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Photonics for Advanced Spectroscopy and Sensing

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 6467

Special Issue Editors


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Guest Editor
PolySense Lab—Dipartimento Interateneo di Fisica, University and Politecnico of Bari, Via Amendola 173, 70126 Bari, Italy
Interests: photoacoustic spectroscopy; gas sensing; environmental monitoring
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy
Interests: design; fabrication and characterization of integrated photonic; graphene-based and plasmonic devices; metasurfaces; optical sensors; microwaves devices; antennas
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Centre for Advanced Photonics and Process Analysis, Munster Technological University, T12 T66T Cork, Ireland
Interests: nanophotonics; integrated photonics; silicon photonics

E-Mail Website
Guest Editor
PolySense Lab—Dipartimento Interateneo di Fisica, University and Politecnico of Bari, Via Amendola 173, 70126 Bari, Italy
Interests: gas sensing; atmospheric spectroscopy; multivariate analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in photonics have opened up new opportunities for gas, liquid, solid sensing, and spectroscopy. This Special Issue is devoted to the Conference on Photonics for Advanced Spectroscopy and Sensing (C-PASS) organized in Castellaneta Marina, Italy, in September 2023. This Special Issue aims to focus on all aspects of research and development related to modeling and the development of integrated photonics alongside its applications in environmental monitoring, bio-medical sensing, agri-food analysis, and industrial process monitoring and control. The C-PASS Special Issue aims to bring submissions from industrial and academic research laboratories that relate to recent developments in integrated photonics, lasers, optical spectroscopy, and sensing in the near- and mid-infrared spectral regions. This Special Issue of Sensors will focus on the design and experimental verification of new photonic devices, spectroscopic instruments, and sensors based on techniques including but not limited to photothermal spectroscopy, photoacoustic spectroscopy, and quartz-enhanced photoacoustic spectroscopy. Original research papers that focus on these topics and papers that focus on their field testing are welcome.

Dr. Marilena Giglio
Dr. Marco Grande
Dr. Liam O’Faolain
Dr. Andrea Zifarelli
Guest Editors

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Keywords

  • integrated photonics
  • laser sources
  • optical spectroscopy
  • optical sensing
  • photoacoustic and photothermal gas and liquid sensing
  • light-induced thermoelastic spectroscopy
  • VOCs and particulate optical detection

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Published Papers (5 papers)

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Research

11 pages, 3182 KiB  
Communication
Micro-Ring Resonator Assisted Photothermal Spectroscopy of Water Vapor
by Maria V. Kotlyar, Jenitta Johnson Mapranathukaran, Gabriele Biagi, Anton Walsh, Bernhard Lendl and Liam O’Faolain
Sensors 2024, 24(11), 3679; https://doi.org/10.3390/s24113679 - 6 Jun 2024
Viewed by 743
Abstract
We demonstrated, for the first time, micro-ring resonator assisted photothermal spectroscopy measurement of a gas phase sample. The experiment used a telecoms wavelength probe laser that was coupled to a silicon nitride photonic integrated circuit using a fibre array. We excited the photothermal [...] Read more.
We demonstrated, for the first time, micro-ring resonator assisted photothermal spectroscopy measurement of a gas phase sample. The experiment used a telecoms wavelength probe laser that was coupled to a silicon nitride photonic integrated circuit using a fibre array. We excited the photothermal effect in the water vapor above the micro-ring using a 1395 nm diode laser. We measured the 1f and 2f wavelength modulation response versus excitation laser wavelength and verified the power scaling behaviour of the signal. Full article
(This article belongs to the Special Issue Photonics for Advanced Spectroscopy and Sensing)
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17 pages, 8111 KiB  
Article
Photothermal Radiometry Data Analysis by Using Machine Learning
by Perry Xiao and Daqing Chen
Sensors 2024, 24(10), 3015; https://doi.org/10.3390/s24103015 - 9 May 2024
Cited by 1 | Viewed by 700
Abstract
Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algorithms for learning from past data and analyzing new [...] Read more.
Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algorithms for learning from past data and analyzing new data, without being explicitly programmed to do so. In this paper, we first review the latest development of machine learning and its applications in photothermal techniques. Next, we present our latest work on machine learning for data analysis in opto-thermal transient emission radiometry (OTTER), which is a type of photothermal technique that has been extensively used in skin hydration, skin hydration depth profiles, skin pigments, as well as topically applied substances and skin penetration measurements. We have investigated different algorithms, such as random forest regression, gradient boosting regression, support vector machine (SVM) regression, and partial least squares regression, as well as deep learning neural network regression. We first introduce the theoretical background, then illustrate its applications with experimental results. Full article
(This article belongs to the Special Issue Photonics for Advanced Spectroscopy and Sensing)
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12 pages, 1340 KiB  
Communication
Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer—Preliminary Data
by Arcangelo Picciariello, Agnese Dezi, Leonardo Vincenti, Marcello Giuseppe Spampinato, Wenzhe Zang, Pamela Riahi, Jared Scott, Ruchi Sharma, Xudong Fan and Donato F. Altomare
Sensors 2024, 24(7), 2343; https://doi.org/10.3390/s24072343 - 7 Apr 2024
Cited by 1 | Viewed by 1368
Abstract
Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC [...] Read more.
Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC patients from healthy controls (HC). The aim of this study was to evaluate the reliability of an innovative portable device containing a micro-gas chromatograph in enabling rapid, on-site CRC diagnosis through analysis of patients’ exhaled breath. In this prospective trial, breath samples were collected in a tertiary referral center of colorectal surgery, and analysis of the chromatograms was performed by the Biomedical Engineering Department. The breath of patients with CRC and HC was collected into Tedlar bags through a Nafion filter and mouthpiece with a one-way valve. The breath samples were analyzed by an automated portable gas chromatography device. Relevant volatile biomarkers and discriminant chromatographic peaks were identified through machine learning, linear discriminant analysis and principal component analysis. A total of 68 subjects, 36 patients affected by histologically proven CRC with no evidence of metastases and 32 HC with negative colonoscopies, were enrolled. After testing a training set (18 CRC and 18 HC) and a testing set (18 CRC and 14 HC), an overall specificity of 87.5%, sensitivity of 94.4% and accuracy of 91.2% in identifying CRC patients was found based on three VOCs. Breath biopsy may represent a promising non-invasive method of discriminating CRC patients from HC. Full article
(This article belongs to the Special Issue Photonics for Advanced Spectroscopy and Sensing)
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12 pages, 3237 KiB  
Article
Mid-Infrared Photothermal Spectroscopy for the Detection of Caffeine in Beverages
by Giovanna Ricchiuti, Lisa Riedlsperger, Alicja Dabrowska, Erwin Rosenberg, Liam O’Faolain and Bernhard Lendl
Sensors 2024, 24(6), 1974; https://doi.org/10.3390/s24061974 - 20 Mar 2024
Cited by 1 | Viewed by 1490
Abstract
Caffeine is the most widely consumed stimulant and is the subject of significant ongoing research and discussions due to its impact on human health. The industry’s need to comply with country-specific food and beverage regulations underscores the importance of monitoring caffeine levels in [...] Read more.
Caffeine is the most widely consumed stimulant and is the subject of significant ongoing research and discussions due to its impact on human health. The industry’s need to comply with country-specific food and beverage regulations underscores the importance of monitoring caffeine levels in commercial products. In this study, we propose an alternative technique for caffeine analysis that relies on mid-infrared laser-based photothermal spectroscopy (PTS). PTS exploits the high-power output of the quantum cascade laser (QCL) sources to enhance the sensitivity of the mid-IR measurement. The laser-induced thermal gradient in the sample scales with the analytes’ absorption coefficient and concentration, thus allowing for both qualitative and quantitative assessment. We evaluated the performance of our experimental PTS spectrometer, incorporating a tunable QCL and a Mach–Zehnder interferometer, for detecting caffeine in coffee, black tea, and an energy drink. We calibrated the setup with caffeine standards (0.1–2.5 mg mL−1) and we benchmarked the setup’s capabilities against gas chromatography (GC) and Fourier-transform infrared (FTIR) spectroscopy. Quantitative results aligned with GC analysis, and limits of detection matched the research-grade FTIR spectrometer, indicating an excellent performance of our custom-made instrument. This method offers an alternative to established techniques, providing a platform for fast, sensitive, and non-destructive analysis without consumables as well as with high potential for miniaturization. Full article
(This article belongs to the Special Issue Photonics for Advanced Spectroscopy and Sensing)
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12 pages, 5944 KiB  
Article
Quartz-Enhanced Photoacoustic Sensor Based on a Multi-Laser Source for In-Sequence Detection of NO2, SO2, and NH3
by Pietro Patimisco, Nicoletta Ardito, Edoardo De Toma, Dominik Burghart, Vladislav Tigaev, Mikhail A. Belkin and Vincenzo Spagnolo
Sensors 2023, 23(21), 9005; https://doi.org/10.3390/s23219005 - 6 Nov 2023
Cited by 4 | Viewed by 1115
Abstract
In this work, we report on the implementation of a multi-quantum cascade laser (QCL) module as an innovative light source for quartz-enhanced photoacoustic spectroscopy (QEPAS) sensing. The source is composed of three different QCLs coupled with a dichroitic beam combiner module that provides [...] Read more.
In this work, we report on the implementation of a multi-quantum cascade laser (QCL) module as an innovative light source for quartz-enhanced photoacoustic spectroscopy (QEPAS) sensing. The source is composed of three different QCLs coupled with a dichroitic beam combiner module that provides an overlapping collimated beam output for all three QCLs. The 3λ-QCL QEPAS sensor was tested for detection of NO2, SO2, and NH3 in sequence in a laboratory environment. Sensitivities of 19.99 mV/ppm, 19.39 mV/ppm, and 73.99 mV/ppm were reached for NO2, SO2, and NH3 gas detection, respectively, with ultimate detection limits of 9 ppb, 9.3 ppb, and 2.4 ppb for these three gases, respectively, at an integration time of 100 ms. The detection limits were well below the values of typical natural abundance of NO2, SO2, and NH3 in air. Full article
(This article belongs to the Special Issue Photonics for Advanced Spectroscopy and Sensing)
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