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We present a combined experimental and theoretical investigation of the radiationless decay spectrum of an O 1s double core hole in liquid water. Our experiments were carried out using liquid-jet electron spectroscopy from cylindrical microjets of normal and deuterated water. The signal of the double-core-hole spectral fingerprints (hypersatellites) of liquid water is clearly identified, with an intensity ratio to Auger decay of singly charged O 1s of 0.0014(5). We observe a significant isotope effect between liquid H2O and D2O. For theoretical modeling, the Auger electron spectrum of the central water molecule in a water pentamer was calculated using an electronic-structure toolkit combined with molecular-dynamics simulations to capture the influence of molecular rearrangement within the ultrashort lifetime of the double core hole. We obtained the static and dynamic Auger spectra for H2O, (H2O)5, D2O, and (D2O)5, instantaneous Auger spectra at selected times after core-level ionization, and the symmetrized oxygen-hydrogen distance as a function of time after double core ionization for all four prototypical systems. We consider this observation of liquid-water double core holes as a new tool to study ultrafast nuclear dynamics. [ABSTRACT FROM AUTHOR]

The singlet fission process involves the conversion of one singlet excited state into two triplet states, which has significant potential for enhancing the energy utilization efficiency of solar cells. Carotenoid, a typical π conjugated chromophore, exhibits specific aggregate morphologies known to display singlet fission behavior. In this study, we investigate the singlet fission process in lycopene H-aggregates using femtosecond stimulated Raman spectroscopy aided by quantum chemical calculation. The experimental results reveal two reaction pathways that effectively relax the S2 (11Bu+) state populations in lycopene H-aggregates: a monomer-like singlet excited state relaxation pathway through S2 (11Bu+) → 11Bu− → S1 (21Ag−) and a dominant sequential singlet fission reaction pathway involving the S2 (11Bu+) state, followed by S* state, a triplet pair state [1(TT)], eventually leading to a long lifetime triplet state T1. Importantly, the presence of both anionic and cationic fingerprint Raman peaks in the S* state is indicative of a substantial charge-transfer character. [ABSTRACT FROM AUTHOR]

Background: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF MS) is proposed for mosquito species identification. The absence of public repositories sharing mass spectra and open-source data analysis pipelines for fingerprint matching to mosquito species limits the widespread use of this technology. The objective of this study was to develop a free open-source data analysis pipeline for Anopheles species identification with MALDI–TOF MS. Methods: Anopheles mosquitoes were captured in 33 villages in Karen (Kayin) state in Myanmar. A subset of 403 specimens was selected for inclusion in either the reference or the test panel (270 and 133 specimens, respectively). Three hundred fifty-nine specimens could be identified with DNA barcodes and were assigned to 21 sensu stricto species and five sibling species pairs or complexes. A total of 3584 mass spectra of the head of these specimens identified with DNA barcoding were acquired and the similarity between mass spectra was quantified using a cross-correlation approach adapted from the published literature. A reference mass spectra database was created using all spectra of the PCR-identified specimens assigned to the reference panel. A simulation experiment was carried out by querying the reference database with the spectra of the test panel to evaluate the performance of species identification with MALDI–TOF MS at varying thresholds of the cross-correlation index for the algorithm to output an identification result and with varying numbers of technical replicates for the tested specimens, considering PCR identification results as the reference. Results: With one spot and a threshold value of −14 for the cross-correlation index on the log scale, the sensitivity was 0.99 [95% credible interval (CrI): 0.98–1.00], the predictive positive value was 0.99 (95% CrI: 0.98–0.99), and the accuracy was 0.98 (95% CrI: 0.97–0.99). It was not possible to directly estimate the sensitivity and negative predictive value because there was no true negative (i.e., queries of species not referenced in the database) in the assessment. Conclusions: The cross-correlation approach can be used to match mass spectral fingerprints to predefined taxa. MALDI–TOF MS is a valuable tool for rapid, accurate, and affordable identification of Anopheles species. [ABSTRACT FROM AUTHOR]

This study explores the application of chemotaxonomic methods to trace the geographic origin of wood used in paper production. Utilizing thermal desorption gas chromatography-mass spectrometry (TD-GC-MS) and principal component analysis (PCA), distinct chemical profiles were identified in both hydrophobic and hydrophilic extracts. Residual wood extractives, that are capable of origin differentiation, were found in acetone extracts of the CITES-protected genus ramin (Gonystylus Teijsm. et Binn. spp.), petroleum ether extracts of red meranti (Rubroshorea (Meijer) P.S.Ashton et J.Heck. spp.) and n-hexane extracts of teak (Tectona grandis L.f.) pulp. The PCA results demonstrate the potential of these chemical fingerprints to serve as reliable indicators of wood provenance, which could potentially be crucial for the practical execution of the European Deforestation Regulation (EUDR) and the US Lacey Act in the paper industry. Despite the promising findings for ramin and teak, the effectiveness of the method to distinguish the origin of red meranti pulps was limited in some cases. [ABSTRACT FROM AUTHOR]

Standardization is paramount for the production and quality control of medicinal plants and their derivatives. One method of achieving standardization is through chemical fingerprinting. Among various techniques, colorimetric sensor arrays are deemed a reliable method for the quality control and diagnosis of medicinal plants. Unlike conventional methods such as DNA barcoding and spectroscopy, chemical fingerprinting offers a different analysis of the plant's unique chemical patterns. Colorimetric sensor arrays were chosen for their cost-effectiveness, ease of use, and rapid results. The objective of this study was to devise an effective method for the classification of ten different commercial brands of thyme and to compare them with three reference original thymes using a colorimetric sensor array technique coupled with mathematical data analysis methods. The findings indicated that colorimetric sensor arrays could effectively represent noticeable color changes between different essential oil samples based on Color Difference Maps (CDMs). The subsequent application of multivariate pattern recognition methods improved the discrimination ability between the samples. Principal Component Analysis (PCA) revealed the discrimination of three classes of the samples with 93% of the total variance, highlighting the distinct separation between different thyme origins. Hierarchical Cluster Analysis (HCA) elucidated the similarity of the studied commercial samples to the reference samples of thyme essential oil, demonstrating clear grouping patterns. Furthermore, Partial Least Squares Discriminant Analysis (PLS-DA) indicated that the majority of commercial samples were as similar as the thymus vulgaris samples, with fitting and validation accuracy of 95% and 91%, respectively. This analysis effectively discriminated the commercial thyme essential oil samples, highlighting their notable similarities to specific reference samples and clarifying the alignment or deviation from different reference standards. Lastly, Linear Discriminant Analysis (LDA) confirmed previous results and differentiated the Thymus samples from zataria multiflora, with 100% fitting and 91% cross-validation accuracy. These methods together provided a comprehensive analysis, each contributing unique insights into the classification and potential standardization of thyme quality detection. Among the used sensing elements, 4 top sensor elements were identified using the factor loadings for the first three PCs of PCA analysis and discrimination ability. While this study primarily focuses on classification, it lays a strong foundation for future standardization efforts in quality control of thyme. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a complex disease of cartilage characterised by joint pain, functional limitation, and reduced quality of life with affected joint movement leading to pain and limited mobility. Current methods to diagnose OA are predominantly limited to X-ray, MRI and invasive joint fluid analysis, all of which lack chemical or molecular specificity and are limited to detection of the disease at later stages. A rapid minimally invasive and non-destructive approach to disease diagnosis is a critical unmet need. Label-free techniques such as Raman Spectroscopy (RS), Coherent anti-Stokes Raman scattering (CARS), Second Harmonic Generation (SHG) and Two Photon Fluorescence (TPF) are increasingly being used to characterise cartilage tissue. However, current studies are based on whole tissue analysis and do not consider the different and structurally distinct layers in cartilage. In this work, we use Raman spectroscopy to obtain signatures from the superficial (top) and deep (bottom) layer of healthy and osteoarthritic cartilage samples from 64 patients (19 control and 45 OA). Spectra were acquired both in the 'fingerprint' region from 700 to 1720 cm− 1 and high-frequency stretching region from 2500 to 3300 cm− 1. Principal component and linear discriminant analysis was used to identify the peaks that contributed significantly to classification accuracy of the different samples. The most pronounced differences were observed at the proline (855 cm− 1 and 921 cm− 1) and hydroxyproline (877 cm− 1 and 938 cm− 1), sulphated glycosaminoglycan (sGAG) (1064 cm− 1 and 1380 cm− 1) frequencies for both control and OA as well as the 1245 cm− 1 and 1272 cm− 1, 1320 cm− 1 and 1345 cm− 1, 1451 cm− 1 collagen modes were altered in OA samples, consistent with expected collagen structural changes. Classification accuracy based on Raman fingerprint spectral analysis of superficial and deep layer cartilage for controls was found to be 97% and 93% on using individual/all spectra and, 100% and 95% on using mean spectra per patient, respectively. OA diseased cartilage was classified with an accuracy of 88% and 84% for individual/all spectra, and 96% and 95% for mean spectra per patient based on analysis of the superficial and the deep layers, respectively. Raman spectra from the C-H stretching region (2500–3300 cm− 1) resulted in high classification accuracy for identification of different layers and OA diseased cartilage but low accuracy for controls. Differential changes in superficial and deep layer cartilage signatures were observed with age (under 60 and over 60 years), in contrast, less significant differences were observed with gender. Prominent chemical changes in the different layers of cartilage were preliminarily imaged using CARS, SHG and TPF. Cell clustering was observed in OA together with differences in pericellular matrix and collagen structure in the superficial and the deep layers correlating with the Raman spectral analysis. The current study demonstrates the potential of Raman Spectroscopy and multimodal imaging to interrogate cartilage tissue and provides insight into the chemical and structural composition of its different layers with significant implications for OA diagnosis for an increasing aging demographic. [ABSTRACT FROM AUTHOR]

Functional foods and their by-products contain a wide range of bioactive components with an array of health benefits and were proposed to improve public health, well-being, and others. To achieve a circular economy, the processing and extraction of flavonoids, phenolic compounds, and others from functional food and agri-food wastes will require the use of environmentally friendly, sustainable, and a low-cost solution. Extraction methods that can eliminate the use of organic solvents, suitable for use in the laboratory and production of extracts will be covered. This will include subcritical water extraction (SBE), pressurized hot water extraction (PHWE), supercritical fluid extraction (SFE), and others. Based on the selected analytical methods, the determination of the marker or bioactive compounds and chemical fingerprints will provide the control measures to identify the batch-to-batch variation of the composition of the functional food products obtained. The combination of chemical standardization with antioxidant assay, such as DPPH and ABTS+ will provide further information on the quality of the extracts. Lastly, to ascertain the biological and physiological relevance of the antioxidant properties of the target sample, treatment of the antioxidant compounds or extracts was carried out using cellular models, and validated using other experimental endpoints, such as metabolomics. [ABSTRACT FROM AUTHOR]

Episodic deposition of light absorbing impurities on glaciers reduces albedo and exacerbates snow melt. In 2019/2020 a devastating Australian bushfire and desert dust event combined with favorable meteorological conditions transported an unprecedented mass of impurities across the Tasman Sea turning the Southern Alps of Aotearoa New Zealand red. Here we use time lapse cameras, airmass back trajectories, snow impurity geochemistry, and remote sensing to quantify the timing, provenance, and mass deposition of the event. Deposited in late November 2019, the impurities were dominated by mineral dust with a distinct southeastern Australian geochemical fingerprint. The event deposited ∼4,500 ± 500 tons of red dust to Southern Alps permanent snow and ice with a mean dust mass concentration of 6.5 ± 0.7 g m−2. A southeast Australian desert dust storm generated by the same type of meteorological conditions as the 2020 New Year bushfires was the main driver of the glacier discoloration. Plain Language Summary: In 2019/2020, snow on the Southern Alps of Aotearoa New Zealand changed color from white to red overnight. While New Zealand air quality dropped following the severe 2020 New Year Australian bushfire event, the glacier discoloration was caused by a desert dust storm and weather conditions that transported an extraordinary amount of red dust from southeastern Australia across the Tasman Sea in late November 2019. Understanding the source and amount of dust deposited to the Southern Alps is required to assess the impact on glacial melt and snow biology. Key Points: Dust deposition to Southern Alps occurred in late November 2019 from Australian bushfire and desert dust storm eventDust geochemical fingerprint and airmass back trajectories pinpoint a southeastern Australian provenanceApproximately 4,500 tons of red dust are estimated to have been deposited to the Southern Alps [ABSTRACT FROM AUTHOR]

Erbium-trioxide (Er2O3) and magnesium oxide (MgO) nanocomposite were effectively synthesized in the form of crystalline powder using a microwave irradiation approach. Various techniques were employed for identifying crystalline structure, FTIR fingerprint region, fluorescence emission behaviors, and surface morphology, dielectric, and magnetic properties using PXRD, FTIR spectroscopy technique, fluorescence spectroscopic technique, SEM analysis, frequency vs. dielectric constant, and MH curve analysis, respectively. Confirmation of the metal-oxygen bond such as Er2O3, and MgO was established through the analysis of stretching frequencies in the FTIR spectrum. The PXRD results using Rietveld refinement confirmed the crystalline nature of the synthesized nanoparticles, consisting of Er2O3, and MgO with unit cell compositions ~ 94.12 and ~ 5.88%, respectively. SEM imaging provided insights into the morphology of the particles, revealing a spherical shape with noticeable agglomeration. The elemental compositions such as Erbium (Er) and Oxygen (O), were validated by the EDS spectrum, confirming the successful achievement of Er2O3, and MgO nanoparticle in the synthesized composite. In addition, the vibrating sample magnetometer (VSM) graph illustrated the paramagnetic behavior of the doped Er2O3/MgO composite at room temperature. The thorough examination of the synthesized Er-MgO NPs, covering structural, morphological, and magnetic characteristics, contributes to a comprehensive understanding of their properties. [ABSTRACT FROM AUTHOR]

Synthetic cannabinoids (SCs), often masqueraded in "e-cigarettes," are novel popular psychoactive substances with diverse structures and complex material compositions, making their detection more challenging for prompt intervention. Herein, a novel electrochemical sensing platform based on Zn/Bi bimetallic organic framework-derived ZnO-Bi2O3 heterojunctions was constructed for the detection of cyclohexanylphenol synthetic cannabinoids (CP-type SCs: CP47,497 and CP55,940). The sensing characteristics of ZnO-Bi2O3 were studied under various conditions, including solvent composition, molar ratio of metal, and calcination temperature. The optimized ZnO-Bi2O3 heterojunction exhibited a larger surface area, more active sites, and stronger stability, conducive to enhanced electrochemical catalytic performance. Under optimal conditions, a ZnO-Bi2O3 modified screen-printed electrode (ZnO-Bi2O3/SPE) showed good linear responses toward CP47,497 and CP55,940 within the concentration ranges 7 × 10−9 ~ 5 × 10−6 M and 1 × 10−9 ~ 5 × 10−6 M, with detection limits of 2.3 × 10−9 M and 3.3 × 10−10 M, respectively. The sensor also depicted excellent reliability and can be used for on-site electrochemical detection of target objects in e-cigarettes with high recovery. Finally, the electrochemical oxidation mechanisms of CP47,497 and CP55,940 were studied for the first time, and electrochemical fingerprints of CP-type SCs were speculated. [ABSTRACT FROM AUTHOR]

The document "Nano-Functional Materials for Sensor Applications" published in the journal Molecules highlights the advancements in sensor technology driven by nanotechnology and materials science. It emphasizes the importance of nano-functional materials in designing high-performance sensors for healthcare monitoring, environmental protection, and security applications. The document discusses the molecular-level interactions and chemical properties of these materials, showcasing their applications in various fields such as healthcare, environmental monitoring, and food safety. The research articles and reviews included in the Special Issue demonstrate innovative approaches to sensor development, combining advanced materials science with practical applications in real-world scenarios. [Extracted from the article]

Background: Pancreatic ductal adenocarcinoma (PDAC) continues to pose profound challenges within the field of oncology due to its notorious resistance to existing therapies and constant high mortality rates. The recent emergence of three‐dimensional patient‐derived organoid (PDO) models marks a significant advancement, opening new avenues for exploring cancer biology and assessing therapeutic approaches. Aims: The aim of this study focuses on the innovative use of Fourier‐transform infrared (FT‐IR) spectroscopy to analyze PDAC organoids, thus illuminating their biochemical intricacies. Materials and Methods: In this study, PDAC organoids, cultivated from specimens sourced from cancer patients, were subjected to FT‐IR spectroscopic imaging. By examining the spectral data within the critical fingerprint region (950–1800 cm−1), and employing principal component analysis (PCA), biochemical disparities were detected and analyzed. Results: The results revealed distinct spectral profiles corresponding to different sample preparation techniques, which in turn highlighted variations in protein content and structure. PCA revealed a high homogeneity within classes and minimal passage number influence on spectral profiles, with variations in lipid content and protein profiles. Significantly, the biochemical fingerprint of these PDOs closely mirrored that of the original human tissue samples. Conclusion: This investigation underscores the efficacy of molecular spectroscopy as a non‐invasive method for profound characterization of PDAC organoids, enhancing our comprehension of tumor biochemistry. The capacity for swift and precise biochemical profiling of PDOs via molecular spectroscopy heralds a promising future for this technique in the realms of cancer diagnostics and personalized medicine. [ABSTRACT FROM AUTHOR]

This work introduces a novel semi-automatic approach to identify elemental lines in spectra obtained via laser-induced breakdown spectroscopy (LIBS). The algorithm is based on unique spectral fingerprints of individual elements that are configured into comb-like filters. The element-specific filters are then correlated with measured spectra for semi-supervised qualitative analysis of samples. Spectral variations are accommodated by adjusting the micro-parameters of the comb filter. This step ensures accurate results despite minor deviations from the instrument's ideal calibration due to instrumental fluctuations, e.g., drift in spectral calibration or line broadening. Additionally, the algorithm can autonomously detect spectral interference regions, aiding the analyst in verifying spectral lines where such interference may occur. The paper presents a comprehensive overview of the algorithm and discusses the main concepts, parameters, optimization steps, and limitations using Echelle spectra of two standard reference materials with different complexity: borosilicate glass (NIST 1411) and low-alloyed steel (SUS1R). Furthermore, the transferability of the approach to different scenarios and real-life applications is demonstrated using a single-channel Czerny–Turner spectrum of an amalgam filling extracted from a hyperspectral image of a human tooth. A demo of the algorithm is publicly available for non-commercial purposes. [ABSTRACT FROM AUTHOR]

Aims: Pistacia atlantica Desf. (Anacardiaceae) is traditionally used in Mediterranean medicine, with previous studies showing antidiabetic potential in its fruits and leaves. This study evaluates the antidiabetic activity of P. atlantica galls (PAG) extracts using in vitro, chemometric, and in silico approaches. Method: The antidiabetic activity of the samples were studied by measuring their half-maximal inhibitory concentrations (IC50s) concentrations according to the in vitro enzyme inhibition assays and modelled as a function of the LC fingerprints using the partial least squares technique. Crystal structures of the human pancreatic α-amylase (HPA) and the α-glucosidase homologue isomaltase were obtained from the Protein Data Bank website (http://www.rcsb.org/pdb). Docking simulations and calculations were carried out using AutoDock Vina. Results: PAG extracts inhibited HPA (IC50s ranging from 1.85 to 2.92 mg/mL) and α-glucosidase (IC50s ranging from 34 to 49 µg/mL) activities, with galls collected from male plants showing higher activity than those from female plants. UPLC fingerprinting, linked to chemometric analysis using a partial least squares regression model, putatively identified five compounds (quinic acid, methyl gallate, digalloyl quinic acid, methyl digallate, and valoneic acid dilactone) responsible for this antidiabetic effect. Molecular docking using AutoDock Vina revealed that the identified compounds interacted with key amino acid residues of HPA and α-glucosidase. Conclusions: By employing UPLC fingerprinting combined with chemometric analysis and molecular docking simulations, quinic acid and digalloyl quinic acid were identified from P. atlantica gall extract as the most promising ligands for further investigation into their antidiabetic potential. [ABSTRACT FROM AUTHOR]

Micro- and nanoplastics (MNPs) pose a significant threat to marine and human life due to their immense toxicity. To protect these ecosystems, the development of reliable technologies for MNP detection, characterisation, and removal is vital. While FTIR and Raman spectroscopy are established methods for MNP analysis, fluorescence (FL) spectroscopy has recently emerged as a promising alternative. However, most prior research relies on FL emission probing with a single excitation wavelength for MNP detection. In this study, we introduce a two-dimensional (2D) fluorescence excitation–emission (FLE) mapping method for the detection of commonly found microplastics, namely polystyrene (PS), polyethylene terephthalate (PET), and polypropylene (PP). The FLE mapping technique enables the collective recording of emission spectra across a range of excitation wavelengths, revealing the dominant excitation–emission features of different microplastics. This research advances the field by offering a non-destructive and label-free identification of MNP contamination through the use of FL spectral fingerprints. [ABSTRACT FROM AUTHOR]

Freshwater salinisation is a growing problem worldwide, affecting surface and groundwater resources. Compared with other global environmental issues, freshwater salinisation has been studied extensively in North America, Australia, and Europe but less so in South America, Asia, and Africa. Both the natural and anthropogenic sources can contribute for freshwater salinisation, through the concentration of dissolved salts in water rising above its normal levels. This review provides a comprehensive assessment of the causes of freshwater salinisation, the impacts on freshwater communities and ecosystem functions, the adaptive mechanisms for survival in an increasingly saline environment, and the management strategies available to control freshwater salinisation. Many human activities contribute to freshwater salinisation, including road salt use, agricultural practices, resource extraction, reservoir construction, and climate change. Aquatic organisms have evolved mechanisms to survive in increasingly saline environments, but excessive salinity can lead to mortality and non-lethal effects. Such effects can have cascading impacts on the structure and function of aquatic communities and ecosystem services. Therefore, monitoring programmes and chemical fingerprinting are needed to identify highly salinised areas, determine how various human activities contribute to freshwater salinisation, and implement management strategies. Furthermore, current research on freshwater salinisation has been limited to a few regions of the world. It is essential to expand the research further into exploring the impacts of salinisation on freshwater resources in unexplored geographic areas of the world that are mainly impacted by climate change scenarios. [ABSTRACT FROM AUTHOR]

Based on chromatographic fingerprinting combined with quantitative analysis on characteristic chemical constituents as well as hierarchical cluster analysis, an easy and sensitive approach utilizing high performance liquid chromatography (HPLC) was developed for the identification and quality evaluation of Rutongshu oral liquid (ROL). What is more, nontargeted metabolomic analysis was conducted to gain a global view in terms of its chemical profile. In this study, 16 peaks from different batches (S1–S10) of ROL samples produced by Taihe Hospital of Chinese Medicine were selected as common peaks for the evaluation of their similarity whose values of all tested 10 batches exceeded 0.90 when compared with the control fingerprints. Meanwhile, simultaneous quantification of five markers in the oral solution, including albiflorin, paeoniflorin, chlorogenic acid, quercetin and ferulic acid was performed, and standard curves established for respective reference substances showed good regression in the linear range (r 2 > 0.999) with recoveries in the range of 98.96–102.35%. The ultra-high performance liquid chromatography (UHPLC) combined with Orbitrap Exploris 120 mass spectrometer resulted in 88 identified compounds. The results of validation showed that the established method was reproducible, precise and stable. This study offers an effective, dependable and useful approach for the systematic evaluation of the hospital formulation ROL. [ABSTRACT FROM AUTHOR]

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This study presents a comprehensive analysis of hydrocarbon pollution in Ghana's coastal sediments, with a focus on aliphatic hydrocarbons and polycyclic aromatic hydrocarbons. The primary objectives were to identify the sources of hydrocarbon pollution, assess its extent, and understand its implications for environmental management and policy. A total of 15 samples were collected from 5 sampling spots. Soxhlet extraction technique was applied. Analysis was conducted by gas chromatography/flame ionization detector for aliphatic hydrocarbons and gas chromatography/mass spectrometry for polycyclic aromatic hydrocarbons. Isomeric ratios, such as the carbon preference index, low molecular weight to high molecular weight n-alkanes, etc., were used to infer the sources of n-alkanes. Polycyclic aromatic hydrocarbons diagnostic ratios, including Benzo[b + k]fluoranthene/Benzo[a]pyrene, Phenanthrene/Anthracene, etc., were used to predict PAHs sources into petrogenic and pyrogenic sources. The study also utilized statistical tools like principal component analysis-absolute principal component scores-multiple linear regression for a detailed source appointment. The type of aliphatic hydrocarbon detected in samples ranged from C10H22 to C33H68. Concerning aliphatic hydrocarbon, C21H44 has the highest average presence at 5.224 μg/kg of dry mass in sediment samples whereas, C10H22 shows the lowest mean concentration of 1.953 µg/kg of dry mass. The mean concentrations of the polycyclic aromatic hydrocarbons detected in samples ranged from 0.544 µg/kg for Anthracene to 2.168 µg/kg for Acenaphthene. Primary findings revealed a mix of petrogenic and pyrogenic sources in the coastal sediments, evidenced by the varying aliphatic hydrocarbons and polycyclic aromatic hydrocarbons ratios. Notably, the presence of carcinogenic PAHs highlighted potential health risks. The APCS-MLR analysis identified specific sources influencing hydrocarbon pollution. These include crude oil, urban runoff, atmospheric deposition, etc. This research contributes to a better understanding of coastal sediment pollution, serving as a foundation for future environmental policies and sustainable coastal management strategies in Ghana. [ABSTRACT FROM AUTHOR]

The quality and flavor of tea leaves are significantly influenced by chemical composition, with the content of free amino acids serving as a key indicator for assessing the quality of Tencha. Accurately and quickly measuring free amino acids during tea processing is crucial for monitoring and optimizing production processes. However, traditional chemical analysis methods are often time-consuming and costly, limiting their application in real-time quality control. Hyperspectral imaging (HSI) has shown significant effectiveness as a component detection tool in various agricultural applications. This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. Four pretreating methods were employed to preprocess the spectra, and partial least squares regression (PLSR) and least squares support vector machine regression (LS–SVR) models were established from the perspectives of individual processes and the entire process. Combining competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA) methods for characteristic band selection, specific bands were chosen to predict the amino acid content. By comparing modeling evaluation indicators for each model, the optimal model was identified: the overall model CT+CARS+PLSR, with predictive indicators Rc2 = 0.9885, Rp2 = 0.9566, RMSEC = 0.0956, RMSEP = 0.1749, RPD = 4.8021, enabling the visualization of total free amino acid content in processed Tencha leaves. Here, we establish a benchmark for machine learning-based HSI, integrating this technology into the tea processing workflow to provide a real-time decision support tool for quality control, offering a novel method for the rapid and accurate prediction of free amino acids during tea processing. This achievement not only provides a scientific basis for the tea processing sector but also opens new avenues for the application of hyperspectral imaging technology in food science. [ABSTRACT FROM AUTHOR]

Polymeric micro and nanoneedles have an exceptionally flexible bio‐SERS structure. However, during detection, Raman signals from the polymer often overlap with those from the target molecules, which is unavoidable and severely compromises the accuracy of the results. To address this issue, this study designed and fabricated a silver (poly(vinyl pyrrolidone)‐coated, PVP)/poly(vinylidene fluoride) (PVDF)/gold nanoneedles structure (Ag/PVDF/Au NNs) and proposed a full‐spectrum intrinsic internal standard (FS‐IS) analytical method. In this method, the entire spectra of PVP and PVDF within the Raman fingerprint region are used as an internal standard, and machine learning algorithms are employed to quantitatively analyze the relevant SERS detection from a statistical perspective. Both the experimental and theoretical results demonstrate that this method is more accurate than traditional analysis methods based on embedded or intrinsic internal standards. Using the FS‐IS analysis method, the Ag/PVDF/Au NNs are successfully applied to various biosensing applications, including cancer cell imaging and in situ monitoring of antibiotics in the urine of surgical patients. This study effectively addresses the issue of low detection accuracy in flexible nanoneedle SERS structures and provides valuable insights for the development of other high‐precision detection technologies based on flexible substrates. [ABSTRACT FROM AUTHOR]

AbstractBicadinanes (C30H52) are a family of non-hopanoid, pentacyclic triterpane biomarkers whose utility in oil spill fingerprinting has, to our knowledge, not been previously explored. Owing to their derivation from Diptocarpaceae tree (dammar) resin, crude oils containing bicadinanes are geographically and temporally limited to Southeast Asian oils sourced from Oligocene or younger strata. Bicadinane-bearing oils produced from Southeast Asia, however, are shipped worldwide and thereby bicadinanes may be present in oils spilled both within and outside of Southeast Asia. Bicadinanes’ relative or absolute concentrations are readily measured using a standard oil spill identification analytical method (GC/MS-SIM) that routinely targets more conventional biomarkers (triterpanes, steranes, and aromatic steroids). In this study, the absolute concentrations of four bicadinane isomers (W, T, T1 and R) were measured in 42 tarballs collected from shorelines along the southern extent of the Straits of Singapore, an area with very high oil tanker and cargo vessel traffic. The tarballs’ specific origins are unknown but safely presumed to be derived from various unknown oil spills and/or oil seeps in the region. GC/FID was used to classify the tarball oils’ level of biodegradation (Levels 1, 4, and 6). The absolute concentration of total bicadinanes ranged from 7.5 to 208 µg/g and varied widely and independent of the level of biodegradation. The relative concentrations among the four bicadinane isomers (T > W>T1∼R; on average, 56% > 20% > 12% each) also showed no obvious or statistically significant trend with biodegradation for the samples studied. Thus, variations in both total and individual isomer concentrations are attributable to the tarballs’ multiple crude oil sources spilled or seeped in the study area. Diagnostic ratios (DRs) are critical components in oil spill identification protocols. A newly proposed DR of (W + T)/hopane varied widely (0.01 to 0.54) and also independently from the more conventionally used DR of 18α(H)-oleanane/hopane (oleanane/hopane; 0.04 to 0.42). Two newly proposed DRs among the four bicadinane isomers, W/T and T/(T1 + R), also varied among the 42 tarball oils studied (0.27 to 0.45 and 1.4 to 3.2, respectively). The variation in the (W + T)/hopane, oleanane/hopane, W/T and T/(T1 + R) ratios was unrelated to the level of biodegradation for the samples studied and exceeded the statistical criteria used in the CEN (2012) oil spill identification protocol (i.e., 95% repeatability limit) and thereby demonstrates these DRs’ utility in comparing and distinguishing among different crude oils containing bicadinanes – and, of course, between crude oils that do and do not contain bicadinanes. The additional specificity offered by bicadinanes argues they should be included as target analytes in oil spill identification studies throughout Southeast Asia or in other areas where Southeast Asian oils are believed to be present (shipped, handled, or used). [ABSTRACT FROM AUTHOR]

Intercalating two-dimensional quantum materials beneath a sheet of graphene provides effective environmental protection and facilitates ex situ device fabrication. However, developing a functional device requires rapid, large-scale screening methods to evaluate the quality of the intercalant, which to date can be monitored only by slow, ultra-high vacuum-based surface science techniques. In this study, we utilize ex situ Raman micro-spectroscopy to optically and nondestructively identify the quantum spin Hall insulator indenene, a monolayer of indium sandwiched between a SiC(0001) substrate and a single sheet of graphene. Color modulation combined with indenene's distinctive low-frequency Raman fingerprint enables rapid assessment of its homogeneity and crystalline quality. Density functional perturbation theory indicates that this Raman signature originates mainly from indenene's shear and breathing modes, while additional higher order modes are tentatively attributed to defect-assisted and two-phonon Raman processes. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a comprehensive quality assessment method for Acanthopanax senticosus using near-infrared (NIR) spectroscopy combined with machine learning algorithms. A qualitative discriminant analysis model was established to distinguish the samples of Acanthopanax from different habitats, and overall prediction accuracy was more than 85%. Moreover, a partial least squares regression (PLSR) model was built to predict the response values of Acanthopanax senticosus at each retention time, with the Acanthopanax senticosus dataset serving as the Y matrix and the NIR spectral dataset serving as the X matrix. The transformed Acanthopanax senticosus fingerprint closely matched the actual fingerprint, exhibiting a high average similarity between the predicted and measured fingerprints for test set samples (Pearson correlation coefficient = 0.8553, cosine similarity = 0.8627). The results of the cluster analysis demonstrated a high level of consistency. Converting NIR spectral fingerprints into HPLC fingerprints enhanced interpretability and more clearly displayed the components responsible for the diversity among samples from different origins. Meanwhile, the partial least squares regression (PLSR) model and support vector machine regression (SVR) model for the prediction of the contents of moisture, total solids, protocatechuic acid, syringin and chlorogenic acid contents were successfully established. The NIR spectra of Acanthopanax senticosus can accurately predict the values of moisture, total solids, protocatechuic acid, syringin and chlorogenic acid content, in which the RMSEP values for PLSR and SVR are 0.176, 0.812, 0.003, 0.026, and 0.007 and 0.182, 0.797, 0.003, 0.025, and 0.006, respectively. The predicted values of the five chemical compounds were 6.09–7.81, 4.8–9.5, 0.003–0.036, 0.066–0.233 and 0.015–0.055. Overall, the results indicate that this rapid quality evaluation system can be utilized for the quality control of Acanthopanax senticosus. The approaches used in this work can be used to identify the compound contents and provenance of herbal medicines and food for quality control measurements, potentially for online and field analyses. [ABSTRACT FROM AUTHOR]

Betalains, which contain nitrogen and are water soluble, are the pigments responsible for many traits of plants and biological activities in different organisms that do not produce them. To better annotate and identify betalains using a spectral library and fingerprint, a database catalog of 140 known betalains (112 betacyanins and 28 betaxanthins) was made in this work to simplify betalain identification in mass spectrometry analysis. Fragmented peaks obtained using MassFrontier, along with chemical structures and protonated precursor ions for each betalain, were added to the database. Product ions made in MS/MS and multistage MS analyses of betanin, beetroot extract, and red pitaya extract revealed the fingerprint of betalains, distinctive ions of betacyanin, betacyanin derivatives such as decarboxylated and dehydrogenated betacyanins, and betaxanthins. A distinctive ion with m/z 211.07 was found in betaxanthins. By using the fingerprint of betalains in the analysis of red pitaya extracts, the catalog of betalains in red pitaya was expanded to 86 (31 betacyanins, 36 betacyanin derivatives, and 19 betaxanthins). Four unknown betalains were detected to have the fingerprint of betalains, but further research will aid in revealing the complete structure. Taken together, we envisage that the further use of the fingerprint of betalains will increase the annotation coverage of identified molecules in studies related to revealing the biological function of betalains or making technologies based on these natural colorants. [ABSTRACT FROM AUTHOR]

Yan Huanglian is the whole grass and rhizome of Corydalis saxicola Bunting (CSB) of the Papaveraceae family. CSB has various pharmacological effects and is a promising ethnopharmaceutical. The chromatographic fingerprint of 16 batches of CSB samples was established in this study. Quantitative analysis of multi‐components by single marker and the external standard method (ESM) was used to quantitatively assess seven constituents of CSB. Because of its high stability and availability, dehydrocavidine was used as a reference to determine the relative correction factors (RCFs) of columbamine, epiberberine, coptisine hydrochloride, palmatine chloride, berberine hydrochloride, and chelerythrine based on high‐performance liquid chromatography. The calculated quantitative analysis of multi‐components by single marker values is consistent with those obtained from the ESM, and the repeatability of the RCFs was demonstrated. For the first time, the quantitative analysis of multi‐components by the single marker method has been established for the simultaneous determination of seven components, and it is demonstrated to be feasible and accurate for the quality evaluation of CSB combined with chromatographic fingerprint analysis. The combination of these methods could be used as a reference for quality control in Chinese medicine. [ABSTRACT FROM AUTHOR]

ABSTRACT Batocera horsfieldi is a major wood‐boring pest in China's timber and economic forests, belonging to the order Coleoptera and the family Cerambycidae. This study headspace solid‐phase microextraction and gas chromatography–mass spectrometry (GC–MS) were used to analyse the volatile components of B. horsfieldi supplementary feeding host plants, and fingerprint profiles were established. The results revealed that the five plants contained 21, 18, 22, 16 and 17 volatile compounds, mostly terpenes, aldehydes and alcohols, along with trace amounts of furans, ketones and ethers. The terpene compounds were mainly monoterpenes and sesquiterpenes. The results of hierarchical cluster analysis (HCA) and principal component analysis (PCA) were consistent. Five common peaks were identified in these host plants, with (E)‐2‐hexenal, caryophyllene, (E)‐cinnamaldehyde, nonanal, and 3,7‐dimethyl‐1,6‐octadien‐3‐ol being the shared volatile components among the five plants. The mantel test, correlation analysis and grey relational analysis indicated that temperature and precipitation are important factors affecting the content of volatile components. These analytical results provide a basis for using plant volatiles as attractants to control B. horsfieldi. [ABSTRACT FROM AUTHOR]

The impact of climate change on medicinal plants has significantly altered their suitable environments, thereby affecting the quality of herbal medicines. Notopterygium franchetii (N. franchetii), a medicinal plant intricately linked to its natural habitat, exhibits substantial quality variations influenced by the ecological conditions of its native region. In this study, comprehensive field surveys were conducted to gather occurrence records and samples of N. franchetii. The Maxent model and ArcGIS software were employed to predict the suitable habitats of N. franchetii during different time periods. A high-performance liquid chromatography (HPLC) method was developed to establish the chemical fingerprints of 21 sample batches. Fourteen common peaks were subjected to cluster analysis, principal component analysis, and orthogonal partial least squares-discriminant analysis. The findings revealed quality variations correlated with their geographic origins, identifying peaks 10, 1, 14, 3, and 4 as crucial for quality differentiation. The study indicates that precipitation, temperature, and altitude significantly influence the distribution of N. franchetii. Under current climate conditions, the suitable habitat area for N. franchetii is estimated to be approximately 94,637.33 km2. However, projections under three future climate scenarios suggest a declining trend in suitable habitat areas. A quality zoning map of N. franchetii was developed, integrating a correlation model between chemical composition and environmental variables with the spatial analysis and visualization capabilities of ArcGIS. The high-quality regions for N. franchetii are predominantly located in the Gannan, Linxia, Dingxi, Longnan, and Wuwei districts. These research outcomes offer a valuable reference for identifying suitable cultivation areas and assessing the quality of N. franchetii in Gansu Province. [ABSTRACT FROM AUTHOR]

The utilization of nutraceuticals consisting of (trans)-resveratrol or quercetin or combination of these two compounds or food supplements contain extracts rich with (trans)-resveratrol or quercetin have become very popular due to their protective and health-beneficial effects especially after the Coronavirus 2019 (COVID-19) pandemic. As the safety and quality control of the food supplements is very important in terms of well-being of consumers, the objective of this study was to qualitatively analyze commercially available (trans)-resveratrol and/or quercetin supplements sold in the Turkish market by using High-Performance Thin-Layer Chromatography (HPTLC) and to measure trans-resveratrol and quercetin quantitatively by using a newly developed and validated High-Performance Liquid Chromatography (HPLC) method. The HPTLC analysis was performed on silica gel 60 F254 glass HPTLC plates using toluene-chloroform-ethyl acetate-formic acid (3:2:10:0.1, v/v/v/v) as a developing solvent system. Standard compounds trans-resveratrol and quercetin were parallelly analyzed with sample test solutions belong to different brands. The chemical fingerprintings of analyzed samples showed that 8 of the 33 of them did not exhibit the expected zones corresponding to reference compounds. According to quantitative analysis via HPLC, the determined contents of trans-resveratrol and quercetin were found to be different from the declared amounts in all of the investigated products. The deviation of determined trans-resveratrol content from the declared content was calculated as ± 10.0% in only four food supplements (R4, R5, R8, QR4; percent of declared trans-resveratrol contents ranged between 90.3% and 101.2 %), whereas deviation of quercetin levels was found higher in all quercetin containing products. To conclude, the majority of the marketed products containing (trans)-resveratrol and/or quercetin in Türkiye had lower levels of the active compounds indicating the inconsistency between the declared and the actual content of these substances. It should be highlighted that quality control of food supplements must be done by authorities before marketing. [ABSTRACT FROM AUTHOR]

Donepezil hydrochloride is a widely used medication for treating Alzheimer's disease. This study utilized terahertz time-domain spectroscopy to analyze the fingerprint spectra of donepezil hydrochloride, identifying five characteristic absorption peaks at 1.65, 2.44, 2.56, 3.31, and 3.75 THz. The vibrational spectrum of the donepezil hydrochloride crystal was further examined using solid-state density functional theory. Based on simulation calculations, the characteristic peaks were identified and analyzed in detail, focusing on long-range ordering and weak interaction networks. The results demonstrate that terahertz spectroscopy is an effective tool for studying intermolecular interactions in drug crystals. [ABSTRACT FROM AUTHOR]

The presence of gray mold can seriously affect the yield and quality of strawberries. Due to their susceptibility and the rapid spread of this disease, it is important to develop early, accurate, rapid, and non-destructive disease identification strategies. In this study, the early detection of strawberry leaf diseases was performed using hyperspectral imaging combining multi-dimensional features like spectral fingerprints and vegetation indices. Firstly, hyperspectral images of healthy and early affected leaves (24 h) were acquired using a hyperspectral imaging system. Then, spectral reflectance (616) and vegetation index (40) were extracted. Next, the CARS algorithm was used to extract spectral fingerprint features (17). Pearson correlation analysis combined with the SPA method was used to select five significant vegetation indices. Finally, we used five deep learning methods (LSTMs, CNNs, BPFs, and KNNs) to build disease detection models for strawberries based on individual and fusion characteristics. The results showed that the accuracy of the recognition model based on fused features ranged from 88.9% to 96.6%. The CNN recognition model based on fused features performed best, with a recognition accuracy of 96.6%. Overall, the fused feature-based model can reduce the dimensionality of the classification data and effectively improve the predicting accuracy and precision of the classification algorithm. [ABSTRACT FROM AUTHOR]

Peanut seeds are harvested at different development stages (early and late) due to their uneven maturation. At the time of harvest, approximately 30% of the seeds are still immature, meaning they are not completely filled with compounds (e.g., oil and minerals) and exhibit reduced vigor. Hypothetically, these compounds can be detected as a "chemical fingerprinting" to classify seed maturation stages. Here, we investigated whether non-destructive techniques such as benchtop nuclear magnetic resonance (NMR), laser-induced breakdown spectroscopy (LIBS), and energy-dispersive X-ray fluorescence (ED-XRF) can identify chemical patterns unique to mature seeds with superior vigor. Field-grown seeds were classified into early (R5 and R6) and late (R7, R8, and R9) stages. Seed weight, germination, vigor, H2O2, and MDA (oxidative stress) were analyzed. Oil, potassium (K), and calcium (Ca) were measured digitally using spectroscopy techniques. We found that: (i) oxidative stress and K levels were higher in seeds from the early stages; (ii) seed oil and Ca were proportional to high-vigor seedlings and successful plant establishment in the field; and (iii) the seed chemical composition could be identified autonomously with 87% to 100% accuracy. In conclusion, LIBS, ED-XRF, and NMR technologies can effectively screen peanut seeds with superior vigor through "chemical fingerprinting". [ABSTRACT FROM AUTHOR]

Humans have used medicinal plants to treat various diseases for thousands of years. Cistus species are also widely used in traditional medicine and have various medicinal applications; therefore, they deserve more in-depth research. The present study evaluated the chemical profile, antioxidant, enzyme inhibition, and cytotoxic properties of the twigs and leaves of C. creticus L., C. laurifolius L., and C. salviifolius L. grown in Türkiye. The methanolic extracts of the three species were rich in phenolics, mainly flavonoids. Exerted potent antioxidant activity with a methanolic extract from the leaves of C. salviifolius displayed the highest total phenolic (97.08-mg gallic acid equivalent/g) and flavonoid (49.60-mg rutin equivalent/g) contents, as well as antiradical (2,2-diphenyl-1-picrylhydrazyl) assay = 612.11 mg TE (trolox equivalent)/g; (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) = 804.66 mg TE/g), reducing ions (cupric reducing antioxidant capacity) = 690.54 mg TE/g; ferric reducing antioxidant power = 459.34 mg TE/g), and chelating (15.58 mg EDTAE/g) properties. It also revealed the best amylase and glucosidase inhibitory activity. Extracts from the twigs of the three Cistus species, except the leaves of C. salviifolius and C. laurifolius, displayed comparable acetylcholinesterase inhibitory activity (2.48-2.57 mg galanthamine equivalent (GALAE)/g). The twig of C. laurifolius also exerted the best antibutyrylcholinesterase (10.50 mg GALAE/g) and antityrosinase (73.15 mg kojic acid equivalent/g) activities. C. creticus leaves revealed toxicity toward the RAW cell line (cell viability reduced to 68.8%) and were not toxic to normal cells (S17). In conclusion, these three Cistus species were shown to be a rich source of bioactive compounds with the potential for future applications in the food, pharmaceutical, and cosmetic industries. [ABSTRACT FROM AUTHOR]

We have demonstrated high-speed, super-resolution infrared (IR) spectroscopy and chemical imaging of autofluorescent biomaterials and organisms using camera-based widefield photothermal detection that takes advantage of temperature-dependent modulations of autofluorescent emission. A variety of biological materials and photosynthetic organisms exhibit strong autofluorescence emission under ultraviolet excitation and the autofluorescent emission has a very strong temperature dependence, of order 1%/K. Illuminating a sample with pulses of IR light from a wavelength-tunable laser source causes periodic localized sample temperature increases that result in a corresponding transient decrease in autofluorescent emission. A low-cost light-emitting diode-based fluorescence excitation source was used in combination with a conventional fluorescence microscopy camera to detect localized variations in autofluorescent emission over a wide area as an indicator of localized IR absorption. IR absorption image stacks were acquired over a range of IR wavelengths, including the fingerprint spectral range, enabling extraction of localized IR absorption spectra. We have applied widefield fluorescence detected photothermal IR (FL-PTIR) to an analysis of autofluorescent biological materials including collagen, leaf tissue, and photosynthetic organisms including diatoms and green microalgae cells. We have also demonstrated the FL-PTIR on live microalgae in water, demonstrating the potential for label-free dynamic chemical imaging of autofluorescent cells. [ABSTRACT FROM AUTHOR]

The mid‐infrared with a characteristic wavelength of 3–20 μm is important for a wealth of technologies. In particular, mid‐infrared spectroscopy can reveal material composition and structure information by fingerprinting chemical bonds' infrared resonances. Despite these merits, state‐of‐the‐art mid‐infrared techniques are spatially limited above tens of micrometers due to the fundamental diffraction law. Herein, recent progress in the scanning probe nanoscale infrared characterization of biochemical materials and natural specimens beyond this spatial limitation is reviewed. By leveraging the strong tip–sample local interactions, scanning probe nano‐infrared methods probe nanoscale optical and mechanical responses to disclose material composition, heterogeneity, orientation, fine structure, and phase transitions at unprecedented length scales. These advances, therefore, revolutionize the understanding of a broad range of biochemical and natural materials and offer new material manipulation and engineering opportunities close to the ultimate length scales of fundamental physical, chemical, and biological processes. [ABSTRACT FROM AUTHOR]

This is the first study to report on high performance thin layer chromatography (HPTLC) generated spectrophotometric data to systematically capture flavonoid compounds using optimized derivatization with either AlCl3 or NaNO2-AlCl3-NaOH as visualisation reagents. While the traditional AlCl3 colorimetric method using UV–Vis analysis provides valuable insights into the presence of flavonoids and allows derivation of the total flavonoid content (TFC) of a sample, HPTLC fingerprints obtained after spraying with AlCl3 or NaNO2-AlCl3-NaOH enable the visualization of the various flavonoids present in a sample based on their respective absorption shifts, thus complementing the traditional TFC assay. In this study, 40 different flavonoids representing different classes (flavonols, flavanolols, flavan-3-ol, flavones, flavanones, and isoflavonoids) were analysed. Upon derivatization with AlCl3 most of the investigated flavonoids recorded bathochromic shifts, yielding characteristic λmax values between 370 and 420 nm, while spraying with NaNO2-AlCl3-NaOH triggered hyperchromic shifts, and thus an increase in absorbance intensity in flavonoids with particular substitution patterns. A few non-flavonoid components with structural similarities to flavonoids (e.g., rosmarinic acid, gallic acid, aspirin, salicylic acid) served as the negative control in this study to determine whether the derivatization reagents allowed exclusive detection of flavonoids. The method was then applied to the analysis of flavonoid containing supplements as well as red clover honey to demonstrate the method's application in the analysis of natural products. [ABSTRACT FROM AUTHOR]

To overcome chondrosarcoma's (CHS) high chemo- and radioresistance, we used polyethylene glycol-encapsulated iron oxide nanoparticles (IONPs) for the controlled delivery of the chemotherapeutic doxorubicin (IONPDOX) to amplify the cytotoxicity of proton radiation therapy. Human 2D CHS SW1353 cells were treated with protons (linear energy transfer (LET): 1.6 and 12.6 keV/µm) with and without IONPDOX. Cell survival was assayed using a clonogenic test, and genotoxicity was tested through the formation of micronuclei (MN) and γH2AX foci, respectively. Morphology together with spectral fingerprints of nuclei were measured using enhanced dark-field microscopy (EDFM) assembled with a hyperspectral imaging (HI) module and an axial scanning fluorescence module, as well as scanning electron microscopy (SEM) coupled with energy-dispersive X-Ray spectroscopy (EDX). Cell survival was also determined in 3D SW3153 spheroids following treatment with low-LET protons with/without the IONPDOX compound. IONPDOX increased radiosensitivity following proton irradiation at both LETs in correlation with DNA damage expressed as MN or γH2AX. The IONPDOX–low-LET proton combination caused a more lethal effect compared to IONPDOX–high-LET protons. CHS cell biological alterations were reflected by the modifications in the hyperspectral images and spectral profiles, emphasizing new possible spectroscopic markers of cancer therapy effects. Our findings show that the proposed treatment combination has the potential to improve the management of CHS. [ABSTRACT FROM AUTHOR]

Phleum pratense is an allergenic grass that pollinates in spring in Pakistan. Databases Allergenonline.org and Allergen.org record ten P. pratense allergens and their isoforms. Phl P 1, Phlp 5, and Phl p 11 are major P. pratense-pollen allergens with demonstrated basophil activity and skin test reactivity. Little is known about P. pratense pollen adaptive variations in different climatic regions and pollen-associated microbial diversity. In this study, we collected P. pratense-pollen and soils in the spring season 2022. Samples were collected from three climatic regions in Pakistan (R1, R2 and R3) with differences in mean monthly air temperature, mean monthly precipitation and elevation. The morphology of pollen was observed by light microscopy, scanning electron microscopy (SEM), biochemical fingerprint analysis, and composition of pollen were investigated by fourier-transform infrared spectroscopy (FTIR). The pollen-associated bacterial populations were identified through a Biolog GEN III microplate system. The pollen water-soluble proteins were isolated and stabilized in phosphate buffer saline (PBS) and tested for allergenicity responses through dot blots and western blots analysis. The morphology study found difference in pollen biochemical composition. Biolog identified Brevibacterium epidermidis and Pantoea agglomerans from P. pratense pollen. Protein extract quantification and sodium dodecyl sulfate-poly acrylamide gel electrophoresis (SDS-PAGE) gel found decreased protein expression in R1 region pollen compared to R2 and R3 region pollen. Allergenicity studies found differential expression of beta-expansin and profilin allergens in pollen obtained from the three regions. Beta-expansin and profilin were suppressed in R1 pollen and expressed in compared to R2 and R3 pollen. This is the first study to identify B. epidermidis and P. agglomerans growth on P. pratense pollen. Variable allergen expression in P. pratense pollen has also been observed in different regions. Soil pH, an increase in mean monthly temperature and a decrease in mean monthly precipitation correlated with pollen biochemical composition, and reduced beta-expansin and profilin expression involved in pollen growth and development. The findings of this research are unique, which enhances basic knowledge and understanding of P. pratense-pollen associated microbiota and climate change impacts on the pollen allergen expression. [ABSTRACT FROM AUTHOR]

Plants have long been recognized as natural sources of therapeutic compounds, with their various parts, including flowers, being used in traditional treatments for centuries. Flowers, in particular, have captivated humans with their beauty. However, the classification and identification of specific flowers for therapeutic use can be challenging using conventional methods. Researchers have turned to modern tools like cameras and computers to aid in this process. Despite their limitations, the need for more efficient and accurate methods has led to the exploration of artificial intelligence (AI). This study seeks to evaluate various AI-based methods utilized by researchers in the field of flower analysis, highlighting their strengths and weaknesses to inform future research. The advanced analytical tools available today are instrumental in creating a chemical fingerprint of flowers. Chromatographic and spectroscopic techniques, used to determine precise chemical composition, offer valuable scientific insights into traditional medicine. Plant part identification often commences with feature extraction. Any plant part is digitally captured multiple times and subjected to different feature extraction methods. Common basic features include color, texture, and shape, while deep learning features like CNNs are also employed. We analyze and review diverse approaches reported in recent literature, examining their advantages and potential applications. [ABSTRACT FROM AUTHOR]

In 2019, Candida auris became the first fungal pathogen included in the list of the urgent antimicrobial threats by the Centers for Disease Control (CDC). Short tandem repeat (STR) analysis and whole-genome sequencing (WGS) are considered the gold standard, and can be complemented by other molecular methods, for the genomic surveillance and clade classification of this multidrug-resistant yeast. However, these methods can be expensive and require time and expertise that are not always available. The long turnaround time is especially not compatible with the speed needed to manage clonal transmission in healthcare settings. Fourier transform infrared (FTIR) spectroscopy, a biochemical fingerprint approach, has been applied in this study to a set of 74 C. auris isolates belonging to the five clades of C. auris (I-V) in combination with an artificial neural network (ANN) algorithm to create and validate "CLADE-FINDER", a tool for C. auris clade determination. The CLADE-FINDER classifier allowed us to discriminate the four primary C. auris clades (I-IV) with a correct classification for 96% of the samples in the validation set. This newly developed genotyping scheme can be reasonably applied for the effective epidemiological monitoring and management of C. auris cases in real time. [ABSTRACT FROM AUTHOR]

As a highly sensitive vibrational technique, Raman spectroscopy (RS) can provide valuable chemical and molecular data useful to characterise animal cell types, tissues and organs. As a label-free, rapid detection method, RS has been considered a valuable asset in forensics, biology and medicine. The technique has been applied to zebrafish for various purposes, including physiological, biochemical or bioaccumulation analyses. The available data point out its potential for the early diagnosis of detrimental effects elicited by toxicant exposure. Nevertheless, no baseline spectra are available for zebrafish embryos and larvae that could allow for suitable planning of toxicological assessments, comparison with toxicant-elicited spectra or mechanistic understanding of biochemical and physiological responses to the exposures. With this in mind, this work carried out a baseline characterisation of Raman spectra of zebrafish embryos and larvae throughout early development. Raman spectra were recorded from the iris, forebrain, melanocytes, heart, muscle and swim bladder between 24 and 168 h post-fertilisation. A chemometrics approach, based on partial least-squares discriminant analysis (PLS-DA), was used to obtain a Raman characterisation of each tissue or organ. In total, 117 Raman bands were identified, of which 24 were well represented and, thus, retained in the data analysed. Only three bands were found to be common to all organs and tissues. The PLS-DA provided a tentative Raman spectral fingerprint typical of each tissue or organ, reflecting the ongoing developmental dynamics. The bands showed frequencies previously assigned to collagen, cholesterol, various essential amino acids, carbohydrates and nucleic acids. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine whether hyper spectral images can be used to identify plant diseases and crop pressure from aerial photographs. With extensive research on the prevalent methods used closer to the problem, this study offers a potent strategy for identifying crop distress and illnesses using this efficient imaging technique. To identify the spectral fingerprints of common indications and symptoms of plant diseases and crop strains, this study evaluates the available hyper spectral photo datasets. After that, the data are examined using two learning algorithms--the highly randomized trees and the Random Woodland set of rules--to create predictions that are entirely dependent on the results that are discovered. In the end, a benchmarked set of test statistics is used to assess the prediction accuracy. The results of this study show that hyper spectral photo evaluation has a strong and promising utility for crop stress and disease identification. Hyper spectral light evaluation is a method for identifying plant diseases caused by strain on crops. By gathering and evaluating high-dimensional spectral reflection data from satellite or aircraft structures, details regarding the physiological homes of flowers can be identified. The health of plants, illnesses brought on by stress, and agricultural productivity predictions can all be made using these facts. Hyper spectral recordings can also be used to create actions that reduce agricultural losses and enhance the health of plants that are prone to disease. [ABSTRACT FROM AUTHOR]

Foodborne illnesses pose a serious threat to public health, with increasing global incidence rates driven by factors such as rising meat consumption. Rapid detection of foodborne pathogens in meat is critical for preventing outbreaks. This study investigates the potential of gas chromatography-mass spectrometry (GC-MS) and Fourier-transform infrared spectroscopy (FTIR) for identifying biomarkers and spectral fingerprints indicative of foodborne pathogens in raw chicken meat. Raw broiler chicken meat samples were surface-sterilized and inoculated with foodborne pathogens. The samples were challenge inoculated with the specific pathogen and the physical quality parameters like pH, color, texture, drip loss, and water activity were assessed. GC-MS analysis identified 113 metabolites, including potential biomarkers like ureidopropionic acid, 5-sulfosalicylic acid, 11,14-eicosadienoic acid, methyl ester for E. coli O157:H7; 11-bromoundecanoic acid, neocurdione, glafenin, eicosanoic acid for Salmonella; azepan-1-yl-acetic acid, methyl ester, tramadol, cytarabine, dipipanone for Staphylococcus and cyclopentaneundecanoic acid, phosphonofluoridic acid, î-n-formyl-l-lysine for Pseudomonas. Pathway analysis revealed the involvement of fatty acid metabolism and amino acid degradation pathways. FTIR spectral data showed significant variances between control and spiked samples, particularly in the fatty acid spectral region. The identified metabolites and spectral patterns could serve as biomarkers for developing rapid pathogen detection methods, contributing to enhanced food safety protocols. [ABSTRACT FROM AUTHOR]

Soil moisture plays a significant role in the global hydrological cycle in the field of agriculture, environment science, urban planning and construction, forestry and many more. Soil moisture can be determined using various approaches, however some of the limitations in these approaches are longer time duration, labor intensive, require installation and maintenance, expensive, lower resolution, etc. This research work proposes a soil moisture detection method which is portable, economic and time efficient. Spectroscopic sensor enables rapid soil moisture estimation using photodiodes of varying sensitivities and wavelengths across a broad spectrum of light, allowing precise identification and analysis of various samples based on their unique spectral fingerprints. In this study, a relation between sensor output data and soil moisture percentage value obtained from gravimetric method has been established. Using various regression methods, the highest value of correlation coefficient was found in case of polynomial function of order 3. Residual values of all wavelengths were then calculated by comparing the calculated with the expected moisture values obtained using the polynomial function. Result showed that 705 nanometers (nm) wavelength has the least residual value, thus signifying that the polynomial equation of order 3 at 705 nm wavelength perfectly demonstrates the relation between soil moisture and sensor reading. Furthermore, the results were verified using optimization method which showed that 705 nm wavelength has the highest closeness coefficient, thus verifying this model and observations. This model can be helpful in determining the soil moisture instantaneously instead of hours as seen in traditional gravimetric methods. [ABSTRACT FROM AUTHOR]

The Cadomian basement of SW Iberian Massif appears in the so-called Ossa-Morena Complex, which contains several allochthonous terranes that are key for deciphering the evolution of the Gondwana margin during the Neoproterozoic. In this context, the Calzadilla Ophiolite represents the remnants of an oceanic Ediacaran lithosphere formed by a boninitic mafic crust that overlies a serpentinized ultramafic section, the latter being the biggest outcrop of Cadomian serpentinites in Europe. In this work, the petrological and geochemical nature of the mantle section is analysed, as well as the tectonothermal evolution of both, the mafic crust and the ultramafic section. The algebraic analysis of whole rock geochemistry of the serpentinites indicates that the protolith corresponds to harzburgite. The geochemical fingerprint of the serpentinites, as well as the primary spinel crystals composition, shows that peridotites underwent high degrees of partial melting (up to 16%) in a forearc setting. Thermodynamic modelling and average PT methods were applied to serpentinites and mafic dykes from the ultramafic section as well as to amphibolites from the mafic section, both of which experienced progradation up to 4–5 kbar and 525–575°C. According to the data, the protoliths of the Calzadilla Ophiolite were formed in a peri-Gondwanan arc system developed during the subduction of oceanic crust below Gondwana. The roll-back of the subducting slab would induce the opening of the forearc basin at c. 602 Ma, protolith age of amphibolites from crustal section, with an extensive partial melting of the mantle wedge and the formation of boninitic-affinity magmas that constitute the mafic section of the Calzadilla Ophiolite. Metasomatism and serpentinization of the mantle wedge would occur via melt- and fluid–rock interactions. A compression-dominated stage at c. 540 led to the emplacement of the Calzadilla Ophiolite onto the Cadomian magmatic arc, with the development of the amphibolite-facies metamorphic imprint. [ABSTRACT FROM AUTHOR]

The chemical synthesis of (CdSe)13 magic-sized clusters (MSCs) allows the replacement of host atoms by individual transition metals such as Mn. By analyzing the spectral fingerprints of the Mn2+ photoluminescence (PL) in MSCs with different dopant concentrations, we are able to distinguish between single Mn2+ ions and coupled Mn2+ pairs. In case of Mn2+ pair emission, temperature-dependent studies show a pronounced red shift, followed by a distinct blue shift of the PL energy upon heating. This is related to a spin ladder formation of the ground and excited states due to Mn2+–Mn2+ exchange interaction at cryogenic temperatures, which is assumed to vanish at higher temperatures. In contrast, single Mn2+ ion PL exhibits a unique redshift with increasing temperature, which can be attributed to a particularly strong coupling to vibronic modes due to the ultimate small size of the MSCs. [ABSTRACT FROM AUTHOR]