Optical-based Techniques Research Articles

A First Extension of the Robust Satellite Technique RST-FLOOD to Sentinel-2 Data for the Mapping of Flooded Areas: The Case of the Emilia Romagna (Italy) 2023 Event

Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time.

Development Of A Spatio-Temporal Acoustic Pressure Measurement Technique Utilizing Background Oriented Schlieren Technique

We developed an image and optical-based technique to measure spatio-temporal acoustic pressure fields using BOS instead of a hydrophone. Fast checker demodulation (FCD) and vector tomography (VT) were employed for displacement detection and axisymmetric tomography, respectively. In experiments, BOS and hydrophones of different diameters measured focused ultrasound, revealing BOS's higher spatial resolution and ability to capture temporal evolution at 4.554 MHz. However, BOS showed systematic errors under amplified pressure. Using synthetic data, we identified that FCD's underestimation issues could be defined by the integrated power spectra of distorted images. Thus, BOS with FCD and VT provides high-resolution measurements but requires criteria to avoid displacement underestimation.

Monte Carlo-Based Optical Simulation of Optical Distribution in Deep Brain Tissues Using Sixteen Optical Sources.

Optical-based imaging has improved from early single-location research to further sophisticated imaging in 2D topography and 3D tomography. These techniques have the benefit of high specificity and non-radiative safety for brain detection and therapy. However, their performance is limited by complex tissue structures. To overcome the difficulty in successful brain imaging applications, we conducted a simulation using 16 optical source types within a brain model that is based on the Monte Carlo method. In addition, we propose an evaluation method of the optical propagating depth and resolution, specifically one based on the optical distribution for brain applications. Based on the results, the best optical source types were determined in each layer. The maximum propagating depth and corresponding source were extracted. The optical source propagating field width was acquired in different depths. The maximum and minimum widths, as well as the corresponding source, were determined. This paper provides a reference for evaluating the optical propagating depth and resolution from an optical simulation aspect, and it has the potential to optimize the performance of optical-based techniques.

Ultrasound biomicroscopy in the management of complex cataract and intraocular lens: A review.

Ultrasound biomicroscopy (UBM) is an invaluable investigation for imaging anterior segment structures. Although it is operator-dependent and time consuming, unlike optical-based imaging techniques, it is able to image structures posterior to the iris, such as the zonules, ciliary body and part of the pars plana. It is especially useful in advanced cataracts, traumatic cataracts, subluxed lenses, posterior polar cataracts, and congenital and developmental anomalies affecting the anterior segment. It provides diagnostic information in eyes with complex cataracts or intraocular lens (IOL)-related pathology, and aids in surgical planning in order to minimise complications. In this review, we describe the UBM features of various lenticular pathologies and demonstrate its application in the diagnosis and surgical management of lens and IOL-related pathologies.

Hyperelastic constitutive model parameters identification using optical-based techniques and hybrid optimisation

AbstractIn this paper we propose a new optical-based technique to identify the constitutive relation coefficients of the hyperelastic material using a hybrid optimisation approach. This technique can be used in place of traditional mechanical testing of elastomers for applications that involve inhomogeneous deformation. The purpose of the proposed method is to identify the incompressible hyperelastic material constitutive relation coefficients using a single experiment under different loading cases. The method comprises sample surface 3D reconstruction and uses finite element simulations to replicate the experiments, and uses a hybrid optimisation technique to minimise the error between actual 3D deformations and FE simulation results. The proposed hybrid technique predicts the hyperelastic constitutive relation coefficients more accurately than other optimisation methods. This study introduces a novel approach by employing a subpixel image registration algorithm for 3D reconstruction. The method requires a single experiment with diverse loading cases to accurately determine the coefficients of hyperelastic constitutive relations. The setup is portable and can be accommodated in a small suitcase. For this purpose, an apparatus was constructed comprising a stereoscopic system with eight cameras and a six-degree-of-freedom force-torque sensor to measure the induced forces and torques during the experiments. We identified the constitutive relation coefficients of Ogden N1, Ogden N3, Yeoh, and Arruda-Boyce relations which are commonly used models for silicone materials, using a traditional uniaxial test, optical uniaxial test (experiments performed using a constructed optical system), and inhomogeneous deformations tests. The study demonstrated that the coefficients obtained from inhomogeneous deformation tests provided the most accurate FE predictions. It was also shown that hyperelastic constitutive relation coefficients obtained from traditional uniaxial tests are insufficient to describe the material behaviour when the material undergoes inhomogeneous deformations.

Investigating the effect of surface termination and thickness of MXene on the detection response of the SPR sensor using FDTD simulation

Surface plasmon resonance (SPR) is an optical-based detection technique that is of great interest in research because of its rapid detection and label-free features. Current SPR sensor development focuses on modifying the sensing surface for maximum bioreceptor and target immobilization. In this research, we proposed SPR sensor with the Kretschmann configuration modified by adding immobilization layers using a few layers of MXene (Ti3C2T2). MXene is an alternative two-dimensional material suitable for sensors because it has surface termination (T2) which active to immobilize the target. The different surface termination (T=O,OH,F) and number of MXene layers was added on the SPR sensor to obtained the optimum parameters of MXene application. Using Finite-Difference Time-Domain (FDTD) method, the SPR sensor is simulated on a different refractive index background which is assumed as the analyte. The different surface termination produces different SPR curves at the same refractive index conditions, the changes are found in the position of the SPR angle and the width of the curve. Thus, the results of surface termination variations are evaluated based on the full-width half-maximum (FWHM) of the curve which indicates the detection accuracy. Meanwhile, the optimum number of MXene layers was evaluated from the sensitivity value of the MXene-modified SPR sensor to changes in refractive index from 1.33 to 1.34. The result shows that the optimum detection performance was obtained from the application of six layers MXene with surface termination F2(Ti3C2F2). This configuration resulted a sensitivity of 150.131°/RIU and the LOD is 0.01633 RIU.

Modeling light propagation in a reconstructed breast model using the Monte Carlo method

Breast cancer is one of the most common types of cancer that causes the highest death among women. One of the main aspects of effectively monitoring the effectiveness of breast cancer treatment is the early detection of abnormalities in breast tissue. It is one of the most critical factors that may determine how well breast cancer therapy is carried out. On the other hand, the techniques used in breast diagnostics today have several limitations, including the hazards connected with radiation exposure, the high price, and the likelihood of creating false-positive results. Therefore, optical techniques are considered feasible strategies for the early diagnosis of breast abnormalities. The optical-based technique has been studied to develop self-diagnostic aids and high-tech devices such as optical tomography. The recent development of therapeutic applications using low-power light also creates a new direction in treating benign tumors through photobiological effects. Therefore, much more research must be conducted to understand how light interacts with breast tissue for developing the diagnosis device and treatment device. In this study, light interaction with tissue was investigated by simulating light propagation at wavelengths of 650 nm, 800 nm, and 950 nm in a breast model that was generated using an MRI data collection. Specifically, the tissue's absorption of these three wavelengths was evaluated to understand better how light interacts with breast tissue. The results of the simulation show that normal breast models and sick breast models have significantly different total quantities of energy absorbed, and these results indicate this discrepancy. The distribution map acquired correlates with the image obtained using optical transillumination imaging. The simulation results can steer further research toward developing a novel breast diagnostics methodology based on optical approaches.

Characterization of agricultural biomass by particle induced x-ray emission and Rutherford backscattering techniques

Particle-induced x-ray emission (PIXE) and Rutherford backscattering spectrometry (RBS) techniques are non-destructive elemental analyses method with high sensitivity and capability for multi-element analysis in one spectrum. These techniques have not been used for the elemental characterization of biomass. So, this study characterized and evaluated elemental compositions of some selected agricultural biomass (Palm kernel shell (PKS), coconut shell (CS), cassava peel (CP), and Africa star apple seed shell (ASASS)) using combined PIXE and RBS. The results were compared with the ultimate analysis and optical-based techniques. Results from PIXE and RBS analysis confirm the presence of C, O, S, Na, Mg, P, Si, K, Ca, Fe, Cl, Al, Ti, Cu, and Zn in the tested biomass samples. Estimation of RBS accuracy by direct comparison with the ultimate analysis of some biomass samples showed a wide disparity of ±10.7% and ±2.57% in the RBS data for carbon and oxygen in PKS and a disparity of ±5.22% and ±0.55% in the RBS data for carbon and oxygen in coconut shell respectively. In the case of minor and trace element concentrations, estimated errors in the range of 1–3% and 2.68–4% were observed for optically based techniques compared with PIXE and RBS analysis, respectively.

Removal of humic acid interference in soil enzymatic analysis using poly-γ-glutamic acid.

Soil enzymes are biological indicators in environmental and agricultural monitoring. However, brownish humic acid (HA) in samples interferes significantly with various analytical methods, especially in optical-based techniques. Here, we implemented a coagulation-flocculation process to carry out continuously an enzymatic reaction without separation and transfer of a sample solution. The elimination of HA in a soil suspension using poly-γ-glutamic acid (PGA) by coagulation to minimize the HA interference in soil enzymatic analysis was investigated. As a result of the optimization of preliminary parameters, the removal efficiency of HA was > 92% in 100mg L-1 HA in neutral pH, using 100mg L-1 PGA and aluminum trivalent as a coagulant aid. However, the fluorescent intensity of the enzyme product (i.e., 4-methylumbelliferone) decreases by about 50% as HA was removed under the conditions used. A decrease in the enzymatic detection of 3,3',5,5'-tetramethylbenzidine (TMB) was not observed from treated samples even though the initial level of HA was different. The results suggested that the coagulation-flocculation approach is suitable for the reduction of HA interference, while maintaining target analyte detection. Therefore, the proposed sample treatment can be used to examine enzyme activity based on TMB product detection without regular standard addition calibration.

Auto-Processing Of Filtered Rayleigh Scattering Images Including Mie And Background Scattering Contributions

Filtered Rayleigh scattering (FRS) is a non-intrusive, optical-based technique that allows for simultaneous, time-averaged, measurements of three-component velocity, static temperature, and static pressure. The method of post-processing the raw images to get these variables is a non-trivial task. The post-processing scheme starts with building a model to generate simulated spectra given known velocity, temperature, and pressure values which can be iterated upon. The model also includes Mie scattering and background scattering contributions that must be taken into account. This iteration scheme allows for the simulated spectra to be matched to the experimental spectra to back out the desired flow variables. This iteration process can be lengthy and so it is important to make spectrum generation/iteration as fast as possible. This is done through the use of support vector spectrum approximation (SVSA), which is a machine learning algorithm, as well as a multivariable minimum error solver based on the least-squares fit between the simulated data and the experimental data. To prove the methodology, simulated results were first compared at different signal-to-noise ratios to determine the expected uncertainty at each SNR level. It was shown that to achieve an error of less than 1\% in all variables (velocity, static temperature, Mie intensity ratio, and background intensity ratio), the SNR must be near 40dB. This kind of SNR level is generally not expected in an experiment. Therefore, a representative experiment was conducted which included Mie and background scattering contributions. It was shown that manually processed data was capable of achieving velocity results that were within 2\% of the probe data. No other variables were achieved. The auto-processing scheme was able to achieve an error of approximately 2.5\% in velocity when compared to probe data. It was also capable of providing static temperature, Mie intensity ratio, and background intensity ratio values. The manual iteration results took several days to achieve while the auto-processing took about an hour. It is clear that this methodology is capable of automatically processing images from experiments with Mie and background scattering contributions while saving time.

Real-time measurements of mineral dust concentration in coarse particulate matter (PM10–2.5) by employing a novel optical-based technique in Los Angeles

As a primary component of coarse particulate matter (PM), ambient mineral dust has been linked to adverse health effects. Los Angeles, the largest metropolitan urban area of the United States, is impacted by both windblown and localized sources of mineral dust, often internally mixed with black carbon. The estimation of mineral dust concentrations with a high time resolution becomes critical in improving our understanding of its sources and temporal trends. Using Aethalometers combined with a high-volume virtual impactor (VI) to enrich coarse (2.5 <dp < 10 μm) particles, the light absorption and mass concentration of mineral dust were estimated in real-time during summer, fall, and winter over 2020–2021. The concentration-enriched coarse PM was collected on Teflon filters, and its chemical composition in terms of trace elements and metals was chemically quantified. The high time-resolution measurements enabled us to calculate the absorption coefficient of enriched dust particles by subtracting the light absorption of the post-VI coarse PM from that of the PM2.5 aerosol fraction to reduce the impact of stronger light absorbers in ambient PM. Mineral dust was more prevalent during the fall and winter campaigns (i.e., 19.3 and 11.4 μg/m3, respectively), lower concentrations were observed during the summer campaign (i.e., 8.50 μg/m3). The calculated absorption Ångström exponent (AAE) was 2.18, highlighting the presence of dust particles during the sampling period. The dust mass absorption coefficient was estimated to be 2.7 ± 1.6 Mm−1 at 370 nm and 0.41 ± 0.16 Mm−1 at 880 nm wavelengths, respectively. The validation of the proposed approach was investigated by comparing the evaluated mineral dust mass concentrations in this study with the reported coarse PM concentrations by the California Air Resources Board (CARB). The results reported by the optical-based approach with high temporal resolution can provide crucial information on identifying sources of mineral dust in urban areas.

Bacteria eat nanoprobes for aggregation-enhanced imaging and killing diverse microorganisms

Currently optical-based techniques for in vivo microbial population imaging are limited by low imaging depth and highly light-scattering tissue; and moreover, are generally effective against only one specific group of bacteria. Here, we introduce an imaging and therapy strategy, in which different bacteria actively eat the glucose polymer (GP)-modified gold nanoparticles through ATP-binding cassette (ABC) transporter pathway, followed by laser irradiation-mediated aggregation in the bacterial cells. As a result, the aggregates display ~15.2-fold enhancement in photoacoustic signals and ~3.0-fold enhancement in antibacterial rate compared with non-aggregated counterparts. Significantly, the developed strategy allows ultrasensitive imaging of bacteria in vivo as low ~105 colony-forming unit (CFU), which is around two orders of magnitude lower than most optical contrast agents. We further demonstrate the developed strategy enables the detection of ~107 CFU bacteria residing within tumour or gut. This technique enables visualization and treatment of diverse bacteria, setting the crucial step forward the study of microbial ecosystem.

Optical techniques in non-destructive detection of wheat quality: A review

Wheat quality detection is essential to ensure the safety of wheat circulation and storage. The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis, which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity, destruction of sample integrity and low efficiency. With the rapid development of optical technology, various optical-based methods, using near-infrared spectroscopy technology, hyperspectral imaging technology and terahertz, etc., have been proposed for wheat quality detection. These methods have the characteristics of non-destructiveness and high efficiency which make them popular in wheat quality detection in recent years. In this paper, various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail. Firstly, the principle and process of common optical non-destructive detection methods for wheat quality are introduced. Then, the optical techniques used in these detection methods are divided into seven categories, and the comparison of these technologies and their advantages and disadvantages are further discussed. It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies, because it can not only detect most types of wheat deterioration, but also has higher accuracy and efficiency. Finally, the research of optical technology in wheat quality detection is prospected. The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database, the fusion of multiple optical detection technologies and multiple quality index information, the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality. These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.

Real-Time Measurements of Mineral Dust Concentration in Coarse Particulate Matter (Pm2.5-10) by Employing a Novel Optical-Based Technique in Los Angeles

Real-Time Measurements of Mineral Dust Concentration in Coarse Particulate Matter (Pm2.5-10) by Employing a Novel Optical-Based Technique in Los Angeles

Quantitative detection of benzoyl peroxide in wheat flour using line-scan short-wave infrared hyperspectral imaging

The addition of benzol peroxide (BPO) to wheat flour as a bleaching agent has been widely recognized as an important food safety issue due to its negative effects on human health. To address this issue, various nondestructive optical-based techniques have been developed to screen for BPO, such as Raman spectroscopy and hyperspectral imaging (HSI). In this study, a shortwave infrared (SWIR) HSI system was developed for the rapid detection of BPO particles in wheat flour. The SWIR HSI system, detailed hyperspectral image processing procedures, and optimal model to detect BPO particles were evaluated. The model was developed using the partial least square regression (PLSR) method. To improve performance of the model, effective wavelength regions were selected, and various pre-processing methods were applied to the PLSR analysis. The developed model was able to detect BPO in wheat flour at 50–6400 ppm with a high determinant coefficient (> 0.985) between predicted and actual values. The developed SWIR HIS system and optimized model demonstrated a high potential for discriminating BPO particles in wheat flour and allowed for its quantitative evaluation.

Electrochemical bioassay coupled to LAMP reaction for determination of high-risk HPV infection in crude lysates

Electrochemical (EC) detection of DNA biomarkers represents an interesting tool in molecular oncology due to its sensitivity, simplicity, low cost or rapid times of measurement. However, majority of EC assays, same as most optical-based techniques, require preceding DNA extraction step to remove other cellular components, making these assays more laborious and time-consuming. One option to circumvent this is to use LAMP (loop-mediated amplification), an isothermal amplification technique that can amplify DNA directly in crude lysates in a short time at a constant temperature. Here, we coupled the LAMP reaction with EC readout to detect DNA from the two most common oncogenic human papillomavirus (HPV) types that cause cervical cancer in women, i.e. HPV 16 and HPV 18, directly in crude lysates without a need for DNA extraction step. We show that in crude lysates, the LAMP reaction was superior to PCR, with very good selectivity on a panel of cancer cell lines and with high sensitivity, enabling detection of HPV DNA from as few as 10 cells. As a proof of principle, we applied the assay to nineteen clinical samples both from uninfected women and from women suffering from cervical precancerous lesions caused by HPV 16 or HPV 18 genotypes. Clinical samples were simply boiled for 5 min in homogenization buffer without DNA extraction step, and amplified with LAMP. We obtained excellent concordance of our assay with PCR, reaching 100% sensitivity for both genotypes, 81.82% specificity for HPV 16 and 94.12% specificity for HPV 18. Proposed assay could be a straightforward, simple, rapid and sensitive alternative for early diagnostics of precancerous cervical lesions.

Measuring perceived sweetness by monitoring sorbitol concentration in apples using a non-destructive polarization-based readout.

The determination and qualification of sugars in fruits are important for quality control and assurance of horticultural produce. The sugars determine the sweetness levels in fruits. The requirement for a universal technique that is also robust to predict the sweetness of the fruit in a non-destructive fashion is immense. The handheld refractometer, hydrometer, electronic tongues, and high-pressure liquid chromatography (HPLC) in combination with other detectors have long been used to determine the sweetness of horticultural produce. Though these techniques are very accurate and useful, they require extensive sample preparation and are generally time-consuming and expensive. Optical techniques like visible to near-infrared spectroscopy (vis/NIRS) are simple in use and can rapidly predict the sweetness of the fruit in a non-destructive fashion. The instrumentation used in these techniques is simple and cost-effective for routine analysis of the fruits. However, their systems need calibration for each sample, and the measurement variation depends on the type of horticultural produce on which measurements are done. An optical-based technique is proposed that uses reflected phase information of the incident light and correlates the same to the presence of sorbitol concentration in apples. The refractive index of sorbitol varies as the fruit ripens due to its change in concentration, and the reflected phase information accordingly changes. Monitoring the reflected phase information allows a prediction mechanism of the sweetness content in the fruit.

Reconstruction of refractive index maps using photogrammetry

Large volume metrology is a key enabler of autonomous precision manufacturing. For component positioning, the optical-based metrology technique of photogrammetry could be used more widely if its accuracy was improved. These positional measurements are subject to uncertainties which can be greater than manufacturing tolerances. One source of uncertainty is due to thermal gradients, which cause the refraction of the light rays in large-scale industrial environments. This paper uses light-based sensor data to reconstruct a heterogeneous spatial map of the refractive index in air. We use this reconstructed refractive index map to discount the refractive effects and thereby reduce the uncertainty of this positioning problem. This new inverse problem employs Voronoi tessellations to spatially parameterize the refractive index map, the Fast Marching Method to solve the forward problem of calculating the light rays through this medium, and a Bayesian approach in the inversion. Using simulated data, this methodology leads to positioning improvements of up to 37.

Development and validation of hybrid Brillouin-Raman spectroscopy for non-contact assessment of mechano-chemical properties of urine proteins as biomarkers of kidney diseases

BackgroundProteinuria is a major marker of chronic kidney disease (CKD) progression and the predictor of cardiovascular mortality. The rapid development of renal failure is expected in those patients who have higher level of proteinuria however, some patients may have slow decline of renal function despite lower level of urinary protein excretion. The different mechanical (visco-elastic) and chemical properties, as well as the proteome profiles of urinary proteins might explain their tubular toxicity mechanism. Brillouin light scattering (BLS) and surface enhanced Raman scattering (SERS) spectroscopies are non-contact, laser optical-based techniques providing visco-elastic and chemical property information of probed human biofluids. We proposed to study and compare these properties of urinary proteins using BLS and SERS spectroscopies in nephrotic patient and validate hybrid BLS-SERS spectroscopy in diagnostic of urinary proteins as well as their profiling. The project ultimately aims for the development of an optical spectroscopic sensor for rapid, non-contact monitoring of urine samples from patients in clinical settings.MethodsBLS and SERS spectroscopies will be used for non-contact assessment of urinary proteins in proteinuric patients and healthy subjects and will be cross-validated by Liquid Chromatography-Mass Spectrometry (LC-MS). Participants will be followed-up during the 1 year and all adverse events such as exacerbation of proteinuria, progression of CKD, complications of nephrotic syndrome, disease relapse rate and inefficacy of treatment regimen will be registered referencing incident dates. Associations between urinary protein profiles (obtained from BLS and SERS as well as LC-MS) and adverse outcomes will be evaluated to identify most unfavored protein profiles.DiscussionThis prospective study is focused on the development of non-contact hybrid BLS - SERS sensing tool and its clinical deployment for diagnosis and prognosis of proteinuria. We will identify the most important types of urine proteins based on their visco-elasticity, amino-acid profile and molecular weight responsible for the most severe cases of proteinuria and progressive renal function decline. We will aim for the developed hybrid BLS - SERS sensor, as a new diagnostic & prognostic tool, to be transferred to other biomedical applications.Trial registrationThe trial has been approved by ClinicalTrials.gov (Trial registration ID NCT04311684). The date of registration was March 17, 2020.

P0666BRILLOUIN AND RAMAN SPECTROSCOPIES FOR NON-CONTACT ASSESSMENT OF MECHANO-CHEMICAL PROPORTIES OF URINARY PROTEINS: A PROOF OF CONCEPT STUDY

Abstract Background and Aims Proteinuria is a major marker of chronic kidney disease (CKD) severity, the predictor of renal function decline and cardiovascular mortality. The main pathway of CKD progressive is caused by tubular toxicity of filtering proteins, so more, the rapid development of renal failure is expected in those patients who have higher level of proteinuria. However, despite high level of proteinuria, paradoxically, some patients may have slow decline of renal function compared to those patients with lower level of urinary protein excretion. This may possibly due to their different mechanical (visco-elastic) and chemical properties, as well as the proteomics of excreting urinary proteins. Brillouin light scattering (BLS) and surface enhanced Raman scattering (SERS) spectroscopies are non-contact (laser optical-based) techniques providing visco-elastic and chemical property information of probed human biofluids. We aimed to study and compare the visco-elastic and chemical properties of urinary proteins using BLS and SERS spectroscopies in nephrotic patient with high protein excretion and healthy subject. Method Two urine samples from nephrotic patient (proteinuria&amp;gt;3.5 g/24h) and healthy subject (undetectable proteinuria) investigated using BLS and SERS spectroscopies. Brillouin spectroscopy performed using Torus single longitudinal mode laser in conjunction with high contrast JRS Instruments 6-pass tandem Fabry-Perot interferometer coupled with confocal microscope. Using Horiba LabRAM HR Raman microscope with 785 nm laser excitation andx100 microscope objective the SERS spectroscopy was performed on urine samples in contact 60 nm gold nanoparticles placed on gold film. Additionally, reversed-phase liquid chromatography-mass spectrometry (LC-MS) was conducted for profiling of urinary proteins LC-MS data was analysed by Mascot software using the SwissProt protein database. Results We found that elevated protein levels in urine alter the urine fluid elasticity sufficiently to be measurable by BLS spectroscopy, with urine from healthy and proteinuric subjects distinguishable to marked significance, which increases with sample concentration. BLS spectroscopy demonstrated that patients with High Protein Loss, characterized by stiffer urine (i.e. higher Brillouin shift and elastic modulus) and less viscous (i.e. less hypersound attenuation) compared to healthy subject. (Figure 1A). SERS measurements of two urine samples taken from patient with high protein loss in healthy subject (Figure 1B) showed the integrated Raman intensity ratio of 1338 cm-1 and 1002 cm-1 peaks is many times higher for the sample with high protein concentration vs sample with low protein concentration. Urinary protein sequencing and Mascot analysis (Table 1) demonstrated that proteinuric patient had different type of proteins with up to 187k Daltons of molecular weight. Conclusion In this proof of concept study, we demonstrated that laser-optical BLS and SERS techniques combined with LC-MS measurements revealed that human urine fluids of healthy and proteinuric subjects had strong variation in the visco-elastic and chemical properties coupled with various level of protein content.