Multivariate Statistical Methods Research Articles

Quality evaluation and health risk assessment of karst groundwater in Southwest China

Groundwater in karst regions is of immense value due to its vital support for regional ecosystems and residents' livelihoods. However, it is simultaneously threatened by multi-source pollution from agricultural non-point sources, industrial and domestic point sources, and mining activities. This study focuses on the Guangxi of China, which features typical karst topography, aiming to thoroughly assess the groundwater quality and related health risks in Guangxi, especially identifying the impacts of various key pollution sources on the groundwater environment. A total of 1912 groundwater samples were collected, covering an area of approximately 237,600 km2. The spatial distribution of pollutants was analysed using the Nemeroww index method and Kriging interpolation, while multivariate statistical and cluster analysis methods were employed to identify the main types of pollution sources. Furthermore, based on the human health risk assessment model of the U.S. Environmental Protection Agency (US EPA), a risk assessment was conducted for key pollutants. The results revealed widespread heavy metal contamination in Guangxi's groundwater, particularly with concentrations of Mn, As, Al, Pb reaching up to 9.4 mg/L, 2.483 mg/L, 37.95 mg/L, 4.761 mg/L, respectively, significantly exceeding China's national Class III groundwater quality standards. Cluster analysis indicated that mining and industrial activities are the primary sources of pollution. The health risk assessment demonstrated that these activities pose a significant risk to public health. The aim of this study is to provide a scientific basis for the protection of the groundwater environment in Guangxi and other karst areas, the formulation of pollution prevention and control strategies, and the optimization of urban and industrial land use layouts. Future research should focus on advanced isotopic and molecular biological techniques to trace pollution sources more precisely and evaluate the effectiveness of pollution control measures.

Effect of Needle Age and Location on the Volatile Composition of Essential Oils from Juniperus communis L.: A Chemometric Study

AbstractProfiles of the volatile organic compound (VOC) content of essential oils (EOs) extracted from young and old Juniperus communis L. needles were generated via gas chromatography combined with mass spectrometry (GC‐MS) and flame ionization detection (GC‐FID) and subsequently processed using multivariate statistical methods. These were then used to differentiate the EOs according to needle age and geographical origin. From a total 49 of VOCs identified, statistically significant differences (p<0.05) between young‐ and old‐needle EOs were found for 21 VOCs. Most of the EOs from the young needles showed higher (p<0.05) contents of α‐ and β‐pinene and sesquiterpenes (β‐ and γ‐elemene, germacrene D and B, α‐muurolene, and δ‐cadinene) and their oxygenated derivatives (α‐ and τ‐cadinol), while most of the monoterpenes (α‐thujene, sabinene, terpinolene, β‐myrcene, β‐phellandrene, and p‐cymene) were present in higher quantities (p<0.05) in the EOs from the old needles. The volatiles germacrene D, α‐ and β‐pinene, α‐cadinol, and sabinene were identified via stepwise and canonical discriminant analysis as the most valuable for EO discrimination according to needle age and geographical origin. Recognition and prediction ability tests of the EOs achieved 100 % accuracy in EO classification according to the above mentioned criterion.

Variability and driving effect of aquatic gross primary productivity across long-distance inter-basin water diversion project

Long-distance water diversion projects significantly affect the water resource cycle and allocation of regions. However, many unknowns still exist in water ecosystem functionality and energy flow in large-scale inter-basin water diversion projects. This study focused on the gross primary production (GPP) in the Middle-Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the world's longest inter-basin water diversion project. The spatiotemporal distribution, driving factors, and pathways of GPP were comprehensively analyzed based on four years of high-frequency water quality monitoring and satellite re-analysis data from 11 national stations, coupling the Bayesian hierarchical models and multivariate statistical methods. The results showed that the daily average GPP in the main canal of the MRSNWDPC over the years was 2.650 g O2 m−2 d−1, with seasonal peak GPP occurring in summer and generally increasing with the distance along the canal. Five structural equation modeling (SEM) were built to analyze the variations of GPP, revealing the surface pressure (PS) and surface carbon dioxide concentrations (CO2) and pH value were the main driving factors in the main canal. The surface pressure showed significant negative impacts on GPP changes in the canal, while the CO2 and pH showed different direction effects in different sections. The daily average carbon equivalent GPPs in the MRSNWDPC is 0.828 g C m−2 d−1, ranging from 0.600 to 1.028 g C m−2 d−1, and close to the Yangtze River and the East Sea of China. This study provides a scientific basis for a deeper understanding and analysis of the energy flow mechanisms in water ecosystems of mega inter-basin water diversion projects.

Analysis of the Volatile Components in Different Parts of Three Species of the Genus Amomum via Combined HS-SPME-GC-TOF-MS and Multivariate Statistical Analysis.

The study used headspace solid-phase microextraction coupled with gas chromatography-time-of-flight mass spectrometry (HS-SPME-GC-TOF-MS) to analyze volatile compounds in leaves and fruits of Amomum tsaoko, Amomum paratsaoko, and Amomum koenigii. The composition and aroma of distinct metabolites were analyzed using multivariate statistical methods. A total of 564 volatile compounds were identified from three species of the genus Amomum, which were further divided into nine categories: terpenoids, carboxylic acids, alcohols, hydrocarbons, aldehydes, ketones, phenols, ethers, and other compounds. Terpenoids and alcohols were the most abundant. The content and types of compounds vary in A. tsaoko, A. paratsaoko, and A. koenigii, so mixing or substituting them is not advisable. We selected 45 metabolites based on the criteria of the variable importance in projection values (VIP > 1.5) and one-way ANOVA (p < 0.05). The top 19 metabolites with the most significant VIP values were chosen. Interestingly, (Z)-2-decenal was only found in Amomum koenigii, while nitroethane and nonanal were only present in cultivated A. tsaoko. Additionally, linalool, cineole, and (D)-limonene were the main components affecting the aroma of three species of the genus Amomum. The volatile components identified in this study provide a theoretical basis for analyzing the unique flavor of A. tsaoko, A. paratsaoko, and A. koenigii.

Enhancing postgraduate education quality: a comprehensive analysis of factors influencing students’ satisfaction in university-business cooperation

ABSTRACT This study investigates the critical factors influencing students’ satisfaction with university-business cooperation (UBC) in postgraduate education, focusing on Chinese universities of applied sciences. Employing a comprehensive research model, we utilised multivariate statistical analysis methods, including canonical correlation analysis, partial correlation analysis, principal component analysis (PCA), and cluster analysis, to validate our measurement and hypotheses. Our findings reveal that teaching, tutor supervision, practical relevance, and individual development are primary determinants of students’ satisfaction with UBC in postgraduate education. Additionally, significant correlations exist between these factors, consistent with our proposed research model. The study emphasises the importance of fostering effective UBC for enhancing postgraduate education quality. Practical implications and recommendations for stakeholders are discussed, contributing both to the academic understanding of UBC and offering actionable insights for educational institutions and businesses. Future research directions in this field are also suggested.

Quantitative assessment of irrigation water and organic/inorganic amendment on biometric growth profiles of Abelmoschus esculentus and Solanum lycopersicum and their varieties

ABSTRACT In recent decades, the use of chemical fertilizers has been recklessly provoked to meet the increased food needs of the rapidly growing population. However, there is some disagreement about the use of chemical fertilizers in agriculture. Hence, the appropriate nitrogen, phosphate, and potassium ratios must be determined before their application in agricultural practices. This study explored three distinct sources of nutrients to support healthy seed germination and reduce nutrient loss: chemical fertilizers, vermicompost, and nutrient-laden irrigation water supply. A sustainable, affordable, and green petri plate seed germination experiment was used to analyze the biometric growth patterns of two plant species (Abelmoschus esculentus and Solanum lycopersicum). To quantify the effects of different irrigation water sources (groundwater, river water), their combinations with chemical fertilizers and vermicompost (3 ton/ha), multivariate statistical methods such as correlation, principal component analysis, and deep neural networks were used. The purpose of this research was to find the optimal nutrient delivery technique for encouraging healthy plant growth while minimizing the environmental stress of excessive nutrient application.

Morphological variation in blackthorn (&lt;i&gt;Prunus spinosa&lt;/i&gt; L.) populations in the Northwestern part of the Balkan Peninsula - Absence of geographical and environmental structure

Blackthorn (&lt;i&gt;Prunus spinosa&lt;/i&gt; L., Rosaceae) is a deciduous, insect-pollinated, and animal-dispersed shrub or a small tree that naturally occurs throughout Europe. The aims of this study were to describe fruit morphological variability of natural populations in the northwestern part of the Balkan Peninsula and to determine the impact of geographical and environmental conditions on shaping the species’ variability. In total, ten fruit and stone morphological traits were studied using data from twelve natural populations. e obtained data was analyzed using descriptive and multivariate statistical methods. High morphological variability was determined both among and within the studied populations. However, our study revealed no clear eco-geographical differentiation of the studied populations. High morphological variability of the populations, but also the absence of geographical and environmental structuring of populations, could be attributed to the animal-mediated dispersal of fruits over greater distances, combined with the vegetative regeneration and high levels of seed flow among populations, as well as the influence of founder effect. Based on the conducted research, valuable information on intra- and interpopulation variability of fruits’ morphological traits was obtained, which can be useful in creating the guidelines for conservation, breeding, and afforestation programs.

Method of thromboembolism prediction in advanced atherosclerosis

Aim. To develop a method for thromboembolism prediction in patients with ad­vanced atherosclerosis.Material and methods. The study involved 170 patients with advanced atheroscle­rosis and 110 patients with local atherosclerotic lesions. At the first stage, speciali­zed experts generated signs characterizing the severity of prothrombotic conditions. To determine whether patients belonged to the first or second group, the multivariate statistics method was used. Decision rules (DRs) was developed based on soft computing (SC), as well as the methodology for the synthesis of hybrid fuzzy DRs.Results. According to the discriminant function coefficient (DFC), the division into classes was made with the formation of an intersection area, which affected the diagnostic sensitivity of this method (=86%). Due to difficulties of separating more heterogeneous groups with a small sample, RP was synthesized according to the atherosclerosis severity classification using SC technology. Atherosclerosis severity classification was adopted according to fuzzy DRs: initiation stage (up to 20%); reverse stage (21-40%); progressive stage (41-65%); critical stage (more than 65%). As­ses­sment of thromboembolism risk in advanced atherosclerosis in the examined pati­ents showed the following trends: progressive stage — 55% of patients; critical stage — 21% of patients; reverse stage — 16% of patients; initiation stage — 7% of patients.Conclusion. DFC for DRs makes it possible to identify groups with high and low thromboembolism risk. The resulting final fuzzy DRs make it possible to differentiate the atherothrombotic condition in advanced atherosclerosis by severity, which can help to timely determine preventive and therapeutic measures.

PROCESS MONITORING IN HYBRID ELECTRIC VEHICLES BASED ON DYNAMIC NONLINEAR METHOD

Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose a multivariate statistical method based on dynamic nonlinear improvement, namely dynamic neural component analysis (DNCA). This method does not require the establishment of precise analytical models; instead, it only necessitates acquiring data from HEV powertrains. Through numerical simulation and real HEV experiments, we demonstrate the effectiveness of this approach in monitoring highway third-level faults. The testing outcomes demonstrate that DNCA outperforms traditional dynamic methods like dynamic principal component analysis (DPCA), conventional nonlinear methods such as kernel PCA (KPCA) and NCA, as well as traditional dynamic nonlinear methods like DKPCA.

Economic Impact and Adaptive Responses in the Global Hotel Industry During the COVID-19 Pandemic: A Comparative Analysis

Abstract The COVID-19 pandemic has become a challenge of unprecedented scale for various industries, and the hotel industry has not escaped the waves of economic uncertainty. Economic indicators in the hotel industry, which once reflected growth and prosperity, abruptly turned into indicators of crisis and the need for adaptation. The main aim of the presented article is the evaluation, categorization, and comparison of hotel-type accommodation facilities in selected countries based on their economic indicators in the pandemic period. Comparison and categorization of countries will be carried out using a multivariate statistical method – cluster analysis. All investigated indicators were obtained by the method of sociological inquiry in the form of a questionnaire. The application of cluster analysis identified the economic importance and potential in the countries in question using the investigated economic indicators in the hotel industry. The results showed a significant economic difference between the countries. The analysis divided the requested countries into five clusters – categories from the lowest (Turkey) to the highest impact of the COVID-19 pandemic on the economic situation in the hotel industry (Slovak Republic). Since the prevention of crises in the hotel industry is a key aspect that can minimize negative consequences and ensure the sustainability of operations, at the end of the presented article we have outlined the measures that accommodation establishments should apply in their normal practice.

Application of the structural equation modeling-partial least square method to analyze factors affecting the quality of life of hypertension patients

Abstract Hypertension is a serious health issue in Pidie Regency, Indonesia. This research utilizes Structural Equation Modeling - Partial Least Squares (SEM-PLS) method to analyze the factors contributing to hypertension in Pidie Regency. Primary data were was gathered via surveys and discussions held with inhabitants from the area. One of the multivariate statistical methods that can be used to explain these complex relationships is SEM-PLS. The direct effects of SEM-PLS analysis indicate the hidden demographic factors notably diminish the concealed variable of quality of life. The concealed indicators of medication adherence showcase a positive impact but lack statistical significance on the hidden variable of quality of life. Additionally, the latent activity variable has a positive and significant effect on quality of life. The research findings revealed an R2 value of 0.234, signifying that 23.4% of the variance in the latent quality of life variable can be explained by latent demographic, medication adherence, and activity variables.

The clinical, radiological, postoperative pathological, and genetic features of nodular lung adenocarcinoma: a real-world single-center data.

The preoperative differential diagnosis of nodular lung adenocarcinoma has long been a challenging issue for thoracic surgeons. This study aimed to explore differential diagnosis of nodular lung adenocarcinoma by comprehensively analyzing its clinical, computed tomography (CT) imaging, and postoperative pathological and genetic features. The clinical, CT imaging, and postoperative pathological features of different classifications of nodular lung adenocarcinoma were retrospectively analyzed through univariate and multivariate statistical methods. There were 132 patients with nodular lung adenocarcinoma enrolled. Firstly, compared with ground-glass nodular lung adenocarcinoma, solid nodular lung adenocarcinoma was more common in women [odds ratio (OR), 3.662; 95% confidence interval (CI): 1.066-12.577] and older adults (OR, 1.061; 95% CI: 1.007-1.119), and CT signs were mostly lobulation (OR, 4.957; 95% CI: 1.714-14.337) and spiculation (OR, 8.214; 95% CI: 2.740-24.621); the mean CT (CTm) value of solid nodular lung adenocarcinoma was significantly higher than that of ground-glass nodular lung adenocarcinoma, and the optimal diagnostic threshold was -267.5 Hounsfield units (HU). Secondly, the maximum diameter of nodule size (NSmax) of invasive adenocarcinoma (IAC) was significantly greater than that of minimally IAC (MIA; OR, 6.306; 95% CI: 1.191-33.400) or atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS; OR, 189.539; 95% CI: 4.720-7,610.476), and the optimal diagnostic threshold between IAC and MIA was 1.35 cm; the CTm value of IAC was significantly higher than that of MIA, and the optimal diagnostic threshold was -460.75 HU. Thirdly, lepidic-predominant adenocarcinoma (LPA) manifest more commonly as pure ground-glass nodule (pGGN; OR, 6.252; 95% CI: 1.429-27.358) or mixed ground-glass nodule (mGGN; OR, 4.224; 95% CI: 1.223-14.585). Moreover, the mutation rate of epidermal growth factor receptor (EGFR) in IAC was 70.69% (41/58). The EGFR mutation rates of mGGNs (OR, 8.794; 95% CI: 1.489-51.933) and solid nodules (SNs; OR, 12.912; 95% CI: 1.597-104.383) were significantly higher than that of pGGNs. Furthermore, compared with those of micropapillary-predominant adenocarcinoma (MPA), solid-predominant adenocarcinoma (SPA), or invasive mucinous adenocarcinoma (IMA), there were significantly higher EGFR mutation rates in acinar-predominant adenocarcinoma/papillary-predominant adenocarcinoma (APA/PPA; OR, 55.925; 95% CI: 4.045-773.284) and LPA (OR, 38.265; 95% CI: 2.307-634.596). Different classifications of nodular lung adenocarcinoma have their own clinicopathological and CT imaging features, and the latter is the main predictor.

Metabolomic differences between non-hydrothermal treated water-soluble (WSPs) and hydrothermally treated water-insoluble (WIPs) Maitake polysaccharides fermented by Lactobacillus acidophilus and L. plantarum

Bacterial Metabolite through a fermentation process is a growing trend and a promising alternative for use as functional components. Non-hydrothermal water-soluble (WSPs) and hydrothermally treated water-insoluble (WIPs) Maitake polysaccharides were fermented with Lactobacillus acidophilus (LA) and Lactobacillus plantarum (LP). Chemical composition analysis indicated that Maitake polysaccharides contained 58.22 ± 1.35 % total sugar and 31.46 % β-glucan, essential for metabolites production. 6-glucanase was used to degrade the WIPs, and hydrothermally treated WIP fibers exhibited smooth microstructure. Hence, the LA and LP bacteria investigated the potential fermented metabolic activities and differences between WSPs(Sp1)and WIP(Sp3) Maitake polysaccharides using LC-MS, and 887 metabolites were identified. Using Venn, Partial least squares discriminant analysis (PLS-DA), VIP Metabolites, and other multivariate statistical analysis methods, metabolites were expressed differently in all samples. Due to hydrothermal processing, WIP induced the highest growth of LA and LP, with an abundance of isocitrate metabolites. Furthermore, 50 metabolite correlations were identified, leading to the classification of 6 distinct metabolic groups. Thus, the study offers the initial comprehensive analysis of metabolites in Lactobacillus-fermented Maitake polysaccharides, aiding in understanding its metabolic interactions and facilitating progress in food engineering research.

Perceptual model for semantic representation of design futures practice cases: Towards the development of a design futures repository

Various media and forms of cases or works have promoted the development and dissemination of "Design Futures", making empirical research focusing on practical cases not only valuable but also urgently needed. While heuristic case libraries enhance creativity and efficiency in traditional design, a resource library for Design Futures has not been established. To fill this gap, our preliminary work includes clarifying the perception of cases by different practitioners through semantic representation to establish a perceptual model. In this paper, we propose the typology of Design Futures practice cases. Using desktop research, expert selection, and empirical methods, we constructed a semantic perceptual space and further extracted it into four categories. Combining multivariate statistical techniques and related visualization methods, we examined the distinctions and relationships between semantic words, dimensions, cases, and their types. We have finally established the perceptual model for semantic representation of design futures practice cases, with 4 main perception dimensions (critical, wellbeing, conceptual, and technological) and 13 key semantics, and explored in depth the implications these semantics may contain individually. We further explored the potential of the model in developing a case library. In summary, our research findings provide a framework for representing, retrieving, and evaluating practice cases and support the development of a Design Futures Repository.

Spontaneous-stimulated Raman co-localization dual-modal analysis approach for efficient identification of tumor cells

The study of highly heterogeneous tumor cells, especially acute myeloid leukemia (AML) cells, usually relies on invasive analytical methods such as morphology, immunology, cytogenetics, and molecular biology classification, which are complex and time-consuming to perform. Mortality is high if patients are not diagnosed in a timely manner, so rapid label-free analysis of gene expression and metabolites within single-cell substructures is extremely important for clinical diagnosis and treatment. As a label-free and non-destructive vibrational detection technique, spontaneous Raman scattering provides molecular information across the full spectrum of the cell but lacks rapid imaging localization capabilities. In contrast, stimulated Raman scattering (SRS) provides a high-speed, high-resolution imaging view that can offer real-time subcellular localization assistance for spontaneous Raman spectroscopic detection. In this paper, we combined multi-color SRS microscopy with spontaneous Raman to develop a co-localized Raman imaging and spectral detection system (CRIS) for high-speed chemical imaging and quantitative spectral analysis of subcellular structures. Combined with multivariate statistical analysis methods, CRIS efficiently differentiated AML from normal leukocytes with an accuracy of 98.1 % and revealed the differences in the composition of nuclei and cytoplasm of AML relative to normal leukocytes. Compared to conventional Raman spectroscopy blind sampling without imaging localization, CRIS increased the efficiency of single-cell detection by at least three times. In addition, using the same approach for further identification of AML subtypes M2 and M3, we demonstrated that intracytoplasmic differential expression of proteins is a marker for their rapid and accurate classifying. CRIS analysis methods are expected to pave the way for clinical translation of rapid tumor cell identification.

Label-Free Identification of AML1-ETO Positive Acute Myeloid Leukemia Using Single-Cell Raman Spectroscopy.

Acute myeloid leukemia (AML) is a malignant hematological tumor disease. Chromosomal abnormality is an independent prognostic factor in AML. AML with t(8:21) (q22; q22)/AML1-ETO (AE) is an independent disease group. In this research, a new method based on Raman spectroscopy is reported for label-free single-cell identification and analysis of AE fusion genes in clinical AML patients. Raman spectroscopy reflects the intrinsic vibration information of molecules in a label-free and non-destructive manner, and the fingerprint Raman spectrum of cells characterizes intracellular molecular types and relative concentration information, so as to realize the identification and molecular metabolism analysis of different kinds of cells. We collected the Raman spectra of bone marrow cells from clinically diagnosed AML M2 patients with and without the AE fusion gene. Through comparison of the average spectra and identification analysis based on multivariate statistical methods such as principal component analysis and linear discriminant analysis, the distinction between AE positive and negative sample cells in M2 AML patients was successfully achieved, and the single-cell identification accuracy was more than 90%. At the same time, the Raman spectra of the two types of cells were analyzed by the multivariate curve resolution alternating least squares decomposition method. It was found that the presence of the AE fusion gene may lead to the metabolic changes of lipid and nucleic acid in AML cells, which was consistent with the results of genomic and metabolomic multi-omics studies. The above results indicate that single-cell Raman spectroscopy has the potential for early identification of AE-positive AML.

Investigating net primary production in climate regions of Khuzestan Province, Iran using CASA model.

This study aimed to investigate the vegetation production changes in Khuzestan province, Iran using MODIS data production, meteorological data, vegetation maps as well as topographic and field monitoring data in CASA model. The study area was divided into different climatic classes based on multivariate statistical method, so the vegetation of each climatic region was examined separately for changes in NPP values. Production changes due to degradation were calculated using the Miami model and subsequently, the rain use efficiency (RUE) and the light use efficiency (LUE) and correlation indices between the CASA model and ground data were determined. The results of this study (R2) showed that the accuracy of this model varies depending on the type of climatic regions (R2 = 0.80 to R2 = 0.15). In different climatic regions, the rate of NPP changes (very humid 68 gC/m2 to ultra-dry 15 gC/m2) varies in rangeland types. The highest rate of vegetation production is observed seasonally in May. Degradation conditions also reduced RUE and LUE. However, in hyper-arid regions, adaptations of plants in some different species (Hammada Spp.) increase their efficiency compared to other vegetation types. The results showed the importance of vegetation and climate classification in vegetation production studies.

Hydrodynamics and dissolved organic matter components shaped the fate of dissolved heavy metals in an intensely anthropogenically disturbed estuary

Anthropogenic activities and natural erosion caused abundant influx of heavy metals (HMs) and organic matter (OM) into estuaries characterized by the dynamic environments governed by tidal action and river flow. Similarities and differences in the fate of HM and OM as well as the influences of OM on HMs remain incomplete in estuaries with seasonal human activity and hydrodynamic force. To address this gap, dissolved HMs (dHMs) and fluorescence dissolved OM (FDOM) were investigated in the Pearl River Estuary, a highly seasonally anthropogenic and dynamic estuary. It aimed to elucidate the effects of hydrodynamic conditions and DOM on the seasonal fate of dHMs via the multivariate statistical methods. Our findings indicated dHMs and FDOM exhibited consistently higher levels in the upper estuarine and coastal waters in both seasons, predominantly controlled by the terrestrial/anthropogenic discharge. In the wet season, dHMs and humic-like substances (HULIS) were positively correlated, showing that dHMs readily combined with HULIS. This association led to a synchronous decrease offshore along the axis of the estuary and the transport following the river plume in the surface affected by the salt wedge. Contrarily, dHMs were prone to complex with protein-like components impacted by the hydrodynamics during the dry season. Principal component analysis (PCA) results revealed the terrestrial/anthropogenic inputs and the fresh-seawater mixing process were the most crucial factors responsible for the fate of dHM in wet and dry seasons, respectively, with DOM identified as a secondary but significant influencing factor in both seasons. This study holds significance in providing valuable insights into the migration, transformation, the ultimate fate of dHMs in anthropogenically influenced estuaries, as well as the intricate dynamics governing coastal ecosystems.

Comparative metabolite analysis of Piper sarmentosum organs approached by LC–MS-based metabolic profiling

Piper sarmentosum Roxb. (Piperaceae) is a traditional medicinal and food plant widely distributed in the tropical and subtropical regions of Asia, offering both health and culinary benefits. In this study the secondary metabolites in different organs of P. sarmentosum were identified and their relative abundances were characterized. The metabolic profiles of leaves, roots, stems and fruits were comprehensively investigated by liquid chromatography high-resolution mass spectrometry (LC-HR-MS) and the data subsequently analyzed using multivariate statistical methods. Manual interpretation of the tandem mass spectrometric (MS/MS) fragmentation patterns revealed the presence of 154 tentatively identified metabolites, mostly represented by alkaloids and flavonoids. Principle component analysis and hierarchical clustering indicated the predominant occurrence of flavonoids, lignans and phenyl propanoids in leaves, aporphines in stems, piperamides in fruits and lignan-amides in roots. Overall, this study provides extensive data on the metabolite composition of P. sarmentosum, supplying useful information for bioactive compounds discovery and patterns of their preferential biosynthesis or storage in specific organs. This can be used to optimize production and harvesting as well as to maximize the plant’s economic value as herbal medicine or in food applications.Graphical

A Study of the Elemental Profiles of Wines from the North-Eastern Coast of the Black Sea

Due to the increasing consumer interest in wines with a controlled place of origin, PGI (Protected Geographical Indication) and PDO (Protected Designation of Origin), the most acute question is how to identify them. One of the most effective ways to confirm the place of origin of wine in global practice is a comprehensive study of the elemental profile using statistical analysis methods. In the period from 2020 to 2023, 152 grape samples of grapes were collected from various wineries in Crimea and Kuban. The grape must that was obtained from them was fermented in laboratory conditions. The elemental profile was determined in the prepared wines, which included 71 indicators. In the conducted work, it was revealed that wines from Crimea and Kuban differ statistically significantly in the concentration of the elements B, Ca, Cu, Mn, Na, Ni, Re, Si, Sn and U. At the same time, the contents of the elements U, Sn and Re prevail in wines from Crimea, and those of B, Ca, Cu, Mn, Na, Ni and Si prevail in wines from Kuban. At the same time, methods of univariate and multivariate statistics do not allow us to reliably classify wine samples from Crimea and Kuban by their place of origin. In order to reveal the non-linear dependence of the studied indicators in wines on the geographical place of grape growing, the method of a supervised learning Random Forest was used. After training the model on the dataset, the proportion of its correct predictions was 96%. The model used 61 parameters, among which the most important were Ni, Re, Ba, Rb, Na, U, Sb, Zn, Bi, Ag and Ti.