Spectroscopy Device Research Articles

In Situ Classification of Original Rocks by Portable Multi-Directional Laser-Induced Breakdown Spectroscopy Device

In situ rapid classification of rock lithology is crucial in various fields, including geological exploration and petroleum logging. Laser-induced breakdown spectroscopy (LIBS) is particularly well-suited for in situ online analysis due to its rapid response time and minimal sample preparation requirements. To facilitate in situ raw rock discrimination analysis, a portable LIBS device was developed specifically for outdoor use. This device built upon a previous multi-directional optimization scheme and integrated machine learning to classify seven types of original rock samples: mudstone, basalt, dolomite, sandstone, conglomerate, gypsolyte, and shale from oil logging sites. Initially, spectral data were collected from random areas of each rock sample, and a series of pre-processing steps and data dimensionality reduction were performed to enhance the accuracy and efficiency of the LIBS device. Subsequently, four classification algorithms—linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost)—were employed for classification discrimination. The results were evaluated using a confusion matrix. The final average classification accuracies achieved were 95.71%, 93.57%, 92.14%, and 98.57%, respectively. This work not only demonstrates the effectiveness of the portable LIBS device in classifying various original rock types, but it also highlights the potential of the XGBoost algorithm in improving LIBS analytical performance in field scenarios and geological applications, such as oil logging sites.

Determination of Ventilatory Thresholds Using Near-Infrared Spectroscopy in Recreational Endurance and CrossFit Athletes.

To define training zones, ventilatory thresholds (VTs) are commonly established by cardiopulmonary gas-exchange analysis during incremental exercise tests. Portable near-infrared spectroscopy (NIRS) devices have emerged as a potential tool for detecting these thresholds by monitoring muscle oxygenation. This study evaluated the accuracy of NIRS measurements to determine VTs or critical power (CP) based on muscle oxygen saturation and assesses the device's consistency across 2 constant-load tests. Data from 2 cross-sectional studies involving trained recreational endurance athletes (26 from study 1) and CrossFit athletes (59 from study 2) were examined. Incremental ramp tests on a cycle ergometer were performed and followed by either a constant-load test (study 1) or a CP test (study 2). When comparing power output or heart rate between NIRS-derived breakpoints and VTs, weak to moderate agreement was found. Mean differences in power output and heart rate ranged from 16.8 to 22.4W and 3.8 to 6.0beats·min-1 at the first threshold and 27.4 to 31.2W and 7.1 to 7.8beats·min-1 at the second threshold. Comparing with CP, mean differences ranged from -0.4 to 0.4W and -0.6 to 0.9beats·min-1. Test-retest reliability showed moderate agreement, with a mean bias of 1.2 percentage points between constant-load tests. Thus, NIRS may not be accurate for determining VTs or CP during exercise due to limited agreement in power output or hear rate, notable variability on individual level, and moderate reproducibility.

Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis.

Learning-enabled spectroscopic analysis, promising for automated real-time analysis of chemicals, is facing several challenges. First, a typical machine learning model requires a large number of training samples that physical systems can not provide. Second, it requires the testing samples to be in range with the training samples, which often is not the case in the real world. Further, a spectroscopy device is limited by its memory size, computing power, and battery capacity. That requires highly efficient learning models for on-site analysis. In this paper, by analyzing multi-gas mixtures and multi-molecule suspensions, we first show that orders of magnitude reduction of data dimension can be achieved as the number of principal components that need to be retained is the same as the independent constituents in the mixture. From this principle, we designed highly compact models in which the essential principal components can be directly extracted from the interrelations between the individual chemical properties and principal components; and only a few training samples are required. Our model can predict the constituent concentrations that have not been seen in the training dataset and provide estimations of measurement noises. This approach can be extended as an effectively standardized method for principle component extraction.

The Effects of Different Treatments on Serum Trace Element Levels in Dogs with Heart Failure.

The prognostic and diagnostic role of some trace elements in heart diseases has been demonstrated. In this study, the effects of min. 30-day and min. 120-day treatment with pimobendan, enalapril, and furosemide, as monotherapy and in combination, on serum trace element levels in dogs with heart failure were evaluated. A total of 107 dogs were treated with pimobendan or enalapril as monotherapy and pimobendan + enalapril (PE), enalapril + furosemide (EF), or pimobendan + enalapril + furosemide (PEF) as combination therapy for a min. of 30 and 120 days. Serum copper (Cu), zinc (Zn), iron (Fe), cobalt (Co), magnesium (Mg), manganese (Mn), selenium (Se), and chromium (Cr) were measured by an inductive coupled plasma optical emission spectroscopy device. Mean serum Mg in dogs treated with pimobendan for a min. of 120 days was significantly lower than that for a min. of 30 days (p < 0.05). In dogs using the PEF combination, mean serum Fe was significantly higher in the min. 120-day treatment group than in the min. 30-day treatment group (p < 0.01). No significant difference was observed in mean serum Cu, Zn, Co, Mn, Se, or Cr between the treatment groups and the time periods (p > 0.05). The short- and long-term use of pimobendan, enalapril, furosemide, and their combinations may cause changes in mean serum Mg and Fe in dogs with heart failure.

Multimodal classifier of medical risk based on a multielectrode bioimpedance converter

The purpose of the research. The aim of the study is Russian and foreign studies have shown that malignant breast tumors have significantly different impedance from normal tissues. However, bioimpedance analysis has limitations in resolution, as well as in the imperfection of bioimpedance models required to generate input vectors for machine learning systems.Methods. The presented study proposes a multimodal classifier, the raw data for which are obtained through an electrode matrix. It also has three channels for processing the results of bioimpedance analysis, with subsequent aggregation of their solutions. An impedance model of biomaterial is proposed, which allows forming descriptors for medical risk classifiers.Results. Hardware and software for bioimpedance studies have been developed, which include a data collection device for bioimpedance spectroscopy based on an electrode matrix, a device for communication with the object of study, and a device for bioimpedance spectroscopy using an electrode matrix. The software includes interface windows for setting up the bioimpedance research program and training and testing fully connected neural networks. An experimental study of the multimodal classifier on a physical model was conducted using inclusions of higher conductivity (tumor imitation) of various types and sizes in the conductivity range from 1.1 to 1.9 of the background. Based on the obtained images in the two-level neural network of the first channel, the integral risk of breast cancer was determined for all pixels of the image. Statistical studies (ROC analysis) showed sufficient sensitivity and specificity for the screening method - &gt; 0.75. Conclusion. Thus, a new model of intelligent support for medical decision-making has been created, integrating the capabilities of bioimpedance spectroscopy, convolutional neural networks and expert assessment of images generated by bioimpedance mapping. However, current data confirming the possibility of separating benign and malignant breast tumors using bioimpedancemetry methods are very limited, which requires further research in this direction.

Abstract 430: The Use of DCS as a Predictive Model for the Development of DCI Following aSAH ‐ Preliminary Experience

Background Delayed cerebral ischemia (DCI) remains the leading cause of morbidity and mortality in patients with subarachnoid hemorrhage (SAH). DCI results in focal neurological deficits in 30‐50% of patients within 4‐10 days following aneurysm rupture. Given the short comings of the current technologies in identifying DCI (mainly, transcranial dopplers), the current study employed the use of non‐invasive optical measurements of cerebral blood flow (CBF) using a custom‐built diffuse correlation spectroscopy (DCS) device. This research is supported by an R01 NIINDS funding mechanism. Methods We preformed daily bilateral cerebral blood flow measurements using a custom built DCS device in six patients following aSAH until discharge from our facility. Daily collection of transcranial doppler readings, NIHSS and GCS scores, in conjunction with other relevant clinical and angiographic procedural data were collected daily from admission until day 14 (or later if the patient remained symptomatic). In patients who underwent intra‐arterial vasospasm treatment in the angio suite, DCS recordings were obtained immediately before, after, and throughout the infusion. Results Recordings were successfully obtained in six patients. No safety concerns or interference with standard workflow procedures were noted. Correlation of daily DCS measurements with changes in clinical exam and occurrence of DCI are being finalized and prepared for the SVIN conference. Conclusions Preliminary analysis have demonstrated that the use of DCS optical measurements is safe and effective in the clinical setting with minimal interruptions to standard clinical operations.

Ultra-broadband emission from Mg7Ga2GeO12:Ni2+,Cr3+ phosphor spanning the NIR I–III regions for spectroscopy applications

Ultra-broadband near-infrared (NIR) lighting sources covering NIR Ⅰ to III regions are pivotal components for NIR spectroscopy devices. However, achieving ultra-broadband and efficient emission covering the entire NIR Ⅰ to III regions remains a significant challenge. To address this, we selected Mg7Ga2GeO12 as host material to realize ultra-broadband NIR emission. Density functional theory calculations on formation energies confirmed the feasibility of doping both Ni2+ and Cr3+ within Mg7Ga2GeO12. Band structure calculations reveal a significantly reduced band gap upon the doping of Ni2+. Following that, we synthesized Mg7Ga2GeO12:Ni2+ through conventional solid-state-reaction method. This phosphor exhibits broadband NIR emission with a full-width-at-half-maximum of 377 nm in the range of NIR II ∼ III (1100–2100 nm). To further enhance the emission efficiency, we co-doped Mg7Ga2GeO12:Ni2+ phosphor with Cr3+. This co-doping strategy not only dramatically enhances the Ni2+ emission intensity, but also extends the lower limit of emission band to 600 nm. However, a spectral gap around 1150 nm remains between Cr3+ and Ni2+ emission centers. To bridge this spectral gap, we employed LiScSiO4:Cr3+ phosphor alongside Mg7Ga2GeO12:Ni2+,Cr3+ phosphor to fabricate a phosphor converted LED (pc-LED) with ultra-broadband NIR emission covering entire NIR Ⅰ to III regions. By utilizing this pc-LED as the lighting source for NIR spectral detection, multiple organic functional group signals can be identified simultaneously in NIR II and III bands from 1000 to 2100 nm. The performance of such an NIR pc-LED not only confirms the application potential of Mg7Ga2GeO12:Ni2+,Cr3+ phosphor in non-destructive spectral analysis but also underscores the feasibility of combining two phosphors to achieve ultra-broadband NIR pc-LEDs, rendering them suitable to be lighting sources for portable spectrometers.

Acute Effect of Fixed vs. Self-Selected Rest Interval Between Sets on Physiological and Performance-Related Responses.

Background: Although the comparison between self-managed rest and fixed rest periods in subjects experienced in lower-limb strength training has been investigated, the results remain unclear due to controversies among some studies. Therefore, the present study aimed to analyze the role of self-managed rest versus fixed rest in athletic performance, mean propulsive velocity, velocity loss, muscle oxygen saturation, and rest time in trained subjects; Methods: Thirteen subjects with a minimum of one year of training experience (age (years): 26.31 ± 3.84; height (cm): 175.46 ± 5.61; weight (kg): 79.24 ± 6.83) were randomly assigned to two groups (self-selected rest group [SR] = 7 and fixed rest group [FR] = 6). The subjects underwent a session for evaluation (one maximum repetition (1RM) estimation, familiarization, and data collection) and another day for a traditional strength training session for the back squat, consisting of five sets of four repetitions at 80% of 1RM. One group took a fixed 2 min break, while the other group managed their breaks autonomously (resuming when they felt ready to perform the next set at maximum velocity). Mean propulsive velocity (MPV) was monitored using a linear position transducer, and muscle oxygen saturation (SmO2) was measured with a near-infrared spectroscopy device; Results: Significant differences between the groups were found for the rest time between the first and second sets (SR 97.29 ± 23.70 seg vs. FR 120 ± 0.00 seg). However, no differences were found for MPV, velocity loss, or SmO2; Conclusions: Given the similarities in performance and physiological outcomes between fixed and self-selected rest conditions, both can be used equally depending on the preferences and training goals of coaches and athletes.

Oxygenation Kinetics of Three Quadriceps Muscles During Squatting Exercise in Trained Men.

This study aimed to monitor the oxygenation and blood supply in three quadriceps muscles [the vastus lateralis (VL), vastus medialis (VM), and rectus femoris (RF)] during squatting exercise to exhaustion. Eighteen young resistance-trained males performed five sets of 15 back squats in a Smith machine, with two warm-up sets [at 14% and 45% of the 15-repetition maximum (15RM)] and three main sets at 100% of the 15RM. Three near-infrared spectroscopy devices were attached to the VL, VM, and RF to record the muscle oxygen saturation (SmO2) and total hemoglobin (tHb, an index of muscle blood supply). The blood lactate concentration was measured after each set with a portable analyzer. The SmO2 and tHb data were analyzed by repeated-measures two-way ANOVA (muscle × set). Lactate data were analyzed by repeated-measures one-way ANOVA. The statistical significance was set at α = 0.05. The SmO2 dropped during each set (hitting zero in many instances) and was reinstated during recovery. The three main sets caused severe deoxygenation in the VL and VM, as opposed to moderate deoxygenation in the RF. From one set to the next, the initial value and the drop in the SmO2 increased, whereas the final SmO2 value decreased. The tHb increased in the VL, did not change considerably in the VM, and decreased in the RF during each set. The blood lactate concentration increased gradually from one set to the next, reaching about 10 mmol/L. These findings show pronounced differences in the physiological and metabolic responses of three quadriceps muscles to squatting exercise, thus highlighting the importance of studying such responses at multiple sites.

Rapid Classification of Milk Using a Cost-Effective Near Infrared Spectroscopy Device and Variable Cluster-Support Vector Machine (VC-SVM) Hybrid Models.

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster-support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud.

Vanadium-dioxide-assisted multifunctional switchable terahertz metamaterial devices

A multifunctional, switchable terahertz (THz) metamaterial (MM) device with wideband absorption and polarization conversion capabilities has been developed, based on the insulator-metal phase transition of vanadium dioxide (VO2). In its metallic state, the device operates as a wideband absorber within the range of 2.56–6.74 THz, achieving a bandwidth of 4.18 THz and an absorption rate of ≥90%. The wideband absorption is insensitive to both oblique incident angles and polarization. When VO2 is in its insulating state, the device switches to a polarization converter, facilitating linear-to-cross polarization (LTX) conversion between 1.04 and 4 THz, and linear-to-circular polarization (LTC) conversion between 1 and 1.04 THz, with a conversion efficiency exceeding 90%. Additionally, the effects of incident angle and polarization angle on the device’s performance were analyzed. This THz device offers advantages of wide angle, wide bandwidth, and high efficiency, making it a valuable reference for research into new multifunctional THz devices. It has great potential applications in short-range wireless THz communication, ultrafast optical switches, high-temperature resistant switches, transient spectroscopy, and optical polarization control devices. In specific application scenarios, particularly in fields requiring efficient detection, transmission, and analysis—such as security and non-destructive testing, secure communication systems, imaging and sensing, multidimensional spectral analysis, pollutant detection, smart stealth coatings, dynamic optical control devices, and integrated optical systems—these devices offer multifunctional capabilities. They enhance system performance and flexibility, meeting diverse application needs.

Assessing Leg Blood Flow In Multiple Locations Using Multiple Near-infrared Spectroscopy Devices

Assessing Leg Blood Flow In Multiple Locations Using Multiple Near-infrared Spectroscopy Devices

Accurate and Reliable Food Nutrition Estimation Based on Uncertainty-Driven Deep Learning Model

Mobile Near-Infrared Spectroscopy (NIR) devices are increasingly being used to estimate food nutrients, offering substantial benefits to individuals with diabetes and obesity, who are particularly sensitive to food intake. However, most existing solutions prioritize accuracy, often neglecting to ensure reliability. This oversight can endanger individuals sensitive to specific foods, as it may lead to significant errors in nutrient estimation. To address these issues, we propose an accurate and reliable food nutrient prediction model. Our model introduces a loss function designed to minimize prediction errors by leveraging the relationships among food nutrients. Additionally, we developed a method that enables the model to autonomously estimate its own uncertainty based on the loss, reducing the risk to users. Comparative experiments demonstrate that our model achieves superior performance, with an R2 value of 0.98 and an RMSE of 0.40, reflecting a 5–15% improvement over other models. The autonomous result rejection mechanism showing a 40.6% improvement further enhances robustness, particularly in handling uncertain predictions. These findings highlight the potential of our approach for precise and trustworthy nutritional assessments in real-world applications.

Functional Exercise Induces Adaptations in Muscle Oxygen Saturation in Division One Collegiate Butterfly Swimmers: A Randomized Controlled Trial

This study investigates the impact of a five-week functional exercise intervention designed to enhance the muscular endurance of the posterior shoulder musculature, aiming to mitigate shoulder fatigue and overuse injury. Twelve Division I collegiate butterfly swimmers were recruited and evenly randomized into exercise (EX) and control (CTRL) groups. Weekly 100-yard butterfly sprints were performed, with Muscle Oxygen Saturation (SmO2) continuously monitored using a wearable near-infrared spectroscopy (NIRS) device. This study is among the first to utilize wearable NIRS devices to monitor SmO2 underwater during swimming, demonstrating that a targeted 5-week exercise program significantly improves posterior shoulder endurance, as evidenced by increased Posterior Shoulder Endurance Test (PSET) scores and distinctive SmO2 adaptations in the EX-group compared to the CTRL group. These findings suggest that targeted dryland exercises can enhance posterior shoulder endurance with long-term implications for potentially reducing injury risk and improving performance.

54768 Skin Cancer Recognition and Efficacy of an Elastic-Scattering Spectroscopy device using Neural Networks (SCREENN)

54768 Skin Cancer Recognition and Efficacy of an Elastic-Scattering Spectroscopy device using Neural Networks (SCREENN)

Spectroscopic analysis of bacterial photoreactivation.

With the rise of bacterial infections and antibiotic resistance, spectroscopic devices originally developed for bacterial detection have shown promise to rapidly identify bacterial strains and determine the ratio of live to dead bacteria. However, the detection of the photoreactivated pathogens remains a critical concern. This study utilizes fluorescence and Raman spectroscopy to analyze bacterial responses to UV irradiation and subsequent photoreactivation. Our experimental results reveal limitations in fluorescence spectroscopy for detecting photoreactivated bacteria, as the intense fluorescence of tryptophan and tyrosine amino acids masks the fluorescence emitted by thymine molecules. Conversely, Raman spectroscopy proves more effective, showing a detectable decrease in band intensities of E. coli bacteria at 1248 and 1665 cm-1 after exposure to UVC radiation. Subsequent UVA irradiation results in the partial restoration of these band intensities, indicating DNA repair and bacterial photoreactivation. This enhanced understanding aims to improve the accuracy and effectiveness of these spectroscopic tools in clinical and environmental settings.

Deep neural network-enabled multifunctional switchable terahertz metamaterial devices

Under the support of deep neural networks (DNN), a multifunctional switchable terahertz metamaterial (THz MMs) device is designed and optimized. This device not only achieves ideal ultra-wideband (UWB) absorption in the THz frequency range but enables dual-functional polarization transformation over UWB. When vanadium dioxide (VO2) is in the metallic state, the device as a UWB absorber with an absorption rate exceeding 90% in the 2.43–10 THz range, with a relative bandwidth (RBW) of 145.2%, and its wideband absorption performance is insensitive to polarization. When VO2 is in the insulating state, the device can switch to a polarization converter, achieving conversions from linear to cross polarization and from linear to circular polarization in the ranges of 4.58–10 THz and 4.16–4.43 THz, respectively. Within the 4.58–10 THz range, the polarization conversion ratio approaches 100% with an RBW of 74.3%, the polarization rotation angle is near 90°. Within the 4.16–4.43 THz range, the RBW is 6.29% and the ellipticity ratio approaches 1, Moreover, the effects of incident angle and polarization angle on the operational characteristics are studied. This THz MMs due to its advantages of wide angle, broad bandwidth, and high efficiency, provides valuable references for the research of new multifunctional THz devices. It has great application potential in short-range wireless THz communication, ultrafast optical switches, high-temperature resistant switches, transient spectroscopy, and optical polarization control devices.

Association of changes in body composition with all-cause mortality in patients undergoing hemodialysis: A prospective cohort study

ObjectivesThis study aimed to explore the effect of longitudinal body composition changes on mortality risk in patients undergoing hemodialysis and identify whether changes in body composition can more accurately predict mortality than baseline status. MethodsA prospective cohort study was conducted on 340 patients undergoing hemodialysis. Lean mass and body fat were determined using a bioimpedance spectroscopy (BIS) device and expressed as the lean tissue index (LTI) or fat tissue index (FTI), respectively. The patients were subjected to BIS at baseline and after 1 year. The hazard ratio (HR) for death was calculated using Cox regression analysis. ResultsAmong 340 patients, 289 were tested with a repeat BIS. LTI loss and FTI gain were observed in 51.2% and 47.1% of the patients, respectively. Low baseline LTI was a significant predictor of all-cause mortality after adjusting for demographic and biochemical parameters (HR, 2.41; P = 0.047), but not when comorbidities were included in the multivariate analysis. However, after adjusting for various confounding factors, LTI loss (HR, 3.40; P = 0.039) and FTI gain (HR, 4.06; P = 0.024) were independent risk factors for all-cause mortality, and the adjusted HR for LTI loss and FTI gain vs. no LTI loss and no FTI gain was 5.34 (P = 0.016). ConclusionsLTI loss and FTI gain, particularly their combination, are important predictors of survival in patients undergoing hemodialysis. Our results emphasize that longitudinal changes in LTI and FTI are more strongly associated with all-cause mortality than single-point values. Therefore, it is important to dynamically assess the muscle and fat tissues and develop potential targeted treatment strategies for this population.

Innovative Integration of Dual Quantum Cascade Lasers on Silicon Photonics Platform.

For the first time, we demonstrate the hybrid integration of dual distributed feedback (DFB) quantum cascade lasers (QCLs) on a silicon photonics platform using an innovative 3D self-aligned flip-chip assembly process. The QCL waveguide geometry was predesigned with alignment fiducials, enabling a sub-micron accuracy during assembly. Laser oscillation was observed at the designed wavelength of 7.2 μm, with a threshold current of 170 mA at room temperature under pulsed mode operation. The optical output power after an on-chip beam combiner reached sub-milliwatt levels under stable continuous wave operation at 15 °C. The specific packaging design miniaturized the entire light source by a factor of 100 compared with traditional free-space dual lasers module. Divergence values of 2.88 mrad along the horizontal axis and 1.84 mrad along the vertical axis were measured after packaging. Promisingly, adhering to i-line lithography and reducing the reliance on high-end flip-chip tools significantly lowers the cost per chip. This approach opens new avenues for QCL integration on silicon photonic chips, with significant implications for portable mid-infrared spectroscopy devices.

Capillary Flow-Based One-Minute Quantification of Amyloid Proteolysis.

Quantifying the formation and decomposition of amyloid is a crucial issue in the development of new drugs and therapies for treating amyloidosis. The current technologies for grasping amyloid formation and decomposition include fluorescence analysis using thioflavin-T, secondary structure analysis using circular dichroism, and image analysis using atomic force microscopy or transmission electron microscopy. These technologies typically require spectroscopic devices or expensive nanoscale imaging equipment and involve lengthy analysis, which limits the rapid screening of amyloid-degrading drugs. In this study, we introduce a technology for rapidly assessing amyloid decomposition using capillary flow-based paper (CFP). Amyloid solutions exhibit gel-like physical properties due to insoluble denatured polymers, resulting in a shorter flow distance on CFP compared to pure water. Experimental conditions were established to consistently control the flow distance based on a hen-egg-white lysozyme amyloid solution. It was confirmed that as amyloid is decomposed by trypsin, the flow distance increases on the CFP. Our method is highly useful for detecting changes in the gel properties of amyloid solutions within a minute, and we anticipate its use in the rapid, large-scale screening of anti-amyloid agents in the future.