Search Results (11)

Grains and their derivatives play a crucial role as staple foods for the global population. Identifying grains in the food chain that are free from mycotoxin contamination is essential. Researchers have explored various traditional detection methods to address this concern. However, as grain consumption becomes increasingly time-sensitive and dynamic, traditional approaches face growing limitations. In recent years, emerging techniques—particularly molecular-based vibrational spectroscopy methods such as visible–near-infrared (Vis–NIR), near-infrared (NIR), Raman, mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI)—have been applied to assess fungal contamination in grains and their products. This review summarizes research advances and applications of vibrational spectroscopy in detecting mycotoxins in grains from 2019 to 2025. The fundamentals of their work, information acquisition characteristics and their applicability in food matrices were outlined. The findings indicate that vibrational spectroscopy techniques can serve as valuable tools for identifying fungal contamination risks during the production, transportation, and storage of grains and related products, with each technique suited to specific applications. Given the close link between grain-based foods and humans, future efforts should further enhance the practicality of vibrational spectroscopy by simultaneously optimizing spectral analysis strategies across multiple aspects, including chemometrics, model transfer, and data-driven artificial intelligence. Full article

Soil spectroscopy offers a rapid, cost-effective alternative to traditional soil analyses for characterization and classification. Previous studies have mainly focused on predicting soil categories using single sensors, particularly visible–near-infrared (vis–NIR) or mid-infrared (MIR) spectroscopy. In this study, we evaluated the performance of vis–NIR, MIR, and their combined spectra for soil classification by partial least-squares discriminant analysis (PLSDA) and random forest (RF). Utilizing 60 typical soil profiles’ data of four soil classes from the global soil spectral library (GSSL), our results demonstrated that in PLSDA models, direct combination (optimal overall accuracy: 70.6%, kappa coefficient: 0.60) and outer product analysis (OPA) fused spectra (optimal overall accuracy: 68.1%, kappa coefficient: 0.57) outperformed vis–NIR (optimal overall accuracy: 62.2%, kappa coefficient: 0.49) but underperformed compared to MIR (optimal overall accuracy: 71.4%, kappa coefficient: 0.62). In RF models, classification accuracy using fused spectra was inferior to single spectral ranges, with MIR achieving the highest classification accuracy (optimal overall accuracy: 89.1%, kappa coefficient: 0.85). Therefore, MIR alone remains the most effective spectral range for accurate soil class discrimination. Our findings highlight the potential of MIR spectroscopy for enhancing global soil classification accuracy and efficiency, with important implications for soil resource management and agricultural planning across diverse environments. Full article

Visible-near infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy are increasingly being used for the fast determination of soil properties. The aim of this study was (i) to test the use of MIR spectra (Agilent 4300 FTIR Handheld spectrometer) for the prediction of soil properties and (ii) to compare the prediction performances of MIR spectra and Vis-NIR (ASD FieldSpecPro) spectra; the Vis-NIR data were adopted from a previous study. Both the MIR and Vis-NIR spectra were coupled with partial least squares regression, different pre-processing techniques, and the same 114 soil samples, collected from the agricultural land located between boreal forests and semi-arid steppe belts (Kastanozems). The prediction accuracy (R² = 0.70–0.99) of both techniques was similar for most of the soil properties assessed. However, (i) the MIR spectra were superior for estimating CaCO₃, pH, SOC, sand, Ca, Mg, Cd, Fe, Mn, and Pb. (ii) The Vis-NIR spectra provided better results for silt, clay, and K, and (iii) the hygroscopic water content, Cu, P, and Zn were poorly predicted by both methods. The importance of the applied pre-processing techniques was evident, and among others, the first derivative spectra produced more reliable predictions for 11 of the 17 soil properties analyzed. The spectrally active CaCO₃ had a dominant contribution in the MIR predictions of spectrally inactive soil properties, followed by SOC and Fe, whereas particle sizes and hygroscopic water content appeared as confounding factors. The estimation of spectrally inactive soil properties was carried out by considering their secondary correlation with carbonates, clay minerals, and organic matter. The soil information covered by the MIR spectra was more meaningful than that covered by the Vis-NIR spectra, while both displayed similar capturing mechanisms. Both the MIR and Vis-NIR spectra seized the same soil information, which may appear as a limiting factor for combining both spectral ranges. The interpretation of MIR spectra allowed us to differentiate non-carbonated and carbonated samples corresponding to carbonate leaching and accumulation zones associated with topography and land use. The prediction capability of the MIR spectra and the content of nutrient elements was highly related to soil-forming factors in the study area, which highlights the importance of local (site-specific) prediction models. Full article

Wheat has played an important role in human agriculture since ancient times. Increasing rates of processed wheat product fabrication require more and more laboratory studies of product quality. This, in turn, requires the use, in production and in field conditions, of sufficiently accurate, fast and relatively low-cost quality control methods, including the detection of fungal diseases. One of the most widespread fungal diseases of wheat in the world is ergot caused by the fungi genus Claviceps. Optical methods are promising for this disease identification due to the relative ease of implementation and the possibility of performing fast analyses in large volumes. However, for application in practice, it is necessary to identify and substantiate characteristic spectral markers that make it possible to judge the sample contamination. In this regard, within the framework of this study, the methods of IR absorption spectroscopy in the MIR region and reflection spectroscopy in the UV-vis-NIR ranges, as well as luminescence spectroscopy, were used to study ergot-infected grains of winter wheat of the “Moskovskaya 56” cultivar. To justify the choice of the most specific spectral ranges, the methods of chemometric analysis with supervised classification, namely PCA-LDA and PCA-SVM, were applied. The possibility of separating infected grains according to the IR absorption, reflection spectra in the UV-vis-NIR ranges and visible luminescence spectra was tested. Full article

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500–25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0–100 cm) or the shallow layers (0–40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm). A total of 90 soil samples containing 450 subsamples (0–10 cm, 10–20 cm, 20–40 cm, 40–70 cm, and 70–100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSE_p = 1.07–3.82 g/kg, RPD = 1.10–5.80; TN: RMSE_p = 0.11–0.15 g/kg, RPD = 1.70–4.39) outperformed those based on vis-NIR (OM: RMSE_p = 1.75–8.95 g/kg, RPD = 0.50–3.61; TN: RMSE_p = 0.12–0.27 g/kg; RPD = 1.00–3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSE_p = 1.09–2.97 g/kg, RPD = 2.13–5.80; TN: RMSE_p = 0.08–0.19 g/kg, RPD = 1.86–4.39) outperformed those based on the shallow layers (OM: RMSE_p = 1.07–8.95 g/kg, RPD = 0.50–3.93; TN: RMSE_p = 0.11–0.27 g/kg, RPD = 1.00–2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths. Full article

As a precious soil resource, black soils in Northeast China are currently facing severe land degradation. Visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–25,000 nm) have shown great potential to predict soil properties. However, there is still limited research on using MIR in situ. The aim of this study was to explore the feasibility of in situ MIR for the prediction of soil total nitrogen (TN) and total phosphorus (TP) and to compare its performance with the use of laboratory MIR, as well as the use of in situ and laboratory vis-NIR. A total of 450 samples from 90 soil profiles, along with their in situ and laboratory spectra of MIR and vis-NIR, were collected in a field with ten different tillage and management practices in a typical black soil area of Northeast China. Partial least square regression (PLSR), random forest (RF) and multivariate adaptive regression splines (MARS) were used to generate the calibrations between the spectra and the two properties. The results showed that both MIR and vis-NIR were able to predict the TN whether in laboratory or in situ conditions, but neither of them could predict the TP quantitatively since there was no sensitive band on both spectra regarding the TP. The prediction accuracy of the TN with laboratory spectra was higher than that with in situ spectra, for both vis-NIR and MIR. The optimal prediction accuracy of the TN with laboratory MIR (RMSE = 0.11 g/kg, RPD = 3.12) was higher than that of laboratory vis-NIR (RMSE = 0.14 g/kg, RPD = 2.45). The optimal prediction accuracy of in situ MIR (RMSE = 0.20 g/kg, RPD = 1.80) was lower than that of in situ vis-NIR (RMSE = 0.16 g/kg, RPD = 2.14). The prediction performance of the spectra followed laboratory MIR > laboratory vis-NIR > in situ vis-NIR > in situ MIR. The performance of in situ MIR was relatively poor, mainly due to the fact that MIR was more influenced by soil moisture. This study verified the feasibility of in situ MIR for soil property prediction and provided an approach for obtaining rapid soil information and a reference for soil research and management in black soil areas. Full article

Despite occupying a large area of the globe and being the next agricultural frontier, sandy soils are seldom explored in scientific studies. Considering the high capacity of remote sensing in soil characterization, this work aimed to: (i) characterize sandy soils’ profiles from proximal sensing; (ii) assess the ability of visible, near, and short-wave infrared (Vis-NIR-SWIR) as well as mid-infrared (MIR) spectroscopy to distinguish soil classes of highly sandy content; (iii) quantify physical and chemical attributes of sandy soil profiles from Vis-NIR-SWIR and MIR spectroscopy as well as X-ray fluorescence (pXRF). Samples were described and collected from 29 sandy soil profiles. The 127 samples went under Vis-NIR-SWIR and MIR spectroscopy, X-ray fluorescence, and chemical and physical analyses. The spectra were analyzed based on “Morphological Interpretation of Reflectance Spectrum” (MIRS), Principal Components Analysis (PCA), and cluster methodology to characterize soils. The integration of different information obtained by remote sensors, such as Vis-NIR-SWIR, MIR, and Portable X-ray Fluorescence (pXRF), allows for pedologically complex characterizations and conclusions in a short period and with low investment in analysis and reagents. The application of MIRS concepts in the VNS spectra of sandy soils showed high potential for distinguishing pedological classes of sandy soils. The MIR spectra did not show distinct patterns in the general shapes of the curves and reflectance intensities between sandy soil classes. However, even so, this region showed potential for identifying mineralogical constitution, texture, and OM contents, assuming high importance for the complementation of soil pedometric characterizations using VNS spectroscopy. The VNS and MIR data, combined or isolated, showed excellent predictive performance for the estimation of sandy soil attributes (R² > 0.8). Sandy soil color indices, which are very important for soil classification, can be predicted with excellent accuracy (R² from 0.74 to 0.99) using VNS spectroscopy or the combination of VNS + MIR. Full article

The number of food frauds in coconut-based products is increasing due to higher consumer demands for these products. Rising health consciousness, public awareness and increased concerns about food safety and quality have made authorities and various other certifying agencies focus more on the authentication of coconut products. As the conventional techniques for determining the quality attributes of coconut are destructive and time-consuming, non-destructive testing methods which are accurate, rapid, and easy to perform with no detrimental sampling methods are currently gaining importance. Spectroscopic methods such as nuclear magnetic resonance (NMR), infrared (IR)spectroscopy, mid-infrared (MIR)spectroscopy, near-infrared (NIR) spectroscopy, ultraviolet-visible (UV-VIS) spectroscopy, fluorescence spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy (RS) are gaining in importance for determining the oxidative stability of coconut oil, the adulteration of oils, and the detection of harmful additives, pathogens, and toxins in coconut products and are also employed in deducing the interactions in food constituents, and microbial contaminations. The objective of this review is to provide a comprehensive analysis on the various spectroscopic techniques along with different chemometric approaches for the successful authentication and quality determination of coconut products. The manuscript was prepared by analyzing and compiling the articles that were collected from various databases such as PubMed, Google Scholar, Scopus and ScienceDirect. The spectroscopic techniques in combination with chemometrics were shown to be successful in the authentication of coconut products. RS and NMR spectroscopy techniques proved their utility and accuracy in assessing the changes in coconut oil’s chemical and viscosity profile. FTIR spectroscopy was successfully utilized to analyze the oxidation levels and determine the authenticity of coconut oils. An FT-NIR-based analysis of various coconut samples confirmed the acceptable levels of accuracy in prediction. These non-destructive methods of spectroscopy offer a broad spectrum of applications in food processing industries to detect adulterants. Moreover, the combined chemometrics and spectroscopy detection method is a versatile and accurate measurement for adulterant identification. Full article

Mid-infrared (MIR) wavelengths (2–10 μ$\mathsf{\mu }$m) open up a new paradigm for femtosecond laser–solid interactions. On a fundamental level, compared to the ubiquitous near-IR (NIR) or visible (VIS) laser interactions, MIR photon energies render semiconductors to behave like high bandgap materials, while driving conduction band electrons harder due to the $\lambda$${}^2$ scaling of the ponderomotive energy. From an applications perspective, many VIS/NIR opaque materials are transparent for MIR. This allows sub-surface modifications for waveguide writing while simultaneously extending interactions to higher order processes. Here, we present the formation of an extreme sub-wavelength structure formation (∼$\lambda /100$) on a single crystal silicon surface by a 3600 nm MIR femtosecond laser with a pulse duration of 200 fs. The 50–100 nm linear structures were aligned parallel to the laser polarization direction with a quasi-periodicity of 700 nm. The dependence of the structure on the native oxide, laser pulse number, and polarization were studied. The properties of the structures were studied using scanning electron microscopy (SEM), atomic force microscopy (AFM), cross-sectional transmission electron-microscopy (CS-TEM), electron diffraction (ED), and energy-dispersive X-ray spectroscopy (EDX). As traditional models for the formation of laser induced periodic surface structure do not explain this structure formation, new theoretical efforts are needed. Full article

This work presents pulsed laser deposition as a method to obtain unoxidized LaNi₅ nanoscale films and describes their temperature and thickness dependent electrical conductivity and the spectral dispersions of some optical properties. AB₅-type rare earth element (REE)-nickel compounds are currently studied from both theoretical and practical points of view. Special challenges are posed during the preparation of these nanomaterials, which can be overcome using finely tuned parameters in a preparation process that always involves the use of high energies. Film deposition was made by laser—induced vaporization, with short and modulated impulses and electro–optical tuning of the quality factor, mainly on glass and one SiO₂ substrate. Deposition geometry dependent linear thickness increase, between 1.5–2.5 nm per laser burst, was achieved. Film structures and phase compositions were determined using XRD and discussed in comparison with films obtained by similar deposition procedures. Temperature and scale dependent properties were determined by studying electrical conductivity and optical properties. Electrical conductivity was measured using the four-probe method. The observed semiconductor-like conductivity for film thicknesses up to 110 nm can be explained by thermal activation of electrons followed by inter-insular hopping or quantum tunneling, which, on the other hand, modulates the material’s native metallic conductance. Films with thicknesses above this value can be considered essentially metallic and bulk-like. The spectral behaviors of the refractive index and absorption coefficient were deduced from differential reflectance spectroscopy data acquired on a broad ultraviolet, visible, near- and mid-infrared (UV-VIS-NIR-MIR) domain, processed using the Kramers-Krönig formalism. Their study led to the identification of the allowed interband transitions. Electronic behavior in the energy bands near the Fermi level and in the surface and interface-states was described, discussing the differences between experimental data and the classical free-electron theoretical model applied for the bulk intermetallic alloy, in correlation with theoretical optical properties or experimental X-ray photoelectron spectroscopy (XPS) results from references. However, the dielectric-like shape of the reflectance of the thinnest film was in accordance with the Lorentz–Drude model. Full article

In this study, the spectral responses obtained from a Typic Red Hapludox (oxisol) were analyzed under different uses and occupations: Ficus elastica cultivation, Citrus + Arachis association cultivation, transitional crops, forest, Mangifera indica, Anacardium occidentale, Elaeis guineensis (18 years), Brachiaria decumbens, Brachiaria brizantha, and Musa × paradisiaca + Zea mays at the La Libertad Research Center in the department of Meta in Colombia (4°04′ North latitude, 73°30′ West longitude, 330 MAMSL). Sampling was performed with four random replicates of the horizon A and B to determine the contents of organic carbon (CO), pH, exchangeable acidity (Ac. I), cation exchange capacity (Cc), P, Ca, Mg, K, Na, sand, lime, and clay and spectral responses were obtained in the visible band (VIS), near infrared (NIR), and infrared (MIR) for each sample under laboratory conditions. A comparison was made between the obtained spectra, determining the main changes in soil properties due to their use and coverage. Variation of soil characteristics such as color, organic carbon content, presence of ferrous compounds, sand, silt, and clay content and mineralogy allow the identification of the main spectral changes of soils, demonstrating the importance of the use of reflectance spectroscopy as a tool of comparison and estimation between physical-chemical properties of the soils. Full article