Forests provide a wide range of ecosystem services essential for human well-being, including provisioning, regulating, and cultural benefits. Among these, the emission of biogenic volatile organic compounds (BVOCs) by trees has emerged as a key factor mediating physiological and psychological health effects. BVOCs, particularly terpenes and phytoncides, influence immune function, reduce stress, support cardiovascular and respiratory health, and contribute to mental well-being. Forest-based therapeutic practices, such as Shinrin-Yoku (forest bathing): capitalize on these natural compounds, demonstrating measurable improvements in mood, stress hormone levels, and immune parameters. Beyond direct health benefits, BVOCs interact with atmospheric chemistry, affecting air quality and urban environmental conditions. Despite growing evidence, the mechanisms linking specific tree species and BVOC profiles to health outcomes remain insufficiently understood, and interdisciplinary collaboration between forestry, medical sciences, and urban planning is limited. This review synthesizes current knowledge on forest ecosystem services and BVOCs in the context of human health, highlighting chemical ecology, therapeutic applications, and potential risks in urban environments. It further identifies knowledge gaps and outlines future research directions to guide forest management, urban green space design, and public health strategies. By integrating ecological, physiological, and environmental perspectives, forests and their volatile compounds can be effectively leveraged as nature-based tools to promote sustainable health and well-being.

Authentication of cereal-based food products has become increasingly important due to the prevalence of food fraud, mislabeling, and the demand for products labeled as gluten-free, wholegrain, or originating from specific regions. These issues not only affect consumer trust but also have significant implications for health, trade, and regulatory compliance. Chemoinformatics, which integrates chemical profiling with multivariate statistical and machine learning techniques, offers a powerful solution for verifying the authenticity and integrity of cereals, pseudocereals, flours, breads, and other bakery products. This mini-review presents an overview of recent advances in chemoinformatics methodologies used for food authentication. It highlights key analytical platforms, such as gas chromatography–mass spectrometry (GC-MS), near-infrared (NIR) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, and inductively coupled plasma–mass spectrometry (ICP-MS). Data derived from these techniques are often processed using principal component analysis (PCA), partial least squares discriminant analysis (PLS- DA), support vector machines (SVM), clustering, and other classification algorithms. We further summarize representative case studies from Europe, Asia, South America, and North Africa that demonstrate the ability of chemoinformatics to differentiate botanical species, trace geographical origins, and detect adulteration in cereal-based matrices. The review outlines current challenges in method standardization and model validation, while emphasizing the future potential of portable devices and artificial intelligence in scalable food authentication strategies.

The phytoremediation capacity of three common poplar species, white poplar (Populus alba L.), Lombardy poplar (Populus nigra ’Italica’), and Euro-American hybrid poplar (Populus × euramericana (Dode) Guinier cl. I-214), grown in a middle-sized city with a continental climate in Serbia was analyzed. For this purpose, 15 polycyclic aromatic hydrocarbons (PAHs), 10 polychlorinated biphenyls (PCBs), and 6 heavy metals (HMs) were tracked in leaves and one-year-old branches. P. × euramericana showed the highest PAH uptake capacity, with concentrations of 821.40 ng g−1 dry weight (DW) and 453.64 ng g−1 DW in leaves and branches, respectively. Likewise, P. euramericana accumulated the highest levels of PCBs in leaves (364.53 ng g−1 DW). Additionally, P. nigra ‘Italica’ demonstrated the greatest accumulation potential for HMs, particularly zinc, with 310.10 µg g−1 DW in leaves. Leaves accumulated ~30% more pollutants compared with branches. Significant differences in pollutant uptake capacities were found among species and plant organs. These findings highlight the importance of species selection in phytoremediation and clarify the role of poplar species in accumulating pollutants to mitigate urban pollution. Finally, this study provides valuable insights for future phytoremediation strategies using poplars, especially in urban environments with similar conditions.

The study is the first analytical approach to evaluate thirteen elements’ profiles of 4 different species (Phaseolus spp., Vicia spp., Pisum spp. and Lathyrus spp.) comprising 38 varieties of legumes cultivated in Serbia. The inductively coupled plasma with an optical emission spectrometer (ICP-OES) was used to determine the levels of macro-, micro- and trace elemental contents, namely, P, K, Ca, Mg, Fe, Cu, Zn, Mn, Cd, Pb, Ni, Cr and As, after microwave-assisted digestion. MANOVA was utilized to reveal significant differences in elemental composition within and between groups, while PCA to reveal the underlying patterns. Among the macroelements, the most abundant was K (8980.7-14177.4mgkg-1), followed by P, Mg and Ca, being the highest in Phaseolus spp. The data revealed that the studied legumes generally contained a high amount of Zn and Fe, with Lathyrus spp. being the richest in Zn. The mean concentration of trace elements in the analyzed legume samples was in the following order: Ni (24.2-57mgkg-1) > Cr (0.8-4.1mgkg-1) > Pb (0.07-1.2mgkg-1) > Cd (0-0.07mgkg-1). The determined Pb and Cd contents in all cultivars exceeded the set maximum limits by European and Serbian legislation, having a potential for human health risk. Pattern recognition techniques applied to the data did not distinguish among the species, revealing a similar elemental profile. In conclusion, this study highlights legumes as an extremely valuable source of macro- and microelements, but also the importance of monitoring the level of heavy metals in this commonly consumed foodstuff.

This study aimed to provide insights into a novel honey screening and authentication approach. Chemometrics associated with a GC/MS instrumentation was applied to verify a total of 13 honey samples having different floral and geographical origin with an aim to detect buckwheat honey and discriminate it from honeys of other floral sources. Non-polar and semi-polar compounds were first extracted and then detected and semi-quantified employing a GC/MS device working in a full scan mode. Fingerprinting signals covering peaks that elute in the first 20 min, comprising 2000 scans of semi-polar compounds, were used as input datasets for unsupervised and supervised chemometric tools. This approach demonstrated excellent classification performances of honey samples according to the declared floral source, regardless of their geographical origin. The obtained results were compared with the outputs of a common melissopalynological procedure. Although exhibiting a high variability in the pollen content, samples declared as buckwheat honey demonstrated similar chemical profiles. The proposed non-targeted and semi-quantitative method showed to be rapid, unbiased, independent of tedious qualitative and quantitative determinations of eluting compounds, thus exhibiting a strong potential to be incorporated into data fusion procedures for honey authentication. Harmonising chemical methodologies with certain indications from pollen analysis to form core-data systems would undoubtedly improve the authentication protocols of honey floral sources.

Vegetable oil blending has become more common lately. The oils are being blended whether to achieve better oil quality and health benefits or market frauds. The aim of this paper was to group binary oil blends based on the botanical origin of the oil components. For that reason, oil samples were prepared by blending extra-virgin olive oil (EVOO) and high-oleic sunflower oil (HOSO) (blend type I), as well as by blending flaxseed oil (FO) and sunflower oil (SO) (blend type II). Fatty acid methyl esters (FAMEs) present in the simulated blend samples and the pure oil samples were analyzed by gas chromatography coupled to a mass spectrometric detector (GC–MS). The ions of prominent FAMEs were extracted from total ion current (TIC) chromatograms and integrated. Peak percentages were used to create a matrix for further data processing. A heatmap was used as a hierarchical clustering tool to separate the blend samples into clusters according to the belonging botanical origin of each oil component in a blend. The simulated blends and pure oil samples were treated by two different multivariate analysis methods: heatmaps and principal component analysis (PCA), thereby dividing the samples into two groups according to the botanical origin of the blends. GC–MS analysis coupled with multivariate statistic tools was shown to be a useful approach to authenticate edible vegetable oils based on FAMEs composition.

In this paper, the retention behavior of thirteen newly synthesized arylidene derivatives of 2-hydamthione was investigated by normal and reversed phases thin-layer chromatography. In normal phase chromatography, thin layers of silica gel and cyano-propyl (CN) silica gel with fluorescent indicator F254 were used as the stationary phase. As the mobile phase, binary systems of non-aqueous solvents benzene-ethyl acetate and hexane-ethyl acetate were used, 8:2 (v/v). Reversed phase chromatography was performed using a thin layer of octadecyl (C-18) silica gel with fluorescent indicator F254 as the stationary phase and a binary solvent mixture of acetonitrile-water in the ratio 7.5:2.5 (v/v) as the mobile phase. Observing under a UV lamp, the spots of all tested derivatives were clearly visible. The RF value was determined for each spot. Based on the obtained results the HPLC technique is recommended for further chromatographic examination of the newly synthesized arylidene derivatives of 2-hydamthione, using commercial columns with polar chemically bound phases and both non-aqueous and aqueous eluents.

This study presents a tentative analysis of the lipid composition of 47 legume samples, encompassing species such as Phaseolus spp., Vicia spp., Pisum spp., and Lathyrus spp. Lipid extraction and GC/MS (gas chromatography with mass spectrometric detection) analysis were conducted, followed by multivariate statistical methods for data interpretation. Hierarchical Cluster Analysis (HCA) revealed two major clusters, distinguishing beans and snap beans (Phaseolus spp.) from faba beans (Vicia faba), peas (Pisum sativum), and grass peas (Lathyrus sativus). Principal Component Analysis (PCA) yielded 2D and 3D score plots, effectively discriminating legume species. Linear Discriminant Analysis (LDA) achieved a 100% accurate classification of the training set and a 90% accuracy of the test set. The lipid-based fingerprinting elucidated compounds crucial for discrimination. Both PCA and LDA biplots highlighted squalene and fatty acid methyl esters (FAMEs) of 9,12,15-octadecatrienoic acid (C18:3) and 5,11,14,17-eicosatetraenoic acid (C20:4) as influential in the clustering of beans and snap beans. Unique compounds, including 13-docosenoic acid (C22:1) and γ-tocopherol, O-methyl-, characterized grass pea samples. Faba bean samples were discriminated by FAMEs of heneicosanoic acid (C21:0) and oxiraneoctanoic acid, 3-octyl- (C18-ox). However, C18-ox was also found in pea samples, but in significantly lower amounts. This research demonstrates the efficacy of lipid analysis coupled with multivariate statistics for accurate differentiation and classification of legumes, according to their botanical origins.

An innovative and rapid approach is described for classifying common types of gluten and non-gluten cereal flour (wheat, rye, triticale, barley, oats, and corn) into the groups defined by their botanical origin. Liposoluble compounds were extracted from flour samples, derivatized, and analyzed using gas chromatography - mass spectrometry (GC-MS). Raw signals used for data processing consisted of mass spectra scans of full chromatograms. These represented unique fingerprints for each class. An automated machine learning framework was applied for classification. The algorithm automatically explored each of the 39 classifiers provided by the software. Using 10-fold cross-validation, a simple logistic classifier was recommended to be optimal. The constructed model resulted in 85.71% correctly classification according to the botanical origin. Furthermore, it unequivocally discriminated samples of non-gluten corn flour. This non-targeted strategy supports the use of artificial intelligence in developing methods for flour authentication.

Context Biofortification of forage crops has become even more important, due to the improvement in livestock nutrition, but it has also had an indirect positive impact on the human diet. Aim This study investigated the effect of nitrogen and microelement (Zn and Se) fertilisation on yield and on the microelement composition of maize (Zea mays L.) silage. Methods Two field experiments were conducted using a two-factorial split-plot design with nitrogen fertilisation in three doses: 0, 120, 180 and 240 kg N/ha. The first experiment included foliar Zn fertilisation as the second factor (0, 1.5 kg Zn/ha and 1.5 kg Zn/ha + urea solution). The second experiment studied the effect of Se (10 g Se/ha). Key results Nitrogen fertilisation increased biomass yield, Cu and Mn concentration in silage maize. Application of Se and Zn did not affect the biomass yield, but it had a positive effect on Se and Zn concentration in plants. Zn and urea application in combination proved to be more efficient in increasing Zn concentration in plants when compared to Zn applied alone. Conclusions Nitrogen and fertilisation with Zn and Se can be a good tool in fodder plant biofortification because their application led to a yield increase (Zn), but at the same time to an improvement in the mineral composition of maize biomass, with essential elements (Zn and Se). Implications Although biofortification with 1.5 kg Zn/ha has achieved the concentration in maize biomass that can meet the nutritional needs of dairy cows, further research is needed to examine the adjuvant doses and forms of Zn to obtain high yields and Zn concentration in forage crops.

The aim of this paper was to quantify individual oils in binary oil blends. Oil samples were prepared by blending different mass proportions of olive oil and high-oleic sunflower oil (Blend I), as well as flaxseed oil and sunflower oil (Blend II). The pure oil samples and simulated blends were analyzed by gas chromatography - mass spectrometry (GC-MS). Peak surface areas of prominent fatty acids were used to create a numerical matrix for further data processing. In order to estimate the exact content of individual oils in each blend, a novel mathematical model was developed and applied, without the application of multivariate statistics. The results of mathematical estimations were compared with theoretical values. Linear dependences were observed in both oil blends, with the R-2 > 0.993 and p < 10(-8). In general, the employed GC-MS method coupled to the proposed mathematical model gave accurate and reliable results. This study represents a meaningful approach for the authentication of vegetable oils in terms to quantify the individual oils in adulterated oil blends.

Organic synthesis could be very demanding, usually due to difficulties related to the separation of main reaction products from by-products. Steroidal compounds could have similar lipophilicity, which is mostly based on the lipophilicity of the steroidal core. This causes many problems during purification, i.e. in obtaining a pure single steroidal compound. In this research, a group of bile acid derivatives were subjected to HPLC analysis using four experimental systems, which presented combinations of C18 and F5 columns with methanol-water and acetonitrile-water as mobile phases. Retention parameters and retention order of the compounds were established and indicated that all experimental systems could be applicable in order to separate and/or purify some individual compounds or a mixture of a few compounds. However, the only experimental system that could separate a mixture of all investigated derivatives proved to be a C18 column with acetonitrile-water as a mobile phase. Since complex interactions between F5 column and the analytes exist, molecular surface polarity (MSP) was tested as a lipophilicity parameter, and also compared with logP using multivariate statistics. Retention parameters obtained on F5 column were used as descriptors, both with MSP and with logP, concluding that logP has shown to be a better lipophilicity descriptor.

Microplastics are present in our environment (in freshwater and marine water, in the air, in our food and drinking water).They are considered an important pollutant since they can take up to thousands of years for these particles to decompose.On the other hand, they can have a negative effect on the health of organisms exposed to them.They can also be the carriers of different sources of pollution through the process of adsorption.Since most of the implemented research on this subject focuses on marine environments, additional investigation regarding freshwater pollution with microplastics can be of interest.This paper gives a brief overview and analysis concerning the measurements of microplastics in freshwater environments.

Microplastics are present in our environment (in freshwater and marine water, in the air, in our food and drinking water). They are considered an important pollutant since they can take up to thousands of years for these particles to decompose. On the other hand, they can have a negative effect on the health of organisms exposed to them. They can also be the carriers of different sources of pollution through the process of adsorption. Since most of the implemented research on this subject focuses on marine environments, additional investigation regarding freshwater pollution with microplastics can be of interest. This paper gives a brief overview and analysis concerning the measurements of microplastics in freshwater environments.

This paper presents an application of a portable microfluidic platform based on a filter paper on which multi walled carbon nanotubes were deposited to quickly determine the quality of olive oil by measuring electrical resistance. Three different types of filter paper with different pore sizes and different filtration rates were used in the middle of the microfluidic platform, as a material for soaking a blend of olive and high-oleic sunflower oil. The rapid prototyping xurographic technique was used to fabricate the complete microfluidic platform. For testing purposes, oil blends in various proportions were deposited through the inlet on the top of the platform. The variation in electrical resistance at room temperature was measured, using the Chemical Impedance Analyzer, successfully indicating oil proportions in measured blends. We obtained the change of resistance in the range from 0.26 M? to 2.79 M? per percentage of olive oil content, for corresponding linearity index from 0.71 to 0.99, for papers labelled with 44?45, respectively. Additionally, a prototype of electronic device was developed for acquisition and displaying measured data, based on the created microfluidic platform.

limitations.Recently, the MP issue has been more acknowledged, but still, many questions arise and wait to be solved.Further studies are necessary to improve existing analysis methods, and more education is needed to raise awareness amongst the general public about the emerging problem of plastics in nature.