Applied Spectroscopy最新文献

Laser-induced breakdown spectroscopy (LIBS) offers a promising alternative due to its minimal sample preparation, real-time analysis capabilities, and versatility in analyzing a broad range of materials. However, the challenge lies in determining its ability to effectively distinguish high-iron ore content from mineralogically similar ores with lower iron content. This study focuses on differentiating iron ore from a variety of ores with lower iron content, including calcite, biotite, dolomite, chalcopyrite, rutile, chromite, olivine, limonite, and astrophyllite, using LIBS. By comparing the obtained spectra and applying receiver operating characteristic (ROC) curve analysis, the study assesses the specificity of the technique. The results demonstrate a high specificity (>70%) in distinguishing iron ore from biotite, dolomite, chalcopyrite, rutile, olivine, and astrophyllite, revealing the potential of LIBS for effectively identifying iron ore from some ore types. However, when comparing iron ore to other ore types, such as limonite, chromite, and calcite, the results are not statistically significant. This means that the spectral or compositional similarities between these ores may limit the method's capacity to give clear separation in certain situations. To further validate the results, two common classification models, principal component analysis followed by linear discriminant analysis (PCA + LDA) and k-nearest neighbors (KNN) were applied to the spectral data. The comparison results demonstrate the resilience of LIBS classification and the impact of mineral matrix influences on diagnostic performance.

Liquid biopsy is revolutionizing cancer management, with circulating tumor cells (CTCs), offering a transformative approach to screening, diagnosis, and treatment monitoring. However, existing CTC isolation methods relying on antigen expression or physical properties lack robustness, are operator-dependent, and suffer from automation challenges, leading to inconsistent and time-intensive analyses. A universal, unbiased methodology for CTC detection across tumor types is critically needed. Here, we present the first proof-of-concept study demonstrating the use of Fourier transform infrared (FT-IR) microspectroscopy to study cytospun blood samples coupled with a random forest (RF) classifier, for the detection of a single CTC in the blood of a lung cancer patient as confirmed via immunohistochemistry. Notably, our method utilizes glass coverslips as substrates, routinely employed in pathology departments, enabling seamless integration with histopathological analyses (e.g., staining, immunohistochemistry). Using FT-IR spectral data from in vitro growing lung cancer cells as a training model, we achieved precise CTC identification based on biochemical composition, specifically within the fingerprint region (1800 cm^-1 to 1350 cm^-1). This study introduces FT-IR microspectroscopy as a novel, label-free approach for CTCs detection in liquid biopsies, with the potential to redefine cancer diagnostics. By enhancing precision and accessibility in CTC identification, the clinical implementation of this methodology may represent a significant advancement in personalized oncology, offering a clinically viable tool for real-time cancer monitoring and improved patient stratification.

Understanding the abundance of atomic oxygen in the vicinity of carbon surfaces exposed to high-enthalpy flows is critical to accurate predictions of the gas-surface interaction. A novel approach for obtaining absolute number density measurements of atomic oxygen in high-enthalpy facilities with nanosecond laser pulses is described and demonstrated using photoionization-dominated, two-photon laser-induced fluorescence. In two-photon laser-induced fluorescence measurements, the depopulation of the excited state is typically dominated by electronic quenching, which depends on the temperature, pressure, and gas composition. To account for the electronic quenching rate, the fluorescence lifetime can be measured by temporally resolving the fluorescence. This can prove challenging in high-temperature and/or high-pressure environments where the fluorescence lifetime can be less than a nanosecond. Instead, by increasing the laser intensity until photoionization dominates the depopulation of the excited state, we create a quenching-independent measurement that is proportional to absolute number density. This technique is demonstrated here in the reacting boundary layer of a graphite sample ablating in the 6000 K plume of an inductively coupled plasma torch. The boundary layer possesses a large temperature gradient that varies from about 2000 K near the sample surface to the plume temperature of 6000 K in a span of approximately 2 mm. The photoionization-dominated technique is calibrated by using the freestream oxygen concentration, assuming the torch plume is in local thermodynamic equilibrium. The spatial resolution of the measurements is 50 µm and we are able to measure the number density of atomic oxygen to within about 60 µm of the graphite sample.

Chromosome characterization is crucial in cytogenetic research and diagnostics, necessitating precise imaging methods to ensure proper analyses. The aim of this project is to identify a reliable method for chromosomal characterization that uses hyperspectral imagery of stained metaphase chromosomes using bright-field microscopy. We analyzed four hyperspectral images of stained chromosomes acquired under bright-field illumination. To address the high dimensionality of the hyperspectral hypercubes, we applied five dimension reduction algorithms based on spectral band selection to determine the most effective approach. A comparative study was conducted between five band selection methods to assess their effectiveness in chromosome characterization. The results indicate that sparse subspace clustering and multi-objective band selection are the most effective methods, outperforming the others in reducing the spectral dimensionality of the hyperspectral data, while preserving key properties essential for accurate chromosomes characterization. This study demonstrates that careful selection of spectral bands can enhance the analysis of spectral hypercubes for chromosome characterization.

A small remote Raman sensor was used to measure the Raman scattering signal from clear, still water as a function of water depth (12 cm and 396 cm depth), sensor distance above the water surface (20-300 cm), and angle of incidence (0-80°) to the normal of the water surface. Under thick- and thin-sample conditions, the signal depends on either the inverse, or the inverse square, of sensor distance from the water surface, respectively. A model is derived that fits data for different sensor distances, water depths, and angles of incidence. Fits to the measured data are consistent with the known intensity of water Raman scattering and the specifications of the detection system. This manuscript provides a mathematical model that can be used to predict and evaluate the performance of remote sensors and can be expanded to account for differing experimental conditions.

Multivariate regression models were optimized for the quantification of sulfuric acid (H₂SO₄) [0-8 M] and temperature (20 °C-80 °C) in the presence of ammonium sulfate ((NH₄)₂SO₄ [0-0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H₂SO₄. The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, partial least squares regression models were optimized for the quantification of H₂SO₄ and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H₂SO₄. The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

Soil reflectance spectroscopy is a powerful tool for rapid, non-destructive assessment of soil properties and the foundation for data-driven soil science applications. However, systematic discrepancies during routine spectral measurement procedures, particularly those arising from contamination or deterioration of white reference (WR) calibration panels, may compromise spectral data stability and hinder harmonization practices across laboratories. This study investigates the impact of using a non-contaminated WR panel as a calibration target to measure soil reflectance across the visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectral regions. The study evaluates the effectiveness of an internal soil standard (ISS) Lucky Bay sand to correct discrepancies within a controlled laboratory setting. Twelve soils from the Israeli Legacy Soil Spectral Library were analyzed using a contact-probe setup that was calibrated with both a clean and a contaminated WR. The spectral correction method, based on IEEE P4005 protocols and the ISS calibration, significantly reduced spectral inconsistencies, especially in the Vis region where contamination effects were most pronounced. Results show that the ISS effectively harmonized spectra acquired under different WR conditions, reducing the modified average spectral difference stability (mASDS) measure across all samples. While ISS correction is commonly employed for cross-laboratory harmonization, our findings highlight its critical role in enhancing intra-laboratory consistency under routine operational variability. We recommend that every WR calibration process will be accompanied with ISS measurements. The continuous use of a well-maintained WR and ISS improves the reliability of soil spectral datasets and supports the long-term harmonization of soil spectral libraries.

Hair fluorescence spectroscopy was evaluated as a novel, non-invasive biomarker for the diagnosis and disease monitoring of systemic lupus erythematosus (SLE). Hair samples were collected from 47 female patients with SLE and 49 age-matched healthy controls (HC), with patients stratified into three clinical groups: active flare, remission of six months to three years (R6M-3Y group), and remission of more than three years (R > 3Y group). Fluorescence emission spectra of hair strands were recorded under ultraviolet excitation and analyzed using multivariate statistical methods, including principal component analysis and hierarchical clustering, to assess group discrimination. The fluorescence profiles differed significantly between SLE patients and the HC group, and within the SLE cohort, spectral signatures varied according to disease activity, enabling discrimination between flare and remission (low disease activity) states. Patients in long-term remission (R > 3Y) showed partial convergence toward the HC group, suggesting progressive normalization over time. Overall, hair fluorescence spectroscopy emerges as a non-invasive, inexpensive, and stable biomarker reflecting both disease presence and remission dynamics in SLE, with potential to complement existing clinical and laboratory indices and to provide rheumatologists with a novel tool for longitudinal disease monitoring.

Identifying characteristic bands, those exhibiting the most distinct features (i.e., minimal correlation), is a critical step in two-dimensional correlation spectroscopy (2D-COS) analysis. This process is essential for establishing effective correlation filters to simplify congested spectral datasets. Historically, such bands were selected using subjective methods, primarily the visual inspection of correlation cross-peaks. We now propose a more systematic and objective procedure based on the sequential multiplication of horizontal slices from a 2D discrimination spectrum. This unsupervised, automatic method is potentially integrable into model-free 2D-COS analyses, making it compatible with automated, machine-based interpretation.