Journal of Spectroscopy
 Journal metrics
See full report
Acceptance rate22%
Submission to final decision107 days
Acceptance to publication15 days
CiteScore3.200
Journal Citation Indicator0.520
Impact Factor2.0

Raman Spectra of PbTe- and GeTe-Based Monocrystalline Epitaxial Layers

Read the full article

 Journal profile

Journal of Spectroscopy publishes research into the theory and application of spectroscopy across all disciplines, including biology, chemistry, engineering, earth sciences, medicine, materials science, physics, and space science.

 Editor spotlight

Chief Editor Dr Daniel Cozzolino is based at the University of Queensland, Australia. His research focuses on the developments of chemometric and spectroscopic methods for use in agriculture and food applications.

 Special Issues

Do you think there is an emerging area of research that really needs to be highlighted? Or an existing research area that has been overlooked or would benefit from deeper investigation? Raise the profile of a research area by leading a Special Issue.

Latest Articles

More articles
Research Article

Gemological and Spectral Characteristics of Gem-Quality Blue Gahnite from Nigeria

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 Eg, T2g(2), and T2g(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.

Research Article

Application of Common Components Analysis to Mid-Infrared Spectra for the Authentication of Lebanese Honey

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.

Research Article

Collaborative Inversion of Soil Water Content in Alpine Meadow Area Based on Multitemporal Polarimetric SAR and Optical Remote Sensing Data

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.

Research Article

Geochemical Characterization of Sedimentary Materials (Limestone, Gypsum, Coal, and Iron Ore) along the Nile River Basin, South Wollo, Ethiopia

Sedimentary rocks are produced by the weathering of preexisting rocks and the subsequent transportation and deposition of the weathering products. Among the sedimentary rocks, especially limestone is a crucial raw material for cement production. The purpose of this study was to characterize the valuable industrial raw materials, limestone, gypsum, clay, coal, and iron ore, along with the Nile River basin. For sample collection, a random sampling method was applied. Different analytical methods were carried out for complete oxide analysis such as LiBO2 fusion, HF attack, and gravimetric, calorimetric, and atomic absorption spectroscopy (AAS). The percentages of oxides detected in this study are in the range of acceptable values (high content of CaO ranging from 47.3 to 50.4% and less content of SiO2 ranging from 8.72 to 11.24%) for good proposal as a potential raw material for cement production. The most dominant and wide-range coverage of limestone along with the Nile basin, particularly near Arsema Monastery, was found as matured limestone. The petrographic analysis of gypsum, sandstone, and clay samples indicated that all the samples taken from Wegidi revealed that the high percentage of gypsum ranges from 90 to 95%. Sandstone is dominantly preset in Kelala to Jamma road along with Beto River with high content of SiO2 ranging from 61 to 95%. The results of this study indicate that the treated coal samples are relative to high calorific value, fixed carbon, and low ash content. Coal and iron ore from Jamma revealed that high calorific value is 4929.24 and hematite content is 52.2, respectively. The result of this study revealed that a huge amount of limestone reservoir is detected in Borena Wereda, Amhara, Ethiopia.

Research Article

Use of Machine Learning Models for Prediction of Organic Carbon and Nitrogen in Soil from Hyperspectral Imagery in Laboratory

Organic carbon and total nitrogen are essential nutrients for plant growth. The presence of these nutrients at acceptable levels can create an optimal environment for the development of crops of interest. The application of spectroscopic techniques and the use of machine learning algorithms have made it possible to calibrate models capable of predicting the number of elements present in the soil. One of these techniques is hyperspectral imaging, which captures portions of the electromagnetic spectrum where the materials present in the soil can be differentiated due to the vibrations of chemical bonds. The objective of this research is to use statistical models to predict OC and N in soils from hyperspectral images. Transformations were applied to spectral and chemical data and the models used were Random Forest (RF) and Support Vector Machine (SVM). To select the best model, the values of the coefficient of determination (), root mean square error of prediction (RMSEP), and the ratio of performance to deviation (RPD) were considered. For OC, the values found for the RF model were an of 0.87, an RMSEP of 0.10, and an RPD of 6.74; the SVM model presented an of 0.92, an RMSEP of 0.20, and an RPD of 3.56. For the variable N, the values found for the RF model were an of 0.79, an RMSEP of 0.03, and an RPD of 5.44; for the SVM model, they were an of 0.87, an RMSEP of 0.08, and an RPD of 2.76. The RF model showed a better fit for both variables. The SVM model also produced acceptable results. The results show that machine learning models are a good alternative for analysing soil-related variables.

Review Article

Vibrational Biospectroscopy in the Clinical Setting: Exploring the Impact of New Advances in the Field of Immunology

The investigation of pathological diseases largely relies on laboratory examinations. The ability to identify and characterise cells is an essential process for clinicians to reach an accurate diagnosis and inform appropriate treatments. There is currently a gap between the advancement of scientific knowledge on cellular and molecular pathways and the development of novel techniques capable of detecting subtle cellular changes associated with disease. Biospectroscopy is the use of spectroscopy techniques to investigate biological materials. Within a biological sample, important molecules such as lipids, carbohydrates, nucleic acids, and proteins are held together by chemical bonds; these bonds will vibrate following excitation with infrared light. By measuring the vibrational energy of each molecule present in a biological sample, a unique spectrum, known as the “molecular fingerprint” is generated. As disease-related changes in biological samples will be reflected in the molecular fingerprint, biospectroscopy is a well-placed candidate for the investigation of disease. Biospectroscopy has been gaining wider acceptance and application in the clinical setting over the past decade; however, it has yet to reach diagnostic laboratories and healthcare clinics as a routine platform for clinical assessment. Immunological disorders are complex, often demonstrating interaction across multiple molecular pathways which results in delayed diagnosis. Vibrational spectroscopy is being applied in many fields, and here we present a review of its use in cellular immunology. Potential benefits, including an enhanced definition of molecular processes and the use of spectroscopy in disease diagnosis, monitoring, and treatment response, are discussed. The translation of vibrational spectroscopic techniques into clinical practice offers rapid, noninvasive, and inexpensive methods to obtain information on the molecular composition of biological samples. The potential clinical benefits of biospectroscopy include providing a more prompt and accurate disease diagnosis, thus improving patient care and resulting in better health outcomes.

Journal of Spectroscopy
 Journal metrics
See full report
Acceptance rate22%
Submission to final decision107 days
Acceptance to publication15 days
CiteScore3.200
Journal Citation Indicator0.520
Impact Factor2.0
 Submit Check your manuscript for errors before submitting

Article of the Year Award: Impactful research contributions of 2022, as selected by our Chief Editors. Discover the winning articles.