Water hyacinth leaves (WHL) are an inexpensive renewable fuel resource that can be employed for energy creation through hydrolysis of simple fermentable reducing sugars. In this work, a hybrid microwave irradiation (MWI)–ternary deep eutectic solvent (TNDES) system involving choline chloride (ChCl) as a hydrogen bond acceptor (HBA), triethanolamine (TEOA) as an amine-based hydrogen bond donor (HBD), monoethylene glycol (MEG), diethylene glycol (DEG), or triethylene glycol (TEG) as polyol-based HBD components was employed as an efficient and green material for pretreatment of WHL for further transformation of the polysaccharide portion. The results showed that hybrid MWI/TNDES (ChCl-TEOA-MEG, ChCl-TEOA-DEG, and ChCl-TEOA-TEG) pretreatments were very efficient for lignin removal from WHL, with efficacy ranging from 80.4 ± 3.2 to 87.7 ± 3.8% compared with pretreatment using hybrid MWI/binary NDES (ChCl-TEOA) (75.6 ± 2.4%). The higher efficacy of the hybrid MWI/TNDES pretreatment was attributed to the impacts of MWI on extracting biological materials and the lower viscosity, higher pH, and lower density associated with the TNDESs. The results indicate that WHL pretreated using hybrid MWI and ChCl-TEOA-MEG, ChCl-TEOA-DEG, and ChCl-TEOA-TEG resulted in significantly boosting cellulose digestibility (4–5 times that of pristine WHL and 1.5 times that of hybrid MWI/ChCl-TEOA-treated WHL). The effect of MWI/TNDES pretreatment was confirmed by scanning electron microscope (SEM) pictures, and lignin and hemicellulose elimination were clearly observed in Fourier transform infrared (FTIR) spectra. The lignin-rich material separated by the hybrid MWI/TNDES pretreatment was analyzed using thermogravimetric analysis (TGA) to obtain the thermal behaviors of this hybrid, pretreated WHL material. In our experimentation with hybrid MWI/TNDES, under optimum circumstances of MWI time of 6 min, MWI power of 300 W, and a temperature of 90 °C, 43–49 g/L TRS yield was achieved by acid-catalyzed hydrolysis employing WHL substrate after being optimized by the single-factor experiments (SFE) approach, while the optimized TRS for untreated WHL and hybrid MWI/binary ChCl-TEOA were estimated to be 12 g/L and 32 g/L, respectively. The hybrid MWI/ChCl-TEOA-TEG pretreated WHL resulted in a high ethanol yield (ca. 22.3 g/L) by Saccharomyces cerevisiae after 72 h of fermentation. This work demonstrates the potential of WHL as a sustainable bioenergy feedstock for bioethanol production in industrial biorefineries. The research establishes effective and green solvent pre-treatment materials and methods (based on hybrid MWI/TNDES) for the efficient removal of lignin and hemicellulose from WHL and cellulose recovery. In general, the research contributes to the development of environmentally friendly and cost-effective hybrid MWI/TNDES processes for WHL biomass conversion and offers strong evidence that hybrid MWI/TNDES processes represent a high-potential method for managing WHL infestations while generating useful products. Future studies should further investigate ways to enhance the efficacy of acid-catalyzed hydrolysis processes and assess the scalability of the technology for industrial applications.
Near-infrared (NIR) spectroscopy with full-spectrum chemometric modeling is widely used in food, agricultural, and pharmaceutical analysis, but calibrations resting on hundreds to thousands of spectral variables are difficult to audit and require full-spectrum instrumentation to deploy. The Spectral Contrast Feature (SCF) framework constructs predictive features as differences between the mean intensities of paired spectral bins, with bin positions, widths, and feature count optimized by a genetic algorithm. SCF-PLSR was evaluated on cocoa bean moisture (n = 72), barley adulteration in roasted coffee (n = 158), wheat grain protein (n = 496), and the IDRC 2002 pharmaceutical tablet shoot-out dataset, against full-spectrum PLSR and four established wavelength-selection methods under repeated evaluation. Using three to seven contrast features in place of 601 to 1559 spectral variables, SCF-PLSR matched or exceeded every comparator on same-instrument prediction. Test-set RMSE fell by 25% for coffee–barley and 15% for wheat protein. On the tablet dataset under second-derivative preprocessing, zero-shot transfer to a second instrument gave RMSE 17% lower than full-spectrum PLSR. Selected features mapped onto established NIR absorption regions, indicating that a calibration built on a few chemically assignable contrasts is both auditable and compatible with targeted, reduced-cost instrumentation.
Photoacoustic spectroscopy (PAS) is a highly sensitive and non-destructive technique widely used for trace gas detection; however, the simultaneous quantification of methane (CH4), ethane (C2H6), and ethylene (C2H4) remains challenging due to severe spectral cross-interference and non-linear responses across broad concentration ranges. In this work, we propose a high-precision, end-to-end detection framework based on a Deep Kernel Extreme Learning Machine (DKELM) optimized using a Mutation–Chaotic Particle Swarm Optimization (MCPSO) algorithm. To enhance diagnostic information in the photoacoustic signals, a multi-scale wavelet transform based on a db4 wavelet basis with 5-layer decomposition and a Heursure soft threshold strategy is first employed for denoising and enhancing absorption features. To address the hyperparameter sensitivity and local-optimum trapping inherent in deep models, the MCPSO algorithm integrates hybrid chaotic initialization, adaptive mutation probability control, Cauchy-based perturbation, temperature-controlled mutation amplitude, and elite-guided population updating. The proposed MCPSO-DKELM model is evaluated on an expanded dataset of 470 mixed-gas spectra and benchmarked against other frameworks, including the previously reported SVM-CPSO-KELM architecture. The experimental results demonstrate that MCPSO-DKELM achieves stable, segmentation-free quantification across the full dynamic range, with an average detection error below 3.5% and the maximum relative error constrained to under 15%, which represents a substantial improvement over existing approaches. Thus, the combination of deep kernel feature extraction and mutation–chaotic global optimization provides a robust and reliable solution for simultaneous multi-component hydrocarbon gas analysis in complex industrial environments.
Ultra-high-performance concrete (UHPC) formulated with alternative binders represents a promising pathway for reducing carbon emissions while enabling multifunctional material performance. This study investigates the mechanical and electrical evolution of two systems: a traditional Portland cement-based UHPC (REF) and a geopolymer counterpart (GEO) where cement is fully replaced by ground granulated blast furnace slag (GGBS) and silica fume. By evaluating both mixes with and without steel fibers, the research assesses how binder chemistry interacts with conductive pathways to influence strength, resistivity, and impedance. Mechanical testing revealed comparable 28-day compressive strengths for the reference and geopolymer mixes (123 MPa and 120 MPa, respectively), which increased to 139 MPa and 130 MPa upon fiber incorporation. Electrical characterization showed that the geopolymer binder significantly enhances conductivity; resistivity values dropped from 9645 Ω·m in the reference mix to 925 Ω·m in the geopolymer and further to 76 Ω·m with fiber reinforcement. Impedance spectroscopy supported these results, as the GEO mixes displayed smaller Nyquist arcs compared to the REF system, indicating greater ionic mobility associated with pore solution chemistry and the GGBS-rich gel structure. Ultimately, this study demonstrates that geopolymer UHPC matches the mechanical integrity of Portland-based systems while offering superior electrical conductivity, making it a strong candidate for low-carbon, self-sensing infrastructure.
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.
This Special Issue is dedicated to honoring the extraordinary scientific career and enduring impact of Professor Henry H [...]
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.
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.
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.
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.
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 1350–2500nm 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 3σ 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.
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 (Aad), Carbon Content (Cd), Sulfur Content (Stad). 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.
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 τc and from local dynamics at the probe site, occurring at a characteristic time scale τloc. The τc and the τloc 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 τc and the τloc. 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 τloc. 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.
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.
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.
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.
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.
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.
