Search Results (82)

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

Background/Objectives: Despite extensive research, the underlying causes of amyotrophic lateral sclerosis (ALS) remain unclear. This umbrella review aims to synthesize a vast body of evidence from advanced magnetic resonance imaging (MRI) studies of ALS, encompassing a wide range of neuroimaging techniques and patient cohorts. Methods: Following the PRISMA guidelines, we conducted an extensive search of four databases (PubMed, Scopus, Web of Science, and Embase) for articles published until 3 December 2024. Data extraction and quality assessment were independently performed using the AMSTAR2 tool. Results: This review included 18 studies that incorporated data from over 29,000 ALS patients. Structural MRI consistently showed gray matter atrophy in the motor and extra-motor regions, with significant white matter (WM) atrophy in the corticospinal tract and corpus callosum. Magnetic resonance spectroscopy revealed metabolic disruptions, including reduced N-acetylaspartate and elevated choline levels. Functional MRI studies have demonstrated altered brain activation patterns and functional connectivity, reflecting compensatory mechanisms and neurodegeneration. fMRI also demonstrated disrupted motor network connectivity and alterations in the default mode network. Diffusion MRI highlighted microstructural changes, particularly reduced fractional anisotropy in the WM tracts. Susceptibility-weighted imaging and quantitative susceptibility mapping revealed iron accumulation in the motor cortex and non-motor regions. Perfusion MRI indicated hypoperfusion in regions associated with cognitive impairment. Conclusions: Multiparametric MRI consistently highlights widespread structural, functional, and metabolic changes in ALS, reflecting neurodegeneration and compensatory mechanisms. Full article

With the surge in digital farming, real-time quality management of fresh produce has become essential. For apples (Malus domestica Borkh.), consumer demand extends beyond sweetness, texture, and appearance to internal quality factors such as moisture content. Existing non-destructive methods, however, involve costly equipment, complex calibration, and sensitivity to environmental conditions. This study hypothesizes that thermal diffusivity indices derived from surface heating and cooling patterns can accurately predict apple moisture content non-destructively. A total of 823 apples from seven varieties were analyzed using a thermal imaging sensor in a 120-s process comprising 40 s of heating and 80 s of cooling. Key thermal diffusivity indices—minimum, maximum, mean, and max–min values—were extracted and correlated with actual moisture content measured via the drying method. Multiple linear regression and leave-one-out cross-validation confirmed that mean temperature-based models provided the most stable predictions ($R_{CV}^2$ ≥ 0.90 for some varieties). Frame optimization and artificial neural networks further improved prediction accuracy for varieties exhibiting higher variability. The proposed method is cost-effective, requires minimal calibration, and is less affected by surface reflectance, outperforming conventional optical methods (e.g., NIR spectroscopy, hyperspectral imaging), especially regarding robustness against surface reflectance variability and calibration complexity. This offers a practical solution for monitoring apple freshness and quality during sorting and distribution processes, with expanded research on sugar content and acidity expected to accelerate commercialization. Full article

Soil color serves as a critical indicator of its properties and conditions. It is shaped by a complex interplay of mineral and organic matter content, moisture levels, and other environmental variables. Additionally, human activities such as land-use changes and intensive agricultural practices can profoundly alter soil color. Soil color, driven by the presence of organic matter, plays a crucial role in understanding soil fertility. Its strong correlation with soil organic carbon content makes it a valuable parameter for assessing soil quality in agricultural practices. A variety of techniques have been developed to measure soil color, ranging from traditional Munsell color matching to modern color meters. Digital image colorimetry enables rapid on-site assessments of soil color, but environmental conditions such as soil water content and lighting conditions should be considered. Spectroscopic methods, particularly diffuse reflectance spectroscopy, pave the way for a more reliable and accurate composition analysis. Advances in remote sensing and computational methods are combined to explore the intricate relationships between soil color and environmental factors. Such an integrated approach not only enhances scalability but also leads to more insights and actionable strategies for environmental management and sustainable agriculture. Full article

Rhabdophane, CePO₄∙H₂O, nanoparticles were prepared by mechanochemical synthesis with different durations and thoroughly characterized by various characterization techniques. X-ray diffraction analysis showed that the optimal synthesis duration was 15 min, since, in this case, pure rhabdophane is obtained, without traces of contamination by the vessel material. The size of the obtained nanoparticles, as determined from high-resolution transmission electron microscopy images, was around 5 nm. According to UV-Vis diffuse reflectance spectroscopy results, rhabdophane nanoparticles show transparency to visible light and high absorption in the UV region, with a direct bandgap of 3.1 eV. The photocatalytic activity in the Castor oil degradation process and the cytotoxicity for human skin cells were determined and compared to commercial TiO₂ nanoparticles, with rhabdophane nanoparticles exhibiting superior properties. Small particle size, purity, absorption in the UV range, transparency to visible light, low photocatalytic activity, and low cytotoxicity indicated the possibility of prepared rhabdophane application as an inorganic UV filter in photoprotective formulations. Full article

The catalytic reduction of organic pollutants in water is a critical environmental challenge due to the persistent and hazardous nature of compounds like azo dyes and nitrophenols. In this study, we synthesized nanostructured CuO/TiO₂ catalysts via a combustion technique, followed by calcination at 700 °C to achieve a rutile-phase TiO₂ structure with varying copper loadings (5–40 wt.%). The catalysts were characterized using X-ray diffraction (XRD), attenuated total reflectance-Fourier transform infrared (ATR–FTIR) spectroscopy, thermogravimetric analysis-differential thermal analysis (TGA–DTA), UV-visible diffuse reflectance spectroscopy (DRS), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS). The XRD results confirmed the presence of the crystalline rutile phase in the CuO/TiO₂ catalysts, with additional peaks indicating successful copper oxide loading onto TiO₂. The FTIR spectra confirmed the presence of all the functional groups in the prepared samples. SEM images revealed irregularly shaped copper oxide and agglomerated TiO₂ particles. The DRS results revealed improved optical properties and a decreased bandgap with increased Cu content, and 4-Nitrophenol (4-NP) and methyl orange (MO), which were chosen for their carcinogenic, mutagenic, and nonbiodegradable properties, were used as model organic pollutants. Catalytic activities were tested by reducing 4-NP and MO with sodium borohydride (NaBH₄) in the presence of a CuO/TiO₂ catalyst. Following the in situ reduction of CuO/TiO₂, Cu (NPs)/TiO₂ was formed, achieving 98% reduction of 4-NP in 480 s and 98% reduction of MO in 420 s. The effects of the NaBH₄ concentration and catalyst mass were investigated. The catalysts exhibited high stability over 10 reuse cycles, maintaining over 96% efficiency for MO and 94% efficiency for 4-NP. These findings demonstrate the potential of nanostructured CuO/TiO₂ catalysts for environmental remediation through efficient catalytic reduction of organic pollutants. Full article

The microstructure of aluminum thin films, including the grain morphology and surface roughness, are key parameters for improving the thermal or electrical properties and optical reflectance of films. The first step in optimizing these parameters is a thorough understanding of the grain growth mechanisms and film structure. To investigate these issues, thin aluminum films with thicknesses ranging from 25 to 280 nm were coated on SiO_x/Si substrates at ambient temperature under high-vacuum conditions and a low argon pressure of 3 × 10⁻³ mbar (0.3 Pa) using the radio frequency magnetron sputtering method. Quantitative analyses of the surface roughness and nanograin characteristics were conducted using atomic force microscopy (AFM), transmission electron microscopy (TEM), and X-ray diffraction. Changes in specular reflectance were measured using ultraviolet–visible and near-infrared spectroscopy. The low roughness values obtained from the AFM images resulted in high film reflectivity, even for thicker films. TEM and AFM results indicate monomodal, randomly oriented grain growth without a distinct columnar or spherical morphology. Using TEM cross-sectional images and the dependence of the grain size on the film thickness, we propose a grain growth mechanism based on the diffusion mobility of aluminum atoms through grain boundaries. Full article

Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes for this specific patient population. The present study aimed to confirm whether sudden metabolic changes due to blood-brain barrier (BBB) disruption during LVO reflect differences in circulating metabolites and RNA between small and large core strokes. The second objective was to evaluate whether integrating molecular markers with existing neurological and imaging tools can enhance outcome predictions in LVO strokes. Methods: The infarction volume in patients was measured using magnetic resonance diffusion-weighted images, and the 90-day stroke outcome was defined by a modified Rankin Scale (mRS). Differential expression patterns of miRNAs were identified by RNA sequencing of serum-driven exosomes. Nuclear magnetic resonance (NMR) spectroscopy was used to identify metabolites associated with AIS with small and large infarctions. Results: We identified 41 miRNAs and 11 metabolites to be significantly associated with infarct volume in a multivariate regression analysis after adjusting for the confounders. Eight miRNAs and ketone bodies correlated significantly with infarct volume, NIHSS (severity), and mRS (outcome). Through integrative analysis of clinical, radiological, and omics data using machine learning, our study identified 11 top features for predicting stroke outcomes with an accuracy of 0.81 and AUC of 0.91. Conclusions: Our study provides a future framework for advancing stroke therapeutics by incorporating molecular markers into the existing neurological and imaging tools to improve predictive efficacy and enhance patient outcomes. Full article

Perovskites can absorb solar energy and are extensively used in various catalytic and photocatalytic reactions. However, noble metal particles may enhance the catalytic, photocatalytic, and antibacterial activities. This study demonstrates the cost-effective green synthesis of the photocatalyst perovskite LaMnO₃ and its modification with noble metal Ag nanoparticles. The green synthesis of nanocomposite was achieved through a hydrothermal method employing aqueous extract derived from Citrus limon (L.) Burm peels. The properties of fabricated perovskites LaMnO₃ and LaMnO₃-Ag nanocomposites were evaluated and characterized by Ultraviolet-Visible spectroscopy (UV-Vis), Diffuse Reflectance Spectroscopy (DRS), X-ray diffraction (XRD), Fourier-Transform Infrared Spectroscopy (FT-IR), High-Resolution Transmission Electron Microscopy (HRTEM), Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX) and Brunauer–Emmett–Teller (BET) surface area techniques. The particle size distribution % of LaMnO₃ and LaMnO₃-Ag was observed to be 20 to 60 nm after using TEM images. The maximum percentage size distribution was 37 nm for LaMnO₃ and 43 nm for LaMnO₃-Ag. In addition, LaMnO₃-Ag nanocomposite was utilized as a photocatalyst for the degradation of Rose Bengal (RB) dye and its antibacterial activities against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). The surface area and band gap for perovskite LaMnO₃ nanoparticles were calculated as 12.642 m²/g and 3.44 eV, respectively. The presence of noble metal and hydrothermal-bio reduction significantly impacted the crystallinity. The BET surface area was found to be 16.209 m²/g, and band gap energy was calculated at 2.94 eV. The LaMnO₃ nanocomposite with noble metal shows enhanced photocatalytic effectiveness against RB dye (20 PPM) degradation (92%, R² = 0.995) with pseudo-first-order chemical kinetics (rate constant, k = 0.05057 min⁻¹) within 50 min due to the ultimate combination of the hydrothermal and bio-reduction technique. The photocatalytic activity of the LaMnO₃-Ag nanocomposite was optimized at different reaction times, photocatalyst doses (0.2, 0.4, 0.6, and 0.8 g/L), and various RB dye concentrations (20, 30, 40, and 50 ppm). The antibacterial activities of green synthesized LaMnO₃ and LaMnO₃-Ag nanoparticles were explored based on colony-forming unit (cfu) reduction and TEM images of bacterial and nanoparticle interactions for S. aureus and E. coli. An amount of 50 µg/mL LaMnO₃-Ag nanocomposite was sufficient to work as the highest antibacterial activity for both bacteria. The perovskite LaMnO₃-Ag nanocomposite synthesis process is economically and environmentally friendly. Additionally, it has a wide range of effective and exclusive applications for remediating pollutants. Full article

The significance of soil in the agricultural industry is profound, with healthy soil representing an important role in ensuring food security. In addition, soil is the largest terrestrial carbon sink on earth. The soil carbon pool is composed of both inorganic and organic forms. The equilibrium of the soil carbon pool directly impacts the carbon cycle via all of the other processes on the planet. With the development of agricultural systems from traditional to conventional ones, and with the current era of precision agriculture, which involves making decisions based on information, the importance of understanding soil is becoming increasingly clear. The control of microenvironment conditions and soil fertility represents a key factor in achieving higher productivity in these systems. Furthermore, agriculture represents a significant contributor to carbon emissions, a topic that has become timely given the necessity for carbon neutrality. In addition to these concerns, updating soil-related data, including information on macro and micronutrient conditions, is important. Carbon represents one of the major nutrients for crops and plays a key role in the retention and release of other nutrients and the management of soil physical properties. Despite the importance of carbon, existing analytical methods are complex and expensive. This discourages frequent analyses, which results in a lack of soil carbon-related data for agricultural fields. From this perspective, in situ soil organic carbon (SOC) analysis can provide timely management information for calibrating fertilizer applications based on the soil–carbon relationship to increase soil productivity. In addition, the available data need frequent updates due to rapid changes in ecosystem services and the use of extensive fertilizers and pesticides. Despite the importance of this topic, few studies have investigated the potential of image analysis based on image processing and spectral data recording. The use of spectroscopy and visual color matching to develop SOC predictions has been considered, and the use of spectroscopic instruments has led to increased precision. Our extensive literature review shows that color models, especially Munsell color charts, are better for qualitative purposes and that Cartesian-type color models are appropriate for quantification. Even for the color model, spectroscopy data could be used, and these data have the potential to improve the precision of measurements. On the other hand, mid-infrared radiation (MIR) and near-infrared radiation (NIR) diffuse reflection has been reported to have a greater ability to predict SOC. Finally, this article reports the availability of inexpensive portable instruments that can enable the development of in situ SOC analysis from reflection and emission information with the integration of images and spectroscopy. This integration refers to machine learning algorithms with a reflection-oriented spectrophotometer and emission-based thermal images which have the potential to predict SOC without the need for expensive instruments and are easy to use in farm applications. Full article

Reflectance imaging spectroscopy (RIS) is invaluable in mapping and identifying artists’ materials in paintings. The analysis of the RIS image cube first involves classifying the cube into spatial regions, each having a unique reflectance spectrum (endmember). Second, endmember spectra are analyzed for spectral features useful to identify the pigments present to create labeled classes. The analysis process for paintings remains semi-automated because of the complex diffuse reflectance spectra due to the use of intimate pigment mixtures and optically thin paint layers by the artist. As a result, even when a group of related paintings are analyzed, each RIS cube is analyzed individually, which is time consuming. There is a need for new approaches to more efficiently analyze RIS cubes of related paintings to address the growing interest in the study of related paintings within a group of artists or artistic schools. This work builds upon prior investigations of 1-D spectral convolutional neural networks (CNNs) to address this need in two ways. First, an expanded training set was used—ten illuminated manuscripts created by artists stylistically grouped under the notname “Master of the Cypresses” (15th century Seville, Spain). Second, two 1-D CNN models were trained from the RIS cubes: reflectance and the first derivative. The results showed that the first derivative-trained CNN generally performed better than the reflectance-trained CNN in creating accurate labeled material maps for these illuminated manuscripts. Full article

Significance: Despite the widespread use of photodynamic therapy in clinical practice, there is a lack of personalized methods for assessing the sufficiency of photodynamic exposure on tumors, depending on tissue parameters that change during light irradiation. This can lead to different treatment results. Aim: The objective of this article was to conduct a comprehensive review of devices and methods employed for the implicit dosimetric monitoring of personalized photodynamic therapy for tumors. Methods: The review included 88 peer-reviewed research articles published between January 2010 and April 2024 that employed implicit monitoring methods, such as fluorescence imaging and diffuse reflectance spectroscopy. Additionally, it encompassed computer modeling methods that are most often and successfully used in preclinical and clinical practice to predict treatment outcomes. The Internet search engine Google Scholar and the Scopus database were used to search the literature for relevant articles. Results: The review analyzed and compared the results of 88 peer-reviewed research articles presenting various methods of implicit dosimetry during photodynamic therapy. The most prominent wavelengths for PDT are in the visible and near-infrared spectral range such as 405, 630, 660, and 690 nm. Conclusions: The problem of developing an accurate, reliable, and easily implemented dosimetry method for photodynamic therapy remains a current problem, since determining the effective light dose for a specific tumor is a decisive factor in achieving a positive treatment outcome. Full article

TiO₂ and TiO₂ films modified with Ag (Ag/TiO₂) were prepared via the DC magnetron sputtering method and the degree of modification was controlled via the sputtering power and time of Ag. The microstructures and properties of these films were characterized using field emission scanning electron microscopy, X-ray diffractometry, ultraviolet–visible diffuse reflectance spectrometry, atomic force microscopy, and X-ray photoelectron spectroscopy (XPS). The results show that the prepared films have an anatase structure. Compared with pure TiO₂, Ag deposition can improve the utilization of light. The three-dimensional images of Ag/TiO₂ clearly show that with the increase in Ag sputtering power and sputtering time, Ag particles on the surface of the film gradually increase, and the structure of the film is relatively dense. The photocatalytic effect of Ag/TiO₂ films is the best when the Ag sputtering power is 5 W and the sputtering time is 50 s. Under high-pressure mercury lamp irradiation, the photocatalytic degradation rate of methyl orange (MO) in pure MO solution with Ag/TiO₂-5 W-50 s can reach 100% within 55 min, whereas that in MO-Na₂SO₄ mixed solution can reach 99.55% within 65 min. The results suggest that the presence of Na₂SO₄ in MO solution can inhibit the degradation of MO using Ag/TiO₂, the result of XPS suggests that Na₂SO₄ accelerates the oxidation of Ag, which may lead to an increase in the recombination rate of photogenerated electron–hole pairs and a decrease in the degradation rate. During the process of recycling photocatalysts, the degradation rate of MO was apparently reduced. A possible reason is that the Ag particles have been oxidized and products of photocatalytic degradation are on the surface of the photocatalyst. The photocatalytic degradation mechanism was studied. Full article

Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue. Full article

Correct classification of skin lesions is a key step in skin cancer screening, which requires high accuracy and interpretability. This paper proposes a multimodal method for differentiating various clinical forms of basal cell carcinoma and benign neoplasms that includes machine learning. This study was conducted on 37 neoplasms, including benign neoplasms and five different clinical forms of basal cell carcinoma. The proposed multimodal screening method combines diffuse reflectance spectroscopy, optical coherence tomography and high-frequency ultrasound. Using diffuse reflectance spectroscopy, the coefficients of melanin pigmentation, erythema, hemoglobin content, and the slope coefficient of diffuse reflectance spectroscopy in the wavelength range 650–800 nm were determined. Statistical texture analysis of optical coherence tomography images was used to calculate first- and second-order statistical parameters. The analysis of ultrasound images assessed the shape of the tumor according to parameters such as area, perimeter, roundness and other characteristics. Based on the calculated parameters, a machine learning algorithm was developed to differentiate the various clinical forms of basal cell carcinoma. The proposed algorithm for classifying various forms of basal cell carcinoma and benign neoplasms provided a sensitivity of 70.6 ± 17.3%, specificity of 95.9 ± 2.5%, precision of 72.6 ± 14.2%, F₁ score of 71.5 ± 15.6% and mean intersection over union of 57.6 ± 20.1%. Moreover, for differentiating basal cell carcinoma and benign neoplasms without taking into account the clinical form, the method achieved a sensitivity of 89.1 ± 8.0%, specificity of 95.1 ± 0.7%, F₁ score of 89.3 ± 3.4% and mean intersection over union of 82.6 ± 10.8%. Full article