Search Results (3,846)

Despite decades of structural and kinetic characterization, the full spectral molecular vibrations that accompany the catalysis in alkaline phosphatase (ALP) have remained largely unexplored. In this study, we combine in situ real-time attenuated total reflection Fourier transform infrared (ATR-FTIR) measurements over a large energy range to track the hydrolysis of p-nitrophenyl phosphate (PNPP) and inorganic phosphate (Pi) over a large range of enzyme concentrations. From the static spectra of the pure components (ALP, PNPP, PNP, Pi), we identify their characteristic vibrational frequencies and use them as reference points for the time-resolved spectra. The reaction reveals a monotonic growth of the inorganic-phosphate band at 1077 cm⁻¹. At the highest alkaline phosphatase concentration, we resolve two blue shifts in the nitro/aromatic region (1510 → 1518 cm⁻¹; 1494 → 1499 cm⁻¹), two red shifts in the fingerprint region (1345 → 1340 cm⁻¹; 1294 → 1290 cm⁻¹), and a splitting of the ~1592 cm⁻¹ band into 1595 and 1583 cm⁻¹. In conclusion, by anchoring the time-resolved spectra to the static spectra of individual constituents, we were able to resolve the infrared readout of the enzymatic reaction, offering a generalizable approach for FTIR-based tracking of catalytic processes. Full article

Accurate mapping of soil organic matter (SOM) is essential for soil management. Remote sensing (RS) provides broad spatial coverage, while visible and near-infrared (VisNIR) laboratory spectroscopy enables accurate point-scale SOM prediction. Conventional data methods for fusing RS and VisNIR data often rely on principal components (PCs) extracted from VisNIR data that have an indirect relationship to SOM and employ ordinary kriging (OK) for their spatialization, resulting in limited accuracy. This study introduces an enhanced fusion method using partial least squares regression (PLSR) to extract supervised latent variables (LVs) related to SOM and residual kriging (RK) for spatialization. Two fusion strategies (four variants)—RS + first i PCs/LVs and RS + ith PC/LV—were evaluated in the contrasting agricultural regions of Da’an City (n = 100) and Fengqiu County (n = 117), China. Laboratory-measured soil spectra (400–2400 nm) were integrated with many temporal combinations of Landsat 8 imagery. The results demonstrate that LVs exhibit stronger correlations with SOM than PCs. For example, in Da’an, LV6 (r = 0.36) substantially outperformed PC6 (r = 0.02), while in Fengqiu, LV3 (r = 0.40) outperformed PC3 (r = −0.05). RK also dramatically improved their spatialization over OK, as demonstrated in Da’an where the R² for LV2 increased from 0.21 to 0.50. More importantly, in SOM prediction performance, all four fusion variants improved accuracy over RS alone, and the LV-based fusion achieved superior results. In terms of mean performance, RS + first i LVs achieved the highest R² (0.39), lowest RMSE (5.76 g/kg), and minimal variability (SD of R² = 0.06; SD of RMSE = 0.28 g/kg) in Da’an, outperforming the PC-based fusion (R² = 0.37, SD = 0.09; RMSE = 5.85 g/kg, SD = 0.42 g/kg). In Fengqiu, two fusion strategies demonstrated comparable performance. Regarding peak performance, the PC-based fusion in Da’an achieved a maximum R² of 0.57 (RMSE = 4.82 g/kg), while the LV-based fusion delivered comparable results (R² = 0.55, RMSE = 4.94 g/kg); both surpassed the RS-only method (R² = 0.54 and RMSE = 4.98 g/kg). In Fengqiu, however, the LV-based fusion demonstrated superiority, reaching the highest R² of 0.40, compared to 0.38 for the PC-based fusion and 0.35 for RS alone. Furthermore, across different temporal scenarios, the LV-based fusion also exhibited greater stability, particularly in Da’an, where the RS + first i LVs method yielded the lowest standard deviation in R² (0.06 vs. 0.09 for PC-based fusion). In summary, integrating LV-derived variables with RS data enhances the accuracy and temporal stability of SOM predictions, making it a preferable approach for practical SOM mapping. Full article

Near-infrared (NIR) spectroscopy is a rapid, non-destructive analytical tool widely used in the food and agricultural sectors. In this study, two NIR instruments were compared for classifying the addition of microplastics (MPs) to high-moisture-content samples such as vegetables and fruit. Polyethylene (PE), polypropylene (PP), and a mix of polymers (PE + PP) MP were added to mixtures of spinach and banana and scanned using benchtop (Bruker Tango) and portable (MicroNIR) instruments. Both principal component analysis (PCA) and partial least squares (PLS) were used to analyze and interpret the spectra of the samples. Quantitative models were developed to predict the addition of Mix, PP, or PE to spinach and banana samples using PLS regression. The R² _CV and the SECV obtained were 0.88 and 0.44 for the benchtop samples, and 0.54 and 0.67 for the portable instruments, respectively. Two wavenumber regions were also evaluated: 11,520–7500 cm⁻¹ (short to medium wavelengths), and 7500–4200 cm⁻¹ (long wavelengths). The R² _CV and the SECV obtained were 0.88 and 0.46, 0.86 and 0.49, respectively, for the prediction of addition in samples analyzed on the benchtop instrument using short and long wavenumbers, respectively. This study provides new insights into the comparison of two instruments for detecting the addition of MPs in high-moisture samples. The results of this study will ensure that NIR can be utilized not only to measure the quality of these samples but also to monitor MPs. Full article

Cerium (Ce³⁺)-doped gadolinium orthoborate (GdBO₃) phosphor powders were synthesized via an aqueous sol–gel route, with systematic variation in solution pH (2, 5, and 8) and annealing temperature (600–1200 °C, in 100 °C increments) to investigate their influence on structural, optical, and scintillation properties. The materials were comprehensively characterized using thermogravimetric and differential thermal analysis (TG–DTA) to assess thermal behavior, X-ray diffraction (XRD) for crystal structure determination, Fourier-transform infrared spectroscopy (FTIR) for vibrational analysis, and both photoluminescence (PL) and radioluminescence (RL) spectroscopies to evaluate optical and scintillation performance. All samples crystallized in the hexagonal GdBO₃ vaterite phase (space group P6₃/mcm). The PL and RL emission spectra were consistent with the Ce³⁺ 5d–4f transitions, and scintillation yields under X-ray excitation were quantified relative to a standard Gadox phosphor. A decrease in photoluminescence quantum yield (PLQY) was observed at annealing temperatures above 800 °C, which is attributed to the incorporation of Ce³⁺ into the host lattice. Scintillation decay profiles were recorded, enabling extraction of timing kinetics parameters. Overall, the results reveal clear correlations between synthesis conditions, structural evolution, and luminescence behavior, providing a rational basis for the optimization of Ce³⁺-doped GdBO₃ phosphors for scintillation applications. Full article

Controlling the rheological properties of liquids allows for the regulation of effective movement, transport of substances, and processes in biological systems. This work presents an experimental investigation into the influence of the proton-exchange polymer membrane Nafion on the surface tension coefficient (STC) of distilled water, aqueous solutions of two methylene blue (MB) forms, and ascorbic acid (AA). Immediately upon membrane immersion in the solutions, a sharp decrease in the surface tension of distilled water, as well as of the oxidized and reduced forms of MB, occurs. The observed narrow time interval is associated with the formation of an exclusion zone near the membrane–solution interface, containing dissociated sulfonate groups $\left(\mathrm{S}{\mathrm{O}}_3^{-}\right)$. The value of the time interval depends on the type of aqueous solution. At long soaking of the membrane in solutions, we obtained: for the aqueous solution of Mb⁺ (blue-coloured solution) the STC value eventually increases by about 5%, and for the reduced form of methylene blue MbH⁰-colourless solution, the STC value decreases by 4%. The STC value of the solutions formed during diffusion into the membrane has a significantly lower value compared to the STC of distilled water by 20% for the Mb⁺ form and by 24% for the MbH⁰ form of MB. The presence of the membrane in the aqueous AA solution causes only an increase in the STC value of the solution. Ultimately, for the solution with a concentration of 5 g/L, this increase reached 15% relative to the STC value of the original AA solution. The change in surface tension of the investigated solutions in the presence of the membrane is due to their adsorption onto the membrane surface. Fourier-transform infrared (FTIR) spectroscopy investigation of distilled water, MB, and AA solution diffusion into the membrane across the range (370–7800) cm⁻¹ confirms the process nonlinearity and enables identification of distinct time intervals corresponding to membrane swelling stages. The positions of IR transmission minima for membranes containing water and solution components remain unchanged; only the numerical values of the transmission coefficients vary. Using spectrophotometry, absorption lines of the membrane with adsorbed components of MB and AA solutions were identified in the range of (190–900) nm. The absorption spectra of dried membranes with adsorbed Mb⁺ and AA solutions show a redshift to the IR region for the Nafion with Mb⁺ and a shift to the UV region for the Nafion soaked in an aqueous ascorbic acid solution. A surface tension gradient at the membrane–solution interface can induce concentration-capillary convection in the liquid. Full article

The development of rapid and intelligent methods is urgently needed for wheat quality evaluation. Using the prediction of wet gluten content as a case study, this work systematically investigated the performance of various machine learning algorithms and their optimization for content prediction, based on hyperspectral data from the visible and near-infrared ranges of wheat grains and flour. The results revealed that the random forest regression (RFR) algorithm delivered the best predictive performance under two conditions: first, when applied directly to visible spectra; and second, when applied to fused visible and near-infrared spectral data. This held true for both grains and flour. Conversely, its direct application to NIR spectra alone yielded relatively worse performance. Following data optimization, the first-derivative (FD) visible spectra of wheat grains were smoothed using a Savitzky–Golay (SG) filter and subsequently used as input for the RFR model. This optimized approach achieved a coefficient of determination (r²) of 0.8579, a root mean square error (RMSE) of 0.0216, and a relative percent deviation (RPD) of 2.6978. Under the same conditions, for wheat flour, the corresponding values were 0.8383, 0.0231, and 2.5293, respectively. Similarly, for wheat flour, the RFR model was applied to the SG-filtered FD spectra derived from the fused visible and near-infrared data, yielding an r² of 0.8474, an RMSE of 0.0224, and an RPD of 2.6034. Under the same conditions, wheat grains yielded an r² of 0.8494, an RMSE of 0.0223, and an RPD of 2.6208. This efficient and rapid intelligent prediction scheme demonstrates considerable potential for the quality assessment and control of relevant food products. Full article

The vibrational properties of the chiral sulfoxide methyl-p-tolyl-sulfoxide (Metoso) were investigated by infrared and Raman spectroscopy in the solid, liquid and aqueous solution phases, for both the enantiopure compounds and their racemic mixture. Experimental data were complemented by DFT calculations on the isolated enantiomer and on the two RR and RS dimeric conformers to support spectral interpretation and mode assignment. The IR and Raman spectra of the crystalline enantiomer and racemic mixture are similar, indicating comparable molecular organization and intermolecular interactions in the solid state. Upon melting, band broadening and frequency shifts are observed, consistent with molecular disorder and the breaking of weak intramolecular interactions, accompanied by changes in the S-O, S-CH₃ and C-H stretching frequencies. In aqueous solution, further broadening and opposite shifts in these bands reflect the formation of Metoso-H₂O complexes through hydrogen bonds. Theoretical spectra reproduce the observed trends and confirm that either solvent or phase transitions control the balance between intra- and intermolecular interactions thus influencing the vibrational degrees of freedom of the model chiral sulfoxide. Full article

Lycopene is a potent antioxidant carotenoid with significant health-promoting properties. However, its practical application is limited by poor water solubility. This study aimed to enhance lycopene dispersibility through the development of solid dispersions obtained by hot-melt extrusion (HME). Polymeric carriers composed of polyvinylpyrrolidone K30 (PVP K30), phosphatidylcholine, and xylitol were designed to achieve optimal processing conditions and thermal stability. Nine formulations containing 10–30% lycopene were prepared and characterized using thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray powder diffraction (XRPD), Fourier transform infrared spectroscopy (FT-IR), and dispersibility testing. TGA confirmed the thermal stability of lycopene at the extrusion temperature (150 °C). DSC and XRPD analyses indicated partial amorphization of lycopene in the extrudates, while FT-IR spectra revealed molecular interactions between lycopene and carrier components, particularly hydroxyl and carbonyl groups. Among the tested systems, the formulation containing PVP K30 and xylitol without phosphatidylcholine exhibited the highest dispersibility (1.0484 mg/mL after 3 h). Dispersibility decreased with increasing lycopene content. These findings demonstrate that HME is an effective technique for producing partially amorphous lycopene dispersions with improved dispersibility, and that polymer–polyol systems are particularly promising carriers for enhancing lycopene bioavailability. Full article

Soil analysis is fundamental to sustainable agriculture; however, traditional laboratory methods are time-consuming, expensive, and environmentally hazardous. Spectroscopy techniques, particularly Near-Infrared (NIR), have gained considerable attention because they require minimal sample preparation, no chemicals, and predict multiple soil properties with a single scan. However, selecting appropriate equipment remains critical, as previous studies have reported inconsistent performance between conventional Near-Infrared (NIR) spectroscopy and advanced Fourier-Transform Near-Infrared (FT-NIR) spectroscopy. Therefore, this study aimed to compare the predictive performance of conventional NIR and advanced FT-NIR spectroscopy for sixteen soil properties. Soil samples (n = 567) were collected from different land-use types across South Korea at a depth of 0–20 cm and analyzed using laboratory methods and spectroscopy techniques. Five models, including partial least squares regression (PLSR), Cubist, support vector machine (SVM), random forest (RF), and memory-based learning (MBL), were evaluated using 15-fold cross-validation to assess prediction accuracy. Overall, conventional NIR spectroscopy yielded consistently higher accuracy for all soil properties than FT-NIR. Strong predictive accuracy was achieved for EC (R² = 0.84), OM (R² = 0.84), avl. P (R² = 0.77), TN (R² = 0.84), and CEC (R² = 0.76). In contrast, FT-NIR provided good prediction accuracy only for Ex. K (R² = 0.72) and TN (R² = 0.84). The average performance of NIR (R² = 0.67) outperformed FT-NIR spectroscopy (R² = 0.63) across all soil properties. These findings demonstrate that, despite their lower spectral resolution, NIR spectra provide robust predictive capability across a wide range of soil properties, which can significantly reduce the investment cost of advanced equipment such as FT-NIR for routine soil analysis. Full article

Goat milk can be a vehicle for beneficial microorganisms, such as probiotic lactic acid bacteria (LAB). During lactic fermentation, the hydrolysis of milk proteins can improve their nutritional properties and sensory attributes and even have beneficial health effects. The objective of this study was to evaluate the caseinolytic activity of LAB strains with probiotic potential and to monitor the changes induced by fermentation and during storage in milk components using Fourier transform infrared (FTIR) spectroscopy. First, the proteolytic activity of 36 LAB strains isolated from dairy products was qualitatively assessed. Then, 17 strains with probiotic potential and moderate to high proteolytic activity were selected for further analysis. Casein proteolysis was found to be strain-dependent, with a decrease in total protein concentration ranging from 28% to 87% and an increase in amino acids ranging from 29% to 88%. Furthermore, a notable difference was observed in the amide bands in the FTIR spectra between the beginning and end of incubation, showing a decrease in the intensities of the bands attributed to proteins. In fermented goat milk, LAB growth resulted in a final count between 0.62 and 2.6 log CFU/mL, a 0.29 to 2.0 drop in pH, and lactic acid production between 0.20 and 1 g/L. FTIR spectra revealed time-dependent modifications in amide I and II bands accompanied by a marked reduction in carbohydrate content and an increase in lactic acid signal. After 21 days of storage, the viability of the strains, pH, and lactic acid in the fermented milks were not substantially modified. These results highlight the potential of lactic fermentation with strains selected for their probiotic potential as an approach to producing value-added goat milk products, as well as the usefulness of FTIR spectroscopy for characterizing complex systems such as goat milk. Full article

Through this work, an experimental setup and pre-processing method for obtaining fluorescence and quasi-noise-free Raman spectra of minerals for in situ mineral identification in an underground environment is proposed. It uses a combination of methodologies like dual excitation wavelengths, Shifted Excitation Raman Difference Spectroscopy (SERDS), and deep learning-based U-Net model for background and noise correction. The dual excitation wavelengths technique employs a near-infrared SERDS laser for the fingerprint and a red laser for the large Raman shift region. The SERDS laser operates at two excitation wavelengths and is tuneable in the vicinity of 785 nm. The red laser uses 671 nm excitation wavelength. The obtained fingerprint and large Raman shift Raman spectra are then fed to a pre-processing method containing the trained U-Net model for obtaining a background-corrected and quasi-noise-free Raman spectrum. The proposed method addresses issues of existing handheld Raman systems in terms of spectrometer sensitivity, spectrum acquisition speed, pre-processing time, fluorescence effects, and other interferences due to surrounding light or vibration. The obtained final processed Raman spectrum is then deconstructed into pseudo-Voigt peaks. The identification of the minerals can be based on the number and the positions of the pseudo-Voigt peaks. Samples of gypsum (CaSO₄·2H₂O) and anhydrite (CaSO₄) were used for evaluating the performance of the proposed method. The influence of measurement time on the reproducibility and precision of the peak identification and, thus, mineral identification is also analyzed. Full article

Soil organic matter (SOM) governs critical soil functions, including carbon storage, nutrient cycling, and microbial activity; yet the specific fractions responsible for its spectral predictability remain poorly understood. This study addresses a fundamental research gap by comparing visible–near-infrared (vis–NIR), mid-infrared (MIR), and fused spectroscopy for predicting SOM and its components: humic acid (HA), fulvic acid (FA), and Humin. Using 93 soil samples from subtropical croplands in southeastern China, we employed partial least squares regression with full spectra and LASSO-selected wavelengths to build predictive models. Results demonstrated that both vis–NIR and MIR individually provided moderately strong predictive performance for SOM and Humin (R² = 0.79–0.90, CCC = 0.85–0.93), while FA remained unpredictable (R² < 0.24) due to weak, overlapping spectral features. The strong predictability of SOM was primarily attributed to the Humin fraction, which comprises approximately 50 percent of total SOM and exhibits abundant spectrally active functional groups. Contrary to expectations, spectral fusion did not improve predictions because both spectral regions already contained complementary information, and fusion introduced redundancy and scale imbalance rather than increasing effective dimensionality. This study establishes that accurate SOM estimation depends fundamentally on the predictability and abundance of the Humin fraction, providing new mechanistic insights for spectroscopic soil carbon monitoring and highlighting the need for component-specific modeling approaches in soil organic matter research. Full article

Low temperatures have a significant impact on the growth, development, and productivity of cucumber plants. The potential of near-infrared spectroscopy and the aquaphotomics approach for investigating chilling stress was studied in Voreas F1 and Gergana cultivars. Changes in the spectral patterns of cucumber plants were compared with physiological and metabolic data. Voreas plants were unable to survive seven days of low-temperature stress due to a drastic increase in electrolyte leakage and a decrease in the net photosynthesis rate, stomatal conductance, and transpiration rate. Gergana plants survived chilling by preserving cell membrane integrity and photosynthesis efficiency. During chilling treatment, the content of most metabolites in both cultivars was reduced compared to the controls, yet it was much more pronounced in Voreas. We observed an increased accumulation of cinnamic acid on the seventh day only in the Gergana cultivar. A MicroNIR spectrometer was used for in vivo spectral measurements of cotyledons and the first two leaves. Differences in absorption spectra were observed among control, stressed, and recovered plants, across different days of stress, and between the studied cultivars. The most significant differences were in the 1300–1600 nm range, much smaller for Gergana than Voreas. Aquagrams of the two cultivars also reveal differences in their responses to low temperatures and changes in water molecular structure in the leaves. The errors of prediction for the days of chilling by using PLS models were from 0.96 to 1.14 days for independent validation, depending on the spectral data of different leaves used. Near-infrared spectroscopy and aquaphotomics can be used as additional tools for early detection of stress and investigation of low-temperature tolerance in cucumber cultivars. Full article

This study presents a systematic investigation of the dynamic and structural characteristics of St. John’s wort (Hypericum perforatum) in alcoholic solutions using experimental and theoretical techniques. Ultrasonic relaxation spectroscopy was employed to investigate medium-range dynamic processes, while density functional theory (DFT) calculations were employed to explore the molecular structure and vibrational properties of the system. Theoretical calculations revealed two Hyperforin conformers, a keto derivative, and three protonated species. Acoustic spectra revealed three distinct Debye-type relaxation processes, corresponding to conformational changes in hyperforin, enol-to-keto tautomerization, and proton transfer mechanisms. In addition, St. John’s wort oil (Oleum Hyperici) was studied, using attenuated total reflection (ATR) infrared spectroscopy for several extraction intervals. These spectra were compared with the theoretical IR spectra of hypericin, hyperforin, and its derivatives, confirming the presence of hyperforin, keto, and two protonated species in the oil. Besides structural and dynamical evaluations, the study assessed the toxicity and biological activity of hyperforin and all species found in the solutions, offering information about potential pharmaceutical uses, suggesting that hyperforin and its keto form have the best antidepressant activity. This comprehensive analysis enhances the understanding of hyperforin’s molecular behavior and strengthens the therapeutic potential of St. John’s wort as a natural antidepressant agent. Full article

Polyphenol oxidase (PPO) is the primary biochemical driver of browning and the subsequent decline of market quality in harvested fruit. In this work, a fully non-invasive analytical framework was built using Visible/Near-Infrared (VIS/NIR) spectroscopy coupled with chemometric modeling in order to estimate PPO activity in two commercially relevant plum cultivars (Khormaei and Khoni). A comprehensive comparative study was conducted utilizing multiple machine learning and linear regression techniques, including Support Vector Regression (SVR), Decision Tree (DT), and Partial Least Squares Regression (PLSR). After acquiring the full VIS/NIR spectra, a suite of metaheuristic feature selection strategies was applied to compress the spectral space to roughly 10–15 highly informative wavelengths. SVR, DT, and PLSR models were then trained and benchmarked using (a) the complete spectral domain and (b) the reduced wavelength subsets. The results consistently demonstrated that non-linear models (DT and SVR) significantly outperformed the linear PLSR method, confirming the inherent complexity and non-linearity of the relationship between the spectra and PPO activity. Across all tests, DT consistently produced the strongest generalization. Under full spectra inputs, DT reached RPD values of 4.93 for Khormaei and 5.41 for Khoni. Even more importantly, the wavelength-reduced configuration further enhanced performance while substantially lowering computational cost, yielding RPDs of 3.32 (Khormaei) and 5.69 (Khoni). The results show that VIS/NIR combined with optimized key-wavelength DT modeling provides a robust, fast, and field-realistic route for quantifying PPO activity in plums without physical destruction of the product. Full article