Optical Detection Techniques Research Articles

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

Multilayered Bioorthogonal SERS Nanoprobes Selectively Aggregating in Human Fluids: A Smart Optical Assay for β-Amyloid Peptide Quantification.

Alzheimer's disease (AD) is a debilitating neurological condition characterized by cognitive decline, memory loss, and behavioral skill impairment, features that worsen with time. Early diagnosis will likely be the most effective therapy for Alzheimer's disease since it can ensure timely pharmacological treatments that can reduce the irreversible progression and delay the symptoms. Amyloid β-peptide 1-42 (Aβ (1-42)) is considered one of the key pathological AD biomarkers that is present in different biological fluids. However, Aβ (1-42) detection still relies on colorimetric and enzyme-linked immunoassays as the gold standard characterized by low accuracy or high costs, respectively. In this context, optical detection techniques based on surface-enhanced Raman spectroscopy (SERS) through advanced nanoconstructs are promising alternatives for the development of novel rapid and low-cost methods for the targeting of Aβ pathological biomarkers in fluids. Here, a multilayered nanoprobe constituted by bioorthogonal Raman reporters (RRs) embedded within two layers of gold nanoparticles (Au@RRs@AuNPs) has been developed and successfully validated for specific detection of Aβ (1-42) in the human cerebrospinal fluid (CSF) with sensitivity down to pg/mL. The smart double-layer configuration enables us to exploit the outer gold NP surfaces for selective absorption of targeted peptide whose concentration controls the aggregation behavior of Au@RRs@AuNPs, proportionally reflected in Raman intensity changes, providing high specificity and sensitivity and representing a significant step ahead of the state of the art on SERS for clinical analyses.

Assessing and detection of multiple bruises in peaches based on structured hyperspectral imaging

This study aimed to detect various types of postharvest damages in peaches based on structured hyperspectral imaging (S-HSI), including impact, falling, and compression damage, which can lead to bruising. The research involved three different spatial frequencies (60, 100, and 150 m−1) and used a 2π/3 phase shift interval to capture S-HSI images. These images were then processed using a mathematical demodulated model to create high-resolution image cubes that included both image and spectral information from the S-HSI data. Artificial neural network and principal component analysis were applied to develop bruise detection models using S-HSI spectra, which showed better discriminating effects compared with the ordinary hyperspectral spectra. The best performing discriminating models for healthy and three kinds of bruised samples were developed using the spectra of spatial frequency with 100 + 150 m−1, respectively. This study demonstrated the potential of S-HSI as an effective optical technique for bruise detection of peach.

Photoacoustic Spectral Response using Ultrasound and Interferometric Sensors: A Correlation Study for a High Bandwidth Real-Time Blood Vasculature Monitoring Application in a Chick-Embryo Chorioallantoic Membrane (CAM) Model.

Photoacoustic (PA) spectral response technique has shown good promise in efficient preclinical tissue diagnosis by depicting mechano-biological properties due to high spatial resolution and penetration depth. The conventional PA-based system is a pump-probe technique that utilizes neodymium-doped yttrium aluminum garnet pulsed laser as a pump and an ultrasound sensor as a probe. For biomedical studies, high-speed PA signals need to be acquired, requiring higher bandwidth ultrasound sensors. While the bandwidth increases, they exhibit a very low signal-to-noise ratio that inhibits acquiring PA signals of biomedical samples. An interferometer-based probe has recently been investigated as a potential ultrasound probe for obtaining PA signals as an alternative. This optical PA detection technique offers high sensitivity by combining low acoustic impedance with high electromechanical coupling. However, there is a lack of exploration of the same for real-time biomedical studies. This work shows the development of a homodyne Mach-Zehnder interferometer-based PA spectral response (PASR) followed by a correlation study between the conventional ultrasound sensor and the interferometer-based sensor. Further, this study demonstrates the capability of continuous monitoring of vascular growth and the effect of an antidrug (Cisplatin) on the vasculature tested on a chick-embryo chorioallantoic membrane model. PASR was able to monitor growth changes within one day, which was not possible with conventional methods. This opens up potential possibilities for using this technique in biomedical applications.

Quantitative analysis of the degree of demineralization for bleached enamel by optical coherence tomography

BackgroundTooth bleaching imparts whitening effects along with adverse effects such as increased tooth sensitivity and enamel surface changes. Herein, we employed optical coherence tomography (OCT), a nondestructive optical detection technique, for evaluation of tooth enamel after treatment with peroxide-based bleaching agents. MethodsFifteen enamel samples were bleached using 38% acidic hydrogen peroxide-based bleach, subjected to OCT scanning, and then cross-sectioned and imaged under polarized light microscopy (PLM) and transverse microradiography (TMR). OCT cross-sectional images were compared with PLM and TMR. The depth and severity of demineralization produced in the bleached enamel were measured by OCT, PLM, and TMR. Comparison between the three techniques was performed using Kruskal-Wallis H non-parametric test and Pearson correlation. ResultsIn comparison with PLM and TMR, OCT clearly detected the changes in the enamel surface after hydrogen peroxide bleaching. Significant correlations (p<0.05) were observed in lesion depth between OCT and PLM (r=0.820), OCT and TMR (r=0.822), and TMR and PLM (r=0.861). There was no statistically significant difference in demineralization depth values measured by OCT, PLM, and TMR (p>0.05). ConclusionOCT can allow real-time, non-invasive imaging of artificially bleached tooth models and automatically measure the early changes in the enamel lesion structure upon exposure to hydrogen peroxide-based bleaching agents.

Rapid screening of Turkey DNA fingerprint using highly sensitive label free plasmonic SERS biosensor

Meat adulteration has been raised posing serious impact on human health upon consumption. In particular, Turkey meat being low-cost is added in higher volume with duck and goose meat to gain profit. Herein SERS (surface enhanced Raman spectroscopy) being a highly sensitive optical detection technique is adopted to target label free Turkey DNA sensing. Pulsed laser deposition was utilized to fabricate various metallic nanomaterial aggregates (Ag, Au, Cu, Al) on solid substrates of Quartz and Si at constant number of laser ablations (3000NLP). The fabricated SERS biosensors are characterized for their structural, morphological and optical properties. Based on their characteristics of metallic nanomaterials, the SERS properties are explained in detail with probe molecule Rhodamine 6 G (R6G). The experimental scrutinization was correlated and validated with Finite difference time domain (FDTD) simulations. Amongst all the nanomaterials, Au/Si was found to possess enhanced sensing (Au/Q:2.8 × 107 & Au/Si:3.9 × 107) with the adopted PLD strategy. Au/Si SERS biosensor was projected with label free turkey DNA sensing which was considered as a major food adulterant with other meat types. The developed Au/Si SERS label free biosensor offers high stability, reproducibility as well ultra-sensitivity with turkey DNA sensing down to 1.8 fg/uL.

Spectroscopic studies on a natural biomarker for the identification of origin and quality of tea extracts for the development of a portable and field deployable prototype

Even in the era of smart technologies and IoT enabled devices, tea testing technique continues to be a person specific subjective task. In this study, we have employed optical spectroscopy-based detection technique for the quantitative validation of tea quality. In this regard, we have employed the external quantum yield of quercetin at 450 nm (λex = 360 nm), which is an enzymatic product generated by the activity of β-glucosidase on rutin, a naturally occurring metabolite responsible for tea-flavour (quality). We have found that a specific point in a graph representing Optical Density and external Quantum Yield as independent and dependent variables respectively of an aqueous tea extract objectively indicates a specific variety of the tea. A variety of tea samples from various geographical origin have been analysed with the developed technique and found to be useful for the tea quality assessment. The principal component analysis distinctly showed the tea samples originated from Nepal and Darjeeling having similar external quantum yield, while the tea samples from Assam region had a lower external quantum yield. Furthermore, we have employed experimental and computational biology techniques for the detection of adulteration and health benefit of the tea extracts. In order to assure the portability/field use, we have also developed a prototype which confirms the results obtained in the laboratory. We are of the opinion that the simple user interface and almost zero maintenance cost of the device will make it useful and attractive with minimally trained manpower at low resource setting.

Optical Techniques for Fungal Disease Detection in Citrus Fruit: A Review

Optical Techniques for Fungal Disease Detection in Citrus Fruit: A Review

A beginner’s guide to surface plasmon resonance

Surface plasmon resonance (SPR) has emerged as a powerful optical detection technique for studying the binding behaviour of immobilized ligands and analytes in solution. The technique makes it possible to measure interactions in real time with high sensitivity. Over the past two decades, SPR has become the gold standard for studying biomolecular interactions in biomedical research and drug discovery. The interactions that can be studied are diverse and include protein–protein, protein–small molecule, protein–nucleic acid, protein–carbohydrate, lipid–protein, hybrid systems of biomolecules and non-biological surfaces. SPR allows researchers to determine which molecules interact, how strongly they bind and inform experiments using mutants, truncations or other variations to probe specificity. This article summarizes the principle and experimental set-up and illustrates the utility of SPR using the example of lipid–protein interactions.

Plasmons in a square of two-dimensional electrons

Microwave absorption spectra of a single square of two-dimensional electrons (2DESs) have been investigated using an optical detection technique. Fundamental dipole and harmonic quadrupole plasmon modes have been identified and compared to those in the disk geometry. In the square-shaped 2DES, a strong interaction is discovered between the neighboring plasmon modes, whereas no such hybridization is observed in the disk-shaped geometry. We establish a rigid theoretical platform to analytically describe the magneto-optical response of confined two-dimensional systems. The developed theory provides a proper description of the obtained experimental results.

A Fiber-Coupled Quartz-Enhanced Photoacoustic Sensor for Dissolved Gas Detection

Detection of dissolved gases in oceans is critically needed for global carbon cycle investigation. However, most in situ optical detection techniques, as far as we know, have restricted measurement efficiency due to large gas consumption. Herein, we develop a sub-mL photoacoustic gas sensor with a simple configuration. A single-mode fiber directly guides the incident laser into the photoacoustic cell without any other free-space optics. Thus, a reduced inner size of 12 mm × 6 mm × 4 mm enables the effective detection of limited dissolved gas. We employ methane (CH4) as an example to demonstrate its sensing performance. The sensor achieves a good linear response with an R-square value of 0.9989 and a minimum detection limit of 1.1 ppmv, corresponding to a normalized noise equivalent absorption coefficient of 7.75 × 10−8 W·cm−1·Hz−1/2.

A Label-Free Gold Nanoparticles-Based Optical Aptasensor for the Detection of Retinol Binding Protein 4.

Retinol-binding protein 4 (RBP4) has been implicated in insulin resistance in rodents and humans with obesity and T2DM, making it a potential biomarker for the early diagnosis of T2DM. However, diagnostic tools for low-level detection of RBP4 are still lagging behind. Therefore, there is an urgent need for the development of T2DM diagnostics that are rapid, cost-effective and that can be used at the point-of-care (POC). Recently, nano-enabled biosensors integrating highly selective optical detection techniques and specificity of aptamers have been widely developed for the rapid detection of various targets. This study reports on the development of a rapid gold nanoparticles (AuNPs)-based aptasensor for the detection of RBP4. The retinol-binding protein aptamer (RBP-A) is adsorbed on the surface of the AuNPs through van der Waals and hydrophobic interactions, stabilizing the AuNPs against sodium chloride (NaCl)-induced aggregation. Upon the addition of RBP4, the RBP-A binds to RBP4 and detaches from the surface of the AuNPs, leaving the AuNPs unprotected. Addition of NaCl causes aggregation of AuNPs, leading to a visible colour change of the AuNPs solution from ruby red to purple/blue. The test result was available within 5 min and the assay had a limit of detection of 90.76 ± 2.81 nM. This study demonstrates the successful development of a simple yet effective, specific, and colorimetric rapid assay for RBP4 detection.

Wearable chemical sensors for biomarker discovery in the omics era.

Biomarkers are crucial biological indicators in medical diagnostics and therapy. However, the process of biomarker discovery and validation is hindered by a lack of standardized protocols for analytical studies, storage and sample collection. Wearable chemical sensors provide a real-time, non-invasive alternative to typical laboratory blood analysis, and are an effective tool for exploring novel biomarkers in alternative body fluids, such as sweat, saliva, tears and interstitial fluid. These devices may enable remote at-home personalized health monitoring and substantially reduce the healthcare costs. This Review introduces criteria, strategies and technologies involved in biomarker discovery using wearable chemical sensors. Electrochemical and optical detection techniques are discussed, along with the materials and system-level considerations for wearable chemical sensors. Lastly, this Review describes how the large sets of temporal data collected by wearable sensors, coupled with modern data analysis approaches, would open the door for discovering new biomarkers towards precision medicine.

Helicity dependent photoresistance measurement vs. beam-shift thermal gradient

Optical detection techniques are among the most powerful methods used to characterize spintronic phenomena. The spin orientation can affect the light polarization, which, by the reciprocal mechanism, can modify the spin density. Numerous recent experiments, report local changes in the spin density induced by a circularly polarized focused laser beam. These effects are typically probed electrically, by detecting the variations of the photoresistance or photocurrent associated to the reversal of the light helicity. Here we show that in general, when the light helicity is modified, the beam profile is slightly altered, and the barycenter of the laser spot is displaced. Consequently, the temperature gradients produced by the laser heating will be modulated, producing thermo-electric signals that alternate in phase with the light polarization. These unintended signals, having no connection with the electron spin, appear under the same experimental conditions and can be easily misinterpreted. We show how this contribution can be experimentally assessed and removed from the measured data. We find that even when the beam profile is optimized, this effect is large, and completely overshadows the spin related signals in all the materials and experimental conditions that we have tested.

Space Object and Background Polarization Models and Detectability Analysis

During the daytime, the commonly used optical intensity imaging techniques for spacecraft detection usually lead to poor detection due to strong background noise. Since some materials with low reflectivity in the visible band have higher reflectivity in the near-infrared band and less sky background noise, this paper carries out a study of near-infrared polarization detection techniques. In this paper, firstly, the parameters in the pBRDF model of common materials are inverted and found to be improved compared with the literature, and secondly, a complex spherical projection is proposed to represent the polarization degree and polarization angle information of the sky background, and this projection has less aberration than the direct projection. Finally, from the above, the polarization detection contrast range and the original intensity contrast range are calculated, and it is found that the maximum of the former is higher than the latter in some angles.

Justification of the Photoplethysmography Sensor Configuration by Monte Carlo Modeling of the Pulse Waveform

Photoplethysmography (PPG) is an optical technique for detection of blood volume changes in the microvascular bed of a biological tissue. Many aspects of the PPG signal formation are still unclear. In particular, it is not known how the shape of a registered PPG signal depends on the geometry of tissue illumination. The aim of this study is to model the PPG waveform using the Monte Carlo (MC) method. For this, we developed a three-layer optical model of the skin in a reflectance geometry and verified it experimentally for different wavelengths (660, 810, and 940 nm) and source-detector distances (from 4 to 10 mm). The MC simulation results showed that the PPG waveform depends on the source-detector distance. The most pronounced diastolic wave is observed at the distance of 6 mm for the wavelength of 810 nm. The results obtained can be used for the development of reflectance PPG sensors.

Elastic net-based non-negative iterative three-operator splitting strategy for Cerenkov luminescence tomography.

Cerenkov luminescence tomography (CLT) provides a powerful optical molecular imaging technique for non-invasive detection and visualization of radiopharmaceuticals in living objects. However, the severe photon scattering effect causes ill-posedness of the inverse problem, and the location accuracy and shape recovery of CLT reconstruction results are unsatisfactory for clinical application. Here, to improve the reconstruction spatial location accuracy and shape recovery ability, a non-negative iterative three operator splitting (NNITOS) strategy based on elastic net (EN) regularization was proposed. NNITOS formalizes the CLT reconstruction as a non-convex optimization problem and splits it into three operators, the least square, L1/2-norm regularization, and adaptive grouping manifold learning, then iteratively solved them. After stepwise iterations, the result of NNITOS converged progressively. Meanwhile, to speed up the convergence and ensure the sparsity of the solution, shrinking the region of interest was utilized in this strategy. To verify the effectiveness of the method, numerical simulations and in vivo experiments were performed. The result of these experiments demonstrated that, compared to several methods, NNITOS can achieve superior performance in terms of location accuracy, shape recovery capability, and robustness. We hope this work can accelerate the clinical application of CLT in the future.

Orbital angular momentum optical communications enhanced by artificial intelligence

Angular momentum of light can be divided into spin angular momentum and orbital angular momentum (OAM). Due to the theoretically unlimited orthogonal states, the physical dimension of OAM provides a potential solution to boost the information capacity. The OAM multiplexing and modulation techniques have been implemented to meet the continuous growth of bandwidth requirements, resulting in the concept of OAM optical communication. However, the performances of the traditional optical OAM detection techniques degrade seriously in the practical application of OAM optical communications. Thanks to the powerful data analysis advantages, the cutting-edge machine learning (ML) algorithms have been widely used in the field of image processing, laying the technical foundation for OAM recognition. This paper reviews the recent advances on OAM optical communications that are enhanced by ML methods. More than the traditional OAM detection methods, the OAM demodulation methods based on multiple network architectures, including the support vector machine, self-organizing map, feed-forward neural network, convolutional neural network, and diffractive deep optical neural network (D2NN), have been summarized. We also discuss the development of the spiking neural network and on-chip D2NN, opening a possible way to facilitate the future ultra-low power and ultra-fast OAM demodulation technology.