Journal of Spectroscopy

Soils exhibit structural heterogeneity across diverse spatio-temporal scales, yielding myriad of microhabitats, highlighting the need for a nuanced understanding of the intricate interactions within the soil matrix. At the nanometer scale, the interplay among organic matter (OM), mineral particles, and microbiota intricately govern the long-term destiny of soil carbon (C), nutrient cycling, and the fate of both organic and inorganic pollutants. Notably, the sorption of soil organic matter (SOM) onto smaller clay particles and its entrapment in microaggregates further contribute to this complex dynamic. Understanding these processes depends on recognizing their scale-dependent nature, necessitating sophisticated techniques for investigation. Although various methods are employed across scales, the current set of techniques still lacks the requisite sensitivity and resolution for microscale data collection. To address this limitation, the adoption of novel microscopic and spectroscopic techniques capable of probing molecular, isotopic, and elemental patterns at the micro to nano scale becomes imperative. Among these cutting-edge methodologies, the nano-scale secondary ion mass spectrometer (NanoSIMS) emerges as a paradigm-shifting tool. Representing the latest evolution in ion microprobes, NanoSIMS seamlessly integrates high-resolution microscopy and isotopic analysis, maintaining unparalleled signal transmission and spatial resolution, reaching as fine as 50 nm. Its capabilities extend beyond conventional applications in science, as evidenced by recent breakthroughs in utilizing NanoSIMS to study biophysical interfaces in soils. This article underscores the pressing need to advance the incorporation of NanoSIMS as a pioneering instrumentation technique in soil studies. Furthering the implementation of this novel instrumentation technique in soil studies will pave avenues and aid in the advancement of future research.

The precise and prompt determination of quality control indicators such as moisture, stilbene glycosides, and anthraquinone glycosides is crucial in assessing the quality of Polygoni Multiflori Radix. Near-infrared spectroscopy is a nondestructive analytical technique that offers a more desirable approach than traditional methods for assessing content levels. In this study, various spectral preprocessing techniques were used to preprocess the raw spectral data. The spectral data were correlated with the determination of three-component contents using the partial least squares regression (PLSR) method. Then different algorithms, such as competitive adaptive weighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE), and random frog hopping (RF), were used for model simplification and feature selection. The data suggest that the first-order deconvolution derivative (1^st Dev.) processing of the spectral data is superior to other methods in all three model evaluation metrics. The PLSR model for moisture, stilbene glycosides, and anthraquinone glycosides produced the calibration coefficient of determination (R²_C) of 0.82, 0.52, and 0.58, the root mean square error of cross validation (RMSE_CV) of 0.91%, 0.77%, and 0.69%, the prediction coefficient of determination (R²_P) of 0.72, 0.28, and 0.54, the root mean square error of prediction (RMSE_P) of 0.65%, 0.81%, and 0.75%, and relative percentage differences (RPDs) of 1.7, 1.0, and 0.8. After optimizing the model using CARS, R²_C increased by 0.15%, 0.41%, and 0.34%, RMSE_CV decreased by 0.53%, 0.32%, and 0.24%, R²_P increased by 0.21%, 0.63%, and 0.35%, RMSE_P decreased by 0.36%, 0.41%, and 0.31%, and RPD increased by 1.1, 0.9, and 0.6, significantly improving the predictive capacity of the model. This research provides a feasible method for rapid compliance testing of Polygoni Multiflori Radix. To further improve the model’s performance and applicability, it is necessary to continuously expand the sample set with different varieties and locations for wide variation.

Lead telluride and germanium telluride are well-known IV-VI semiconductors, which is now the focus of research due to the perspective of application as thermoelectrics for midrange temperatures. Solid solutions and heterostructures on this basis, obtained by molecular beam epitaxy, are a promising direction for the development of these materials. In this paper, we have focused on the Raman spectra excited by the 514.5 nm laser line (out of resonance) of PbTe, GeTe, (Pb, Ge)Te, and (Pb, Ge, Eu)Te layers grown on BaF₂ (111) monocrystalline substrates. The obtained phonon properties are related to the properties of the corresponding bulk materials or can be explained by a model that takes into account the difference in the masses of the constituent elements only, as is the case with the local mode of Ge in PbTe (registered at about 181 cm⁻¹). Multiphonon processes registered for this phonon are a consequence of the change in the electronic structure of PbTe and electron-phonon interaction. An improvement in the quality of thin films due to doping with Eu ions was also registered.

Gem-quality blue octahedral crystalline gahnite was produced in Nigeria. This paper investigated gemological and spectroscopic characteristics by basic gemological experiments, electron probes, infrared reflectance spectroscopy, laser Raman spectroscopy, photoluminescence spectroscopy, and ultraviolet-visible spectroscopy. The results show that the refractive index (RI) of Nigerian gahnite is 1.792∼1.794, and the specific gravity is 4.45∼4.66, with no fluorescence. The main chemical composition is ZnAl₂O₄, accounting for 93.57%, and the rest is mainly FeAl₂O₄, which also contains Na, Mg, Co, Mn, Cr, Cu, Si, K, and Ca elements. The infrared spectra showed midinfrared absorption bands near 510 cm⁻¹, 559 cm⁻¹, and 664 cm⁻¹ in the fingerprint region, corresponding to the Zn-O stretching vibration, bending vibration, and Al-O bending vibration, respectively. The Raman spectra showed three of the five Raman active modes of the spinel group, with characteristic Raman absorption peaks located at 418 cm⁻¹, 508 cm⁻¹, and 660 cm⁻¹, corresponding to E_g, T_2g(2), and T_2g(3) modes, respectively, and the comparison revealed a higher degree of Zn and Al ordering in this paper for gahnite. The photoluminescence spectra show the common Cr³⁺-activated fluorescence splitting peaks of natural spinel, of which the 686 nm (R-line) fluorescence peak is obvious and sharp. The UV-vis absorption spectra located at 444 nm and 489 nm are the most obvious, which are caused by the d-d electron leap of ^TFe²⁺ (⁵E ⟶ ⁵T₂), and the blue-gray tones of the samples are mainly caused by the spin-forbidden electronic transitions in ^TFe²⁺ and ^MFe²⁺ ↔ ^MFe³⁺; the weak absorption peak at 609 nm was determined to be associated with Co²⁺ by derivative spectra.

Honey is considered as a premium food produced by honeybees. It is highly appreciated by consumers around the world and raises a major concern nowadays which is ensuring its authenticity in respect to its production and its botanical origin. In Lebanon, honey is mainly multifloral which makes its authentication rather difficult. While mid-infrared (MIR) spectroscopy combined with multivariate analysis has proven to be successful in authenticating unifloral honey, the challenge with Lebanese honey lies in assessing its performance with multifloral honey. Therefore, this work aims to test the performance of common components analysis (CCA) applied on mid-infrared spectra in the authentication of multifloral Lebanese honey. For this purpose, 96 multifloral Lebanese honey samples of different floral sources were collected from different regions of the Lebanese territory and analyzed using MIR spectroscopy. CCA applied to the spectral data, allowed a separation between honeydew honey samples and floral honey samples. In addition, honey samples collected from the Bekaa plain region were differentiated from the other honey samples collected from all the other Lebanese geographical regions. This discrimination between the groups of honey samples is based essentially on their sugar composition.

Soil water content is a critical environmental parameter in research and practice, though various technological and contextual constraints limit its estimation in arid areas with vegetation cover. This study combined the multitemporal remote sensing data of Sentinel-1 and Landsat 8 to conduct an inversion study on surface soil water content under low vegetation cover in Nagqu, central Tibetan Plateau. Four vegetation indices (NDVI, ARVI, EVI, and RVI) were extracted from optical remote sensing data. A water cloud model was used to eliminate the influence of the vegetation layer on the backscattering coefficient associated with vegetation cover, and a predictive model suitable for the Nagqu area was constructed. The water cloud model effectively incorporated a vegetation index instead of vegetation water content. We found that VV polarization was more suitable for soil water content inversion than VH polarization. Among the four vegetation indices, the soil water content inversion model constructed with RVI under VV polarization had the best fit (R² = 0.8212; RMSE = 6.30). The second-best fit was observed for vegetation water content-NDVI (R² = 0.8201). The soil water content inversion models all had an R² > 0.6, regardless of the vegetation index used, though the RVI had the best fitting effect, indicating that this vegetation index is highly applicable in the water cloud model, as a substitute for vegetation water content, and is expected to perform well in similar study sites.