Optical Detection Techniques Research Articles

A Review on Photoacoustic Spectroscopy Techniques for Gas Sensing.

The rapid growth of industry and the global drive for modernization have led to an increase in gas emissions, which present significant environmental and health risks. As a result, there is a growing need for precise and sensitive gas-monitoring technologies. This review delves into the progress made regarding photoacoustic gas sensors, with a specific focus on the vital components of acoustic cells and acoustic detectors. This review highlights photoacoustic spectroscopy (PAS) as an optical detection technique, lauding its high sensitivity, selectivity, and capability to detect a wide range of gaseous species. The principles of photoacoustic gas sensors are outlined, emphasizing the use of modulated light absorption to generate heat and subsequently detect gas pressure as acoustic pressure. Additionally, this review provides an overview of recent advancements in photoacoustic gas sensor components while also discussing the applications, challenges, and limitations of these sensors. It also includes a comparative analysis of photoacoustic gas sensors and other types of gas sensors, along with potential future research directions and opportunities. The main aim of this review is to advance the understanding and development of photoacoustic gas detection technology.

A Sensitive Fluorescence Analysis Method of Pathogenic Microorganisms Based on Silicon Photomultiplier.

The monitoring of pathogenic microorganisms in water is important for public health and disease outbreaks prediction. Recently, optical detection techniques have drawn much attention due to the advantages of rapid response, security and high sensitivity. In this paper, a fluorescence spectrometer based on 375nm exciting laser and the microchannel liquid sample flow technology is proposed. The 4 × 4 narrowband filter array was coupled to a Silicon Photomultiplier (SiPM) array with single-photon sensitivity. B500 fluorescent microspheres and Escherichia coli were used for performance evaluation of the spectrometer. As a result, it is feasible to use random particle counting method to detect the bacteria concentration level in water even low to several CFU/mL. In addition, based on Python tools and neural network algorithm models, the fluorescence spectra of different kinds of substances (biotic and abiotic) can be classified with an accuracy of more than 97%. The method was successfully applied to tap water samples. The results suggest that the proposed method is applicable for on-site bacteria detection.

SPR humidity dynamic monitoring method via PVA sensing membrane thickness variation and image processing techniques

Humidity monitoring is paramount in diverse applications, industrial, and medical applications. Surface Plasmon Resonance (SPR) is an optical detection technique capable of sensing various environmental parameters through changes in reflected optical spectra and has garnered significant attention. Typically, SPR sensing employs a single-point detection strategy with the sample at a fixed concentration to achieve optimal sensitivity, limiting its application in dynamic environmental testing. This study proposes an image-based SPR humidity monitoring method, integrating SPR with image processing, enabling dynamic parameter reconstruction, and achieving high responsiveness. Au-PVA is used as a sensing film. To attain the best sensing film thickness, sensing film thicknesses ranging from 94.0 nm to 243.3 nm were tested. Through optimizing film thickness and image data processing, high precision and dynamic responsiveness were achieved. Experimental results demonstrate a response time of 84 ms and an average relative prediction error of 1.57 % for the sensor. Our research holds significant promise for dynamic and accurate humidity detection.

Advanced optical imaging techniques for bladder cancer detection and diagnosis: a systematic review.

To systematically assess the current available literature concerning advanced optical imaging methods for the detection and diagnosis of bladder cancer (BCa), focusing particularly on the sensitivity and specificity of these techniques. First a scoping search was performed to identify all available optical techniques for BCa detection and diagnosis. The optical imaging techniques used for detecting BCa are: the Storz professional image enhancement system (IMAGE1 S), narrow-band imaging (NBI), photoacoustic imaging (PAI), autofluorescence imaging (AFI), photodynamic diagnosis (PDD), and scanning fibre endoscopy (SFE). The staging and grading techniques for BCa are: optical coherence tomography (OCT), confocal laser endomicroscopy (CLE), Raman spectroscopy, endocytoscopy, and non-linear optical microscopy (NLO). Then a systematic literature search was conducted using MEDLINE, EMBASE and Web of Science from inception to 21 November 2023. Articles were screened and selected by two independent reviewers. Inclusion criteria were: reporting on both the sensitivity and specificity of a particular technique and comparison to histopathology, and in the case of a detection technique comparison to white light cystoscopy (WLC). Out of 6707 articles, 189 underwent full-text review, resulting in 52 inclusions. No articles met criteria for IMAGE1 S, PAI, SFE, Raman spectroscopy, and endocytoscopy. All detection techniques showed higher sensitivity than WLC, with NBI leading (87.8-100%). Overall, detection technique specificity was comparable to WLC, with PDD being most specific (23.3-100%). CLE and OCT varied in sensitivity and specificity, with OCT showing higher specificity for BCa diagnosis, notably for carcinoma in situ (97-99%) compared to CLE (62.5-81.3%). NLO demonstrated high sensitivity and specificity (90-97% and 77-100%, respectively) based on limited data from two small ex vivo studies. Optical techniques with the most potential are PDD for detecting and OCT for staging and grading BCa. Further research is crucial to validate their integration into routine practice and explore the value of other imaging techniques.

High-Precision Fiber Noise Detection and Comparison over a 260 km Field Fiber Link.

In this paper, we present a high-precision optical frequency noise detection and comparison technique using a two-way transfer method over a 260 km field fiber link. This method allows for the comparison of optical frequencies between remote optical references without the need for data transfer through communication. We extend a previously established two-way comparison technique to obtain all data at the local site. Two optical carrier signals are injected into the bidirectional fiber from both ends, and one carrier is reflected back from the remote end. This enables the phase comparison of the two carrier signals at a single site without the need to transmit experimental data. The common-mode frequency noise induced by the bidirectional fiber link is detected and effectively suppressed without the need for sophisticated active fiber noise control. Our demonstration system, which uses a 260 km field fiber link and a common laser source, achieves a fractional instability of 2.5×10-17 at 1 s averaging time and scales down to 3.5×10-21 at 8000 s. This scheme offers the distinct advantage of completing the comparison at a single site, eliminating the need for remote data transfer via communication. This method is expected to enhance reliability for high-precision frequency comparisons between remote optical clocks and advanced atomic clocks.

Rapid and real-time analysis of multi-component dissolved gas in seawater by Raman spectroscopy combined with continuous gas−liquid separator

Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle. Large quantities of in situ optical detection techniques showed restricted measurement efficiency, owing to the single gas sensor without the identification ability of multiple gases. In this work, a novel gas−liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas−liquid separator under a large difference of partial pressure. The limit of detection (LOD) of the gas Raman spectrometer could arrive at about 14 μl·L−1 for N2 gas. Moreover, based on the continuous gas−liquid separation process, the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method (30 min). Effect of equilibrium time on gas−liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO2 >O2 >N2. In addition, the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed. The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.

Fluorescence molecular tomography based on an online maximum a posteriori estimation algorithm

Fluorescence molecular tomography (FMT) is a non-invasive, radiation-free, and highly sensitive optical molecular imaging technique for early tumor detection. However, inadequate measurement information along with significant scattering of near-infrared light within the tissue leads to high ill-posedness in the inverse problem of FMT. To improve the quality and efficiency of FMT reconstruction, we build a reconstruction model based on log-sum regularization and introduce an online maximum a posteriori estimation (OPE) algorithm to solve the non-convex optimization problem. The OPE algorithm approximates a stationary point by evaluating the gradient of the objective function at each iteration, and its notable strength lies in the remarkable speed of convergence. The results of simulations and experiments demonstrate that the OPE algorithm ensures good reconstruction quality and exhibits outstanding performance in terms of reconstruction efficiency.

Identification of medium- and mechanism-related pitfalls towards improved performance and applicability of electrochemical mercury(II) aptasensors.

The importance of understanding the mercury (II) ion interactions with thymine-rich DNA sequences is the reason for multiple comparative investigations carried out with the use of optical detection techniques directly in the depth of solution. However, the results of such investigations have limited applicability in the interpretation of the Hg2+ binding phenomenon by DNA sequences in thin, interfacial (electrode/solution), self-organized monolayers immobilized on polarizable surfaces, often used forsensing purposes in electrochemical biosensors. Overlooking the careful optimization of the measurement conditions is the source of discrepancies in the interpretation of the registered electrochemical signal. In this study, the chosen effects accompanying the efficiency of surface related recognition of Hg2+ by polyThymine DNA sequences labelled with methylene blue were investigated by voltammetry, QCM and spectro-electrochemical techniques. As was shown, the composition of the biosensing layer and buffers or the analytical procedures have a significant impact on the registered electrochemical readout which translates into signal stability, the biosensor's working parameters or even the mechanism of detection. After elucidation of the above factors, the complete and ready-to-use biosensor-based analytical solution was proposed offering subpicomolar mercury ion determination with high selectivity (also in aqueous real samples), reusability, and high signal stability even after long-term storage. The developed procedures were successfully used during the miniaturization process with self-prepared (PVD) elastic transducers. The obtained sensor, together with the simplicity of its use, low manufacturing cost, and attractive analytical parameters (i.e., LOD < < Hg2+ WHO limit) can present an interesting alternative for on-site mercury ion detection in environmental samples.

Multi-scale representation of surface-enhanced Raman, spectroscopy data for deep learning-based liver, cancer detection

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

Point Projection Mapping System for Tracking, Registering, Labeling, and Validating Optical Tissue Measurements.

The validation of newly developed optical tissue-sensing techniques for tumor detection during cancer surgery requires an accurate correlation with the histological results. Additionally, such an accurate correlation facilitates precise data labeling for developing high-performance machine learning tissue-classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with the histopathology results is proposed and validated on a case study. The proposed framework provides a more-robust and accurate method for the tracking and validation of optical tissue-sensing techniques, which saves time and resources compared to the available conventional techniques.

A colour sensor integrated into a microcontroller platform as a reliable tool for measuring pH changes in biochemistry applications.

Colorimetry is a widely used technique for optical detection in point-of-care testing and on-site detection. Although some studies employ a multiplex approach to analyse coloured solutions, many still analyse one sample at a time. We have prepared a simple and affordable colorimetric assay based on a TCS34725 colour sensor (ams-OSRAM) integrated into an M5Stack module and an RGB LED module both inserted into a 3D printed frame. We found that the colorimetric assay can be easily transferred to a colour sensing platform, and the signal range obtained using the prepared colorimeter is more than 200 times larger than that obtained using digital image colorimetry (DIC) for the same samples containing cholinesterase or urease as a model enzyme providing a change in pH of the processed solution. The assay appears to be ready for practical use.

A Review of Gas Generation Mechanisms and Optical Detection Techniques for Mineral Oil

A Review of Gas Generation Mechanisms and Optical Detection Techniques for Mineral Oil

Research on temperature and pressure interference in photoacoustic spectroscopy gas detection

AbstractPhotoacoustic spectroscopy (PAS) is a highly sensitive optical detection technique for trace gases. However, PAS is sensitive to environmental factors such as temperature and pressure during actual measurement. In this paper, we study the interference characteristics of temperature and pressure in PAS gas detection. The influence of pressure and temperature on PAS is theoretically analyzed based on the basic principle of gas photoacoustic spectroscopy. The effect of different pressure and temperature conditions on the resonance frequency and signal amplitude of PAS is studied through COMSOL software simulation and validated through experiments. Results show that changes in temperature and pressure will affect the resonance frequency and signal amplitude of PAS, thereby impacting the system's sensitivity and accuracy. To reduce the impact of temperature on the PAS sensor, a temperature compensation model was derived based on experimental analysis. Real‐time temperature detection is used to compensate for the photoacoustic signal based on temperature changes, which can improve the accuracy and stability of photoacoustic spectroscopy gas detection. For 5% CO2, the photoacoustic signal and experimental temperature data were measured for 3612 s, and after temperature compensation, the detection limit was 0.0192% at an integration time of 365 s, which is 0.0128% higher than before temperature compensation. The system's normalized noise equivalent absorption coefficient is 3.30 × 10−8 cm−1 WHz−1/2. This research improves the accuracy and reliability of photoacoustic spectroscopy gas detection technology and provides useful references for temperature correction in similar gas detection fields.

Deep Mutual Learning-Based Mode Recognition of Orbital Angular Momentum

Due to its orbital angular momentum (OAM), optical vortex has been widely used in communications and LIDAR target detection. The OAM mode recognition based on deep learning is mostly based on the basic convolutional neural network. To ensure high-precision OAM state detection, a deeper network structure is required to overcome the problem of similar light intensity distribution of different superimposed vortex beams and the effect of atmospheric turbulence disturbance. However, the large number of parameters and the computation of the OAM state detection network conflict with the requirements of deploying optical communication system equipment. In this paper, an online knowledge distillation scheme is selected to achieve an end-to-end single-stage training and the inter-class dark knowledge of similar modes are fully utilized. An optical vortex OAM state detection technique based on deep mutual learning (DML) is proposed. The simulation results show that after mutual learning training, a small detection network with higher accuracy can be obtained, which is more suitable for terminal deployment. Based on the scalability of the number of networks in the DML queue, it provides a new possibility to further improve the detection accuracy of the optical communication.

A 3D-Printed Micro-Optofluidic Chamber for Fluid Characterization and Microparticle Velocity Detection.

This work proposes a multi-objective polydimethylsiloxane (PDMS) micro-optofluidic (MoF) device suitably designed and manufactured through a 3D-printed-based master-slave approach. It exploits optical detection techniques to characterize immiscible fluids or microparticles in suspension inside a compartment specifically designed at the core of the device referred to as the MoF chamber. In addition, we show our novel, fast, and cost-effective methodology, dual-slit particle signal velocimetry (DPSV), for fluids and microparticle velocity detection. Different from the standard state-of-the-art approaches, the methodology focuses on signal processing rather than image processing. This alternative has several advantages, including the ability to circumvent the requirement of complex and extensive setups and cost reduction. Additionally, its rapid processing speed allows for real-time sample manipulations in ongoing image-based analyses. For our specific design, optical signals have been detected from the micro-optics components placed in two slots designed ad hoc in the device. To show the devices' multipurpose capabilities, the device has been tested with fluids of various colors and densities and the inclusion of synthetic microparticles. Additionally, several experiments have been conducted to prove the effectiveness of the DPSV approach in estimating microparticle velocities. A digital particle image velocimetry (DPIV)-based approach has been used as a baseline against which the outcomes of our methods have been evaluated. The combination of the suitability of the micro-optical components for integration, along with the MoF chamber device and the DPSV approach, demonstrates a proof of concept towards the challenge of real-time total-on-chip analysis.

Remote detection of bovine serum albumin (BSA) using cantilever beam magnetometer

Detection of bovine serum albumin (BSA) is important to comprehend the severity of a certain diseases. Here, we report on the remote detection of bovine serum albumin (BSA) using modified cantilever beam magnetometery (CBM). A magnetostrictive Fe70Ga30 cantilever in combination with optical detection technique allowed us to detect BSA concentration up to 1 mg/mL in a remote way. Essentially, a position sensitive detector (PSD) is used to detect reflected light from cantilever without any optical mirrors. Apart from the above, magnetostriction of Fe70Ga30 cantilever estimated as 80 ppm at 180 mT. We ascertain that our method is easy and efficient in estimating the magnetostriction of thin films up to 10-6 order. We believe that CBM technique that we developed would be helpful in estimating magnetostriction of bulk and thin films apart from the detection of bio – molecules.

Au-Coated ZnO Surface-Enhanced Raman Scattering (SERS) Substrates: Synthesis, Characterization, and Applications in Exosome Detection.

Developing a biomolecular detection method that minimizes photodamage while preserving an environment suitable for biological constituents to maintain their physiological state is expected to drive new diagnostic and mechanistic breakthroughs. In addition, ultra-sensitive diagnostic platforms are needed for rapid and point-of-care technologies for various diseases. Considering this, surface-enhanced Raman scattering (SERS) is proposed as a non-destructive and sensitive approach to address the limitations of fluorescence, electrochemical, and other optical detection techniques. However, to advance the applications of SERS, novel approaches that can enhance the signal of substrate materials are needed to improve reproducibility and costs associated with manufacture and scale-up. Due to their physical properties and synthesis, semiconductor-based nanostructures have gained increasing recognition as SERS substrates; however, low signal enhancements have offset their widespread adoption. To address this limitation and assess the potential for use in biological applications, zinc oxide (ZnO) was coated with different concentrations (0.01-0.1 M) of gold (Au) precursor. When crystal violet (CV) was used as a model target with the synthesized substrates, the highest enhancement was obtained with ZnO coated with 0.05 M Au precursor. This substrate was subsequently applied to differentiate exosomes derived from three cell types to provide insight into their molecular diversity. We anticipate this work will serve as a platform for colloidal hybrid SERS substrates in future bio-sensing applications.

Point‐of‐Care Testing (POCT) Devices for DNA Detection: A Comprehensive Review

DNA chips play a crucial role in point‐of‐care diagnostics by enabling rapid and accurate detection of genetic information. These chips offer high sensitivity and selectivity, allowing for the identification of specific DNA sequences associated with diseases and pathogens. Integration into lab‐on‐chip platforms streamlines and miniaturizes diagnostic workflows, paving the way for cost‐effective, portable, and user‐friendly testing devices that can revolutionize healthcare delivery. In this review, a comprehensive description of the platforms utilized in DNA analysis, including microfluidic devices and integrated DNA chips, is provided. It explores the selection and immobilization of DNA probes for improved selectivity. Additionally, it covers diverse detection techniques such as optical detection (colorimetry and fluorescence) and electrochemical techniques. In these discussions, it is aimed to provide a thorough understanding of the current state of the art in DNA biosensor‐integrated lab‐on‐chip technology for point‐of‐care testing. The continued advancements in DNA chip technology hold immense promise for the development of next‐generation point‐of‐care diagnostics, where integrated sample preparation and rapid results generation can further enhance patient outcomes and contribute to the effective management of diseases.

Recent advances of application of optical imaging techniques for disease detection in fruits and vegetables: A review

Fruits and vegetables are among the agricultural products that enjoy a high demand in the market. However, the most critical challenge in the production of fruits and vegetables is disease infections which can lead to economic loss. The current assessment method of disease infection still relies on conventional methods and laboratory analysis. The conventional methods although simple and easy are laborious and have less accuracy while laboratory analysis is costly and time-consuming. Over the last two decades, optical imaging techniques have emerged and are gaining interest in the agricultural and food industries. These techniques offer easy, rapid, accurate, reliable as well as user-friendly sensing tools for the identification of quality and disease infection in agricultural products. The fundamental aspect in the techniques is the interaction of light with the tissue and the manipulation of light when interacting with a surface. The integration of edge computing along with artificial intelligence has also reshaped the technology to another level. The use of other agricultural technologies such as drones or robots enables real-time monitoring and efficient farm management. Thus, this paper reviews recent advances in the application of optical imaging techniques for disease detection in fruits and vegetables. This includes computer vision, multispectral imaging, hyperspectral imaging, biospeckle and thermal imaging. The feasibility of the optical imaging techniques is discussed. Challenges and considerations for future research are also highlighted. This review provides a new insight into the recent application of optical imaging which is not only beneficial for the agricultural and food industries but also to other relevant industries.

A novel SVMA and K-NN classifier based optical ML technique for seizure detection

A novel SVMA and K-NN classifier based optical ML technique for seizure detection