with scale-adaptive weighting to achieve more accurate bounding box localization. The experiments demonstrated that YOLOv11-XRBS achieves better performance compared to both existing YOLO variants and classical detection models such as Faster R-CNN, SSD512, RetinaNet, DETR, and VGGNet, achieving a mean average precision (mAP) of 94.8%. These results confirm the robustness and practicality of the proposed framework, highlighting its potential deployment in XRBS-based security inspection systems. Full article
(This article belongs to the Special Issue Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology)
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15 pages, 2185 KB  
Article
High Sensitivity Online Sensor for BTEX in Ambient Air Based on Multiphoton Electron Extraction Spectroscopy
by Uriah H. Sharon, Lea Birkan, Valery Bulatov, Roman Schuetz, Tikhon Filippov and Israel Schechter
Sensors 2025, 25(14), 4268; https://doi.org/10.3390/s25144268 - 9 Jul 2025
Cited by 2 | Viewed by 1350
Abstract
Benzene, toluene, ethylbenzene, and xylene (BTEX) are widespread volatile organic compounds commonly present in fuels and various industrial materials. Their release into the atmosphere significantly contributes to air pollution, prompting strict regulatory concentration limits in ambient air. In this work, we introduce Multiphoton [...] Read more.
Benzene, toluene, ethylbenzene, and xylene (BTEX) are widespread volatile organic compounds commonly present in fuels and various industrial materials. Their release into the atmosphere significantly contributes to air pollution, prompting strict regulatory concentration limits in ambient air. In this work, we introduce Multiphoton Electron Extraction Spectroscopy (MEES) as an innovative technique for the sensitive, selective, and online detection and quantitation of BTEX compounds under ambient conditions. MEES employs tunable UV laser pulses to induce the resonant ionization of target molecules under a high electrical field, with subsequent measurement of the generated photocurrent. We now demonstrate the method’s ability to detect BTEX in ambient air, at part-per-trillion (ppt) concentration range, providing distinct spectral signatures for each compound, including individual xylene isomers. The technique represents a significant advancement in BTEX monitoring, with potential applications in environmental sensing and industrial air quality control. Full article
(This article belongs to the Special Issue Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology)
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19 pages, 1468 KB  
Article
Adaptive Kalman Filtering for Compensating External Effects in On-Line Spectroscopic Measurements
by Daniel Sbarbaro, Tor Arne Johansen and Jorge Yañez
Sensors 2025, 25(8), 2513; https://doi.org/10.3390/s25082513 - 16 Apr 2025
Cited by 2 | Viewed by 2033
Abstract
This study addresses the challenges of real-time spectroscopic sensing in industrial applications, where external factors such as temperature fluctuations, pressure variations, and particle size distribution significantly impact measurement accuracy. Conventional quantitative analytical methods often neglect these dynamic influences, leading to erroneous concentration estimates. [...] Read more.
This study addresses the challenges of real-time spectroscopic sensing in industrial applications, where external factors such as temperature fluctuations, pressure variations, and particle size distribution significantly impact measurement accuracy. Conventional quantitative analytical methods often neglect these dynamic influences, leading to erroneous concentration estimates. To overcome these limitations, we propose an integrated modeling framework that combines a discrete-time process model with a physics-based spectroscopic sensor model, explicitly accounting for the dynamic properties of the system. A key innovation of this work is the development and application of an Adaptive Kalman Filter (AKF) to systematically correct for measurement distortions caused by external disturbances. Unlike conventional filtering techniques, the AKF dynamically adjusts to changing process conditions by leveraging real-time observability analysis, ensuring robustness even in the presence of sensor noise and environmental variability. Furthermore, to address cases where full observability is not achievable, we introduce a reduced-order Adaptive Kalman Filter (rAKF), which optimally estimates concentrations while minimizing computational complexity. A comprehensive series of simulations was conducted to assess the sensitivity of the estimation to variations in external signal type, noise levels, and initial values for parameters and states. The findings of this study demonstrate the superior performance of both AKF and rAKF in comparison to conventional filtering techniques, including the Extended Kalman Filter. The proposed approaches have been shown to enhance the reliability of spectroscopic sensor measurements, enabling more precise real-time estimations that can be used for monitoring and advanced process control strategies in industrial settings. Full article
(This article belongs to the Special Issue Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology)
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13 pages, 2175 KB  
Article
Compensation for Matrix Effects in High-Dimensional Spectral Data Using Standard Addition
by Elena Khanonkin, Israel Schechter and Itai Dattner
Sensors 2025, 25(3), 612; https://doi.org/10.3390/s25030612 - 21 Jan 2025
Cited by 1 | Viewed by 3643
Abstract
The standard addition method is widely used in analytical chemistry to compensate for matrix effects. While effective with single signals (e.g., absorbance at a single wavelength) and independent of matrix composition or blank measurements, it has limitations with high-dimensional data (e.g., full spectra). [...] Read more.
The standard addition method is widely used in analytical chemistry to compensate for matrix effects. While effective with single signals (e.g., absorbance at a single wavelength) and independent of matrix composition or blank measurements, it has limitations with high-dimensional data (e.g., full spectra). Existing methods for high-dimensional data require knowledge of the matrix composition and blank measurements, restricting their applicability. We propose a novel algorithm for standard addition that works with high-dimensional data without requiring matrix composition knowledge or blank measurements. By modifying experimental data (e.g., spectra) before applying chemometric models, the algorithm accurately determines analyte concentrations even in complex matrices like seawater or food, where blanks are unavailable. A performance evaluation shows the algorithm compensates effectively for matrix effects, outperforms previously published standard addition algorithms and direct applications of multivariate chemometric algorithms, and is robust to variations in SNR and matrix effect intensity. Full article
(This article belongs to the Special Issue Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology)
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14 pages, 920 KB  
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
Cited by 1 | Viewed by 2455
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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Review

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30 pages, 2615 KB  
Review
Laser-Induced Breakdown Spectroscopy Analysis of Lithium: A Comprehensive Review
by Stefano Legnaioli, Giulia Lorenzetti, Francesco Poggialini, Beatrice Campanella, Vincenzo Palleschi, Silvana De Iuliis, Laura Eleonora Depero, Laura Borgese, Elza Bontempi and Simona Raneri
Sensors 2025, 25(24), 7689; https://doi.org/10.3390/s25247689 - 18 Dec 2025
Viewed by 1439
Abstract
Lithium has emerged as a pivotal material for the global energy transition, yet its supply security is challenged by limited geographical availability and growing demand. These constraints highlight the need for analytical methods that are not only accurate but also sustainable and deployable [...] Read more.
Lithium has emerged as a pivotal material for the global energy transition, yet its supply security is challenged by limited geographical availability and growing demand. These constraints highlight the need for analytical methods that are not only accurate but also sustainable and deployable across the entire lithium value chain. In this context, Laser-Induced Breakdown Spectroscopy (LIBS) offers distinctive advantages, including minimal sample preparation, real-time and in situ analysis and the potential for portable and automated implementation. Such features make LIBS a valuable tool for monitoring and optimizing lithium extraction, refining and recycling processes. This review critically examines the recent progress in the use of LIBS for lithium detection and quantification in geological, industrial, biological and extraterrestrial matrices. It also discusses emerging applications in closed-loop recycling and highlights future prospects related to the integration of LIBS with artificial intelligence and machine learning to enhance analytical accuracy and material classification. Full article
(This article belongs to the Special Issue Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology)
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