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This study aimed to determine the impact of tetrabutylphosphonium bromide [TBP][Br] on the soil environment through an experiment on loamy sand samples. The tested salt was added to soil samples at doses of 0 (control), 1, 10, 100, and 1000 mg kg−1 dry matter (DM). During the experiment, the activity of selected enzymes involved in carbon, phosphorus, and nitrogen cycles, characteristics of organic matter with Fourier-transform infrared (FT-IR) spectroscopy, and toxicity of soil samples in relation to Aliivibrio fischeri were determined at weekly intervals. The results showed that low doses of [TBP][Br] (1 and 10 mg kg−1 DM) did not have much influence on the analyzed parameters. However, the addition of higher doses of the salt into the soil samples (100 and 1000 mg kg−1 DM) resulted in a decrease in the activity of enzymes participating in the carbon and phosphorus cycle and affected the activation of those enzymes involved in the nitrogen cycle. This may be due to changes in aerobic conditions and in the qualitative and quantitative composition of soil microorganisms. It was also observed that the hydrophobicity of soil organic matter was increased. Moreover, the findings suggested that the soil samples containing the highest dose of [TBP][Br] (1000 mg kg−1 DM) can be characterized as acute environmental hazard based on their toxicity to Aliivibrio fischeri bacteria. The increased hydrophobicity and ecotoxicity of the soil samples exposed to the tested salt were also positively correlated with the activity of dehydrogenases, proteases, and nitrate reductase. Observed changes may indicate a disturbance of the soil ecochemical state caused by the presence of [TBP][Br].

Recent advancements in plant sensing technologies have significantly improved agricultural productivity while reducing resource inputs, resulting in higher yields by enabling early disease detection, precise diagnostics, and optimized fertilizer and pesticide applications. Each adopted technology offers unique advantages suitable for various farm operations, breeding programs, and laboratory research. This review article first summarizes key target traits, endogenous structures, and metabolites that serve as focal points for plant diagnostic and sensing technologies. Next, conventional plant sensing technologies based on light reflectance and fluorescence, which rely on foliar phytopigments and fluorophores such as chlorophylls are discussed. These methods, along with advanced analytical strategies incorporating machine learning, enable accurate stress detection and classification beyond general assessments of plant health and stress status. Advanced optical techniques such as Fourier transform infrared spectroscopy (FT‐IR) and Raman spectroscopy, which allow specific measurements of various plant metabolites and structural components are then highlighted. Furthermore, the design and applications of nanotechnology chemical sensors capable of highly sensitive and selective detection of specific phytochemicals, including phytohormones and signaling second messengers, which regulate physiological and developmental processes at micro‐ to sub‐micromolar concentrations are introduced. By selecting appropriate sensing methodologies, agricultural production, and relevant research activities can be significantly improved. This review explores plant diagnostic and sensing technologies, highlighting key traits, structures, and metabolites. It covers conventional methods like leaf light reflectance and fluorescence, as well as advanced optical techniques such as Fourier transform infrared spectroscopy and Raman spectroscopy. The integration of machine learning, statistical methods, and chemical sensors is discussed for phytochemical detection, crop production, breeding, and agrochemical development.

The lime juice is one of the products that has always fallen victim to fraud by manufacturers for reducing the cost of products. The aim of this research was to determine fraud in distributed lime juice products from different factories in Iran. In this study, 101 samples were collected from markets and also prepared manually and finally derived into 5 classes as follows: two natural classes (Citrus limetta, Citrus aurantifolia), including 17 samples, and three reconstructed classes, including 84 samples (made from Spanish concentrate, Chinese concentrate, and concentrate containing adulteration compounds). The lime juice samples were freeze‐dried and analyzed using FT‐IR spectroscopy. At first, principal component analysis (PCA) was applied for clustering, but the samples were not thoroughly clustered with respect to their original groups in score plots. To enhance the classification rates, different chemometric algorithms including variable importance in projection (VIP), partial least square‐discriminant analysis (PLS‐DA), and counter propagation artificial neural networks (CPANN) were used. The best discriminatory wavenumbers related to each class were selected using the VIP‐PLS‐DA algorithm. Then, the CPANN algorithm was used as a nonlinear mapping tool for classification of the samples based on their original groups. The lime juice samples were correctly designated to their original groups in CPANN maps and the overall accuracy of the model reached up to 0.96 and 0.87 for the training and validation procedures. This level of accuracy indicated the FT‐IR spectroscopy coupled with VIP‐PLS‐DA and CPANN methods can be used successfully for detection of authenticity of lime juice samples. In this work, the authenticity of commercial lime juice was detected and quantified using FT‐IR spectroscopy coupled with the VIP variable selection and CPANN models. The main advantage of the present contribution is the diversity of the calibrating samples which include broad ranges of natural, synthetic, and adulterated lime juice samples. Therefore, applicability domain of the developed discriminative model in this work would be broad and wide which is a needed property in fraud detection in lime juice industry.

The comparison of the antioxidant activity of the studied seeds of fiber crop (hemp and flax) emphasized a hierarchy of antioxidant capacity. The purpose of the study was to investigate the antioxidant capacity and nutritional value of flax seeds (Linum usitatissimum L.) and hemp seeds (Cannabis sativa L.) in powder form. In this study, the FT-IR technique was utilized in order to detect molecular components in analyzed samples. Antioxidant capacity was evaluated with photochemical assay as well as humidity, protein, fiber, lipid and carbohydrate content. The FT-IR results reveal the presence of different bio-active compounds in hemp such as flavonoids, tannins, sugars, acids, proanthocyanidins, carotenoids and citric metabolites. The highest antioxidant capacity was observed in flax seeds, 18.32 ± 0.98, in comparison with hemp seeds, 4 ± 0.71 (μg/mg dry weight equivalent ascorbic acid). Regarding nutritional parameters, flax seeds showed the most increased content of protein, displaying average values of 534.08 ± 3.08, as well as 42.20 ± 0.89 of lipids and 27.30 ± 0.89 of fiber (g/100 g/sample). Hemp seeds showed the highest protein content of 33 ± 1.24 (g/100 g/sample).

In this paper, a spectral collection of over 150 ATR-FT-IR spectra of materials related to cultural heritage and conservation science has been presented that have been measured in the extended region of 4000-80 cm(-1) (mid-IR and far-IR region). The applicability of the spectra and, in particular, the extended spectral range, for investigation of art-related materials is demonstrated on a case study. This collection of ATRFT-IR reference spectra is freely available online (http://tera.chem.ut.ee/IR_spectra/) and is meant to be a useful tool for researchers in the field of conservation and materials science.

This study aims to develop a new FT–IR spectral imaging of tumoral tissue permitting a better characterization of tumor heterogeneity and tumor/surrounding tissue interface. Infrared (IR) data were acquired on 13 biopsies of paraffin-embedded human skin carcinomas. Our approach relies on an innovative fuzzy C-means (FCM)-based clustering algorithm, allowing the automatic and simultaneous estimation of the optimal FCM parameters (number of clusters K and fuzziness index m). FCM seems more suitable than classical ‘hard' clusterings, as it permits the assignment of each IR spectrum to every cluster with a specific membership value. This characteristic allows differentiating the nuances in the assignment of pixels, particularly those corresponding to tumoral tissue and those located at the tumor/peritumoral tissue interface. FCM images permit to highlight a marked heterogeneity within the tumor and characterize the interconnection between tissular structures. For the infiltrative tumors, a progressive gradient in the membership values of the pixels of the invasive front was also revealed.

Microplastics are a widespread contaminant found not only in various natural habitats but also in drinking waters. With spectroscopic methods, the polymer type, number, size, and size distribution as well as the shape of microplastic particles in waters can be determined, which is of great relevance to toxicological studies. Methods used in studies so far show a huge diversity regarding experimental setups and often a lack of certain quality assurance aspects. To overcome these problems, this critical review and consensus paper of 12 European analytical laboratories and institutions, dealing with microplastic particle identification and quantification with spectroscopic methods, gives guidance toward harmonized microplastic particle analysis in clean waters. The aims of this paper are to (i) improve the reliability of microplastic analysis, (ii) facilitate and improve the planning of sample preparation and microplastic detection, and (iii) provide a better understanding regarding the evaluation of already existing studies. With these aims, we hope to make an important step toward harmonization of microplastic particle analysis in clean water samples and, thus, allow the comparability of results obtained in different studies by using similar or harmonized methods. Clean water samples, for the purpose of this paper, are considered to comprise all water samples with low matrix content, in particular drinking, tap, and bottled water, but also other water types such as clean freshwater.

The development of sensitive and robust screening tool(s) for assuring the quality of incoming raw materials would supplement the assurances provided by food manufacturer vendor auditing programs. Our aim was to evaluate the ability of attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy in combination with multivariate analysis as a screening tool for the diverse cocoa butter supply. Forty different cocoa butter samples encompassing an acceptable range of compositional variability for the chocolate industry were included. Cocoa butters were characterized for their melt characteristics (melting heat), triacylglycerol content and fatty acid composition (GC-FAME). Soft independent modeling of class analogy (SIMCA) and partial least squares regression (PLSR) were used for classification and quantification analysis. SIMCA classified all cocoa butters in distinct clusters in a 3-dimensional space but no sample clustering patterns were associated with melt characteristics. Spectral differences responsible for the separation of classes were attributed to stretching vibrations of the ester (–C=O) linkage (1,660–1,720 cm⁻¹). PLSR models showed correlation coefficients >0.93 and prediction errors (SECV) of 1.5 units for melt characteristics, 0.2–0.3 and 0.4–0.8 % for major fatty acids and triacylglycerols, respectively. ATR-MIR spectroscopy combined with pattern recognition analysis provides robust models for characterization and determination of cocoa butter composition.

Ultrathin amorphous alumina layers are excellent barriers making them ideal (electro)catalyst overlayers to prevent undesirable side‐reactions, e.g. in O2‐containing environments. Here, 2.5, 5, and 10 nm ultrathin Al2O3 and 2Al2O3‐3SiO2 (mullite) films are deposited onto Pt electrodes using pulsed laser deposition to evaluate their permeability to protons and O2. Cyclic voltammetry revealed that aluminosilicate layers are proton‐permeable but fail to effectively block O2, while amorphous alumina quantitatively suppresses oxygen reduction, enabling selective electrochemical conversions in oxygen‐rich environments. Electrochemical impedance spectroscopy and FT‐IR reflection‐absorption spectroscopy revealed structural transformations in alumina upon applying cathodic potentials, leading to new proton diffusion pathways. The effective proton diffusion coefficient (Deff,H+) remained in the range of 10−18 to 10−17 m2/s, as determined from Pt‐H vibrational mode growth and Warburg analysis. The observed decrease in diffusion and charge transfer resistance results from structural relaxation or increased hydration at the Pt/alumina interface, enhancing proton transport without altering the fundamental diffusion properties of the material. This highlights the ability of Al2O3 overlayers to enable additional transport pathways without fundamentally altering proton diffusivity. Furthermore, it highlights the importance of active site accessibility at buried catalyst interfaces in governing proton reduction kinetics under electrochemical conditions. Ultrathin amorphous alumina electrocatalyst overlayers block oxygen reduction while cathodic‐driven structural relaxation accelerates proton transport over time. By contrast, ultrathin aluminosilicate coatings allow proton permeation but cannot suppress oxygen. These insights enable new strategies for selective electrocatalysis in oxygen‐rich environments.

The need to identify highly related bacterial strains is ancient in clinical, industrial, or environmental microbiology. Strategies based on different phenotypic and genotypic principles have been used since the early 1930s with variable outcomes and performances, accompanying the evolution of bacterial features’ knowledge as well as technologies, instruments, and data analysis tools. Today, more than ever, the implementation of bacterial typing methods that combine a high reliability and accuracy with a rapid, low-cost, and user-friendly performance is highly desirable, especially for clinical microbiology. FT-IR developments for bacterial discrimination at the infra-species level settled on the identification of bacterial groups previously defined by phenotypic or genotypic typing methods. Therefore, this review provides a brief historical overview of main bacterial strain typing methods, and a comprehensive analysis of the fundamentals and applications of Fourier transform infrared spectroscopy, a phenotypic-based method with potential for routine strain typing. The different studies on FT-IR-based strain typing of diverse Gram-negative and Gram-positive bacterial species are discussed in light of genotypic, phenotypic, and biochemical aspects, in order to definitively give this methodology credit to be widely accepted by microbiologists. Importantly, the discriminatory biochemical fingerprints observed on FT-IR spectra have been consistently correlated with sugar-based coating structures that besides reflecting strain variation are also of high relevance for the specificity in pathogen-host interactions. Thus, FT-IR-based bacterial typing might not only be useful for quick and reliable strain typing but also to help understanding the diversity, evolution, and host adaptation factors of key bacterial pathogens or subpopulations.