Search Results (513)

In the saline–alkaline lake (SAL) systems of the Nhecolândia region, Brazilian Pantanal, soils exhibit complex mineralogical assemblages controlled by sediment inheritance, pedogenesis, and hydrogeochemical gradients. This study investigates the distribution and transformation of 2:1 phyllosilicates along representative SAL toposequences. Soil samples were characterized by Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD), supported by granulometry and adjustment of the FTIR spectra. Mineralogical data were integrated with geochemical (Al, K, Mg, Ca, Na) and pH data and examined using principal component analysis (PCA). Greenish loamy horizons act as key morphological controls on hydrogeochemistry, regulating solute retention along mid- to downslope transitions. Illite is more strongly associated with upslope positions, whereas downslope alkaline environments are associated with smectitic phases (e.g., montmorillonite and Mg-rich varieties such as saponite) and mixed-layer minerals structures (e.g., illite–smectite and montmorillonite–vermiculite structures). These assemblages are consistent with non-linear transformation pathways, with illite as a possible transitional phase between micas and expandable structures. The PCA results suggest a primary mineral distribution structured by fine-material content and depth, while pH and alkalinity emerge as key geochemical controls that differentiate mineral stability fields and reinforce the hydrogeochemical compartmentalization of the profiles. Geochemical data show strong associations of Al, Mg, and K with fine-fraction accumulation. The integration of these approaches highlights that a 2:1 phyllosilicate assemblage results from multiple superimposed pedogenetic pathways, offering a conceptual framework for studying complex soil–lake systems. Full article

This study evaluated the use of visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning (ML) algorithms to predict soil fertility-related properties in two contrasting agroecological regions of Peru: the Highlands and the Rainforest. A total of 297 soil samples were analyzed using portable spectroradiometers covering a spectral range of 350–2500 nm, applying transformations such as Savitzky–Golay smoothing, first derivative, and band depth. Predictive models were developed using PLSR, Random Forest, Support Vector Machines, and neural networks. Results show variable predictive performance across soil properties and ecosystems. Organic matter in Highland soils and calcium in Rainforest soils achieved the strongest test-set accuracy (R² > 0.70), while pH and texture fractions showed moderate performance (R² = 0.42–0.67), and mobile nutrients including phosphorus, potassium, and sodium showed limited predictive accuracy due to their weak spectral expression. Spectral predictions were further integrated into a structured nutrient balance framework to assess agronomic reliability. Nitrogen fertilizer recommendations showed the strongest agreement between observed and predicted values across both ecosystems, whereas K₂O and CaO recommendations in Highland soils were substantially underestimated, demonstrating that property-level statistical performance does not guarantee agronomic reliability. These findings confirm that Vis-NIR spectroscopy combined with ML represents a fast, cost-effective, and sustainable alternative to conventional soil analysis, especially in rural areas with limited laboratory infrastructure. Expanding regional calibration datasets and exploring mid-infrared FTIR spectroscopy as a complementary technology are identified as priority directions for improving predictions of agronomically critical nutrients. Full article

Monitoring dissolved carbon dioxide (CO₂) in aquatic and sediment systems is critical for understanding carbon cycling and climate feedback. This study develops and characterizes a compact, low-cost microbolometer-based attenuated total reflectance (ATR) mid-infrared sensor for direct dissolved CO₂ measurement in liquid and soil-water environments. The system integrates a ZnSe ATR crystal with custom suspended SiN membrane microbolometers and uses evanescent-wave absorption at 4.26 μm with a broadband LED source and computational spectral reconstruction, eliminating the need for an interferometer. Calibration shows excellent linearity (R² ≈ 0.99) over 50–1000 ppm CO₂, with a practical limit of detection (LOD) of ~26–35 ppm at 5–25 °C. UV-induced CO₂ generation from soil-water mixtures was investigated across UV wavelengths, revealing UV-C (254 nm) as optimal, producing net ΔCO₂ ≈ 339 ppm above ambient levels in 30 min. Environmental factors (temperature 5–35 °C, pH 5–11, pressure 1–1.5 ATM, dissolved organic carbon) were systematically evaluated, confirming robust sensor performance (accuracy >90%, correlation r > 0.98 with reference instrument). This sensor represents the first integration of MEMS microbolometer detectors with ATR evanescent-wave spectroscopy for liquid-phase dissolved CO₂, enabling real-time monitoring and rapid sediment organic carbon assessment in a field-deployable platform. Full article

Ultrafast fiber lasers operating near 2 μm have emerged as a critical platform for advancing mid-infrared photonics due to their narrow pulse durations, high peak powers, and broad tunability. These sources exploit the rich energy-level structures of Tm³⁺ and Ho³⁺ doped fibers and reside within an atmospheric transmission window, enabling applications spanning nonlinear microscopy, precision micromachining, optical frequency metrology, biophotonics, and free-space optical communication. Recent progress in low-loss fiber fabrication, dispersion-engineered cavity design, and mode-locking technologies has significantly expanded the performance boundaries of 2 μm ultrafast fiber lasers. This review systematically examines the underlying pulse-formation mechanisms and categorizes state-of-the-art mode-locking approaches. Representative laser architectures are compared with respect to pulse duration, energy scalability, repetition-rate enhancement, spectral characteristics, and environmental stability. Key application pathways in high-resolution spectroscopy, biomedical diagnostics, and mid-IR supercontinuum generation are highlighted. Finally, the remaining challenges and prospective research directions are discussed to inform the development of next-generation ultrafast photonic sources in the 2 μm band. Full article

This study presents a comparative evaluation of multiple-approach optical spectroscopic sensor—Laser-Induced Breakdown Spectroscopy (LIBS), Mid-Infrared Spectroscopic sensing (MIRS), and Hyperspectral Imaging (HSI)-based sensors operating in the Visible–Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) ranges—for the quantitative detection of calcium carbonate (CaCO₃) in pelletized CaCO₃-CaO mixtures. The objective was to assess and compare the sensing performance of these optical sensor platforms for carbonate quantification. Each spectroscopic sensor dataset was processed using chemometric calibration methods, including Partial Least Squares Regression (PLSR), to ensure robust and reproducible quantitative predictions. Although the samples consisted of binary CaCO₃-CaO mixtures, the sensing task focused exclusively on CaCO₃ content. Results indicate that LIBS, MIRS, and HSI-SWIR-based sensing approaches achieved comparable quantitative performance, with LIBS providing the highest prediction accuracy. In contrast, the HSI-VNIR sensor configuration demonstrated lower predictive capability relative to the other optical sensing modalities. These findings highlight the potential and limitations of different optical sensor technologies for carbonate detection in heterogeneous mineral systems. Full article

Non-obstructive azoospermia (NOA), a severe male infertility condition with impaired or absent sperm production, is treated by microsurgical testicular sperm extraction (micro-TESE), whose success depends on identifying seminiferous tubules with active spermatogenesis. To address this challenge, we demonstrate that mid-infrared photothermal (MIP) microscopy can provide label-free molecular signatures to distinguish different NOA subtypes in patient tissues. We applied MIP microscopy and MIP-guided IR spectroscopy to testicular tissues from obstructive azoospermia (normal spermatogenesis) and idiopathic NOA (abnormal spermatogenesis) patients. Tissue classification was performed using a Singular Value Decomposition–Random Forest (SVD-RF) pipeline. MIP imaging revealed distinct lipid distribution and reduced lipid content in NOA tissues compared to normal spermatogenic tissues. Using SVD to extract spectroscopic features and RF for classification, we achieved 94.03% accuracy in distinguishing testicular tissues as normal spermatogenesis or three pathological subtypes of idiopathic NOA. These findings demonstrate MIP microscopy as an effective tool for characterizing the spermatogenic potential of seminiferous tubules based on their molecular composition, potentially facilitating improved sperm retrieval strategies. Full article

The concepts of food matrix and holistic analysis have been used in a wide range of scientific disciplines to describe the sum of the parts of a whole that provide a specific property or functionality to the sample. Traditional chemical and physical analysis needs to destroy the sample (e.g., dilution, extraction, drying) before analysis. The utilization of vibrational spectroscopy techniques, like near (NIR), mid infrared (MIR) and Raman spectroscopy, allows for the non-destructive analysis of food ingredients and products. The resulting output of this analysis is based on the information provided by the vibrational modes of atoms present in the different molecules, allowing the measurement of different chemical and physical characteristics of the food. The objective of this paper is to discuss the ability of vibrational spectroscopy methods to provide robust tools to analyze the food matrix holistically, moving away from the traditional analysis of individual compounds or chemical parameters. Studies discussed and presented in this review demonstrated the ability of vibrational spectroscopy (e.g., NIR, MIR and Raman spectroscopy, hyperspectral imaging) to assess the whole food matrix beyond the traditional notion of developing a calibration model. Full article

The authentication of wines with Protected Designation of Origin (PDO) status is a key requirement for quality assurance, traceability, and consumer trust, particularly in traditional wine-producing regions such as the Douro Demarcated Region (Portugal). Among the certification criteria, the reliable identification of grape varieties remains technically challenging, especially when rapid and non-destructive analytical approaches are required. In this study, Fourier-transform infrared spectroscopy coupled with chemometric analysis was evaluated as a rapid screening approach for the differentiation of monovarietal Douro wines produced under standardized microvinification conditions. Twenty-one monovarietal wines were analyzed using mid-infrared spectra (1800–1000 cm⁻¹) and classification models were developed using Partial Least Squares Discriminant Analysis (PLS-DA). The PLS-DA models showed preliminary discriminatory capacity, with apparent error rates of 10.2% for calibration and 19.3% under leave-one-out cross-validation. The results indicate that FTIR-ATR spectroscopy combined with chemometrics captures chemically relevant spectral variability associated with grape varietal differences and shows potential as a rapid exploratory screening approach within PDO traceability frameworks. Although the study is based on a limited number of biological replicates from a single vintage and sub-region, the findings provide a methodological baseline for future multi-vintage and multi-region investigations aimed at consolidating FTIR-based approaches for varietal authentication of Douro wines. Full article

Lonicera caerulea L. (blue honeysuckle) is rich in antioxidant polyphenols, and rapid and accurate determination of its polyphenol content is of great significance for functional food quality control. This study proposed a hybrid variable selection strategy designed for high-dimensional small-sample scenarios and developed a quantitative prediction model for polyphenol content based on mid-infrared (MIR) spectroscopy. A total of 191 Lonicera caerulea samples were collected from Northeast China, and 7468-dimensional spectral data were acquired using a Fourier transform infrared spectrometer. Polyphenol reference values were determined by the Folin–Ciocalteu method. Samples were divided into calibration (n = 152) and prediction (n = 39) sets using the SPXY algorithm. Among the 10 preprocessing methods evaluated, MSC combined with Savitzky–Golay first derivative achieved the best performance and was therefore used for subsequent modeling. The proposed hybrid variable selection method (VIP1.0∩RFR30%) intersected PLS variable importance in projection (VIP ≥ 1.0) with the top 30% important variables from random forest regression, selecting 984 key wavelengths and achieving 86.8% dimensionality reduction. A three-stage hyperparameter tuning strategy was implemented across four models (PLS, RFR, SVR, and XGBoost) to validate feature stability and control overfitting. The optimized XGBoost model achieved excellent performance on the independent test set ($R^2$ = 0.92, RMSE = 0.098, RPD = 3.47). Compared with the classical CARS method ($R^2$ = 0.78, RPD = 2.14), $R^2$ improved by 16.3% and RPD improved by 55.2%. The results demonstrate that the proposed hybrid variable selection strategy can effectively address the challenges of high-dimensional MIR spectral data in small-sample modeling, providing a reliable tool for rapid and non-destructive quantitative analysis of polyphenols in Lonicera caerulea. Full article

Black pepper is the most widely used spice crop globally and has significant economic value, making it a target for economically motivated adulteration. A wide range of organic and inorganic bulking materials has been used as adulterants in black pepper. Development of rapid non-targeted screening methods for use at different stages of the black pepper supply chain is extremely important for the identification and prevention of evolving fraudulent practices. This study has assessed the potential of benchtop Fourier Transform infrared with attenuated total reflectance (FTIR-ATR), benchtop Fourier Transform near-infrared (FT-NIR), and two handheld NIR spectrometers, coupled with chemometrics, for the discrimination of black pepper (Piper nigrum), pepper from other species and genera (non-Piper nigrum) and a broad range (n = 27) of endogenous and exogenous adulterants. Spiked samples were prepared to imitate pepper adulteration with seven different adulterants at five levels of adulteration (5%, 25%, 50%, 75%, 95% w/w). Orthogonal partial least squares discriminant analysis (OPLS-DA) achieved 100% total prediction accuracy for both FTIR-ATR and FT-NIR in differentiating authentic Piper nigrum and adulterant samples. The handheld microNIR 1700ES resulted in a 91.30% correct classification rate, while the SCiO model achieved 86.96% prediction accuracy. Detection of black pepper adulteration with multiple adulterants was performed using data-driven soft independent modelling of class analogy (DD-SIMCA). The highest performance of the DD-SIMCA model was achieved by FTIR-ATR (100% sensitivity and 100% specificity) followed by FT-NIR (98% sensitivity and 99% specificity). The handheld microNIR 1700ES resulted in 95% sensitivity and 90% specificity. This study demonstrated that FTIR-ATR and FT-NIR, coupled with DD-SIMCA, can effectively detect black pepper adulteration with multiple endogenous and exogenous adulterants. The handheld NIR (microNIR1700ES) clearly demonstrated the potential for rapid and effective verification of Piper nigrum authenticity outside the laboratory. Full article

Demand for rapid and cost-effective soil analysis has increased the use of spectroscopy, particularly in the visible–near-infrared (Vis–NIR) and mid-infrared (MIR) regions. Using 8304 soil samples from the United States Department of Agriculture spectral library, this study evaluated the effects of raw and preprocessed spectra on the prediction accuracy of eleven key soil properties across Vis–NIR and MIR regions using multiple machine learning algorithms. Spectral preprocessing, combining baseline correction and standard normal variate transformation, consistently improved prediction accuracy compared to the raw spectra. Overall, MIR-based models consistently outperformed Vis–NIR across all soil properties, with the largest performance gains observed for potassium, bulk density, and nitrate nitrogen. Among the machine learning approaches evaluated, artificial neural networks and categorical boosting algorithms provided the strongest and most consistent predictive performance across both spectral regions. These findings demonstrate that combining appropriate spectral preprocessing, spectral region selection, and advanced machine learning algorithms can substantially improve soil property prediction using spectroscopy. Full article

There is an increasing need to establish reliable safety controls in the food industry and to protect public health. Consequently, there are numerous efforts to develop sensitive, robust, and selective analytical strategies. As regulatory requirements for food and the concentration for target biomarkers in clinical analysis evolve, the food and health sectors are showing a growing interest in developing non-destructive, rapid, on-site, and environmentally safe methodologies. One alternative that meets the conditions is non-destructive spectroscopic sensors, such as those based on vibrational spectroscopy (Raman, surface-enhanced Raman—SERS, mid- and near-infrared spectroscopy, and hyperspectral imaging built on those techniques). The use of vibrational spectroscopy in food safety and health applications is expanding rapidly, moving beyond the laboratory bench to include on-the-go and in-line deployment. The dominant trends include the following: (1) the miniaturisation and portability of instruments; (2) surface-enhanced Raman spectroscopy (SERS) and nanostructured substrates for the detection of trace contaminants; (3) hyperspectral imaging (HSI) and deep learning for the spatial screening of quality and contamination; (4) the stronger integration of chemometrics and machine learning for robust classification and quantification; (5) growing attention to calibration transfer, validation, and regulatory readiness. These advances will bring together a variety of tools to create a real-time decision-making system that will address the issue in question. This article review aims to highlight the trends in vibrational spectroscopy tools for health and food safety control, with a particular focus on handheld and miniaturised instruments. Full article

The design features and applications of button sample holders are described. The similarities and contrasts between the button method and the transmission cell and attenuated total reflection techniques are discussed. Different button sample holder analysis methodologies are outlined, and examples are provided for mid-infrared spectroscopy measurements of solids, liquids, and pastes. Results obtained for 10-nonadecanone powder, a vitamin C tablet, a soil sample, and poly(methyl methacrylate) are used to illustrate different solid sample analysis approaches. Time-dependent spectrum variations detected during evaporation of a blood drop are elucidated and spectra obtained from different quantities of liquid chlorobenzene loaded into buttons and transmission cells are characterized. Infrared spectra derived from three toothpaste brands are compared and a sample perturbation study to identify temperature-dependent changes to the structure of poly(bisphenol A carbonate) is provided as an example of variable temperature infrared spectroscopy. Full article

Topaz is one of the most economically important fluorine-rich nesosilicates, which are predominantly colorless in natural crystals. Hence, the trade relies almost entirely on irradiated blue topaz with an unstable color center, which has been shown to fade over time. The cobalt (Co) diffusion treatment is a stable alternative process for converting colorless topaz to blue by a solid-state diffusion mechanism. To investigate the potential role of Co²⁺ substitution in the formation of the blue layer and the coupled behavior of F/OH dehydroxylation in facilitating this process, systematic diffusion treatments have been successfully conducted and compared. In this study, gem-quality topazes were annealed in air at 1000 °C for 20–40 h (hr) along with CoO, Fe₂O₃, Cr₂O₃, and CuO powders. The diffused products were characterized using Scanning Electron Microscope (SEM), Ultraviolet-Visible absorption spectroscopy (UV-Vis), Near-Mid Infrared spectroscopy (NMIR), and X-ray photoelectron spectroscopy (XPS). Parallel runs with CuO, Fe₂O₃, or Cr₂O₃ alone confirmed that none of these oxides produces a stable blue layer, underscoring the unique role of Co. The Co-diffused sample displays an intense blue layer characterized by a Co²⁺ octahedral isomorphism triplet at 540, 580, and 630 nm, which are absent from both untreated and heat-only controls. XPS analysis reveals the emergence of Co²⁺ (binding energy: 780.63 eV) and a concomitant depletion in F⁻, along with the disappearance of the OH overtone absorption at 7123 cm⁻¹. These observations confirm that defluorination generates octahedral vacancies accommodated by the coupled substitution: CoF₂ (solid reactant) + (AlO₂)⁻ (fragment of topaz structure) → AlOF (solid product) + (CoOF)⁻ (fragment of topaz structure). Prolonged annealing leads to decreased relative atomic percentages of K⁺ and F⁻ ions, consistent with volatilization losses during the high-temperature process, thereby directly correlating color intensity with cobalt valence state, which transfers from Co²⁺ to Co³⁺. These findings establish a Co-incorporation chronometer for F–rich aluminosilicate systems, with an optimal annealing time of approximately 20 hr at 1000 °C. Furthermore, the above results demonstrate that the color mechanism in nesosilicate gems is simultaneously governed by volatile release and cation availability. Full article

Diabetes is a developing global health concern that cannot be cured, necessitating frequent blood glucose monitoring and dietary management. Photoacoustic Spectroscopy (PAS) in the mid-infrared (MIR) region has recently emerged as a viable noninvasive blood glucose monitoring technique. However, MIR-PAS confronts significant challenges: (i) Water absorption, which reduces light penetration, and (ii) interference from other blood components. This paper systematically analyzes the background of photoacoustic signal generation and proposes a differential PAS (DPAS) in the MIR region for removing the background signals arising from water and other interfering components of blood, which improves the overall detection sensitivity. A detailed mathematical model with an explanation for choosing two suitable MIR quantum cascade lasers for this differential scheme is presented here. For single-wavelength PAS (SPAS), a detection sensitivity of $1.537$ µPa mg⁻¹ dL was obtained from the proposed model. Alternatively, $2.333$µPa mg⁻¹ dL detection sensitivity was found by implementing the DPAS scheme, which is about 1.5 times higher than SPAS. Moreover, DPAS facilitates an additional parameter, a differential phase shift between two laser responses, that has an effective correlation with the glucose concentration variation. Thus, MIR-based DPAS could be an effective way of monitoring blood glucose levels noninvasively in the near future. Full article