Reflection Intensity Research Articles

Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR

Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud data visually represent spatial features but lack attribute information, posing challenges for automated object classification and effective management. Commercial software primarily relies on manual classification, which is time-intensive. This study addresses these challenges by using the laser reflectance intensity (I-value) for automated classification. Boxplot theory is applied to calibrate the data, remove noise, and establish polynomial regression equations correlating intensity with scanning distances. These equations serve as attribute functions for classifying datasets. Focusing on materials in traditional Minnan architecture on Kinmen Island, controlled indoor experiments and outdoor case studies validate the approach. The results show classification accuracies of 74% for wood, 98% for stone, and 93% for brick, demonstrating this method’s effectiveness in enhancing point cloud data applications and management.

Sequential Multimodal Underwater Single-Photon Lidar Adaptive Target Reconstruction Algorithm Based on Spatiotemporal Sequence Fusion

For the demand for long-range and high-resolution target reconstruction of slow-moving small underwater targets, research on single-photon lidar target reconstruction technology is being carried out. This paper reports the sequential multimodal underwater single-photon lidar adaptive target reconstruction algorithm based on spatiotemporal sequence fusion, which has strong information extraction and noise filtering ability and can reconstruct the target depth and reflective intensity information from complex echo photon time counts and spatial pixel relationships. The method consists of three steps: data preprocessing, sequence-optimized extreme value inference filtering, and collaborative variation strategy for image optimization to achieve high-quality target reconstruction in complex underwater environments. Simulation and test results show that the target reconstruction method outperforms the current imaging algorithms, and the built single-photon lidar system achieves underwater lateral and distance resolution of 5 mm and 2.5cm@6AL, respectively. This indicates that the method has a great advantage in sparse photon counting imaging and possesses the capability of underwater target imaging under the background of strong light noise. It also provides a good solution for underwater target imaging of small slow-moving targets with long-distance and high-resolution.

A hyperspectral stealth material design method based on the composition and mixing spectral feature of desert soil

In this study, we used desert soil from Gansu, China, as a sample to propose a method for designing hyperspectral stealth coatings against desert soil backgrounds within the spectral range of 400–2500 nm, and the corresponding coating was prepared. Firstly, the correlation between the composition and typical spectral detected characteristics of the desert soil was systematically analyzed. It was found that the color and the spectrum of the desert soil in the range of 400–1000 nm were influenced by different types of iron oxides. The main spectral characteristic and reflection intensity at 1000–2500 nm were impacted by quartz and montmorillonite. Subsequently, the design method for hyperspectral stealth coatings was developed by analyzing the differences in spectral and structural characteristics between the coatings and the soil. The prepared coating exhibited similar color and spectral shape to the soil in the range of 400–1000 nm, with comparable spectral features around 1414 nm, 1915 nm, 2212 nm, 2250 nm, and 2346 nm. The correlation coefficient and the spectral cosine angle between the reflectance spectra of the coating and the soil within the 400–2500 nm wavelength were calculated to be 0.989 and only 0.05 radians, respectively. The effectiveness of the coating in achieving excellent camouflage against the desert soil background was confirmed through the analysis of multispectral images and thermal infrared temperature. This study holds significant importance for the application of hyperspectral stealth techniques in desert soil scenarios.

Impact of foam metal hoods on pressure waves generated by high-speed trains traversing tunnels

The high-speed trains traveling at 400 km/h will generate severe alternating pressure and potential sonic boom when passing through tunnels. This paper proposed foam metal hoods (FMH) to mitigate the pressure waves induced by trains traversing tunnels. 1:20 scaled moving-model experiments were conducted to investigate the mitigation mechanisms of FMH on micro-pressure waves (MPW), residual pressure, and aerodynamic loads on the train and tunnel. The impact of FMH's installation position and length on MPW and residual pressure were discussed. The results indicate that the entrance FMH can weaken the expansion wave generated by the tail train entering the tunnel, thereby reducing the pressure amplitude on the train surface and tunnel wall. FMH can reduce the reflection intensity of pressure waves, effectively lowering the root mean square (RMS) of residual pressure. Installing FMH at both ends can reduce the RMS of residual pressure in the middle of the tunnel by 25%. The exit FMH enables the initial wavefront to gradually release pressure outward, thereby reducing MPW intensity. The radiation range of the MPW iso-surface is narrowed by energy consumption as the wavefront passes through the porous structures. The mitigation ratio of MPW intensifies as the length of the exit FMH increases. Using a 4-m-long exit FMH can decrease the MPW amplitude by 83.2% at 20 m from the FMH exit. The FMH facilitates a low-noise environment near tunnel portals, reducing the aerodynamic loads on the tunnel structures, and mitigating the train aerodynamic loads.

Real-time data processing for serial crystallography experiments.

We report the use of streaming data interfaces to perform fully online data processing for serial crystallography experiments, without storing intermediate data on disk. The system produces Bragg reflection intensity measurements suitable for scaling and merging, with a latency of less than 1 s per frame. Our system uses the CrystFEL software in combination with the ASAP::O data framework. In a series of user experiments at PETRA III, frames from a 16 megapixel Dectris EIGER2 X detector were searched for peaks, indexed and integrated at the maximum full-frame readout speed of 133 frames per second. The computational resources required depend on various factors, most significantly the fraction of non-blank frames (`hits'). The average single-thread processing time per frame was 242 ms for blank frames and 455 ms for hits, meaning that a single 96-core computing node was sufficient to keep up with the data, with ample headroom for unexpected throughput reductions. Further significant improvements are expected, for example by binning pixel intensities together to reduce the pixel count. We discuss the implications of real-time data processing on the `data deluge' problem from recent and future photon-science experiments, in particular on calibration requirements, computing access patterns and the need for the preservation of raw data.

Comparing and Optimizing Four Machine Learning Approaches to Radar-Based Quantitative Precipitation Estimation

To improve radar-based quantitative precipitation estimation (QPE) methods, this study investigated the relationship between radar reflectivity (Z) and hourly rainfall intensity (R) using data from 289 precipitation events in Shanghai between September 2020 and March 2024. Two Z-R relationship models were compared in terms of their fitting performance: Z = 270.81 R1.09 (empirically fitted relationship) and Z = 300 R1.4 (standard relationship). The results show that the Z = 270.81 R1.09 model outperforms the Z = 300 R1.4 model in terms of fitting accuracy. Specifically, the Z = 270.81 R1.09 model more effectively captures the nonlinear relationship between radar reflectivity and rainfall intensity, with a higher degree of agreement between the fitted curve and the observed data points. This model demonstrated superior performance across all 289 precipitation events. This study evaluated the performance of four machine learning approaches while incorporating five meteorological features: specific differential phase shift (KDP), echo-top height (ET), vertical liquid water content (VIL), differential reflectivity (ZDR), and correlation coefficient (CC). Nine QPE models were constructed using these inputs. The key findings are as follows: (1) For models with a single-variable input, the KAN deep learning model outperformed Random Forest, Gradient Boosting Decision Trees, Support Vector Machines, and the traditional Z-R relationship. (2) When six features were used as inputs, the accuracy of the machine learning models improved significantly, with the KAN deep learning model outperforming other machine learning methods. Compared to using only radar reflectivity, the KAN deep learning model reduced the MRE by 20.78%, MAE by 4.07%, and RMSE by 12.74%, while increasing the coefficient of determination (R2) by 18.74%. (3) The integration of multiple meteorological features and machine learning optimization significantly enhanced QPE accuracy, with the KAN deep learning model performing best under varying meteorological conditions. This approach offers a promising method for improving radar-based QPE, particularly considering seasonal, weather system, and precipitation stage differentiation.

A small-scale spatial heterogeneity in photochemical reflectance index and intensity of reflected light at 530 nm in pea (Pisum sativum) leaves is sensitive to action of salinization.

Remote sensing of stressor action on plants is an important step of their protection. Measurement of photochemical reflectance index (PRI) can be used to detect action of stressors including salinization; potentially, a small-scale spatial heterogeneity of PRI (within leaf or its part) can be an indicator of this action. The current work was devoted to analysis of sensitivity of the small-scale heterogeneity in PRI and in the reflected light intensity at 530nm (approximately corresponding to the measuring wavelength for PRI) in leaves of pea (Pisum sativum ) plants to action of salinization. Plants were cultivated under controlled conditions of a vegetation room and under open-air conditions. It was shown that both the standard deviation of PRI and coefficient of variation of the reflected light intensity at 530nm were sensitive to action of salinization on plants. Moreover, this variation coefficient was negatively corelated to the potential quantum yield of PSII; i.e. increasing the coefficient could be used to estimate decreasing this yield caused by photodamage of PSII under salinization. Our results show that the small-scale spatial heterogeneity in PRI and the reflected light intensity at 530nm can be used as additional tools of the remote sensing of plant responses under action of salinization.

Nonlinear optical characterization of opaque and scattering media: from rough surfaces to powder

We introduce the reflection intensity correlation scan (RICO-scan), a nonlinear (NL) optical technique designed to characterize opaque and scattering media, where traditional transmittance methods fail. By analyzing variations in the intensity correlation functions of speckle patterns generated from backscattered light, the RICO-scan was applied to an unpolished silicon surface and silicon powders, providing information on the intensity dependence of the complex refractive index. Numerical simulations based on Fresnel equations and speckle propagation corroborated the experimental results, demonstrating RICO-scan’s robustness and versatility. The RICO-scan is the first, to the best of our knowledge, NL optical technique capable of characterizing the third-order nonlinearity of powdered materials, offering a reliable tool for studying disordered complex systems.

Exploring Imaging Techniques for Detecting Tomato Spotted Wilt Virus (TSWV) Infection in Pepper (Capsicum spp.) Germplasms.

Due to the vulnerability of pepper (Capsicum spp.) and the virulence of tomato spotted wilt virus (TSWV), seasonal shortages and surges of prices are a challenge and thus threaten household income. Traditional bioassays for detecting TSWV, such as observation for symptoms and reverse transcription-PCR, are time-consuming, labor-intensive, and sometimes lack precision, highlighting the need for a faster and more reliable approach to plant disease assessment. Here, two imaging techniques-Red-Green-Blue (RGB) and hyperspectral imaging (using NDVI and wavelength intensities)-were compared with a bioassay method to study the incidence and severity of TSWV in different pepper accessions. The bioassay results gave TSWV an incidence from 0 to 100% among the accessions, while severity ranged from 0 to 5.68% based on RGB analysis. The normalized difference vegetative index (NDVI) scored from 0.21 to 0.23 for healthy spots on the leaf but from 0.14 to 0.19 for disease spots, depending on the severity of the damage. The peak reflectance of the disease spots on the leaves was identified in the visible light spectrum (430-470 nm) when spectral bands were studied in the broad spectrum (400.93-1004.5 nm). For the selected wavelength in the visible light spectrum, a high reflectance intensity of 340 to 430 was identified for disease areas, but between 270 and 290 for healthy leaves. RGB and hyperspectral imaging techniques can be recommended for precise and accurate detection and quantification of TSWV infection.

Amyloid beta biomarker for dementia detection by hyperspectral ophthalmoscope images.

The escalating prevalence and economic burden of dementia underscore the urgency for innovative detection methods. This study investigates the potential of hyperspectral imaging (HSI) to detect dementia by analyzing retinal changes associated with amyloid beta (Aβ) formations. Leveraging a dataset of 3,256 ophthalmoscopic images from 137 participants aged 60 to 85 years, categorized into dementia and non-dementia groups via the Mini-Mental State Examination (MMSE), we extracted features from five key regions of interest (ROIs) identified for their pronounced changes in Aβ biomarkers. The analysis revealed that gender does not significantly influence dementia levels, and no substantial spectral differences were observed within the 380 nm to 600 nm wavelength range. However, significant variations in spectral reflection intensity were noted between 600 nm and 780 nm across both genders, suggesting a potential avenue for distinguishing stages of dementia. Despite the impact of diabetes on the vascular system, its stages did not significantly influence dementia development. This research highlights the utility of HSI in identifying dementia-related retinal changes and calls for further exploration into its effectiveness as a diagnostic tool, potentially offering a non-invasive method for early detection of dementia.

Spatial Sequential Matching Enhanced Underwater Single-Photon Lidar Imaging Algorithm

Traditional LiDAR and air-medium-based single-photon LiDAR struggle to perform effectively in high-scattering environments. The laser beams are subject to severe medium absorption and multiple scattering phenomena in such conditions, greatly limiting the maximum operational range and imaging quality of the system. The high sensitivity and high temporal resolution of single-photon LiDAR enable high-resolution depth information acquisition under limited illumination power, making it highly suitable for operation in environments with extremely poor visibility. In this study, we focus on the data distribution characteristics of active single-photon LiDAR operating underwater, without relying on time-consuming deep learning frameworks. By leveraging the differences in time-domain distribution between noise and echo signals, as well as the hidden spatial information among echo signals from different pixels, we rapidly obtain imaging results across various distances and attenuation coefficients. We have experimentally verified that the proposed spatial sequential matching enhanced (SSME) algorithm can effectively enhance the reconstruction quality of reflection intensity maps and depth maps in strong scattering underwater environments. Through additional experiments, we demonstrated the algorithm’s reconstruction effect on different geometric shapes and the system’s resolution at different distances. This rapidly implementable reconstruction algorithm provides a convenient way for researchers to preview data during underwater single-photon LiDAR studies.

Designs for photovoltaic glass surface texturing to improve transmittance and minimize glare

Planar glass cover creates optical reflection loss and glare, which is harmful to energy efficiency and effective operation of PV modules, especially at larger angles of incidence (AOIs). Textured surfaces can reduce reflections and glare intensity. In this work, three textured glass surfaces are described and simulated numerically over a wide range of AOIs. The anti-reflection effect and light trapping effect are provided to analyze the transmission gain across a wide range of AOIs. The reflection times is proposed to assess the anti-glare effect of the textured surfaces. It is found that the hexagonal array structured surface exhibits the highest transmission gain and anti-glare effect. The optimized hexagonal array structured surface could improve the average transmission by ∼5% relative to planar surface for AOIs of 0°–80°. In addition, the reflection times is doubled, which helps to diminish the reflection and glare intensity.

Advanced Detection of Underwater Gas Seep Sites Through Multibeam Echosounder Water Column Data and Numerical Analysis

Abstract Water column imaging of multibeam echosounder systems (MBES) are sensitive and potential devices for investigating free gas bubbles release and their ascent up the water column. These data could demonstrate previously undetected characteristics on the water’s surface and seabed which are related to Sustainable Development Goal 14 about life below water. The research utilizes a MBES to map the volume of seabed gas emission bubbles in the Adriatic Sea, Italy, using water column data. The survey covered 1.5 km2 around a four-legged gas platform at a depth of 77 meters. To achieve a 50% overlap, ten parallel transects of 1.5 km each were used, with a vessel speed range of 2-2.6 m/s and a transect spacing of 100 m. Acoustic waves from seabed seepage were visualized using water column data, with reflection intensities ranging from −63.5 dB to 29 dB, reflecting the acoustic reflectance of various suspended materials. More precise thresholds were obtained by filtering and clustering the gas bubbles using the point weight approach to separate them from the noise and water bubbles. The uneven Digital Terrain Model (DTM) indicates gas emissions through water column data. The volume of gas bubbles was determined by visualizing the points in a 3D format using XYZ coordinates. Through interpolation techniques and 3D volumetric analysis, six bubble locations were obtained with volumes of 651.12 m3, 108.30 m3, 42.00 m3, 167.20 m3, 186.00 m3, 287.81 m3, and 45.00 m3. This study is crucial because it questions the methods of Carbon Capture and Storage (CCS) by investigating the discharge of carbon into the sea. Furthermore, this study helps to identify emission sources, measure the volume of released gas, and explain the depth distribution of emissions, providing essential data for marine CCS assessment.

In-situ synthesis and integration of gold nanoparticles into 3D printed optical fiber probes

This work uses the polymeric reduction method to explore the in-situ synthesis of gold nanoparticles (AuNPs) within 3D-printed optical fiber probes (OFPs). Digital light processing (DLP) 3D printing is employed to fabricate the OFPs using a resin consisting of hydroxyethyl methacrylate (HEMA) and polyethylene glycol diacrylate (PEGDA). After printing, OFPs were immersed in a boiling gold precursor solution to facilitate the synthesis of AuNPs inside the polymer matrix. We produced single material (HEMA/PEGDA) and multimaterial (HEMA/PEGDA + Dentaclear) OFPs loaded with AuNPs at different concentrations. Scanning electron microscopy analysis confirmed the effective distribution and dispersion of AuNPs within the polymer matrix. The optical properties, including reflection and transmission spectra, are comprehensively measured using customized setups. The localized surface plasmon resonance of the embedded AuNPs created a distinct dip in the 500–600 nm wavelength range. Higher AuNP concentrations and longer dipping times enhanced light absorption, reducing reflection and transmission intensities. Multimaterial OFPs also exhibited tunable wavelength filtering capabilities based on the AuNP concentration. The AuNP-loaded OFPs demonstrated stable optical performance across varying temperatures and pH environments, highlighting their potential for diverse applications.

Study of the Local and Average Structure of Vanadium Oxides As Potential Cathodes for Mg-Ion Batteries

Being a divalent cation with the lowest mass, Mg2+ has garnered quite attention in recent times due to the high volumetric capacity of it’s batteries. Theoretical studies reveal that the combination of a Mg anode and a spinel oxide cathode would be able to surpass the current limits found in widely used Li-ion batteries Therefore, to design materials with high diffusion and reversible insertion capacity, a conclusive demonstration of the intercalation mechanism of Mg2+ into metal oxides, is required.Among the numerous options for cathodes, vanadium oxides show evidence of the largest capability toward reversible intercalation of Mg ions. In particular, spinel-type MgV2O4 shows a capacity of 200 mAh/g, corresponding to the cycling of ~0.7 mol Mg2+ per mol of oxide, and layered α-V2O5 reaches 300 mAhg-1, intercalating 1 mol Mg2+ per mol of oxide. The extent and reversibility of these reactions were confirmed by elemental, redox and structural characterization in recent work [1,2]. This high capacity is only observed at 110°C and it is accompanied by a large hysteresis in potential during the electrochemical cycling. The push to overcome these bottlenecks demands a more comprehensive definition of the underlying mechanisms of reaction than is currently available.Previous X-ray Diffraction (XRD) measurements conducted on the oxide indicated a loss of reflection intensity due to a loss in their crystallinity which makes structural characterization using typical diffraction techniques difficult. Through this work, we mainly make use of pair distribution function techniques to study the changes in the local structures of these oxides in both their pristine and charged state, to find out the local symmetry is being preserved even at the cycled state, thus giving new insights in further pushing the efficiency of multivalent battery materials.Reference:1. Hu, L., Jokisaari, J.R., Kwon, B.J., Yin, L., Kim, S., Park, H., Lapidus, S.H., Klie, R.F., Key, B., Zapol, P. and Ingram, B.J., 2020. High capacity for Mg2+ deintercalation in spinel vanadium oxide nanocrystals. ACS Energy Letters, 5(8), pp.2721-2727.2. Yoo, H.D., Jokisaari, J.R., Yu, Y.S., Kwon, B.J., Hu, L., Kim, S., Han, S.D., Lopez, M., Lapidus, S.H., Nolis, G.M. and Ingram, B.J., 2019. Intercalation of magnesium into a layered vanadium oxide with high capacity. ACS Energy Letters, 4(7), pp.1528-1534.

Enhanced Second‐Harmonic Generation in Quadratically Nonlinear Weyl Semimetal NbAs for Broadband Photodetection Applications

AbstractQuadratically nonlinear photodetectors (QNPDs) typically focus on 2D materials with high second‐order nonlinear polarizability, thereby severely disregarding bulk nonlinear optical (NLO) crystals as these rely on phase‐matching technology and achieving efficient bulk QNPDs remains a significant challenge. Weyl semimetal crystals have some signatures of inversion symmetry breaking, most notably second‐order NLO polarizability, while the inability to balance the low transmittance limits frequency conversion of the zero‐band gap absorption‐induced crystal. Herein, this study investigates an efficient QNPD based on bulk NbAs crystals designed with a strong second‐harmonic effect due to its large refractive index (≈5.0), resulting in an intense laser reflectivity of 50% on its surface, which creates a favorable environment for achieving second‐harmonic generation (SHG) without phase matching. The QNPD has a rectification ratio exceeding 107 with a dark current of 164 pA and an enhanced photoresponse in the 355‒1900 nm range, exhibiting a maximum responsivity of 4.1 mA W−1 with a detectivity of 0.8 × 1010 Jones at 355 nm. The responsivity improvement rate is 88% higher than that of linear NbAs (001) photodetector. This study opens new avenues for designing QNPDs by utilizing the second harmonic effect in bulk Weyl semimetal crystals.

VIS-NIR hyperspectral imaging and multivariate analysis for direct characterization of pelagic fish species

The identification of fish species and their physical and chemical characterization play a crucial role in the fishing industry, fish-food research and the management of marine resources. Traditional methods for species identification, such as expert observation, DNA barcoding and meta-barcoding, though effective, require labor-intensive laboratory work. Consequently, there is a pressing need for more objective and efficient methodologies for accurate fish species identification and characterization. This study proposes the use of multivariate analysis and visible-near infrared hyperspectral imaging (HSI) for a rapid characterization of fish, including the evaluation of specific morphological regions of interest (ROIs) in fish images or intrasample spectral variability, species differentiation, and freshness assessment. The study involves three pelagic species: sardine (Strangomera bentincki), silverside (Odontesthes regia) and anchovy (Engraulis ringens). Principal component analysis (PCA), support vector machine regression (SVM-R), partial least squares regression (PLS-R), and partial least squares discriminant analysis (PLS-DA) were applied as multivariate techniques for these purposes. Comparative studies of morphological ROIs revealed significant differences between the spectral characteristics of various fish zones. A decrease in reflectance intensity due to freshness loss was detected, and the prediction of this freshness, quantified as “time after capture,” was achievable using SVM-R, with a 9% relative error of prediction. Overall, VIS-NIR HSI, supported by multivariate analysis, enables differentiation between the studied species, highlighting its potential as a robust fish species identification and characterization tool.

In situ X‐Ray Powder Diffraction Investigation on the Development of Zeolite‐Templated Carbons in FAU Zeolite

AbstractA time resolved in situ X‐ray powder diffraction study using synchrotron radiation allowed for describing the evolution of the zeolite FAU structure during the development of a zeolite‐templated carbon (ZTC) in its porous voids. During the ZTC formation the intensity decrease of most zeolite reflections and the simultaneous rise in intensity of the 222 reflection (of null intensity in the pristine zeolite) was observed. Full pattern profile fitting by Rietveld refinement allowed for achieving a detailed description of the underlying chemistry, with coincident pore filling with carbon atoms in specific positions and framework distortion. Monitoring the intensity profiles of the 222 reflection allowed assessment of the energetics of the ZTC formation. Our results contribute to a better understanding of the phenomena involved on the atomic scale in ZTC synthesis.

Studies on the Powerful Photoluminescence of the Lu2O3:Eu3+ System in the Form of Ceramic Powders and Crystallized Aerogels.

This study compared the chemical, structural, and luminescent properties of xerogel-based ceramic powders (CPs) with those of a new series of crystallized aerogels (CAs) synthesized by the epoxy-assisted sol-gel process. Materials with different proportions of Eu3+ (2, 5, 8, and 10 mol%) were synthesized in Lu2O3 host matrices, as well as a Eu2O3 matrix for comparative purposes. The products were analyzed by infrared spectroscopy (IR), X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS), transmission electron microscopy (TEM), photoluminescence analysis, and by the Brunauer-Emmett-Teller (BET) technique. The results show a band associated with the M-O bond, located at around 575 cm-1. XRD enabled us to check two ensembles: matrices (Lu2O3 or Eu2O3) and doping (Lu2O3:Eu3+) with appropriate chemical compositions featuring C-type crystal structures and intense reflections by the (222) plane, with an interplanar distance of around 0.3 nm. Also, the porous morphology presented by the materials consisted of interconnected particles that formed three-dimensional networks. Finally, emission bands due to the energy transitions (5DJ, where J = 0, 1, 2, and 3) were caused by the Eu3+ ions. The samples doped at 10 mol% showed orange-pink photoluminescence and had the longest disintegration times and greatest quantum yields with respect to the crystallized Eu2O3 aerogel.

Designing mouthwash formulations with innovative molecular components to control initial dental erosion in vivo.

This study aimed to examine and compare the efficacy of mouthwashes containing different proteins and peptide on the prevention of enamel erosion in vivo, as well as to evaluate the participants' satisfaction with the formulations. Twelve participants were selected and underwent five cross-over mouthwash phases: Water (control); 0.1mg/mL CaneCPI-5; 0.5mg/mL MaquiCPI-3; 0.1mg/mL CsinCPI-2; and 0.037mg/mL Stn15pSpS. After prophylaxis, the participants rinsed (1min), followed by the acquired enamel pellicle (AEP) formation (2h). An erosive challenge was made (biopsy, citric acid 1%, 15s) on the buccal surface of the central maxillary incisors. The Relative Surface Reflection Intensity (%SRI) was assessed and analyzed by ANOVA/Tukey's tests. The calcium release in acid was measured by the Arsenazo method and verified by Kruskal-Wallis/Dunn's tests. The Spearman's correlation was used between analyses. A questionnaire evaluated the satisfaction of participants. For both analyses, the results showed that mouthwashes containing the proteins or peptide were significantly more effective in preventing enamel erosion compared to deionized water, with no significant differences among the active ingredients (p < 0.05). Also, there was a significant negative correlation between %SRI and calcium released (r=-0.5754). The questionnaire revealed that the volunteers were satisfied with the taste of the products. In addition, the experimental procedures were well tolerable, and no side effects were reported. All mouthwashes containing proteins or peptide were acceptable and effective in protecting enamel against initial dental erosion in vivo. This study highlights the potential of these pioneer organic components for the development of mouthwashes designed for people with risk of erosive tooth wear.