Society for Applied Spectroscopy

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www.s-a-s.org The Society for Applied Spectroscopy is a nonprofit organization formed to advance and disseminate knowledge and information concerning the art and science of spectroscopy.

Abstract: Partial least squares discriminant analysis (PLS-DA) is often used for data sets that consist of a large number of potential predictors but relatively few observations such that chance correlations between predictors and response can occur that lead to false conclusions. Hence, there is a need for data adequacy testing before model building but currently no such method exists. In this work we propose one where we used random permutations to destroy the correlation structure between predictor and response data. This produced normal distributions of chance correlation coefficients that were used to find correlation coefficients in the non-permuted data that differed significantly from chance occurrences. Based on these distributions, we defined two novel null hypotheses to control for when a true null hypothesis is incorrectly rejected and the other for when a false null hypothesis is not rejected. To counter false positive errors, the standard significance levels were adjusted with predictor-based Bonferroni corrections. To counter false negative errors, we compared the true and permuted correlation coefficients in distribution tails. The outcomes of the hypothesis tests then indicated whether or not PLS-DA models could be successfully built from these data sets. We also investigated how to determine the number of samples needed for a data set with a given number of predictors. Simulations showed that our method produced significantly fewer false positives than PLS-DA (P = 0.0018, our method error rate 12 × less than PLS-DA error rate) but significantly more false negatives (P = 0.0003, our method error rate 4.5 × more than PLS-DA error rate). Data from Raman spectroscopy showed that the method transferred to real data. By pre-screening such data, our method can aid in assessing whether to proceed with model building and, when there is a need to increase the sample size, we show by how much.
Abstract: Exhaust gases generated by fossil fuel combustion significantly contribute to air pollution and climate change, emphasizing the need for efficient combustion diagnostics. OH radicals are key indicators of flame behavior; however, conventional laser-induced fluorescence (LIF) techniques are impractical for industrial monitoring due to their complexity. In this study, wavelength modulation spectroscopy (WMS) employing a near-infrared laser at 1.49 μm was used to measure OH radical concentrations, incorporating a correction for spectral interference from H2O. A wavelength division multiplexer (WDM) was used to enable the simultaneous operation of two lasers at 1.49 μm (OH) and 1.39 μm (H2O, temperature), allowing for correction of water interference. OH concentrations were determined using both WMS and direct absorption spectroscopy (DAS), and the results were evaluated through comparison with CHEMKIN simulations. The proposed dual-laser system demonstrated reliable and interference-corrected quantification of OH radicals in methane (CH4)/air premixed flames over a range of equivalence ratios. Comparisons with thermocouple-based temperature measurements and CHEMKIN-predicted species concentrations confirmed the reliability of the proposed technique. This study highlights the robustness and applicability of WMS-based OH diagnostics for combustion monitoring and demonstrates the potential for future implementation in industrial burner systems.
Abstract: Optical photothermal infrared (O-PTIR) spectroscopy is rapidly transforming molecular imaging by combining the chemical specificity of infrared absorption with the high spatial resolution enabled by visible-light excitation. This review provides a comprehensive and forward-looking overview of O-PTIR, with a particular focus on its expanding applications in biological and biomedical research. We begin by outlining the development of O-PTIR from earlier photoacoustic and photothermal infrared methodologies, placing it within the broader field of vibrational spectroscopy and emphasizing its unique advantages including sub-micrometer spatial resolution, label-free detection, and compatibility with heterogeneous biological samples. The foundational principles of O-PTIR, such as the photothermal effect, instrumental configurations, and the integration of simultaneous IR and Raman measurements, are discussed to establish the basis for its analytical capabilities. Leveraging these strengths, O-PTIR enables high-resolution and chemically specific imaging of key biomolecules including lipids, proteins, nucleic acids, and metabolites across cells, tissues, and microbial systems. Current applications span diverse areas such as cellular metabolism, microbial phenotyping, cancer diagnostics, biomarker identification, and pharmaceutical analysis. Alongside these advances, we critically evaluate existing limitations, including challenges associated with sample preparation, instrumental complexity, signal interpretation, and standardization. Finally, we highlight emerging directions such as live-cell O-PTIR measurements, artificial intelligence (AI)-driven spectral analysis, and the development of hybrid modalities including FISH-O-PTIR. Together, these innovations reinforce O-PTIR's potential as a transformative technology for biological and biomedical sciences, poised to bridge fundamental molecular studies with future clinical and translational applications.
Abstract: Raman spectroscopy has been widely demonstrated as a powerful tool for analyzing macromolecules characteristic of living organisms. This research focuses on a detailed spectroscopic analysis of lymphocytes affected by infectious mononucleosis caused by Epstein–Barr virus (EBV, HHV-4). As shown, at least 98% of the population is infected with EBV, making it one of the most common viruses. The main aim of this study is to evaluate the capability of Raman spectroscopy to distinguish between healthy lymphocytes from those infected with EBV at an early stage of infection. Blood samples were collected from patients with infectious mononucleosis who were receiving treatment at the University Hospital in Kraków. For comparative purposes, samples from healthy volunteers were also included. To guarantee detailed analysis, Raman spectra were acquired using two excitation wavelengths: 514.5 nm and 785 nm. The two-trace two-dimensional (2T2D) correlation method was performed to recognize the difference between EBV-infected and healthy cells. Initially, mean Raman spectra were calculated for healthy and infected cells and subsequently used to generate synchronous and asynchronous 2T2D correlation maps. Analysis of the asynchronous correlation spectra reveals distinct differences between healthy and diseased lymphocytes for both excitation wavelengths. For example, asynchronous peaks at 514.5 nm excitation indicate the beginning of immunoglobulin formation, while at 785 nm excitation indicate Tyrosine phosphorylation, which corresponds to activation of lymphocytes. The detection of specific biochemical alterations shows the potential of Raman spectroscopy as a non-invasive diagnostic tool in viral infections.
Abstract: Phthalate-based plasticizers in polyvinyl chloride (PVC) polymer were examined using infrared (IR) spectroscopy and evolved gas analysis–mass spectrometry (EGA-MS), a type of mass spectrometry (MS). IR signals arising from the C=O stretching vibrations of the plasticizers are observed but it is difficult to distinguish individual components due to their structural similarity. In contrast, temperature-dependent mass spectra of the PVC sample revealed a characteristic increase in total ion signals within the 100–220 °C range, indicating that plasticizer desorption occurs predominantly before decomposition of the PVC polymer. The application of two-dimensional correlation spectroscopy (2D-COS) to the mass spectra elucidated the detailed sequence of spectral changes during thermal desorption. The 2D correlation spectra derived from the mass spectra within the 100–220 °C range exhibited distinct correlation peaks associated with bis(2-ethylhexyl) phthalate, bis(2-ethylhexyl) adipate, and bis(2-ethylhexyl) sebacate. These results demonstrate that EGA-MS, combined with 2D-COS, can effectively identify individual additives in polymer systems.
Abstract: A polarized near-infrared (NIR) study for characterizing the orientation distribution of polymer chains is described. A low-density polyethylene (LDPE) sheet prepared with a hot rolling press is gradually rotated while being probed with a high-speed polarized NIR spectrometer based on an acousto-optic tunable filter (AOTF) to obtain a series of NIR spectra. During the change in the rotation angle, the spectral feature varies depending on the direction of the molecular vibration within the sample. The fine features of the spectral changes are readily analyzed with the two-trace two-dimensional (2T2D) correlation method by using the spectrum measured at 0° as a sample spectrum. The asynchronous correlation intensities between the crystalline and amorphous bands of the LDPE exhibited a sinusoidal variation depending on the selected reference spectrum (i.e., polarization angle). In particular, the intensities provide local maximum points at 90° and 270° where the direction of the polarized NIR light becomes parallel to the hot rolling direction. Consequently, the cyclic pattern of the asynchronous correlation intensity suggests that the hot rolling treatment substantially induces the reorientation of the polymer chains and subsequent additional crystallization, which makes the LDPE hard and brittle.
Abstract: Poly(3-hydrxybutyrate-co-3-hydroxyhexanoate) (PHBHx) is a biopolymer that is produced and degraded by microbes. Because of the potential to replace polymers derived from petrochemicals with these materials, there is a high level of expectation for its commercial uses if its physical and chemical properties can be understood and controlled. Among other things these properties are determined by the polymer’s morphology – that is its crystallinity, and orientation of both crystalline and amorphous phases. The focus on the Raman characteristics of the crystalline phase enables elucidation of the characteristics of the polymer experiencing dynamic crystallization under various conditions. In this article we will start by reviewing the changes in the Raman spectrum from an amorphous to a crystalline material in an isothermal crystallization study. In that study a correlation field splitting between a CH stretching band that interacts with the carbonyl group on the opposite chain in the unit cell was identified. Then we will show the polarized Raman spectra of single crystals which enable an explanation of the residual amorphous material seen in the spectra of single crystals. Using the information from the single crystal measurements we can then study the Raman behavior of spherulites and confirm the model that proposes an explanation for the appearance of rings in the polarized light microscope (PLM) images of some spherulites. The polarized Raman studies confirm that the crystal ribbons that grow along the radii are twisting about the growth direction. The two-dimensional correlation spectroscopy (2D-COS) analysis of the polarized spectra of spherulites suggest the presence of strain that has been proposed to induce the twisting.
Abstract: Understanding the biochemical aging mechanisms of bloodstains is essential for developing reliable forensic methods to estimate the time since deposition (TSD). Although fluorescence spectroscopy is effective for tracking endogenous fluorophores such as tryptophan, nicotinamide adenine dinucleotide (NADH), and flavins, its utility is limited by spectral overlap and sample variability. In this study, we employed two-dimensional correlation spectroscopy (2D-COS) and 2D gradient mapping method to investigate the time-dependent fluorescence changes in bloodstains, gaining molecular-level insights into the aging process. 2D-COS uncovered hidden spectral components and revealed sequential molecular changes, especially in NADH- and flavin-associated bands. The 2D gradient maps further visualized these spectral trends quantitatively over 24 hours of aging. This study focuses on uncovering the biochemical mechanisms underlying bloodstain aging, probed by fluorescence spectroscopy. These findings deepen our fundamental understanding of ex vivo blood degradation and establish a foundation for more accurate and robust forensic applications.
Abstract Due to the growing interest in high-purity bioplastics for emerging applications, we investigated the solution crystallization of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHx) from a chloroform solution using advanced spectroscopic techniques. Compared to melt crystallization, solution crystallization proved to be a more complex process. Solvent evaporation led to the formation of amorphous PHBHx and less-ordered crystallites. Subsequently, a distinct intermediate species of slightly disordered crystals formed, eventually transforming into more ordered crystalline structures. To analyze the time-dependent attenuated total reflection infrared (ATR-IR) spectra during crystallization, we employed two-dimensional correlation spectroscopy (2D-COS) and codistribution spectroscopy (2D-CDS). To streamline the 2D analysis of this complex dynamic system, we utilized a correlation filter (CF) technique to attenuate interfering contributions from overlapping peaks.
Abstract: Identifying characteristic bands, those exhibiting the most distinct features (i.e., minimal correlation among themselves), 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.
Abstract: A method is proposed whereby the range of equilibrium constants that describe how a ternary mixture of molecules is adsorbed onto a solid substrate may be obtained. This information may then be related to the surface preference of a particular species. The technique utilizes infrared and/or Raman spectra of the bulk phase, together with sum frequency generation spectra of the surface in a heterospectral two-dimensional correlation analysis. The method consists of a series of up to three experiments in which the bulk concentrations are varied, followed by a set of rules for interpreting the resulting correlation maps to identify adsorption sequences and surface preference. A scheme is provided to direct the design of the experiments and analyses for such multi-component mixtures.
Abstract: This comprehensive survey review offers the trajectory of research dissemination in the field of two-dimensional correlation spectroscopy (2D-COS) from 1 July 2022 to 17 June 2024. It covers books, reviews, tutorials, innovative concepts and theories, patents applications, and diverse applications that emerged during this period, as well as instances of improper usage or citations of 2D-COS. Innovations in probe integration, data analysis, and technical methodologies highlight the expanding role of 2D-COS in materials science, environmental monitoring, and biological system. This review shows the dynamic growth and diversification of 2D-COS across in many scientific fields.
Abstract: Commercial glyphosate-based herbicides are widely used in agriculture. They can be environmental contaminants and may pose a risk to human health through water and the consumption of agricultural products. However, methods for detecting glyphosate in different matrices are expensive and tedious procedures because it is a challenging molecule to detect. Therefore, in this study, we employed surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles (AgNPs) as a sensitive, simple, and rapid method to indirectly detect glyphosate in commercial glyphosate-based products with the ninhydrin reaction. The glyphosate-ninhydrin reaction product exhibits a band at approximately 567 nm in the visible light spectrum, and Raman analysis reveals two distinct peaks at 660 and 790 cm−1 in the standard. This confirms that the peaks are consistent with commercial glyphosate-based products and directly related to the concentration of glyphosate. The proposed SERS method may be practical for analyzing environmental samples with relatively high concentrations when compared to regulated levels in some matrices or to those reported in other scientific studies. Although its application is mainly geared toward detection in high ranges, it is a functional method that can be adjusted to improve its sensitivity and adapt to different analytical conditions. Our results provide an effective strategy for detecting this pollutant, which is crucial for monitoring, controlling, and preventing population exposure. A reliable and straightforward glyphosate detection method can thus support environmental safety and public health.
Abstract: Raman spectroscopy is an analytical technique of choice for Earth and planetary sciences, which was recently selected as part of robotic exploration missions on Mars. Indeed, several miniaturized Raman spectrometers have been included into the scientific payload of rovers for the remote surface exploration of Mars: SuperCam and Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) for the NASA Mars 2020 mission and the Raman laser spectrometer (RLS) for the European Space Agency's (ESA) ExoMars mission. In preparation for these missions, a number of Mars analogue biogeological samples retrieved on Earth are extensively interrogated using Raman spectrometers, including flight prototype instruments but not only. Some studies also used flight representative portable instruments, as well as benchtop instruments. Commonly, authors reported the excitation laser wavelength and its power but often omitted the laser spot size on the sample which is a key factor for comparing several studies in term of spectrometer capabilities. In this study, we reported an easy, fast and universal experimental approach for determining the effective laser spot size, defined as the diameter of the sample section which is effectively probed by the Raman spectrometer during the analyses. Here, we characterized the effective laser spot size for a benchtop micro-Raman system and two different portable spectrometers, using a standard silicon wafer and gypsum powders with various average grain sizes. The dependence of the laser spot size with the grain size of the samples is discussed with regards to qualitative and quantitative analyses of solid dispersions in the scope of remote planetary missions.
Abstract: It has long been of interest to understand how optical spectra change when the principal axis of a uniaxial crystal is oriented not only within the measurement plane but also out of the plane. Although several theoretical frameworks have been developed to address this situation, relatively few studies have directly compared these models with experimentally measured spectra. In this study, Lekner’s formalism, which is applicable when the principal axes are not confined to the measurement plane, was applied to crystalline quartz. The reliability of this method for three-dimensional orientation analysis of uniaxial crystals was evaluated by comparing calculated reflection spectra with experimental measurements. The results showed that large frequency dispersion produces orientation-dependent spectral features. These features arise from the mixing of the optical responses along the principal axes when out-of-plane orientations are present. The potential and limitations of this approach for determining crystal orientation angles are also discussed.
Abstract: Despite their small size, postage stamps are culturally significant artifacts that reflect the artistic, technological, and political contexts of their production. Although the material composition of stamps from many European countries, including Italy, Portugal, and the United Kingdom, has been studied, Swiss postage stamps remain largely unexplored. In this study, we present the first systematic material characterization of 98 Swiss stamps issued between 1850 and 1908 using a combination of non-invasive analytical techniques including Raman spectroscopy, attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) together with scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX). In specific cases, high-performance liquid chromatography coupled with diode array detection (HPLC-DAD) was also applied. Our results highlight the predominant use of Prussian blue as a blue pigment, whereas ultramarine appears more frequently in early editions. There is greater diversity in red inks, which consist of both inorganic pigments such as vermilion, chrome orange, and red lead, and organic dyes such as eosin Y, carminic acid, and alizarin from 1882 onwards. Yellow and green inks reflect standard formulations, the latter resulting from the combination of Prussian blue and chrome yellow, broadly corresponding to those used in contemporary foreign issues. Brown and gray inks were obtained from a mixture of carbon black and a red or white pigment, respectively. This study sheds new light on the material history of Swiss postage stamps and highlights the value of combining spectroscopy, imaging, and chromatography techniques in philatelic research and, more broadly, in heritage science.
Abstract: Using spectroscopic technology for the accurate and non-destructive determination of moisture content (MC) in husk-on fresh corn (Zea maize L. sinensis Kulesh) is crucial for optimizing harvesting periods, ensuring quality, and maintaining nutritional value. However, corn husks interfere with the propagation of incident photons within corn kernels, leading to acquired spectral signals that contain information unrelated to the kernels themselves, thereby decreasing the accuracy of moisture detection in the kernels. This study developed a multichannel visible and near-infrared (Vis-NIR) spectral acquisition system based on spatially resolved diffuse reflectance technology for MC detection in husk-on fresh corn. The developed system mitigates the interference of husks on the acquired spectral signals by collecting spectral information from multiple detection positions offset at specific distances from the incident light source. Meanwhile, three model building strategies based on deep learning frameworks, including feature-level fusion, data-level fusion, and decision-level fusion, were proposed and compared. Results showed that the decision-level fusion model with standard normal variate (SNV) preprocessing achieved the highest prediction accuracy, with a coefficient of determination (R2p) of 0.897 and a root mean square error of prediction (RMSEP) of 4.13%. Furthermore, multichannel data relatively enhanced model performance, with the four-channel combination achieving the best performance. This study demonstrates the potential of deep learning and multichannel spectral data fusion in improving MC prediction accuracy, offering a practical solution for non-destructive moisture measurement in fresh corn.
Abstract A novel flow cell allowing for multiple optical spectroscopy measurements on flowing molten salts was designed, and demonstrative calibrations of impurities in aqueous samples were performed. Online compositional measurements of molten salts are of high interest to monitor the state of relevant solar and nuclear systems. The Spectroscopic Configuration for Optical Real-Time Characterization of High-Temperature (SCORCH) fluids cell was designed to meet this need by providing optical access to a high-temperature molten salt sample stream without physical contact between the sample and window materials. Laser-induced breakdown spectroscopy (LIBS) was utilized to quantify Li, Cr, Fe, Ni, Sr, and Pr at concentrations ranging nominally from 0 to 315 mmol L−1. Laser power, frequency, and plasma position were optimized to mitigate challenges associated with sample splashing. Univariate calibration models were built with R2 > 0.98, percent root mean square error of cross-validation (%RMSECV) as low as 2.7%, and limits of quantification (LOQs) down to 4.1 mmol L−1. Simultaneously, absorbance calibrations were developed for the applicable analytes (Cr, Ni, and Pr) using Beer’s law with a pathlength of 4.41 ± 0.10 mm. These models provide excellent quantification performance with R2 > 0.999, %RMSECV as low as 0.6%, and LODs down to 0.08 mmol L−1. Although these calibrations were performed for each spectroscopic technique separately, the two methods may be combined in the future through multivariate modeling and sensor fusion to provide more robust models with the benefits of both techniques (e.g., absorbance: oxidation state concentrations, LIBS: elemental concentration). Additionally, optimized spectrometers may be deployed to enhance sensitivity.
Abstract A sensitive, simple, inexpensive, rapid, and eco-friendly spectrofluorimetric method was created to assess the folic acid (FA) concentration in tablets using acriflavine (ACF) as an eco-friendly photoprobe. Based on its ability to quench the ACF fluorescence intensity in water at pH 8.0 and λex = 460nm. FA concentration was measured by quenching the fluorescence intensity of the ACF at λem =508 nm within the linear range of 3.5 × 10–6–30.0 × 10–6 mol L–1 with a correlation coefficient r2 = 0.9991. The limit of quantification (LOQ) and the limit of detection (LOD) are 1.159 × 10−6 mol·L–1 and 0.383 × 10−6 mol·L–1, respectively. This method was easy to use and accurately and effectively evaluated FA in pharmaceutical tablet samples. No influence was observed from the excipients usually contained in pharmaceutical formulations. The method was verified as valid according to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines. In addition, its greenness has been estimated using environmental assessment tools.
Abstract Arthroscopic procedures rely on qualitative methods for cartilage assessment, such as tissue visualization and mechanical probing. Visible–near-infrared (Vis-NIR) spectroscopy offers the potential to include compositional tissue characterization which could improve surgical guidance. The primary objective of this study was to assess the feasibility of using a fiber optic Vis-NIR probe in environments typically experienced during arthroscopy. Given the geometric constraints of articulating joints, a probe was fabricated with a 90-degree bend at the tip to enable movement and access to tissues. Absorbances from arthroscopic irrigation fluid (saline) are prominent in the NIR spectral region and thus need to be minimized during spectral collection. The current study aims to identify spectral data where the probe was not in contact with the tissues and/or where environmental saline contributed to the spectra. Porcine patella tissues were used to model how spectra collection in various conditions (probe offset from tissue and presence of fluid) impact spectra. Spectra were collected from cartilage, bone, and osteochondral tissues (n = 6 each) in experimental configurations with and without tissue contact and/or saline. Additionally, arthroscopic spectra collection in an equine stifle joint was investigated. Spectra collected while the fiber optic probe was in contact with the tissues resulted in minimal impact of environmental saline. Principal component analysis of spectra resulted in the separation of groups based on experimental configuration, demonstrating the potential for the development of more advanced machine learning algorithms focused on exclusion of spectra without appropriate tissue contact and with saline interference.
Abstract: Complex-valued chemometrics utilizes both the absorption index and refractive index spectra. Through application of a Kramers–Kronig transformation, it can also be extended to absorbance and Raman spectra. In this work, we expand complex-valued chemometrics to include partial least squares (PLS) regression. Several strategies for implementing complex-valued PLS are explored. One approach builds on the nonlinear iterative partial least squares (NIPALS) formalism to compute real and imaginary components of the PLS solutions in parallel. Additionally, as both the real and imaginary parts can assume positive or negative values, this results in 2N possible solutions for N components. In this case, the optimal solutions are selected using a brute-force approach combined with a nested leave-one-out (LOO) scheme. Additionally, single-value decomposition (SVD) can be directly applied to the complex matrix product of the spectral and concentration matrices. We compare these approaches using complex refractive index spectra of mixtures from the thermodynamically ideal systems benzene–toluene, benzene–cyclohexane, and benzene–carbon tetrachloride (CCl4). In particular, when the high-wavenumber refractive index differs between the neat components, complex-valued PLS achieves errors more than an order of magnitude lower than conventional PLS based solely on the imaginary part.
Abstract: Laser-induced breakdown spectroscopy (LIBS) has broad application potential, yet its analytical accuracy is often limited by poor spectral stability. Since plasma optical signals directly reflect plasma fluctuations, they offer a promising basis for spectral correction. In a novel approach, we introduce a neuromorphic dynamic vision sensor (DVS) to capture plasma dynamics with microsecond temporal resolution. The DVS provides a 120 dB dynamic range and a low data rate (∼10 MB/s), enabling acquisition of plasma optical signals over a wide range of conditions. We further propose an event-enhanced spectroscopy correction network (EESCN), which employs a dual-stream convolutional neural network (CNN) to extract key features from spectra and plasma images, respectively. A multihead attention module then performs cross-modal fusion by dynamically weighting spectral and image features to predict and correct signal fluctuations. To emulate challenging conditions, we introduced laser energy fluctuations and selected spectral lines affected by self-absorption. EESCN substantially suppressed spectral fluctuations arising from self-absorption and laser energy fluctuations for C(I) 493.202 nm and Mn(I) 403.076 nm in carbon steel, and for Cu(I) 327.395 nm and Zn(I) 328.233 nm in copper alloys, reducing the mean relative standard deviations by 71%, 79%, 81%, and 72%, respectively. Calibration curves constructed from the corrected spectra all achieved R2 values above 0.99, markedly outperforming the original spectra, normalization, and other correction methods. By integrating a low-cost, high-speed DVS with a cross-modal fusion model, this work provides a practical and powerful solution for mitigating spectral instability in LIBS and supports robust on-site analytical applications.
Abstract: 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.
Abstract: Sediment contamination by trace elements (TE) is a major environmental issue. In particular, TE speciation is of great importance because the form of the TE determines their mobility, bioavailability, and consequently their potential toxicity. Characterizing the chemical speciation of TEs can be complex and costly with current analytical methods. Non-destructive spectroscopic methods, which require limited sample preparation, are therefore useful tools for characterizing and possibly quantifying TEs in complex sedimentary matrices. Thus, this study explores the potential of visible and near-infrared hyperspectral imaging (HSI) to estimate the speciation of some TEs in sediments based on their spectral properties. Standard ranges of sixteen chemical species of six TEs, i.e., arsenic (As), cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn), were produced using three model sediment matrices (clay, silt, and organic matter). The results obtained show specific absorptions for each of the TE species, and nine of them could be quantified with detection limits of around 1 g/kg in the visible range and around 10 g/kg in the short-wave infrared range. This approach enables a more accurate and rapid assessment of environmental risk using HSI, in addition to conventional analytical methods.
Abstract: Raman spectroscopy (RS) is a label-free, non-destructive optical modality that provides a detailed profile of the molecular composition of a sample. There is growing interest in the clinical application of RS to characterize biomolecular signatures associated with radiotherapy response in tumor cells and tissues. A critical step before analyzing Raman data consists of performing spectral pre-processing to increase the quality of the measurements. Spectral pre-processing comprises baseline subtraction, signal smoothing, cosmic ray (CR) correction, and removal of poor-quality measurements. Herein, we present a convolutional autoencoder (AE) for single-step, automated pre-processing of Raman spectra obtained from tumor cells and tumor tissue. We trained two separate models using the same proposed architecture, one for eliminating spectral artifacts from preclinical single-cell line and xenografted tissue spectra exposed to single-fraction radiation, and the other for correcting clinical prostate tumor biopsy spectra collected from patients receiving high-dose-rate brachytherapy (HDR-BT). The autoencoder demonstrated fast, excellent performance in removing baseline, noise, and CRs. For the preclinical data, the model obtained a root mean squared error (RMSE), and a percentage root mean squared difference (PRD) of 7.1 × 10−5 and 3.1%, respectively, between the AE-corrected spectra and their corresponding target data (pre-processed by our current baseline-removal algorithm). Also, the autoencoder successfully removed 94.0% of CRs from the spectra. For the clinical biopsy data, the AE achieved an RMSE and a PRD of 8.1 × 10−5 and 3.7%, respectively, and a CR removal rate of 90.2%. Overall, the AE corrected approximately 11 000 spectra within 2.4  s without the need of a GPU. Furthermore, comparative supervised learning-based post-processing data analyses were performed separately on the spectra pre-processed by the autoencoder versus the target data, and we show consistency in the biochemical radiation response profiles extracted. Finally, the AE architecture was leveraged to train a reconstruction AE to facilitate semi-automated identification of poor-quality prostate biopsy spectra, and we demonstrate 96.4% agreement between AE and manually removed outliers. These results support the development of a deep learning framework for efficient, automated pre-processing of tumor cell and tissue Raman spectra collected for radiation response monitoring studies.
Abstract: Attenuated total reflection infrared–far-infrared (ATR IR-FIR) spectra (4000–50 cm–1) and Raman spectra (2000–50 cm–1) were measured for twelve types of biogenic minerals (shells), including Corbicula sandai (aragonite), Corbicula fluminea (aragonite), Corbicula japonica (aragonite), Ruditapes philippinarum (aragonite), and Mytilus galloprovincialis (aragonite and calcite) from different origins and growing environments. In this study, we investigated the crystal structures of these biogenic minerals, the water contents and structure in them, and the differences in the crystal structures among the aragonite forms of these minerals. In the 4000–3000 cm–1 region and around the 1650 cm–1 band region in the IR spectra, the proportion of the IR absorption bands related to weak and strong hydrogen bonds was significantly different among the shellfish species investigated. Therefore, it has been found that IR spectroscopy is useful for discriminating among shells based on the content and structure of water such as hydrogen bonds. In the low-frequency region below 500 cm–1, where bands corresponding to lattice vibrational modes are observed, we investigated the lattice vibration modes of aragonite of shells and discussed particularly the full width at half-maximum (FWHM) of the bands at around 267 cm–1 in the FIR spectra and the intensity of the side band at around 140 cm–1 in the Raman spectra. As a result, we demonstrated that using both IR and Raman spectroscopies including the low-frequency regions allows us to distinguish various biogenic minerals from different habitats and growing environments. Additionally, it suggests that both IR and Raman spectroscopies including low-frequency regions are useful for characterizing habitats of shellfish.
Abstract: Bamboo craftsmanship is highly valued for its aesthetics and cultural significance. The classification of bamboo craftsmanship plays a key role in preserving its heritage and ensuring its quality. However, the surface characteristics of bamboo exhibit substantial variation due to environmental factors. This study proposes a novel method using laser-induced breakdown spectroscopy (LIBS) combined with spectral data fusion to enhance the identification accuracy of bamboo age ranges. By fusing spectra from different bamboo parts, a broader range of elemental compositions can be captured while minimizing the influence of regional variations. A total of 50 bamboo craftsmanship samples of five different age ranges were prepared, and their internal and external surface LIBS spectra were collected for data analysis. Experimental results demonstrate that the peak selection–linear discriminant analysis model presents the highest classification accuracy of 99.0% before spectral data fusion. After fusion, the accuracy can be further improved to 99.9%. Additionally, a comparison of various data fusion methods reveals that the Concat method, which increases the dimensionality of the feature space and provides richer data representation, exhibits the best compatibility with LIBS spectral characteristics and classification models. In conclusion, the combination of LIBS and data fusion methods proves to be an effective approach for accurately identifying bamboo age ranges.
Abstract: Optical phantoms are widely used to characterize diffuse optical setups and data analysis methods for in-vivo/ex-vivo measurements. Coconut oil is an interesting compound to use in phantoms, because it could be used to model lipidic tissues, such as the one in breast tissue. In this paper, we measure the absorption and scattering spectra of coconut oil from 600 to 1600 nm, encompassing the so-called “therapeutic window”. To cover the entire range, we exploit a supercontinuum pulsed laser and a superconducting nanowire single photon detector operating in the time domain. Finally, we demonstrate the use of a homogeneous coconut oil phantom to characterize a hyperspectral continuous-wave (CW) setup.