Applied Spectroscopy

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.

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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 λ_ex = 460nm. FA concentration was measured by quenching the fluorescence intensity of the ACF at λ_em =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.

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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.

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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 (R²_p) 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.

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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.

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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.

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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.

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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.

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