Spectroscopy Journal

Beryllium (Be), a critical strategic metal element, is predominantly extracted from beryl, which serves as a key mineral combining significant strategic importance with essential industrial applications. Significant debate remains, however, regarding the mineralogical characteristics and color-causing mechanisms of beryl. In this study, we integrate Electron Probe Microanalysis (EPMA), Fourier transform infrared spectrometer (FTIR), laser Raman spectrometer (LRS), X-ray diffractometer (XRD), and ultraviolet–visible spectrophotometer (UV-VIS) to elucidate the mineralogy and spectral characteristics of pegmatitic beryl from Xinjiang, Northwest China. The results indicate that the beryl mainly presents a yellowish-green color, associated with minerals such as feldspar, quartz, and garnet. The EPMA results confirm the chemical composition of the typical beryl and indicate that the Al content is lower than the theoretical value, reflecting the substitution of Al³⁺. The FTIR shows characteristic vibrations of Si-O tetrahedral groups within the range of 1400~400 cm⁻¹, along with distinct bending and stretching vibration peaks of H₂O molecules observed in the range of 1700~1500 cm⁻¹ and 3500~3800 cm⁻¹, respectively. Combined with spectral analysis, it can be determined that both Type I water and Type II H₂O are present in the samples. Raman spectroscopy reveals that the two distinct peaks of beryl are located at approximately 685 cm⁻¹ (attributed to the stretching vibration of Be-O) and 1067 cm⁻¹ (corresponding to the bending vibration of Si-O), respectively. The XRD analysis shows that the ratio of unit cell parameters c/a of the samples ranges from 0.9950 to 1.0068, and the isomorphous substitution in its structure is mainly manifested as the replacement of octahedral coordination sites by Al³⁺. The UV-VIS shows that Fe³⁺ exhibits a broad absorption band in the range of 200~300 nm, while no obvious absorption peaks are observed in the range of 300~800 nm. The above characteristics indicate that Fe³⁺ has a significant impact on the color of beryl. For green beryl samples, a portion of Fe³⁺ occupies the structural channel sites and interacts with H₂O molecules within the channels, which contributes to the yellowish hue of beryl. Our study highlights crucial data for mineralogical identification, genetic tracing, as well as efficient utilization of beryl resources. Full article

To address the need for rapid evaluation of large batches of Mee rough tea during the acceptance stage, this study aims to explore the feasibility of using portable Fourier transform near-infrared (FT-NIR) spectroscopy for preliminary quality screening. The goal is to develop a rapid, non-destructive, and relatively objective assessment method that is applicable to practical acceptance scenarios. This work represents an exploratory proof-of-concept study rather than a finalized industrial grading solution. Spectral data of three reference categories and thirty-six test samples were collected in the wavelength range of 1350–$2500\mathrm{nm}$ using a portable FT-NIR spectrometer. The sample configuration was designed to simulate practical acceptance sampling conditions. The spectra were preprocessed using multiplicative scatter correction, first-order derivative transformation, and mean-centering. Independent principal component analysis (PCA) models were constructed for each reference category to achieve class-wise feature dimensionality reduction, with cumulative explained variance exceeding $95\%$. Distance thresholds were determined using the $3\sigma$ principle based on Euclidean distance and Mahalanobis distance. Classification was performed by distance-based matching between test samples and reference categories. Under optimized matching degree threshold settings of $0.9$ and $0.7$, the two distance models achieved classification accuracies of $86.11\%$ and $83.33\%$, respectively, demonstrating the feasibility of the proposed approach. The main contribution of this study is the application of class-wise PCA combined with distance-based discrimination to the acceptance stage of Mee rough tea. The proposed framework provides a practical exploratory approach for rapid screening and offers a preliminary digital tool to support acceptance decisions. Further validation using larger and more diverse datasets will be necessary prior to large-scale industrial implementation. Full article

Forests are vital regulators of global carbon balance, yet accelerating deforestation and land-use conversion continue to erode their capacity to sequester carbon. This research quantifies forest restoration and carbon sequestration potential across Visakhapatnam, India, by integrating imaging spectroscopy with machine learning at medium spatial resolution. Using 33 spectral and environmental predictors, an ensemble Random Forest model was developed and benchmarked against a K-Nearest Neighbors algorithm. The Random Forest approach demonstrated markedly higher predictive strength, explaining 87% of the spatial variability in tree cover, while maintaining low error margins. By excluding agricultural and urban areas, the analysis identified approximately 104,800 hectares of restorable land. The restorable area corresponds to an estimated carbon sequestration potential of about 0.12 petagrams, underscoring the district’s significant yet underutilized capacity to contribute to regional and national climate goals. The research highlights how integrating spectroscopy-derived vegetation metrics with ensemble learning enables spatially precise, policy-relevant restoration planning. By linking medium-resolution environmental data with carbon accounting, this framework advances a scalable pathway for data-driven forest recovery and nature-based climate mitigation, bridging the gap between site-specific ecological assessments and large-scale sustainability initiatives. Full article

To achieve accurate identification of early subcutaneous bruising regions in fragrant pears, this study developed a detection system based on Structured-Illumination Reflectance Imaging (SIRI) and integrated it with both machine learning and deep learning models. Structured-illumination images were acquired at six spatial frequencies (50, 100, 150, 200, 250, and 300 cycle·m⁻¹) and evaluated after demodulation through both visual assessment and contrast index (CI) analysis. The optimal spatial frequency of 150 cycle·m⁻¹ was selected for subsequent analysis. Texture features were extracted from AC, DC, and RT images based on the gray-level co-occurrence matrix (GLCM), and classification was performed using three machine learning models KNN, PLS-DA, LightGBM and the deep learning Mask R-CNN model. The results showed that the classification performance of RT images was superior to that of AC and DC images. Among them, the PLS-DA model achieved an accuracy of 95.00% on the test set for RT images. The Mask R-CNN model achieved a recognition accuracy of 99.17% on the RT image test set. These results demonstrate that the combination of SIRI and deep learning enables highly sensitive and nondestructive detection of early subcutaneous bruising in Korla pears, providing an efficient and reliable technical approach for fruit quality grading and postharvest intelligent inspection. Full article

The Esquel pallasite provides a valuable record of metal–silicate interaction in differentiated planetesimals, yet many aspects of its formation and thermal evolution remain uncertain. Here, we present a comprehensive multi-technique characterization of a single Esquel specimen, integrating SC-XRD, Raman spectroscopy, SEM–EDS, XPS, magnetic force microscopy, and X-ray computed tomography. Olivine grains are shown to be structurally pristine, with the first full crystallographic refinement for Esquel confirming a single-domain silicate lattice. XPS demonstrates a stoichiometric silicate surface containing only lattice O²⁻, Si⁴⁺, Mg²⁺, and Fe²⁺, indicating that olivine remained chemically unaltered. The Fe–Ni metal preserves diffusion-controlled taenite–kamacite exsolution, compositionally distinct plessite, accessory schreibersite and troilite as resolved by SEM. Quantitative Ni zoning, evaluated through interface-to-center gradients and a width–center-Ni correlation method, yields a self-consistent cooling rate of ~10–20 °C/Myr. MFM reveals microscale magnetic structures that correlate directly with Fe–Ni chemical zoning, providing magnetic confirmation of slow cooling. CT analysis further identifies interconnected metal networks, inclusions, and micro-porosity reflecting melt migration and late-stage modification. These results establish Esquel as an exceptionally well-preserved pallasite and demonstrate the value of integrated, multi-scale analytical workflows for reconstructing early Solar System processes. Full article

The purpose of this study was to examine the use of portable spectroscopy technologies for rapid milk composition and hygiene quality assessment in ovine milk. Two portable analyzers, namely SmartAnalysis (UV/Vis absorbance) and SpectraPod (NIR transmittance), were used to obtain spectral data of raw milk samples. Additionally, reference values of the milk’s compositional, physical, and hygienic traits were measured. Machine learning algorithms were used to explore the correlations between spectral data and milk traits. The initial results indicated a promising potential of utilizing spectral technologies to predict milk quality and hygienic parameters. Regression models presented a moderate predictive accuracy, with R² values between 0.55 and 0.34, respectively, regarding fat (RF-NIR) and protein (LR-UV/Vis). Classification models indicated high accuracy for hygienic parameters, with the highest accuracy and AUC values up to 0.87 and 0.83, respectively, predicting increased levels of total bacterial count (TBC), while somatic cell count (SCC) level was less accurately predicted by the model, with AUC values lower than 0.70. The results demonstrate the applicability potential of UV/Vis and NIR portable devices in milk quality assessment, enabling its rapid evaluation, including milk composition and hygiene parameters at the point of service. Full article

Aerinite is a rare blue aluminosilicate mineral whose identification as a pigment in Pyrenean medieval artworks typically requires invasive microsampling. This study evaluates portable X-ray fluorescence spectroscopy (pXRF) as a noninvasive screening tool for aerinite in Alto Aragón (Spain) cultural heritage. Elemental compositions of aerinite and lapis lazuli references, ceramics, polychromed capitals, and thirteenth- to fifteenth-century painted panels were measured with a Niton XL3t GOLDD+ spectrometer. Data were analyzed using log-ratio linear discriminant analysis (LDA), with silicon as an internal normalizer. Aerinite references showed Cu and Co levels below instrumental detection limits, along with Fe (6.99 ± 1.04 wt%), Al (4.91 ± 1.38 wt%), and Si (15.95 ± 1.60 wt%). High-confidence aerinite classifications were obtained for Cu-free and Co-free blue pigments in the Barbastro Chrismon, the Buira altar frontal, and other panels. Extension of the protocol to green pigments revealed that two samples—from the Saint Anthony Abbot panel and Portaspana retable—were also classified as aerinite, providing the analytical evidence for “verde de Juseu” as a naturally occurring greenish aerinite variety. Despite known pXRF limitations, this technique effectively screens candidate aerinite-containing passages for subsequent microanalytical confirmation. Full article

In PAC spectroscopy, hyperfine interactions of a radioactive probe nucleus with its surroundings are measured, providing information about the local atomic structure and dynamics at the probe site. In the so-called fast reorientation time regime for fluctuating nuclear quadrupole interactions (NQIs), the PAC signal is an exponentially decaying function, with decay constant λ depending on both the hyperfine interaction and dynamics. For a molecular system in solution, dynamics may originate from Brownian molecular tumbling (rotational diffusion) with rotational correlation time τ_c and from local dynamics at the probe site, occurring at a characteristic time scale τ_loc. The τ_c and the τ_loc cannot be discriminated in a single PAC spectrum; however, assuming that they scale differently with viscosity and temperature, a series of experiments in which these parameters are varied may allow for discrimination of τ_c and the τ_loc. Three models are presented for the effect of dynamics on the PAC signal: (1) the Stokes–Einstein–Debye model with linear scaling of λ with viscosity ξ; (2) a more general model presenting a power law scaling of λ with (ξ/ξ₀)ⁿ; and (3) a model that includes rotational and local dynamics leading to an expression for λ that scales with ξ/(ξ + c), where c is a constant that depends on temperature, molecular volume, and τ_loc. These models may serve as different approaches to analyze PAC data and their dependence on temperature and solvent viscosity in the fast reorientation time regime, and they can be applied to design experiments for optimal discrimination of global rotational diffusion and local dynamics at the probe site. Full article

The study presents a novel approach that integrates laser-induced breakdown spectroscopy (LIBS) data with machine learning algorithms for the rapid evaluation of coal quality. The developed framework enables the determination of three critical parameters: Ash Content (A_ad), Carbon Content (C_d), Sulfur Content (S_tad). The experimental implementation utilized an optimized dataset to construct and evaluate the predictive model. The LIBS prototype system enables spectral data acquisition under controlled experimental conditions. Data preprocessing is carried out by systematically removing background interference and substrate effects using adaptive filtering techniques. Characteristic emission peaks corresponding to target elements are identified through multivariate analysis, and Partial Least Squares Regression (PLSR) serves as the core algorithm for analysis. Systematic iterative optimization of multivariate preprocessing parameters and adaptive peak selection strategies yields substantial improvements in both predictive accuracy and computational efficiency, with determination coefficients (R² > 0.90) demonstrated for all target analytes. This enhanced accuracy validates the viability of LIBS as a robust alternative to conventional analytical methods for coal composition analysis. The LIBS demonstrates substantial advantages in coal quality assessment, thereby enhancing the overall efficiency of both coal extraction and quality evaluation processes. Full article

Biological clusters, encompassing proteins, nucleic acids, and lipids, represent functional assemblies that underpin cellular physiology and contribute to disease pathogenesis. Their detection and characterization remain technically challenging due to their multistep, heterogeneous, and often transient nature. Fluorescence correlation spectroscopy (FCS) has become a powerful tool for quantifying particle numbers, diffusion states, and brightness changes, thereby providing direct insights into finite molecular assemblies. Applications include diverse oligomers and complexes of proteins, lipids, and nucleic acids, underscoring both physiological and pathological relevance. Recent methodological extensions—including multi-color cross-correlation FCS, image- and super-resolution-based approaches, and brightness analyses—have expanded the capacity to resolve complex molecular interactions. Transient state (TRAST) monitoring provides additional sensitivity to photophysical state transitions of fluorophores and to their physicochemical environments. Looking ahead, integration with AI promises to lower technical barriers and accelerate broader adoption. This review highlights the conceptual framework, recent advances, and future opportunities of FCS in probing biological clusters and aggregates. Full article

Fumarate, succinate, maleate, dihydroxyfumarate, D–tartarate, L–tartarate, DL–tartarate, L-malate, D-malate, oxaloacetate, citrate, and DL-isocitrate in the 5–100 μM concentration range were incubated in 12.5 mM HEPES/25 mM TRIS base containing 200 μM Eu³⁺–tetracycline and 60% (v/v) formamide (pH unadjusted). After 30 min of incubation, they were separated at 4 °C by capillary electrophoresis utilizing laser-induced luminescence detection with 12.5 mM HEPES/25 mM TRIS base containing 60% formamide as the running buffer. All analytes yielded peaks, with the exception of fumarate, succinate, and maleate. L-Malate was detected down to 100 nM. The main component of this study was the analysis of malate. The objective was to develop a stereoselective methodology for the detection of L-malate. This was achieved by varying the formamide concentration and separation temperature. When the temperature was increased to 22 °C and the formamide concentration decreased to 40%, the sensitivity for L-malate was diminished about 10-fold, but that for D-malate was eliminated. This combination of conditions allowed for the stereospecific analysis of L-malate. Full article

This article introduces the main characteristics of PyMossFit, a software for Mössbauer spectra fitting. It is explained how each aspect of the code works. Based on the Lmfit Python package, it is a robust data fitting tool. Designed to run through Jupyter Notebook in the Google Colab cloud, it also allows one to work via multiple devices and operating systems. In addition, it allows the fitting procedure to be performed collaboratively among researchers. The software performs the folding of raw data with a discrete Fourier transform. Data smoothing is available with the use of a Savitzky–Golay algorithm. Moreover, a K-nearest neighbor algorithm enables users to determine the present phases by matching the correlations of hyperfine parameters from a local database. Full article

Achieving long-lived room-temperature phosphorescence (RTP) with high quantum efficiency is of significant interest for applications in anti-counterfeiting, flexible optoelectronic displays, and multi-level information encryption. Here, we presented a hydrogen-bond engineering strategy to enhance RTP performance by progressively increasing the number of hydrogen-bonding sites within a polyvinyl alcohol (PVA) matrix. A series of carbazole-based chromophores (Cz, ICz and 2ICz) were embedded into the PVA network, and their photophysical properties were systematically characterized using steady-state photoluminescence spectra, time-decay spectra, Fourier-transform infrared (FTIR), and Raman and X-ray photoelectron spectroscopy (XPS). Spectroscopic analysis revealed that the increased number of N-H groups significantly strengthened hydrogen-bonding interactions, effectively suppressing non-radiative decay pathways and stabilizing triplet excitons. As a result, the phosphorescence lifetime was prolonged up to 1.68 s with a quantum yield of 38.63%. Furthermore, leveraging the spectral overlap integral between the phosphorescent emission and dye absorption, efficient Förster resonance energy transfer (FRET) was realized, enabling tunable multi-color afterglow emissions. This study establishes a design strategy validated by spectroscopy for high-performance RTP materials and highlights their promising potential in advanced optical encryption and flexible photonic applications. Full article

Wild Physalis angulata L. has promising medicinal potential due to its rich flavonoids. However, a green analytical approach for these compounds from this plant has not yet been thoroughly optimized. Therefore, this study optimized ultrasound-assisted extraction using the response surface method for the UV-VIS spectroscopic determination of the total flavonoid content in P. angulata in Vietnam. Notably, the greenness of the whole procedure was evaluated by AGREE, Eco-Scale, GAPI, BAGI methodologies. The Box–Behnken model was applied to design the experiments with four variables: ethanol concentration, solid-to-liquid ratio, extraction temperature, and time. The UV-Vis spectrophotometric method was validated at 510 nm according to AOAC guidelines and met all the requirements, including specificity, linearity (R² = 0.9996) in the working range of 15–120 µg/mL, repeatability (RSD = 1.89%), intermediate precision (RSD = 2.21%), and accuracy (recoveries from 99.52 to 104.06%). The limits of detection (LOD) and quantification (LOQ) were 2.48 µg/mL and 7.52 µg/mL, respectively; however, to avoid noise signal at lower concentrations, the validated lower limit of quantification (LLOQ) was set at 15 µg/mL. Data were analyzed using second-order regression. The R² = 0.9726 shows a close correlation between variables and the experimental data. The optimal extraction conditions were 31.66% ethanol, 30:1 mL/g ratio, 80 °C and 48.73 min. The predicted values (38.09 ± 1.70 mg RU/g) were not statistically different from the experimental values (34.58 ± 0.87 mg RU/g), confirming the model’s accuracy and applicability in optimizing the extraction process. The ultrasound-assisted extraction was optimized to enhance the flavonoid extraction yield from P. angulata, providing a solid scientific foundation for further pharmacological research. Full article

The development of tunable optical filters in the mid-infrared (MIR) region is crucial for a variety of applications, including environmental monitoring, medical diagnostics, and communication systems. This paper presents the design, fabrication, and characterization of a novel Twisted Liquid Crystal (TLC) electro-tunable optical cavity filter for the MIR region 3–5 μm. The filter is based on a Fabry–Perot interferometer configuration, which includes a polarization-independent TLC to achieve electrical control over the filter’s transmission characteristics. Two distinct filters were fabricated, differing in their substrate materials: silicon and glass. The silicon-based filter demonstrated an impressive 80% transmission with a tuning range of ∼13.6 nm and ∼14.64 nm in two separate bands, achieved by varying the applied voltage from 0 to 20 V. In contrast, the glass substrate filter exhibited a slightly higher transmission of 82% with tuning ranges of ∼10.5 nm and ∼7.2 nm across the spectral band when the voltage was adjusted from 0 to 27 V. Experimental validation showed a strong alignment between the simulations and results, demonstrating the feasibility of integrating tunable liquid crystals into mid-infrared optical cavities. This advancement highlights their potential for applications that require precise and dynamic control of the mid-infrared spectrum. Full article

Polarization volume gratings (PVGs) have been recognized as a groundbreaking technology with the potential to revolutionize holographic waveguides and facilitate immersive experiences in augmented and virtual reality (AR/VR) applications. This study investigated the design, fabrication, and utilization of PVGs within the framework of holographic waveguides for immersive encounters. The essay begins by presenting a comprehensive overview of waveguide technologies. The display devices and optical combiners combine the actual and virtual worlds that the naked eye can see. This review categorizes current visual solutions for micro-optical combiners in augmented reality head-mounted displays (AR-HMDs). Subsequently, an investigation was carried out into the manufacturing process, physical principles, optical structures, performance characteristics, and other aspects. Furthermore, a review and assessment of their merits and drawbacks were conducted. Full article

Thermoelectric (TE) materials represent a critical frontier in sustainable energy conversion technologies, providing direct thermal-to-electrical energy conversion with solid-state reliability. The optimizations of TE performance demand a nuanced comprehension of structure–property relationships across diverse length scales. This review summarizes established and emerging spectroscopic and microscopic techniques used to characterize inorganic and polymer TE materials, specifically poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS). For inorganic TE, ultraviolet–visible (UV–Vis) spectroscopy, energy-dispersive X-ray (EDX) spectroscopy, and X-ray photoelectron spectroscopy (XPS) are widely applied for electronic structure characterization. For phase analysis of inorganic TE materials, Raman spectroscopy (RS), electron energy loss spectroscopy (EELS), and nuclear magnetic resonance (NMR) spectroscopy are utilized. For analyzing the surface morphology and crystalline structure, chemical scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD) are commonly used. For polymer TE materials, ultraviolet−visible–near-infrared (UV−Vis−NIR) spectroscopy and ultraviolet photoelectron spectroscopy (UPS) are generally employed for determining electronic structure. For functional group analysis of polymer TE, attenuated total reflectance–Fourier-transform infrared (ATR−FTIR) spectroscopy and RS are broadly utilized. XPS is used for elemental composition analysis of polymer TE. For the surface morphology of polymer TE, atomic force microscopic (AFM) and SEM are applied. Grazing incidence wide-angle X-ray scattering (GIWAXS) and XRD are employed for analyzing the crystalline structures of polymer TE materials. These techniques elucidate electronic, structural, morphological, and chemical properties, aiding in optimizing TE properties like conductivity, thermal stability, and mechanical strength. This review also suggests future research directions, including in situ methods and machine learning-assisted multi-dimensional spectroscopy to enhance TE performance for applications in electronic devices, energy storage, and solar cells. Full article

The consumption of illicit psychoactive substances, such as cocaine, poses significant public health and socioeconomic challenges due to its widespread use and impact on the central nervous system. This study aimed to develop and validate an analytical method for quantifying cocaine in surface water using an adapted QuEChERS extraction procedure and gas chromatography–mass spectrometry (GC-MS). The research included a bibliographic review of about 40 articles and laboratory analyses conducted at the Federal University of Northern Tocantins (UFNT). The results showed a matrix effect of −54.24%. This negative matrix effect impacted accuracy, as interference from other sample components can suppress the analyte signal, resulting in a smaller measured quantity. This indicates signal suppression, which can be corrected through matrix-matched calibration. Recovery values ranged from 61.3% to 107.7%, demonstrating satisfactory accuracy. The validated method proved suitable for monitoring cocaine in surface water and can serve as a biomarker for untreated sewage discharges. Full article