Search Results (4,069)

Soluble solids content (SSC) is an important indicator of sweetness-related quality, maturity, and postharvest grading in cherry tomato. Destructive physicochemical measurements remain accurate but are labor-intensive and unsuitable for rapid batch assessment. This study developed an RGSW-SBL framework for predicting SSC in Zheyingfen cherry tomato using portable near-infrared (NIR) spectra. In this framework, region-guided stable wavelength selection (RGSW) was used to select informative wavelengths, whereas sparse Bayesian learning (SBL) served as the quantitative regression model for SSC prediction. RGSW integrates adaptive candidate waveband estimation with robust competitive wavelength screening, thereby retaining continuous spectral regions while reducing redundant and unstable variables. The prediction performance of SBL was compared with that of partial least squares (PLS) regression under full-spectrum and different wavelength-selection conditions. Model performance was evaluated using R² and RMSE, with the principal test-set results reported in terms of R² and RMSE. Under the training–validation–test evaluation protocol, RGSW-SBL achieved the numerically best internal-test result among the reported SBL combinations, with a test-set R² of 0.847 and an RMSE of 0.369 °Brix using 157 selected wavelengths. The selected and high-contribution wavelengths were concentrated in chemically meaningful NIR regions related to C-H and O-H overtone absorption, sugar responses, and water-related tissue information. These results support RGSW-SBL as an interpretable framework for controlled-condition SSC prediction in cherry tomato, although multi-season, multi-cultivar, and multi-instrument validation remains necessary before deployment. Full article

This study investigated the adsorption capabilities of three solids derived from dead Pinus halepensis needles, a renewable and low-cost natural material, for the removal of cadmium ions from wastewater: the raw material (P-H₂O) and materials chemically modified with NaOH (P-NaOH) and HCl (P-HCl) solutions. The Fourier transform infrared spectra of the three materials revealed the presence of active functional groups, such as hydroxyl and carbonyl groups, which may be involved in the adsorption process. The effects of pH, adsorption kinetics, adsorption isotherms, and the presence of Na⁺, K⁺, Ca²⁺, and Mg²⁺ ions were studied. Continuous adsorption tests in a fixed-bed column were carried out using the P-H₂O biosorbent. The amount of Cd(II) adsorbed increased with increasing pH and followed the order P-NaOH > P-H₂O > P-HCl. Cadmium adsorption kinetics were very rapid (less than 30 min in all cases), and the pseudo-second-order kinetic model adequately described the adsorption process. The Sips isotherm model accurately described the adsorption equilibrium and predicted maximum adsorption capacities of 30.50 mg·g⁻¹, 70.11 mg·g⁻¹, and 30.46 mg·g⁻¹ for the P-H₂O, P-NaOH, and P-HCl solids, respectively. It was also verified that the amount of cadmium adsorbed decreased substantially in the presence of Na⁺, K⁺, Ca²⁺, and Mg²⁺ ions. These results highlight that dead pine needles may be a promising, inexpensive, and effective adsorbent for the removal of Cd(II) ions from aqueous solutions. Full article

Near-infrared (NIR) spectroscopy-based adulteration detection approaches are still dominated by one-dimensional (1D) spectral analysis, which inherently limits the exploration of complex patterns and nonlinear interactions in spectral data. Therefore, the objective of this study is to use a two-dimensional (2D) NIR spectrogram, combined with Explainable Artificial Intelligence (XAI), to predict the level of adulteration in palmyrah sap. The dataset matrix dimension is 110 × 1101, derived from the sample adulteration level (0–100%) and the NIR wavenumber (4000–12,500 cm⁻¹). Following Kennard–Stone partitioning, the evaluated preprocessing methods were applied using parameters derived exclusively from the training set. For the 2D modeling branch, the resulting training and testing spectra were subsequently transformed separately using the Continuous Wavelet Transform (CWT). A total of six AI algorithms, three from machine learning (PLS, kNN, ANN) and three from deep learning (CNN, AlexNet, ResNet), were applied in this study. The best model AI was interpreted using Shapley Additive Explanations (SHAP) for 1D NIRs and the Gradient-weighted Class Activation Mapping (Grad-CAM) for 2D NIR spectrograms. The four best-performing model configurations can predict the level of palmyrah sap adulteration, with R² values ranging from 0.969 to 0.994 and RMSE ranging from 2.333% to 5.547% in the training. In the testing, the model’s performance is in the R² range of 0.959–0.990, RMSE of 3.093–6.396%, MAE of 2.358–4.252%, RPD of 5.06–10.46 and Bias of 0.03–0.93%. The SHAP and Grad-CAM XAI revealed that the wavenumber associated with this sap counterfeiting is critical to the level of adulteration of palmyrah sap. This approach provides a quantitative method that accounts for advanced dimensions and treats them as essential information to support large-scale data matrices in AI modeling. The application of this method is an alternative that is easy to interpret and implement, and can be applied to long- and short-wavelength data from continuous NIR or discrete multi-wavelength NIR. Full article

This study presents a comprehensive investigation of the structural, morphological, and optical properties of aluminum phthalocyanine (AlPc) and silicon phthalocyanine (SiPc) thin films prepared by the close space sublimation (CSS) method, deposited at different evaporator temperatures of 350 °C, 400 °C, and 450 °C, including heterostructures incorporating an indium tin oxide (ITO) layer. Scanning electron microscopy revealed a clear temperature-dependent evolution of surface morphology, with both materials transitioning from isolated crystallites to dense, highly crystalline films. SiPc exhibited higher nucleation density and earlier film densification, while AlPc showed more pronounced grain growth at elevated temperatures, accompanied by crack formation due to internal stress. Optical absorption spectra indicated a red shift in absorption maxima with increasing deposition temperature, associated with improved crystallinity and reduced defect density. The presence of ITO significantly modified the optical response, introducing additional absorption features in the near-infrared region due to interference effects and free-carrier contributions. Fluorescence measurements revealed enhanced emission intensity with increasing temperature for AlPc, while SiPc showed weaker emission overall. The incorporation of ITO led to substantial fluorescence enhancement and the appearance of additional near-infrared emission bands, highlighting the importance of interface engineering. Transmittance spectra demonstrated that ITO-based heterostructures provide a balance between transparency and absorption, with selective attenuation in the 600–800 nm range, enabling band-stop filter behavior. Raman analysis revealed opposite temperature-dependent trends: increasing structural order in AlPc and gradual disorder in SiPc. X-ray diffraction confirmed the crystalline nature of both materials, showing temperature-induced improvements in crystallinity and crystallite size, as well as distinct differences in molecular packing and preferred orientation. These results demonstrate the potential of AlPc and SiPc thin films as functional optical materials with tunable structural and optical properties. Full article

Wear metal monitoring is an important component of lubricant condition monitoring but commonly relies on elemental techniques such as inductively coupled plasma optical emission spectroscopy (ICP-OES), which require dedicated laboratory infrastructure and sample preparation. This study evaluates whether Fourier-transform infrared (FT-IR) spectra of used engine oils can be combined with partial least squares (PLS) regression to provide a rapid screening estimate of iron (Fe) concentration. Used petrol and diesel engine oil samples were analyzed by FT-IR spectroscopy and ICP-OES. PLS models were developed using processed FT-IR spectra as predictor variables and ICP-OES-derived Fe concentrations as response variables. For petrol used oil samples, the optimized model employing 18 latent variables achieved a root mean squared error of 5.02 ppm and a coefficient of determination of 0.97 between measured and predicted Fe concentrations. Model loadings indicated contributions from spectral features associated with soot, oxidation, nitration, antioxidant (AO) depletion, and zinc dialkyldithiophosphate depletion. Combining petrol and diesel samples in a single model reduced predictive performance and increased uncertainty, indicating that their differing degradation pathways cannot be adequately represented by one common latent variable model. The approach does not directly measure Fe and is not intended to replace elemental analysis. Instead, it provides a rapid, low-cost screening tool for identifying samples with potentially elevated wear metal concentrations and prioritizing them for confirmatory analysis. Full article

Luminescence thermometry has evolved through decades of research focused on optimising materials and on extracting temperature information from isolated spectral features such as luminescence intensity ratios, bandwidth, line shift and excited-state lifetime. Despite extensive material development, these conventional methods remain fundamentally limited by construction: only a small subset of pre-selected spectral features is exploited, while the bulk of the temperature-relevant information encoded in the full spectrum is systematically discarded. A paradigm shift is presented here: Luminescence Thermometry 2.0 (LT 2.0), implemented through the newly developed SpectraSensML platform, in which machine learning regression operates on the entire emission spectrum to deliver temperature readout. The approach is demonstrated on a Yb³⁺-doped phosphor emitting in the near-infrared biological transparency window across 100 to 700 K. Yb³⁺ is a particularly demanding case: only the single ²F_5/2 multiplet emits, and its weakly thermally coupled Stark sub-levels yield modest sensitivity under conventional intensity-ratio thermometry. A total of 27 regression algorithms drawn from four families, namely tree ensembles, physics-aware regression models, kernel and instance methods, and neural networks, are systematically benchmarked. A sensor-fusion estimator that combines the first three principal components reaches an average root-mean-square error of 0.36 K on an unseen-temperature test set, a seven-fold improvement over the best luminescence intensity ratio variant. Standard normal variate (SNV) normalisation is identified as the most effective preprocessing strategy because it isolates the band-shape deformations that encode temperature. Single-component approaches that rely on the first principal component alone are shown to be quantitatively sub-optimal: multi-component regressors that exploit the first three principal components reduce the temperature uncertainty by close to an order of magnitude. The structural reason behind the failure of decision-tree ensembles on unseen temperatures is explained: their piecewise-constant predictions cannot interpolate beyond training set-points. The open-source SpectraSensML application used to obtain the results is released alongside the manuscript to enable reproducible community benchmarks. Full article

Mobile near-infrared (NIR) detection of wheat moisture is susceptible to random noise, scattering effects, baseline variations, and local spectral misalignment under dynamic acquisition conditions. In this study, a mobile online NIR detection platform was developed to collect wheat spectra over 660–1080 nm. A total of 169 modeling samples were divided into a calibration set (118 samples) and a prediction set (51 samples), while 50 samples from a different source were used for external validation. Savitzky–Golay (SG) smoothing was used to suppress random noise, extended multiplicative scatter correction (EMSC) was applied to correct scattering effects and baseline variations, and correlation optimized warping (COW) was employed for wavelength alignment. CARS–VIP was subsequently used to select informative wavelength variables, and an RF model was developed for moisture prediction. Among the evaluated strategies, SG–EMSC–COW–CARS–VIP–RF achieved the best overall performance and outperformed the corresponding full-spectrum RF model. The optimal model retained 17 wavelength variables, accounting for 6.8% of the original 250 variables. It achieved an R²_p of 0.9923, an RMSE_p of 0.3678, and an MAE_p of 0.2323 on the prediction set. For the external validation set, the corresponding R², RMSE, and MAE values were 0.9803, 0.4428, and 0.3682, respectively. The proposed method effectively mitigated spectral interference and enhanced prediction stability, providing a technical basis for the online determination of moisture content in stored grain. Full article

The ultrafast photochemical reaction dynamics of a cyclic(alkyl)(amino)carbene–carbon disulfide (CAAC–CS₂) dimer containing two adjacent S–S bonds were investigated using femtosecond time-resolved infrared spectroscopy in combination with multireference electronic structure calculations. Time-resolved vibrational spectra and global kinetic analysis reveal that photoexcitation of the S–S n → σ* transition at 375 nm induces subpicosecond (<0.3 ps) homolytic cleavage of one S–S bond, generating a bis-thiyl diradical intermediate. This intermediate undergoes two competing pathways: recombination to regenerate the parent dimer with a time constant of 5.7–8.5 ps, or secondary cleavage of the remaining S–S bond to yield two CAAC–CS₂ monomers with a time constant of 30–35 ps. Wavelength- and temperature-dependent kinetic measurements demonstrate that the branching between these pathways is governed by excess excitation energy and thermally driven radical-pair fluctuations. Multireference electronic structure calculations support a sequential S–S bond cleavage mechanism, in good agreement with the experimental observations. These findings provide direct spectroscopic evidence for a bis-thiyl diradical intermediate and offer new mechanistic insight into the ultrafast photochemistry of adjacent S–S bonds. Full article

Accurate identification of mango varieties holds substantial significance for the elevation of product added value and the facilitation of market differentiation through quality-based pricing. Near-infrared (NIR) spectral analysis offers a rapid, non-destructive solution for mango variety identification. To address the challenges in fine-grained classification of NIR spectra, namely, high spectral similarity and severe overlap of absorption peaks, which make it difficult to extract nonlinear features using chemometrics, as well as the excessive complexity of existing deep learning models, a lightweight multi-scale spatial global context network is proposed. One-dimensional NIR spectra are converted into two-dimensional images through the Gramian angular difference field. Multi-scale partial convolution, coordinate-aware global context, efficient multi-scale attention, and structural re-parameterization are integrated to capture local spectral features and long-range band correlations effectively. Evaluated on two mango spectral datasets with different distributions, the proposed model achieves variety identification accuracies of 99.46% and 97.83%, with only 19.08 M parameters. Computational complexity, throughput, and latency reach 120.29 M FLOPs, 2848.5 FPS, and 0.351 ms, respectively, realizing a balance between classification accuracy and computational speed. Ablation and robustness experiments demonstrate that the accuracy of the model is improved by 5.91% and 2.15% compared with one-dimensional convolutional neural network and FasterNet, respectively. Important wavelengths obtained by threshold screening of activation maps exhibit consistency with the majority of conclusions from analysis of variance and VIP methods, while the remainder represent newly identified important bands. Validation across different temperature and batch scenarios reveals strong generalization capability. Future refinement will be pursued through increased sample diversity. Overall, high-precision identification is attained by the model at comparatively low computational overhead, indicating potential for advancing the practical application of NIR spectroscopy in agricultural quality inspection. Full article

Natural blue calcite is relatively rare, and its coloration and luminescence mechanisms have not been systematically established. In this study, four natural blue calcite samples from China were comprehensively characterized using mineralogical testing, X-ray diffraction (XRD), electron probe microanalysis (EPMA), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, ultraviolet–visible spectroscopy (UV-Vis), photoluminescence (PL), and electron paramagnetic resonance (EPR). XRD confirms single-phase trigonal calcite (space group R-3c). EPMA detects minor Mg, Fe, Cu, and Sr, with smaller-radius Mg²⁺, Fe²⁺, and Cu²⁺ being the main contributors to the contraction through isomorphous substitution for Ca²⁺. UV-Vis spectra show characteristic absorptions at 270 nm and 340 nm related to lattice defects with a broad emission band centered at 480 nm in the PL spectra. EPR detects a CO₂⁻ radical center (g = 2.003), and the same signal is also observed in the colorless sample. The colorless sample also contains the same CO₂⁻ radicals, indicating that these radicals alone do not account for the blue coloration. A broad 480 nm blue-violet fluorescence band is observed in the four blue samples under 405 nm excitation. These findings provide a spectroscopic and crystallographic basis for distinguishing natural blue calcite from analogous materials and for understanding the origin of its color and luminescence. Full article

This study aimed to synthesize copper(I) iodide nanoparticles (CuI NPs) through a green route using taxo (banana passion fruit) extract as a natural capping and stabilizing agent, and to evaluate their antimicrobial performance against microorganisms isolated from concrete, together with a preliminary cytotoxicity screening. The obtained nanoparticles were characterized by ultraviolet–visible spectroscopy (UV–Vis), Fourier-transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM–EDS), dynamic light scattering (DLS), and X-ray diffraction (XRD). UV–Vis spectra recorded in the 200–704 nm range showed a strong absorption band at 224 nm, consistent with electronic transitions associated with nanostructured CuI. FTIR analysis revealed extract-derived biomolecules adsorbed on the nanoparticle surface, with bands assigned to aliphatic C–H, aromatic moieties, and C–O/C–O–C vibrations, supporting the formation of an organic capping layer. DLS analysis showed a mean hydrodynamic diameter of approximately 32 nm in aqueous suspension, whereas TEM revealed particle sizes ranging from 13 to 42 nm. XRD confirmed a predominantly cubic CuI phase, while SEM–EDS identified Cu and I as the main elements, with minor signals attributed to residual organic coating and/or trace species from the synthesis medium. The CuI NPs exhibited antimicrobial activity against microorganisms isolated from medium-strength concrete, producing inhibitory effects at all tested concentrations (0.014, 0.0087, and 0.0035 mol/L). Preliminary cytotoxicity screening using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay in human foreskin fibroblast (HFF), human breast adenocarcinoma (MCF7), and human glioblastoma (U251) cell lines showed dose- and time-dependent reductions in metabolic viability. The 1.0 mol/L formulations, particularly the precipitated fraction, produced stronger cytotoxic effects, whereas the 0.1 mol/L formulations, especially the residual fraction, preserved comparatively higher metabolic viability. Overall, these findings suggest that taxo-mediated CuI NPs are promising antimicrobial candidates for concrete biodeterioration control, while further colloidal and biological studies are required to better define their behavior under cell-culture conditions and optimize their safe application. Full article

Soil organic carbon (SOC) is a key indicator of soil quality, farmland productivity, and the terrestrial carbon cycle. Visible and near-infrared (Vis-NIR) spectroscopy offers a rapid approach for SOC estimation, but wavelength point selection may disrupt continuous spectral structures, whereas conventional wavelength interval selection may fail to fully exploit complementary information across spectral regions. In this study, a synergistic interval-constrained Ridge regression framework, termed sicRidge, was developed for SOC prediction. Continuous candidate intervals were generated using a sliding-window strategy, and a dual-interval synergistic search with an overlap constraint was applied to identify complementary and low-redundancy interval combinations. The selected intervals were then used to construct Ridge regression models. Using Vis-NIR spectra from 168 soil samples, sicRidge was compared with full-spectrum Ridge regression, five wavelength point selection-based Ridge models, and several wavelength interval selection-related benchmark models. sicRidge achieved the best prediction performance using 140 selected bands, with an R²_P of 0.834, RMSE_P of 2.010 g kg⁻¹, RPD of 2.483, and RPIQ of 3.777. The optimal intervals were 570~649 nm and 1880~1939 nm. These results indicate that sicRidge can improve SOC prediction by preserving continuous spectral structures while exploiting complementary cross-region information. Full article

Capturing reliable hyperspectral imager data at a meaningful frame rate for explosions and other fast-changing scenes is not possible in the mid-wave infrared region under traditional sensor operating configurations and processing techniques, which typically have frame rates on the order of 0.5–2.0 Hz. To combat these shortcomings, the scene acquisition parameters were tailored for explosions and a new method for processing optical signatures of fast transient scenes with Fourier-transform infrared hyperspectral imagers was developed. For this technique, the instrument was first configured to collect asymmetric interferograms while optimizing the number of measurement points on the short side of the interferogram. Additionally, pixel-wise zero path distance offset and phase corrections were applied to the interferograms, a reduced spectral resolution of 8 cm⁻¹ was selected, and the window size was narrowed to 32 × 64 pixels while using a lens with a wide field of view. The smooth offset correction for scene change artifacts was then applied in post-processing to address any remaining artifacts in the Fourier-transformed spectra. These procedures yielded a 29× increase in frame rate and significant improvements in spectra fidelity. This work makes reliable field calibrations and measurements of explosions with Fourier-transform infrared hyperspectral imagers more achievable than before. Full article

Indocyanine green (ICG) is a clinically approved near-infrared fluorescent dye used in medical imaging and diagnostics but has limitations owing to its poor photostability in aqueous environments. This paper has explored the role of human serum albumin (HSA) and cholesterol in protecting ICG photostability. Pure ICG, an HSA-ICG complex, and an ICG–cholesterol colloidal assembly solution were stirred in the dark (control) and under continuous broadband irradiation (400–1600 nm, approximately 1.4 W), and absorption spectra (550–950 nm) were taken every 1 min, over 15 min. All the formulations were stable in the dark, with minimal total variance in absorbance. Pure ICG significantly photodegraded under irradiation (55 ± 2.75–65 ± 3.25% loss of maximum absorbance). The introduction of HSA and cholesterol limited the photodegradation, resulting in 15 ± 0.75–30 ± 1.5% and 25 ± 1.25% losses in maximum absorbance, respectively, upon irradiation. The modulators produced a significant increment in initial NIR absorbance (p < 0.001) and retained significantly high stability during irradiation (p < 0.01). Moreover, both modulators reduced photooxidative damage, as shown by the lower level of singlet oxygen (¹O₂) generation in the presence of HSA-ICG (35 ± 1.75%) and ICG–cholesterol (19 ± 0.95%) compared to pure ICG (57 ± 2.85% after 15 min). These results reveal that cholesterol is the best stabilizer of ICG photostability. By safely dissipating excitation energy via non-radiative decay, cholesterol demonstrates strong potential for enhancing ICG performance in photothermal therapy (PTT), whereas HSA remains the optimal modulator for near-infrared fluorescence imaging and photodynamic therapy. Full article

Crop residue cover (CRC) plays a crucial role in sustainable farming systems by improving soil structure, regulating water retention, and reducing soil erosion. Accurate CRC monitoring is therefore important for evaluating field management practices at scale. Existing field methods (e.g., line-transect and visual estimation) are labor-intensive and difficult to scale, while optical retrievals are often confounded by soil moisture. Moisture introduces nonlinear spectral distortions that can bias residue estimates, particularly in the shortwave infrared range. We propose a Deep Moisture-Invariant Autoencoder (DMIA) framework that performs conditional spectral normalization—referred to as moisture normalization (dry-equivalent spectral transformation)—before linear spectral unmixing. The workflow has two stages: (1) a conditional autoencoder that transforms moisture-affected spectra to dry-equivalent spectra, and (2) fully constrained linear unmixing on dry-equivalent spectra. The experiment included 63 controlled wheat-residue scenes at a semi-arid site in Morocco, spanning three moisture levels and seven residue proportions (0–100%) measured with ASD spectroscopy. Within this controlled experimental dataset, DMIA achieved a global coefficient of determination of R² = 0.93, outperforming ordinary least squares (R² = 0.65), fully constrained least squares (R² = 0.68), and ELMM (R² = 0.71), and matching the performance of MESMA (R² = 0.93) while requiring only a single forward pass at inference rather than iterative library matching. Although both methods showed similar overall accuracy, a closer analysis reveals that DMIA’s advantage over MESMA widens under wetter, coarser-resolution conditions, which are highly representative of operational monitoring. This finding is further validated by a Monte Carlo uncertainty propagation, proving the results are unaffected by reference noise. Using spectrally resampled ground data to simulate satellite responses, performance remained robust for PRISMA (R² = 0.93) and Sentinel-2 simulation (R² = 0.87). Reconstruction diagnostics (mean SAM below 5°) support the physical plausibility of the learned transformation. These results suggest that conditional spectral normalization can reduce moisture-related distortions while preserving compositional signals under controlled experimental conditions; however, the use of three discrete moisture levels represents an experimental simplification; in open operational fields, soil moisture varies continuously and pixel-level states are unknown. This framework provides a proof-of-concept basis for further investigation across diverse soils, residue types, and operational sensor configurations. Full article